Methods for detection of maternal mosaic aneuploidy in fetal aneuploidy screening

Abstract

The present disclosure relates to methods of analyzing cell-free DNA samples from expecting mothers or pregnant women. Also provided are methods of enhancing the positive predictive value of fetal aneuploidy screening involving detecting maternal aneuploidy in a cell-free DNA sample obtained from a pregnant mother.

Description

Atty. Dkt. No.: 131588-1664METHODS FOR DETECTION OF MATERNAL MOSAIC ANEUPLOIDY IN FETAL ANEUPLOIDY SCREENING FIELD[00011 Described herein are methods of preparing samples from expecting mothers, and related methods of analysis of such samples.BACKGROUND[00021 The following description of the background of the present technology is provided simply as an aid in understanding the present technology and is not admitted to describe or constitute prior art to the present technology.

[0003] Non-invasive pre-natal screening (NIPS) has become a routine component of healthcare for expecting mothers. NIPS can involve both screening for aneuploidy (e.g., Down syndrome and the like) and screening for other genetic abnormalities in the mother or fetus. Many such screens utilize cell-free DNA (cfDNA); however, utilization of cfDNA suffers from a number of challenges because only a small portion of the cfDNA in maternal plasma is derived from the fetus.10004] Additionally, pre-natal screening for certain inheritable conditions has traditionally required obtaining DNA samples from both a mother and a father. For example, a traditional approach for detecting aneuploidy and various genetic conditions required obtaining samples of genomic DNA (gDNA) from both mother and father of the fetus, as well as cfDNA from the mother. Thus, such testing required at least three samples, each of which may be processed and assessed in a different manner.

[0005] The present disclosure addresses those challenges by providing methods of identifying maternal aneuploidy in a sample containing both fetal and maternal cell-free DNA, such that fetal aneuploidy can be identified with enhanced true positive confidence.-1- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664SUMMARY[0006J The present disclosure is generally directed to screens for aneuploidy in a sample obtained from a pregnant subject. These processes improve non-invasive pre-natal screening (NIPS) by streamlining and simplifying the necessary analysis, utilizing fewer samples, and reducing background noise, all with less complexity and requiring less time compared to conventional pre-natal screening analysis. In particular, the disclosed methods increase the positive predictive value (PPV) of screens for fetal aneuploidy, such as fetal sex chromosome aneuploidy. Such methods enable the identification of false-positive.

[0007] In one aspect, the present disclosure provides a method of detecting maternal mosaic aneuploidy in a sample comprising cell-free fetal DNA (cffDNA) and cell-free maternal DNA (cfrnDNA), comprising: (a) extracting cffDNA and cfmDNA from a biological sample obtained from a pregnant subject; (b) sequencing the cffDNA and cfmDNA fragments to obtain a sequence library; (c) preparing at least two subsets of sequencing reads from the sequence library, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length in a range from about 145 nucleotides to about 200 nucleotides, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with different maximum fragment lengths; (d) calculating, for each subset of sequencing reads, an estimated fetal fraction and an estimated normalized read depth for each of one or more calling regions; (e) calculating, for each calling region, a depth trajectory, wherein the depth trajectory is a rate of change in normalized read depth as a function of fetal fraction; and (f) identifying the presence of maternal mosaic aneuploidy when: (i) a copy number greater than 2 is identified in the calling region and the depth trajectory is negative, or (ii) a copy number less than 2 is identified in the calling region and the depth trajectory is positive. In some embodiments, the aneuploidy is selected from a monosomy, a trisomy, a tetrasomy, a pentasomy, a microdeletion, a microduplication, and mosaic versions of monosomy, trisomy, tetrasomy, and pentasomy. In some embodiments, the aneuploidy is a sex chromosome aneuploidy. In some embodiments, the biological sample is blood or plasma. In some embodiments, the method comprises preparing at least 3 subsets of sequencing reads, at least 4 subsets of sequencing reads, at least 5 subsets of sequencing reads, at least 6 subsets of sequencing reads, at least 7 subsets of sequencing reads, at least 8-2- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664subsets of sequencing reads, at least 9 subsets of sequencing reads, or at least 10 subsets of sequencing reads. In some embodiments, each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length of about 145 nucleotides, about 150 nucleotides, about 155 nucleotides, about 160 nucleotides, about 165 nucleotides, about 170 nucleotides, about 175 nucleotides, about 180 nucleotides, about 185 nucleotides, about 190 nucleotides, about 195 nucleotides, or about 200 nucleotides. In some embodiments, the method comprises preparing 7 subsets of sequencing reads, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length of 145 nucleotides, 150 nucleotides, 165 nucleotides, 168 nucleotides, 170 nucleotides, 175 nucleotides in length, or 190 nucleotides. In some embodiments, the estimated fetal fraction is calculated based on an analysis of variant allele frequencies, region-specific depth-of-coverage anomalies, insert size, or any combination thereof. In some embodiments, the estimated fetal fraction is calculated based on an analysis of variant allele frequencies. In some embodiments, the calling region is selected from a chromosome, a chromosome arm, or microdeletion region. In some embodiments, the normalized read depth is normalized to control for GC-bias, sample background, hybridization probe capture, or a combination thereof. In some embodiments, the depth trajectory is calculated by linear regression analysis. In some embodiments, the method further comprises (g) identifying fetal aneuploidy when: (i) a copy number greater than 2 is identified in the calling region and the depth trajectory is positive, or (ii) a copy number less than 2 is identified in the calling region and the depth trajectory is negative.

[0008] In one aspect, the present disclosure provides a method of screening for fetal sex chromosome aneuploidy (SCA) in a sample comprising cell-free fetal DNA (cffDNA) and cell-free maternal DNA (cfmDNA), comprising: (a) extracting cffDNA and cfrnDNA from a biological sample obtained from a pregnant subject; (b) sequencing the cffDNA and cfmDNA fragments to obtain a sequence library; (c) preparing at least two subsets of sequencing reads from the sequence library, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length in a range from about 145 nucleotides to about 200 nucleotides, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with different maximum fragment lengths; (d) calculating, for each subset of sequencing reads, an estimated fetal fraction and an estimated -3- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664normalized read depth for each of one or more calling regions; (e) calculating, for each calling region, a depth trajectory, wherein the depth trajectory is a rate of change in normalized read depth as a function of fetal fraction; (f) detecting maternal mosaic aneuploidy in the one or more calling region, wherein maternal mosaic aneuploidy is detected when: (i) a copy number greater than 2 is identified in the calling region and the depth trajectory is negative, or (ii) a copy number less than 2 is identified in the calling region and depth trajectory is positive; and (g) detecting fetal aneuploidy in the one or more calling region, wherein fetal aneuploidy is detected when: (i) a copy number greater than 2 is identified in the calling region and the depth trajectory is positive, or (ii) a copy number less than 2 is identified in the calling region and the depth trajectory is negative. In some embodiments, the aneuploidy is selected from a monosomy, a trisomy, a tetrasomy, a pentasomy, a microdeletion, a microduplication, and mosaic versions of monosomy, trisomy, tetrasomy, and pentasomy. In some embodiments, the aneuploidy is a sex chromosome aneuploidy. In some embodiments, the biological sample is blood or plasma. In some embodiments, the method comprises preparing at least 3 subsets of sequencing reads, at least 4 subsets of sequencing reads, at least 5 subsets of sequencing reads, at least 6 subsets of sequencing reads, at least 7 subsets of sequencing reads, at least 8 subsets of sequencing reads, at least 9 subsets of sequencing reads, or at least 10 subsets of sequencing reads. In some embodiments, each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length of about 145 nucleotides, about 150 nucleotides, about 155 nucleotides, about 160 nucleotides, about 165 nucleotides, about 170 nucleotides, about 175 nucleotides, about 180 nucleotides, about 185 nucleotides, about 190 nucleotides, about 195 nucleotides, or about 200 nucleotides. In some embodiments, the method comprises preparing 7 subsets of sequencing reads, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length of 145 nucleotides, 150 nucleotides, 165 nucleotides, 168 nucleotides, 170 nucleotides, 175 nucleotides, or 190 nucleotides. In some embodiments, the estimated fetal fraction is calculated based on an analysis of variant allele frequencies, region-specific depth-of-coverage anomalies, insert size, or any combination thereof. In some embodiments, the estimated fetal fraction is calculated based on an analysis of variant allele frequencies. In some embodiments, the calling region is selected from a chromosome, a chromosome arm, or-4- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664microdeletion region. In some embodiments, the normalized read depth is normalized to control for GC-bias, sample background, hybridization probe capture, or a combination thereof. In some embodiments, the depth trajectory is calculated by linear regression analysis.

[0009] In one aspect, the present disclosure provides a method of in silico processing of cell-free DNA (cfDNA), comprising (a) sequencing cfDNA in a sample obtained from a pregnant subject to obtain a sequence library, wherein the biological sample comprises cell-free fetal DNA (cffDNA) and cell-free maternal DNA (cfmDNA); (b) determining an estimated fetal fraction and an estimated normalized read depth for each of one or more calling regions in at least two size selection windows of the sequence library in a read-length-based size analysis; and (c) determining, for each calling region, a depth trajectory, wherein the depth trajectory is a rate of change in normalized read depth as a function of fetal fraction. In some embodiments, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 size selection windows of the sequence library are assessed, thereby obtaining, respectively, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 fetal fraction-enriched subsets of sequencing reads. In some embodiments, the at least two size selection windows of the sequence library are selected from (i) sequences that are 0-145 nucleotides, (ii) sequences that are 0-150 nucleotides, (iii) 0-155 nucleotides, (iv) 0-160 nucleotides, (v) 0-165 nucleotides, (vi) 0-168 nucleotides, (vii) 0-170 nucleotides, (viii) 0-175 nucleotides, (ix) 0-180 nucleotides, (x) 0-185 nucleotides, (xi) 0-190 nucleotides, (xii) 0-195 nucleotides, (xiii) 0-200 nucleotides, and (xiv) ungated. In some embodiments, the method further comprises detecting aneuploidy. In some embodiments, the aneuploidy is maternal mosaic aneuploidy. In some embodiments, maternal mosaic aneuploidy is detected when: (i) a copy number greater than 2 is identified in the calling region and the depth trajectory is negative, or (ii) a copy number less than 2 is identified in the calling region and depth trajectory is positive. In some embodiments, the aneuploidy is fetal aneuploidy. In some embodiments, fetal aneuploidy is detected when: (i) a copy number greater than 2 is identified in the calling region and the depth trajectory is positive, or (ii) a copy number less than 2 is identified in the calling region and depth trajectory is negative. In some embodiments, the estimated fetal fraction is determined based on an analysis of variant allele frequencies, region-specific depth-of-coverage anomalies, insert size, or any combination thereof. In some embodiments, the estimated fetal fraction is determined based on an analysis -5- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664of variant allele frequencies. In some embodiments, the calling region is selected from a chromosome, a chromosome arm, or microdeletion region. In some embodiments, the normalized read depth is normalized to control for GC-bias, sample background, hybridization probe capture, or a combination thereof. In some embodiments, the depth trajectory is determined by linear regression analysis.

[0010] Both the foregoing summary and the following description of the drawings and detailed description are exemplary and explanatory. They are intended to provide further details of the disclosure but are not to be construed as limiting. Other objects, advantages, and novel features will be readily apparent to those skilled in the art from the following detailed description of the disclosure.[OOH] It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below are provided as being part of the inventive subject matter disclosed herein and may be employed in any combination to achieve the benefits described herein.BRIEF DESCRIPTION OF THE DRAWINGS

[0012] FIG. 1 shows a panel of graphs illustrating the effect of in silico size selection on fetal fraction and read depth.

[0013] FIG. 2 shows a panel of graphs showing sex chromosome depth trajectories observed in screened samples. Increasing fetal fraction is expected to decrease X chromosome depth in samples with an X, XYY, or XY fetus (blue, orange, and green lines). Maternal mosaic loss of X shows the opposite pattern, with normalized depth increasing along with fetal fraction (purple lines).

[0014] FIG. 3 shows a plot illustrating concordance of WGS and targeted cffDNA fetal sex calls.

[0015] FIG. 4 is a visualization of the expected allele fractions for each given mixture of fetal and maternal genotypes with simulated data.-6- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[0016| FIG. 5 is a panel of graphs showing the effect of reference bias at various levels in simulated data.

[0017] FIG. 6 is a panel of plots illustrating depth normalization procedures.DETAILED DESCRIPTION

[0018] Embodiments according to the present disclosure will be described more fully hereinafter. Aspects of the disclosure may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting.10019] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the present application and relevant art and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein.Although not explicitly defined below, such terms should be interpreted according to their common meaning.

[0020] Unless the context indicates otherwise, it is specifically intended that the various features described herein can be used in any combination. Moreover, the disclosure also contemplates that in some embodiments, any feature or combination of features set forth herein can be excluded or omitted. To illustrate, if the specification states that a complex comprises components A, B, and C (or A, B, and / or C), it is specifically intended that any of A, B or C, or a combination thereof, can be omitted and disclaimed singularly or in any combination.-7- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[00211 Unless explicitly indicated otherwise, all specified embodiments, features, and terms intend to include both the recited embodiment, feature, or term and biological equivalents thereof.I. Definitions

[0022] As used herein, the term “about” is to be understood as a relative term that encompasses both the stated numerical value and a range of + / - 10%. For example, the phrase “about 10” should be understood as meaning both “10” and “9 to 11.”[0023J Also as used herein, “and / or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (“or”).

[0024] As used herein, “optional” or “optionally” means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where said event or circumstance occurs and instances where it does not.

[0025] As used herein, a “DNA-binding particle” refers to any conventional solid-phase material that interacts with, or that has been modified to interact with, a DNA fragment, such as a cfDNA fragment. The solid-phase phase material, for example, is any type of an insoluble, usually rigid material, matrix or stationary phase material that interacts with a DNA, either directly or indirectly, in a reaction solution. In certain example embodiments, the DNA-binding particle is a bead.

[0026] As used herein, a “bead” refers to a solid-phase particle of any convenient size and can have an irregular or regular shape. In certain example embodiments, the surface of the bead is modified to bind DNA, either directly and / or indirectly. For example, the bead can include silanol groups, carboxylic groups, or other groups that facilitate the direct and / or interaction of the bead with DNA. In certain example embodiments, silica beads (and gels) can be functionalized by adding primary amines, thiols, sulfhydryls, propyl, octyl, as well as other derivatives to the hydroxyl group (silanol) attached to silica. The bead can fabricated from any number of known materials, including cellulose, cellulose derivatives, acrylic resins, glass, silica gels, polystyrene, gelatin, polyvinyl pyrrolidone, co-polymers of vinyl and -8- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664acrylamide, polystyrene cross-linked with divinylbenzene, or the like, polyacrylamides, latex gels, polystyrene, dextran, rubber, silicon, plastics, nitrocellulose, natural sponges, silica gels, controlled pore glass (CPG), metals, cross-linked dextrans (e.g., Sephadex®), agarose gel (Sepharose®), and other solid phase bead supports known to those of skill in the art. In certain example embodiments, the beads can be packed together so as to form a column that can be used with conventional column chromatography.

[0027] As used herein, the term “genetic variant” when used in reference to a screening, call, or process described herein refers to an alteration from what is considered a non-pathogenic or wild-type gene sequence. Accordingly, the term “genetic variant” includes pathogenic single nucleotide polymorphisms (SNPs), insertions or deletions of bases within a subject’s genome (INDELs), substitution mutations, single gene copy number variations, and the like. Additionally, it should be noted that the term “genetic variant” as used herein is distinct from aneuploidy and the term “genetic variant” does not relate to missing or extra chromosomes. Rather, the term “genetic variant” is to be understood as relating to features or alterations (pathogenic or otherwise) in a subject’s genome sequence and not chromosomal abnormalities.

[0028] As used herein, the terms “cfDNA library” or “nucleic acid library” may be used interchangeably to refer to a collection of nucleic acids, e.g., a collection of cell free nucleic acids derived from a biological sample. In some embodiments, the cfDNA library or nucleic acid library is generated by amplifying the nucleic acid in a sample or otherwise preparing the library using PCR-free based methods. In some embodiments, the cfDNA library or nucleic acid library is generated by amplifying specific target fragments within a sample, as detailed below. In some embodiments, a portion or all of the nucleic acids in the cfDNA library or nucleic acid library comprise an adapter sequence. The adapter sequence can be located at one or both ends. The adapter sequence can be useful, e.g., for a sequencing method (e.g., an NGS method), for amplification, for reverse transcription, or for cloning into a vector.

[0029] The cfDNA library or nucleic acid library can comprise a collection of nucleic acid fragments, which may comprise a target nucleic acid sequence (e.g., a nucleic acid sequence in which a genetic variant associated with a disease can be detected), a reference nucleic acid-9- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664sequence, or a combination thereof. In some embodiments, two or more cfDNA or nucleic acid libraries from the same subject can be combined.10030] As used herein, a “sequence library” is a collection of nucleic acid sequences that have been prepared by sequence a cfDNA library or nucleic acid library e.g., using massively parallel methods, such as next generation sequencing or NGS. NGS generally refers to sequencing methods that allow for massively parallel sequencing of clonally amplified and of single nucleic acid molecules during which a plurality, e.g., millions, of nucleic acid fragments from a single sample or from multiple different samples are sequenced in unison. Non-limiting examples of NGS include sequencing-by-synthesis, sequencing-by-ligation, real-time sequencing, and nanopore sequencing.[00311 As used herein, the term “sample” or “biological sample,” refers to a biological sample obtained or derived from a source of interest, as described herein. In certain embodiments, a source of interest comprises an organism, such as a microbe, a plant, an animal or a human. In certain embodiments, a biological sample is or comprises biological tissue or fluid. In certain embodiments, a biological sample may be or comprise bone marrow; blood (or a fraction thereof, such as plasma or serum); blood cells; ascites; tissue or fine needle biopsy samples; cell-containing body fluids; free floating nucleic acids (e.g., cell free DNA); sputum; saliva; urine; cerebrospinal fluid, peritoneal fluid; pleural fluid; lymph; gynecological fluids; skin swabs; vaginal swabs; oral swabs; nasal swabs; washings or lavages such as a ductal lavages or broncheoalveolar lavages; aspirates; scrapings; bone marrow specimens; tissue biopsy specimens; surgical specimens; feces, other body fluids, secretions, and / or excretions; and / or cells therefrom, etc.II. Sample Preparations

[0032] Cell-free DNA (cfDNA) is a mixture of DNA which varies in properties (e.g., size, sequence, abundance) as well as tissue of origin (e.g., maternal vs. fetal). For example, cfDNA obtained from pregnant women contains DNA of both maternal and fetal origin. A primary driver of NIPS sensitivity when utilizing cfDNA in a given maternal plasma sample is the fetal fraction (FF). The fetal fraction comprises the portion of the total cell-free DNA-10- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664that is from the fetus or derived from cell-free fetal DNA (cffDNA). For most samples, FF values are between 1% and 30%, but in many instances, the amount can be even lower.

[0033] The present disclosure provides sample preparations and methods of preparing samples from pregnant subjects (i.e., an expecting mother or biological mother) that can be used to improve sensitivity, specificity, and minimize noise when performing NIPS. In particular, the sample preparations may rely on physical processing of a cfDNA sample obtained from a pregnant woman, in silico processing of sequencing reads produced from a cfDNA sample obtained from a pregnant woman, or a combination thereof.(a) Physical Enrichment of the Fetal Fraction

[0034] Physical processing of a cfDNA sample (e.g., blood) obtained from a pregnant woman by methods of this disclosure can enrich the fetal fraction of a cfDNA sample by up to 3 times. In particular, the fetal fraction can be enriched in a sample by size selection using a size cut-off that retains most of the fetal cell-free DNA fragments and removes some of the large cell-free maternal DNA fragments. For example, a cut-off may be set to retain cfDNA fragments that are less than about 145 nucleotides in length, less than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in lengths, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, or about 180 nucleotides in length.

[0035] In some embodiments, the methods may be used to select and isolate fragments that are 75 nucleotides or less, 80 nucleotides or less, 85 nucleotides or less, 90 nucleotides or less, 95 nucleotides or less, 100 nucleotides or less, 105 nucleotides or less, 110 nucleotides or less, 115 nucleotides or less, 120 nucleotides or less, 125 nucleotides or less, 130 nucleotides or less, 135 nucleotides or less, 140 nucleotides or less, 145 nucleotides or less, 150 nucleotides or less, 155 nucleotides or less, 160 nucleotides or less, 165 nucleotides or less, 170 nucleotides or less, 175 nucleotides or less, 180 nucleotides or less, 195 nucleotides or less, 200 nucleotides or less, 205 nucleotides or less, 206 nucleotides or less, 210 nucleotides or less, 215 nucleotides or less, 220 nucleotides or less, 225 nucleotides or less, 230 nucleotides or less, 235 nucleotides or less, 240 nucleotides or less, 245 nucleotides or less, 250 nucleotides or less, 255 nucleotides or less, 260 nucleotides or less, 265 nucleotides -11- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664or less, 270 nucleotides or less, 275 nucleotides or less, 280 nucleotides or less, 285 nucleotides or less, 290 nucleotides or less, 295 nucleotides or less, 300 nucleotides or less, 305 nucleotides or less, 310 nucleotides or less, 311 nucleotides or less, 315 nucleotides or less, 320 nucleotides or less, or 325 nucleotides or less. In some embodiments, the target size may be 125 nucleotides or less, 130 nucleotides or less, 135 nucleotides or less, 140 nucleotides or less, 145 nucleotides or less, 150 nucleotides or less, 155 nucleotides or less, 160 nucleotides or less, 165 nucleotides or less, 170 nucleotides or less, 175 nucleotides or less, 180 nucleotides or less, 195 nucleotides or less, or 200 nucleotides or less. Regardless of the precise cut-off or target size, the goal of the process is to retain cffDNA with little or no loss and minimize or deplete cfrnDNA.[0036| This type of size-base exclusion can be performed using electrophoresis (e.g., gel electrophoresis or capillary electrophoresis) and other known methods, which may utilize, for example a DNA binding particle, such as a bead (e.g., an AMPURE™ bead). In one embodiment, nucleic acid electrophoretic separation followed by the recovery of the desired fragment lengths is used. Various known electrophoretic processes may be used for this purpose, but in one embodiment, the NIMBUS Select™ workstation with Ranger Technology™ for high throughput nucleic acid size selection may be used. Other strategies for fragment size selection include electrophoresis on agarose cassettes (BluePippin, Sage Science) following the manufacturer’s instructions for “range” mode. Short fragments are eluted from the gel until the desired target size of the eluted DNA is obtained. Still other methods include, but are not limited to, solid support capture (e.g., affinity column), such as an antibody-coated spin column; synchronous (or non-synchronous) coefficient of drag alteration sizing (SCODA); solid phase reversible immobilization sizing (e.g., using carboxylated magnetic beads); affinity chromatography processes, or combinations of PCR amplification with varied lengths of amplicons and microchip separation.[0037J The disclosed size-based exclusion methods may enrich the fetal fraction in a cfDNA sample by at least 1.1X, 1.2X 1.25X, 1.5X, 1.75X, 2X, 2.25X, 2.5X, 2.75X, 3X, 3.25X, 3.5X, 3.75X, 4X, 4.25X, 4.5X, 4.75X, 5X, 5.5X, 6X, 6.5X, 7X, 7.5X, 8X, 8.5X, 9X, 9.5X, 10X, 15X, 20X, 25X or more.-12- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[0038| Thus, the present disclosure provides methods of size selection of cell-free fetal DNA (cffDNA), comprising subjecting a cell-free DNA (cfDNA) sample comprising cffDNA and cell-free maternal DNA (cfmDNA) to a size exclusion process in order to enrich a fetal fraction in a DNA sample obtained from a pregnant woman.(b) In Silico Enrichment of the Fetal Fraction

[0039] The present disclosure additionally provides in silico enrichment of a cfDNA sample (e.g., blood, plasma, serum, or urine) obtained from a pregnant subject (e.g., an expecting mother or biological mother), which are further able to enrich the fetal fraction of a cfDNA sample. In particular, the disclosed in silico enrichment comprises read-length-based size analysis. For the purposes of the present disclose, a “read-length-based size analysis” is an in silico process that establishes a trajectory from a range of windows that is applied to sequencing read data. The established trajectory can be based on allele balances (ABs) observed across a set of FF levels. Thus, the FF levels are determined via in silico size selection from different size selection windows, thus allowing for distinguishing between maternal and fetal DNA (cfmDNA and cffDNA, respectively). For example, a trajectory could show an AB of 55% at 10% FF, an AB of 60% at 15% FF, and an AB of 65% at 20% FF. This is an upward-sloping trajectory because the AB increases as FF increases. Both the slope and the offset (or intercept) of such a trajectory are useful. For instance, if cfmDNA are primarily selected by a given window, such that FF is as low as possible, the resulting AB mostly reflects the maternal genotype. As more FF is picked up by windows with smaller fragments, the deflection in AB is indicative of the fetal genotype. As a result, if the intercept is -50% (meaning that the mother is heterozygous for the variant), then a trajectory with negative slope suggests the fetus has not inherited a particular maternal variant.10040] Understanding the allele balance in the cfDNA sample improves the ability to focus on the desired sample fraction (e.g., fetal fraction or maternal fraction). In some embodiments, a moderate size selection in vitro (i.e., physical processing / size exclusion) followed by a size-based moving window analysis may provide the best results.[0041| Once a sequence library has been prepared, the fetal fraction of the sequence library may be further processed or enriched using an in silico moving window analysis. For the -13- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664purposes of the disclosed methods, a “window” or “size selection window” is a selection or sub-section of the sequence library that includes a specific size range of sequences. For example, a “window” or “size selection window” may encompass all of the sequences in the sequence library that are 0-145 nucleotides, 0-150 nucleotides, 0-155 nucleotides, 0-160 nucleotides, 0-165 nucleotides, 0-170 nucleotides, 0-175 nucleotides, 0-180 nucleotides, 0-185 nucleotides, 0-190 nucleotides, 0-195 nucleotides, 0-200 nucleotides, 0-205 nucleotide, 0-210 nucleotides, 0-215 nucleotides, 0-220 nucleotides, 0-225 nucleotides, 25-145 nucleotides, 25-150 nucleotides, 25-155 nucleotides, 25-160 nucleotides, 25-165 nucleotides, 25-170 nucleotides, 25-175 nucleotides, 25-180 nucleotides, 25-185 nucleotides, 25-190 nucleotides, 25-195 nucleotides, 25-200 nucleotides, 25-205 nucleotide, 25-210 nucleotides, 25-215 nucleotides, 25-220 nucleotides, 25-225 nucleotides, 50-145 nucleotides, 50-150 nucleotides, 50-155 nucleotides, 50-160 nucleotides, 50-165 nucleotides, 50-170 nucleotides, 50-175 nucleotides, 50-180 nucleotides, 50-185 nucleotides, 50-190 nucleotides, 50-195 nucleotides, 50-200 nucleotides, 50-205 nucleotide, 50-210 nucleotides, 50-215 nucleotides, 50-220 nucleotides, 50-225 nucleotides, 75-145 nucleotides, 75-150 nucleotides, 75-155 nucleotides, 75-160 nucleotides, 75-165 nucleotides, 75-170 nucleotides, 75-175 nucleotides, 75-180 nucleotides, 75-185 nucleotides, 75-190 nucleotides, 75-195 nucleotides, 75-200 nucleotides, 75-205 nucleotide, 75-210 nucleotides, 75-215 nucleotides, 75-220 nucleotides, 75-225 nucleotides, 100-145 nucleotides, 100-150 nucleotides, 100-155 nucleotides, 100-160 nucleotides, 100-165 nucleotides, 100-170 nucleotides, 100-175 nucleotides, 100-180 nucleotides, 100-185 nucleotides, 100-190 nucleotides, 100-195 nucleotides, 100-200 nucleotides, 100-205 nucleotide, 100-210 nucleotides, 100-215 nucleotides, 100-220 nucleotides, 100-225 nucleotides, or any ranges in between. A window can be considered “ungated” if a specific maximum and minimum are not set, and instead the window includes the entire sequence library. FIG. 1 shows an example of the use of size selection windows in assessing fetal and maternal cfDNA frequency.

[0042] Thus, the disclosed methods of in silico enrichment can comprise a read-lengthbased size exclusion of sequences in at least two size selection windows of the sequence library, thereby obtaining at least two fetal fraction-enriched sequence libraries. In some embodiments, 3, 4, 5, 6, 7, 8, 9, 10, or more windows may be assessed. In some embodiments, at least 5, at least 6 at least 7, or at least 8 windows may be assessed. In some -14- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664embodiments, the windows are the same size (e.g., each window encompasses a set range of nucleotides, such as 0-100, 5-105, 10-110, etc.). In some embodiments, the windows are different sizes. For example, the size of each additional window may increase while the minimum remains the same (e.g., a set of windows with size cutoffs of 0-145, 0-150, 0-155, 0-160, 0-165, 0-170, etc.). Comparing the allele balance in each window allows for the calculation of an allele balance trajectory between the various fetal -fraction enriched sequence libraries. The trajectory is the change across the observed windows of the percentage of the allele balance for any given genetic sequence of interest. The allele balance trajectory can be calculated as a slope of the allele balance in each observed window, and it can be visualized in a number of ways.[O043| Further, the library of cfmDNA sequences can be enriched by focusing analysis between two fragment sizes, such as 100-200 nucleotides, 105-200 nucleotides, 110-200 nucleotides, 115-200 nucleotides, 120-200 nucleotides, 125-200 nucleotides, 130-200 nucleotides, 135-200 nucleotides, 140-200 nucleotides, 140-200 nucleotides, 145-200 nucleotides, 150-200 nucleotides, 155-200 nucleotides, 160-200 nucleotides, 165-200 nucleotides, 170-200 nucleotides, or 175-200 nucleotides or any size range in between. In some embodiments, the size range selected for enrichment may be about 155 to about 200 nucleotides.

[0044] In some embodiments, the at least two size selection windows of the sequence library are selected from (i) sequences that are 0-145 nucleotides, (ii) sequences that are 0-150 nucleotides, (iii) 0-155 nucleotides, (iv) 0-160 nucleotides, (v) 0-165 nucleotides, (vi) 0-168 nucleotides, (vii) 0-170 nucleotides, (viii) 0-175 nucleotides, (ix) 0-180 nucleotides, (x) 0-185 nucleotides, (xi) 0-190 nucleotides, (xii) 0-195 nucleotides, (xiii) 0-200 nucleotides, and (xiv) ungated. In some embodiments, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 windows are selected from (i) sequences that are 0-145 nucleotides, (ii) sequences that are 0-150 nucleotides, (iii) 0-155 nucleotides, (iv) 0-160 nucleotides, (v) 0-165 nucleotides, (vi) 0-168 nucleotides, (vii) 0-170 nucleotides, (viii) 0-175 nucleotides, (ix) 0-180 nucleotides, (x) 0-185 nucleotides, (xi) 0-190 nucleotides, (xii) 0-195 nucleotides, (xiii) 0-200 nucleotides, and (xiv) ungated.-15- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[00451 Enriching the fetal fraction of the sequence library in silico can also further comprise identifying and separating cffDNA from cfrnDNA by comparing sequence reads of cffDNA and cfrnDNA in the first sequence library to a reference genome, demultiplexing sequence reads from the first library, removing duplicate sequences from the first sequence library, or a combination thereof.[00461 For instance, sample preparation can include in silico binary alignment processing in which the collected DNA sample may be computationally reconstructed by using overlaps between short sequencing reads. The reconstruction of a genome can be facilitated if a reference genome is available to which the sequencing reads can be aligned. A sequence alignment tool can be used to map short reads stored in a file to the reference genome.Subsequently, depth and variant processing can be used to identify and isolate specific gene sequences to inform follow-on analyses, which may be directed to, for example, identification of specific aneuploidies and / or genetic variants. In this way, with only a limited amount of initially collected cfDNA, specific portions of the collected DNA may be delineated and assembled for use with specific assay detections.[0047| The collected DNA sample may be computationally reconstructed by using overlaps between short sequencing reads. Thus, DNA samples may be delineated at a first pass using a demultiplexer (e.g., demux), which allows for the determination unique molecule identifiers that may be needed for assessment for specific screenings (e.g., carrier, prenatal, and the like). Unique molecular identifiers (UMIs), (sometimes called molecular barcodes (MBC)) are short sequences (e.g., tags) added to DNA fragments during sequencing library preparation protocols to identify the desired DNA molecule upon which a specific screen may be directed. These tags are added before any amplification and can be used to reduce errors and quantitative bias introduced by the amplification.

[0048] Once tagged, the specific tagged DNA sequences may be initially aligned using an alignment processing to delineate the desired DNA sequences from each other. Then a duplication reduction (e.g., “deduping”) can clean up any errant identification and / or misalignments, which may comprise retaining a consensus sequence of overlapping portions-16- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664of paired end reads. Thereafter, a realignment process can be performed to produce a more robust delineation between desired and tagged DNA sequences.

[0049] Amplification may be used to isolate specific nucleic acid sequences that are of interest or desirable for subsequent screening. For example, in silico amplification can be accomplished using computational tools to calculate theoretical polymerase chain reaction (PCR) results using a given set of primers (probes) to amplify DNA sequences from a sequenced DNA sample. After amplification, the quality of the specific read sequences may be improved by removing (c.g, trimming) partial (e.g., incomplete) sequences that are at beginnings and ending of sequences. One exemplary, but non-limiting, method for accomplishing this is called Paired-End (PE) trimming, which can include two input files (for forward and reverse reads) and four output files (for forward paired, forward unpaired, reverse paired and reverse unpaired reads) to identify and remove partial sequences. The reconstruction of a useful DNA sample can be facilitated and stored in a ready to use file. Further, the file may be delineated into different bins regarding fragment length (in terms of number of nucleotides).[0050| Specific gene sequences stored in the file may be identified and isolated to inform follow-on analyses directed to specific aneuploidies and / or causal genetic variants as part of a depth and variant processing. This file may be used during specific procedures to alleviate biasing in the initial collected sample. The foregoing in silico steps and computational preparations can optimize the DNA sample for specific DNA sequences for the specific goals of a given test or screen.[0051| The disclosed in silico processing may enrich the fetal fraction in a cfDNA sample by at least 1.1X, 1.2X 1.25X, 1.5X, 1.75X, 2X, 2.25X, 2.5X, 2.75X, 3X, 3.25X, 3.5X, 3.75X, 4X, 4.25X, 4.5X, 5.75X, 5X, 5.5X, 6X, 6.5X, 7X, 7.5X, 8X, 8.5X, 9X, 9.5X, 10X, 15X, 20X, 25X or more.

[0052] Alternatively, if desirable, the disclosed in silico processing may also be used to enrich the maternal fraction of a sample by selecting for larger fragments. In some embodiments, the disclosed in silico processing may enrich the maternal fraction in a cfDNA sample by at least 1.1X, 1.2X 1.25X, 1.5X, 1.75X, 2X, 2.25X, 2.5X, 2.75X, 3X, 3.25X, 3.5X,-17- 4919-8854-3250.1Atty. Dkt. No.: 131588-16643.75X, 4X, 4.25X, 4.5X, 5.75X, 5X, 5.5X, 6X, 6.5X, 7X, 7.5X, 8X, 8.5X, 9X, 9.5X, 10X, 15X, 20X, 25X or more.

[0053] Thus, the present disclosure provides methods of in silico sorting and enrichment of cffDNA, comprising sequencing a cell-free DNA (cfDNA) sample comprising cffDNA and cell-free DNA maternal (cfmDNA), and performing read-length-based size analysis, wherein a size-based moving window is used to establish a trajectory based on allele balances between cfmDNA and cffDNA to elucidate a genotype for the cfmDNA or cffDNA in a given sample. The present disclosure also thus provides methods of in silico processing of cell-free DNA (cfDNA), comprising sequencing cfDNA in a sample obtained from a pregnant subject to obtain a sequence library, wherein the biological sample comprises cell-free fetal DNA (cffDNA) and cell-free maternal DNA (cfmDNA); determining an estimated fetal fraction and an estimated normalized read depth for each of one or more calling regions in at least two size selection windows of the sequence library in a read-length-based size analysis; and determining, for each calling region, a depth trajectory, wherein the depth trajectory is a rate of change in normalized read depth as a function of fetal fraction. In some embodiments, such methods may further comprise identifying and separating cffDNA from cfmDNA by comparing sequence reads of cffDNA and cfmDNA to a reference genome, demultiplexing the sequence reads, and removing duplicate sequences.(c) Combination of Physical Enrichment and In Silico Enrichment

[0054] The foregoing methods of sample preparation can be performed individually or in combination to enrich the fetal fraction of a given sample. Prior to either physical enrichment or in silico enrichment, total cfDNA may be isolated from a maternal sample (e.g., blood, plasma, serum, or urine) by conventional means. For example, total cfDNA can extracted from clarified plasma obtained from a sample using an APOSTLE™ Cell-Free DNA Extraction kit. Other known methods and commercially available kits for cfDNA extraction can also be used, including but not limited to, kits produced Molzym GmbH & Co KG (Bremen, DE), Qiagen (Hilden, DE), Macherey-Nagel (Duren, DE), Roche (Basel, CH), and Sigma (Deisenhofen, DE).-18- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[00551 After physical enrichment and in silico enrichment, the fetal fraction may be 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 85%, 90%, 95%, 99% or 100% of the DNA sample that is used for further testing, screening, or analysis.Additionally or alternatively, after physical enrichment and in silico enrichment, the fetal fraction may be about 5% to 100%, about 5% to about 95%, about 5% to about 90%, about 5% to about 85%, about 5% to about 80%, about 5% to about 75%, about 10% to 100%, about 10% to about 95%, about 10% to about 90%, about 10% to about 85%, about 10% to about 80%, about 10% to about 75%, about 15% to 100%, about 15% to about 95%, about 15% to about 90%, about 15% to about 85%, about 15% to about 80%, about 15% to about 75%, about 20% to 100%, about 20% to about 95%, about 20% to about 90%, about 20% to about 85%, about 20% to about 80%, about 20% to about 75%, about 25% to 100%, about 25% to about 95%, about 25% to about 90%, about 25% to about 85%, about 25% to about 80%, about 25% to about 75%, about 30% to 100%, about 30% to about 95%, about 30% to about 90%, about 30% to about 85%, about 30% to about 80%, about 30% to about 75%, about 35% to 100%, about 35% to about 95%, about 35% to about 90%, about 35% to about 85%, about 35% to about 80%, about 35% to about 75%, about 40% to 100%, about 40% to about 95%, about 40% to about 90%, about 40% to about 85%, about 40% to about 80%, about 40% to about 75%, about 45% to 100%, about 45% to about 95%, about 45% to about 90%, about 45% to about 85%, about 45% to about 80%, about 45% to about 75%, about 50% to 100%, about 50% to about 95%, about 50% to about 90%, about 50% to about 85%, about 50% to about 80%, and about 50% to about 75%.

[0056] Thus, the present disclosure provides methods of preparing a cell-free DNA sample with an enriched fetal fraction, comprising processing of a cfDNA sample using size exclusion to retain cell-free fetal DNA (cffDNA) and remove cell-free maternal DNA (cfmDNA), in silico processing to identify and isolate cffDNA from cfmDNA, or a combination thereof.-19- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664III. Methods of Screening

[0057] The present disclosure provides methods of assessing or screening for aneuploidy in a fetus utilizing only a single biological sample (e.g., blood, plasma, serum, or urine) from the biological mother of the fetus. Conventionally, testing for aneuploidy was subject to a high-false-positive rate. Such testing also often required multiple samples. The disclosed methods overcome these issues and function to provide new and useful methods that improve conventional non-invasive pre-natal screening (NIPS).

[0058] The disclosed methods may comprise screens that utilize the same single sample of cfDNA from the biological mother. Such a screen may involve determining a depth trajectory, the pattern and sign of which may inform the nature of a detected aneuploidy. An analysis of the covariation of read depth and cfDNA fragment size can reveal with origin of a detected aneuploidy.[0059| In a screen, a specific subsection of the collected sample (e.g., a subsection of smaller cfDNA fragments) can be used for optimizing the fetal fraction to assess the presence or absence of an aneuploidy condition. The presence or absence of the aneuploidy can be established by determining depth trajectories that allow for distinguishing maternal and fetal DNA. In this way, the disclosed screen can concurrently assess fetal aneuploidy and maternal aneuploidy, which was not previously possible. The screen may additionally or alternatively rely on sequencing depth to determine whether an aneuploidy is present or absent in a given sample.

[0060] For example, after sequencing cfDNA from a biological sample obtained from a pregnant mother, long DNA fragments can be computationally removed from further analysis, thus enriching the sample for cell-free fetal DNA in silico. The number of sequencing reads covering a region of the genome (often called “read depth”) is partially a function of the copy number of that region in the mother and fetus. For example, if there is a fetal trisomy on chromosome 21, one should observe more reads aligning to a given region of chromosome 21 than to similar regions of euploid chromosomes. If a trisomy is in the fetus, the strength of the read depth signal should be positively-correlated with the fetal fraction. That is, if, prior to in silico size selection, a sample has a fetal fraction 0.1 and an estimated -20- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664normalized read depth 1.05 on chromosome 21, and then the sample is filtered in silico to remove long fragments causing the fetal fraction to increase to 0.2, the normalized read depth should also go up to 1.1. In the case of a monosomy, the sign of the relationship between the read depth signal and the fetal fraction should be reversed. That is, if a sample containing a fetal monosomy X initially measures a fetal fraction of 0.1 and an estimated read depth 0.95, and is then filtered in silico to achieve a fetal fraction 0.2, then the read depth should decrease to 0.9. In contrast if a sample has a euploid fetus and a mother with an aneuploidy (typically but not always mosaic), increasing the fetal fraction via in silico size selection should decrease the strength of the read depth signal. In this manner, a maternal aneuploidy can be distinguished from a fetal aneuploidy by assessing only the depth trajectory - the rate of change of the estimated read depth as a function of fetal fraction.|0061] The methods may begin with collecting a sample from a biological mother, typically through a blood draw, though other biological samples are contemplated (e.g., plasma, serum, urine, etc.). This sample comprises cell free DNA (cfDNA). cfDNA may include various DNA freely circulating, including circulating tumor DNA (ctDNA), cell-free mitochondrial DNA (cf mtDNA), cell-free maternal DNA (cfmDNA) and cell-free fetal DNA (cffDNA). As the subject is an expecting mother, a certain level of fetal DNA will also be present in the cfDNA sample. Further, a targeted DNA capture suited to specific gene sequences may also be performed. Thus, aspects of both cfDNA as well as targeted capture may be employed for the purposes of the disclosed methods.[00621 In one aspect, the present disclosure provides methods of screening for fetal sex chromosome aneuploidy (SCA) in a sample comprising cell-free fetal DNA (cffDNA) and cell-free maternal DNA (cfmDNA), comprising: (a) extracting cffDNA and cfmDNA from a biological sample obtained from a pregnant subject; (b) sequencing the cffDNA and cfmDNA fragments to obtain a sequence library; (c) preparing at least two subsets of sequencing reads from the sequence library, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length in a range from about 145 nucleotides to about 200 nucleotides, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with different maximum fragment lengths; (d) calculating, for each subset of sequencing reads, an estimated fetal fraction and an estimated-21- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664normalized read depth for each of one or more calling regions; (e) calculating, for each calling region, a depth trajectory, wherein the depth trajectory is a rate of change in normalized read depth as a function of fetal fraction; (f) detecting maternal mosaic aneuploidy in the one or more calling region, wherein maternal mosaic aneuploidy is detected when: (i) a copy number greater than 2 is identified in the calling region and the depth trajectory is negative, or (ii) a copy number less than 2 is identified in the calling region and depth trajectory is positive; and (g) detecting fetal aneuploidy in the one or more calling region, wherein fetal aneuploidy is detected when: (iii) a copy number greater than 2 is identified in the calling region and the depth trajectory is positive, or (iv) a copy number less than 2 is identified in the calling region and the depth trajectory is negative. In some embodiments, both the cfDNA library is enriched to increase the fetal fraction and the sequence library in enriched to increase the fetal fraction. The following sections provide more detail regarding relevant processes for each form of enrichment.(a) Biological Sample

[0063] For the purposes of the disclosed methods, the biological sample needs to contain cfDNA, including cffDNA. Examples of samples that may be obtained from a biological mother for use in the disclosed methods include, but are not limited to, blood, serum, plasma, and urine. In some embodiments, the sample for use in the disclosed methods is blood. In some embodiments, the sample for use in the disclosed methods is serum. In some embodiments, the sample for use in the disclosed methods is plasma. In some embodiments, the sample for use in the disclosed methods is urine.[00641 In some embodiments, nucleic acid extraction will be performed prior to amplification of the cfDNA in the sample and preparation of the cfDNA library or cfDNA libraries. Various protocols for nucleic acid extraction may be used in the methods of the present technology. Examples of commercially available nucleic acid purification kits include Apostle MiniMax Kit, Molzym GmbH & Co KG (Bremen, DE), Qiagen (Hilden, DE), Macherey-Nagel (Duren, DE), Roche (Basel, CH) or Sigma (Deisenhofen, DE). Other systems for nucleic acid purification, which are based on the use of polystyrene beads etc., as-22- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664support material may also be used. Automated DNA extraction platforms may also be used, such as the QIAsymphony®, Hamilton® automation, or a Biorobot® EZ1™ automated system.(b) cfDNA Library Preparation|0065] cfDNA library preparation can be performed using known methods of amplification (e.g., an xGen Prism Library Prep kit (IDT™)) as well as PCR-free methods of library preparation, such as COLLIBRI™, NEBNEXT® and TRUSEQ™ kits produced by Illumina, the KAPA™ HyperPrep kit produced by Roche, and the MGIEasy kit produced by MG Tech. Optionally, preparation of the cfDNAlibrary can include a step of end repair. cfDNA may comprise overhangs of other damage to the ends of a given nucleic acid sequence, and end repair can convert such damaged or sheared DNA into blunt-ended molecules that are more easily ligated to adaptors, tags, or barcodes. One or more ligation reactions can be implemented to attach adaptors to the nucleic acid sequences from the sample. The adaptors are used to both facilitate amplification by providing a uniform sequence to which primers can anneal, and to separate the sequences of interest. Adaptors may be a unique length (to allow separation and isolation via electrophoresis), a unique sequence, or comprise other features to aid in isolation of target nucleic acid sequences after amplification.]0066j PCR-based methods are commonly used to generate an amplified library in advance of sequencing or analysis of a given nucleic acid sample; however, PCR is not required, and those skilled in the art will know of PCR-free methods of library preparation as well. Various PCR methods utilizing commercially available reagents and polymerases may be utilized for the nucleic acid amplification portion of library preparation (e.g., KAPA™ HiFi HotStart Ready Mix).

[0067] Using any of the approaches described herein or otherwise known to those skilled in the art, a cfDNA library can be prepared from a maternal sample. Optionally, the cfDNA library can be cleaned using known methods, such as isolation of the amplified fragments in the library using AMPURE beads or other similar methods that allow for the removal of salts, unwanted macromolecules, and other debris from the sample.-23- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[0068| Prior to sequencing of the cfDNA library, the fetal fraction may be enriched as described herein. Additionally or alternatively, the fetal fraction may be enriched the maternal sample prior to preparation of the cfDNA library. Briefly, enriching the fetal fraction of the cfDNA library or maternal sample is a physical processing of the sample, which can comprise removing from the cfDNA library any DNA fragments that are greater than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in lengths, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, about 180 nucleotides in length about 185 nucleotides in length, about 190 nucleotides in length, about 195 nucleotides in length, or about 200 nucleotides in length.

[0069] This type of size-base exclusion can be performed using electrophoresis (e.g., gel electrophoresis or capillary electrophoresis) and other known methods, which may utilize, for example a DNA binding particle, such as a bead (e.g., an AMPURE™ bead). In one embodiment, nucleic acid electrophoretic separation followed by the recovery of the desired fragment lengths is used. Various known electrophoretic processes may be used for this purpose. For example, in one embodiment, the NIMBUS Select™ workstation with Ranger Technology™ for high throughput nucleic acid size selection may be used. In another embodiments, the BluePippin electrophoresis system may be used.

[0070] Prior methods of size-based exclusion have been used to enrich the fetal fraction of cfDNA libraries, but unlike those prior methods, the present inventors discovered that using a higher cutoff value can improve noise reduction when combined with further in silico selection, as described herein. Briefly, while not being bound by theory, noise may be reduced because of retention of a higher total number of cffDNA molecules via more permissive size selection. It was conventionally believed that using a lower cutoff value was superior because it excluded more maternal cfDNA. FIG. 1 shows a comparison of the disclosed size exclusion process compared to traditional approaches. These more restrictive, traditional methods also discarded a not-insignificant amount of cffDNA. The disclosed approach of combining a more “permissive” size exclusion technique with a further in silico enrichment is thus an improvement that specifically addresses a critical problem in the field of pre-natal screening: enrichment of the fetal fraction without inadvertently or unnecessarily discarding cffDNA, which may be in preciously limited supply within a given sample.-24- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[00711 The disclosed size-based exclusion methods may enrich the fetal fraction in a cfDNA sample by at least 1.1X, 1.2X 1.25X, 1.5X, 1.75X, 2X, 2.25X, 2.5X, 2.75X, 3X, 3.25X, 3.5X, 3.75X, 4X, 4.25X, 4.5X, 5.75X, 5X, 5.5X, 6X, 6.5X, 7X, 7.5X, 8X, 8.5X, 9X, 9.5X, 10X, 15X, 20X, 25X or more.(c) Sequencing the Nucleic Acid Library

[0072] The nucleic acid library, which may be enriched for fetal fraction, can be sequenced using known sequencing methods (e.g., NovaSeq sequencers and flowcells, Illumina sequencers, pyrosequencing, Reversible dye-terminator sequencing, SOLiD sequencing, Ion semiconductor sequencing, Helioscope single molecule sequencing, Ion Torrent™ (Life Technologies, Carlsbad, CA) amplicon sequencing system, 454™ GS FLX ™ sequencing system, SMRT™ sequencing, etc.). In some embodiments, the cfDNA fragments in the nucleic acid library are sequenced from both ends (i.e., paired-end mode). In some embodiments, the cfDNA fragments in the nucleic acid library are sequenced are one end (i.e., single-end mode). In some embodiments, the cfDNA fragments in the nucleic acid library may be isolated or bound using a targeted capture method, such as hybrid capture. Sequencing from both ends of each fragment allows the fragment lengths to be determined. In some embodiments, the resulting sequences can be used to map the cfDNA fragments.

[0073] In some embodiments, the disclosed methods may utilize target capture methods to sequence only the particular fragments of interest. Fragments of interest may, for example, correspond to cfDNA that encodes a gene related to a genetic disease, condition, or trait (i.e., a genetic variant of interest) or cfDNA that corresponds to a particular chromosome.[00741 Once the cfDNA fragments in the nucleic acid library have been sequenced, the fetal fraction of the sequence library may be further enriched using an in silico moving window analysis described herein. For the purposed of the disclosed methods, a “window” of “size selection window” is a selection or sub-section of the sequence library that includes a specific size range of sequences. For example, a “window” or “size selection window” may encompass all of the sequences in the sequence library that are 0-145 nucleotides, 0-150 nucleotides, 0-155 nucleotides, 0-160 nucleotides, 0-165 nucleotides, 0-170 nucleotides, 0-175 nucleotides, 0-180 nucleotides, 0-185 nucleotides, 0-190 nucleotides, 0-195 nucleotides,-25- 4919-8854-3250.1Atty. Dkt. No.: 131588-16640-200 nucleotides, 25-145 nucleotides, 25-150 nucleotides, 25-155 nucleotides, 25-160 nucleotides, 25-165 nucleotides, 25-170 nucleotides, 25-175 nucleotides, 25-180 nucleotides, 25-185 nucleotides, 25-190 nucleotides, 25-195 nucleotides, 25-200 nucleotides, 50-145 nucleotides, 50-150 nucleotides, 50-155 nucleotides, 50-160 nucleotides, 50-165 nucleotides, 50-170 nucleotides, 50-175 nucleotides, 50-180 nucleotides, 50-185 nucleotides, 50-190 nucleotides, 50-195 nucleotides, 50-200 nucleotides, 75-145 nucleotides, 75-150 nucleotides, 75-155 nucleotides, 75-160 nucleotides, 75-165 nucleotides, 75-170 nucleotides, 75-175 nucleotides, 75-180 nucleotides, 75-185 nucleotides, 75-190 nucleotides, 75-195 nucleotides, 75-200 nucleotides, 100-145 nucleotides, 100-150 nucleotides, 100-155 nucleotides, 100-160 nucleotides, 100-165 nucleotides, 100-170 nucleotides, 100-175 nucleotides, 100-180 nucleotides, 100-185 nucleotides, 100-190 nucleotides, 100-195 nucleotides, 100-200 nucleotides, or any ranges in between. In some embodiments, the disclosed methods may utilize two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more) windows that encompass fragments in two or more (e.g., 2,3 ,4, 5, 6, 7, 8, 9, or 10 or more) size ranges selected from 0-145 nucleotides, 0-146 nucleotides, 0-147 nucleotides, 0-148 nucleotides, 0-149 nucleotides, 0-150 nucleotides, 0-151 nucleotides, 0-152 nucleotides, 0-153 nucleotides, 0-154 nucleotides, 0-155 nucleotides, 0-156 nucleotides, -157 nucleotides, 0-158 nucleotides, 0-159 nucleotides, 0-160 nucleotides, 0-161 nucleotides, 0-162 nucleotides, 0-163 nucleotides, 0-164 nucleotides, 0-165 nucleotides, 0-166 nucleotides, 0-167 nucleotides, 0-168 nucleotides, 0-169 nucleotides, 0-170 nucleotides, 0-171 nucleotides, 0-172 nucleotides, 0-173 nucleotides, 0-174 nucleotides, 0-175 nucleotides, 0-176 nucleotides, 0-177 nucleotides, 0-178 nucleotides, 0-179 nucleotides, 0-180 nucleotides, 0-181 nucleotides, 0-182 nucleotides, 0-183 nucleotides, 0-184 nucleotides, 0-185 nucleotides, 0-186 nucleotides, 0-187 nucleotides, 0-188 nucleotides, 0-189 nucleotides, 0-190 nucleotides, 0-191 nucleotides, 0-192 nucleotides, 0-193 nucleotides, 0-194 nucleotides, 0-195 nucleotides, 0-196 nucleotides, 0-197 nucleotides, 0-198 nucleotides, 0-199 nucleotides, 0-200 nucleotides, 5-145 nucleotides, 5-146 nucleotides, 5-147 nucleotides, 5-148 nucleotides, 5-149 nucleotides, 5-150 nucleotides, 5-151 nucleotides, 5-152 nucleotides, 5-153 nucleotides, 5-154 nucleotides, 5-155 nucleotides, 5-156 nucleotides, -157 nucleotides, 5-158 nucleotides, 5-159 nucleotides, 5-160 nucleotides, 5-161 nucleotides, 5-162 nucleotides, 5-163 nucleotides, 5-164 nucleotides, 5-165 nucleotides, 5-166 nucleotides, 5-167 nucleotides, 5--26- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664168 nucleotides, 5-169 nucleotides, 5-170 nucleotides, 5-171 nucleotides, 5-172 nucleotides, 5-173 nucleotides, 5-174 nucleotides, 5-175 nucleotides, 5-176 nucleotides, 5-177 nucleotides, 5-178 nucleotides, 5-179 nucleotides, 5-180 nucleotides, 5-181 nucleotides, 5-182 nucleotides, 5-183 nucleotides, 5-184 nucleotides, 5-185 nucleotides, 5-186 nucleotides, 5-187 nucleotides, 5-188 nucleotides, 5-189 nucleotides, 5-190 nucleotides, 5-191 nucleotides, 5-192 nucleotides, 5-193 nucleotides, 5-194 nucleotides, 5-195 nucleotides, 5-196 nucleotides, 5-197 nucleotides, 5-198 nucleotides, 5-199 nucleotides, 5-200 nucleotides, 10-145 nucleotides, 10-146 nucleotides, 10-147 nucleotides, 10-148 nucleotides, 10-149 nucleotides, 10-150 nucleotides, 10-151 nucleotides, 10-152 nucleotides, 10-153 nucleotides, 10-154 nucleotides, 10-155 nucleotides, 10-156 nucleotides, -157 nucleotides, 10-158 nucleotides, 10-159 nucleotides, 10-160 nucleotides, 10-161 nucleotides, 10-162 nucleotides, 10-163 nucleotides, 10-164 nucleotides, 10-165 nucleotides, 10-166 nucleotides, 10-167 nucleotides, 10-168 nucleotides, 10-169 nucleotides, 10-170 nucleotides, 10-171 nucleotides, 10-172 nucleotides, 10-173 nucleotides, 10-174 nucleotides, 10-175 nucleotides, 10-176 nucleotides, 10-177 nucleotides, 10-178 nucleotides, 10-179 nucleotides, 10-180 nucleotides, 10-181 nucleotides, 10-182 nucleotides, 10-183 nucleotides, 10-184 nucleotides, 10-185 nucleotides, 10-186 nucleotides, 10-187 nucleotides, 10-188 nucleotides, 10-189 nucleotides, 10-190 nucleotides, 10-191 nucleotides, 10-192 nucleotides, 10-193 nucleotides, 10-194 nucleotides, 10-195 nucleotides, 10-196 nucleotides, 10-197 nucleotides, 10-198 nucleotides, 10-199 nucleotides, 10-200 nucleotides, 15-145 nucleotides, 15-146 nucleotides, 15-147 nucleotides, 15-148 nucleotides, 15-149 nucleotides, 15-150 nucleotides, 15-151 nucleotides, 15-152 nucleotides, 15-153 nucleotides, 15-154 nucleotides, 15-155 nucleotides, 15-156 nucleotides, -157 nucleotides, 15-158 nucleotides, 15-159 nucleotides, 15-160 nucleotides, 15-161 nucleotides, 15-162 nucleotides, 15-163 nucleotides, 15-164 nucleotides, 15-165 nucleotides, 15-166 nucleotides, 15-167 nucleotides, 15-168 nucleotides, 15-169 nucleotides, 15-170 nucleotides, 15-171 nucleotides, 15-172 nucleotides, 15-173 nucleotides, 15-174 nucleotides, 15-175 nucleotides, 15-176 nucleotides, 15-177 nucleotides, 15-178 nucleotides, 15-179 nucleotides, 15-180 nucleotides, 15-181 nucleotides, 15-182 nucleotides, 15-183 nucleotides, 15-184 nucleotides, 15-185 nucleotides, 15-186 nucleotides, 15-187 nucleotides, 15-188 nucleotides, 15-189 nucleotides, 15-190 nucleotides, 15-191 nucleotides, 15-192 nucleotides, 15-193 nucleotides, 15-194 nucleotides, 15-195 nucleotides, 15-196 nucleotides,-27- 4919-8854-3250.1Atty. Dkt. No.: 131588-166415-197 nucleotides, 15-198 nucleotides, 15-199 nucleotides, 15-200 nucleotides, or any ranges in between. In some embodiments, the disclosed methods may utilize at least eight windows comprising size ranges including 0 to about 145 nucleotides, 0 to about 150 nucleotides, 0 to about 155 nucleotides, 0 to about 160 nucleotides, 0 to about 165 nucleotides, 0 to about 168 nucleotides, 0 to about 175 nucleotides, and 0 to about 190 nucleotides. In some embodiments, the disclosed methods may utilize eight windows comprising the size ranges 0-145 nucleotides, 0-150 nucleotides, 0-155 nucleotides, 0-160 nucleotides, 0-165 nucleotides, 0-168 nucleotides, 0-175 nucleotides, and 0-190 nucleotides.

[0075] In some embodiments, the disclosed methods may utilize two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more) windows that encompass fragments in two or more (e.g., 2,3 ,4, 5, 6, 7, 8, 9, or 10 or more) size ranges selected from about 20 to about 145 nucleotides, about 20 to about 150 nucleotides, about 20 to about 155 nucleotides, about 20 to about 160 nucleotides, about 20 to about 165 nucleotides, about 20 to about 170 nucleotides, about 20 to about 175 nucleotides, about 20 to about 180 nucleotides, about 20 to about 185 nucleotides, about 20 to about 190 nucleotides, about 20 to about 195 nucleotides, about 20 to about 200 nucleotides, about 25 to about 145 nucleotides, about 25 to about 150 nucleotides, about 25 to about 155 nucleotides, about 25 to about 160 nucleotides, about 25 to about 165 nucleotides, about 25 to about 170 nucleotides, about 25 to about 175 nucleotides, about 25 to about 180 nucleotides, about 25 to about 185 nucleotides, about 25 to about 190 nucleotides, about 25 to about 195 nucleotides, about 25 to about 200 nucleotides, about 50 to about 145 nucleotides, about 50 to about 150 nucleotides, about 50 to about 155 nucleotides, about 50 to about 160 nucleotides, about 50 to about 165 nucleotides, about 50 to about 170 nucleotides, about 50 to about 175 nucleotides, about 50 to about 180 nucleotides, about 50 to about 185 nucleotides, about 50 to about 190 nucleotides, about 50 to about 195 nucleotides, about 50 to about 200 nucleotides, about 75 to about 145 nucleotides, about 75 to about 150 nucleotides, about 75 to about 155 nucleotides, about 75 to about 160 nucleotides, about 75 to about 165 nucleotides, about 75 to about 170 nucleotides, about 75 to about 175 nucleotides, about 75 to about 180 nucleotides, about 75 to about 185 nucleotides, about 75 to about 190 nucleotides, about 75 to about 195 nucleotides, about 75 to about 200 nucleotides, about 100 to about 145 nucleotides, about 100 to about 150 nucleotides, about 100 to about 155 nucleotides, about 100 to about 160 nucleotides, about 100 to about 165 nucleotides, about 100 to about 170 nucleotides, -28- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664about 100 to about 175 nucleotides, about 100 to about 180 nucleotides, about 100 to about 185 nucleotides, about 100 to about 190 nucleotides, about 100 to about 195 nucleotides, about 100 to about 200 nucleotides, or any ranges in between.

[0076] For the purposes of the disclosed methods, the windows used for subsequent analysis and trajectory calculations can be different sizes (i.e., each window encompassing a different range of fragment sizes, such as 0-145, 0-150, 0-155, etc.) or the windows may be the same size (i.e., each window encompassing different fragments but across a set size range, such as 0-145, 5-150, 10-155, etc.). A window can be considered “ungated” is a specific maximum and minimum are not set, and instead the window includes the entire sequence library.

[0077] As described above, enriching the fetal fraction of the sequence library is a form of in silico enrichment, which can comprise a read-length-based size exclusion of sequences in at least two windows of the sequence library, thereby obtaining at least two fetal fraction-enriched sequence libraries. Comparing the allele balance in each window allows for the calculation of an allele balance trajectory between the various fetal -fraction enriched sequence libraries. The allele balance trajectory is the change across the observed windows of the percentage of the allele balance for any given genetic sequence of interest. The allele balance trajectory can be calculated as a slope of the allele balance in each observed window, and it can be visualized in a number of ways. It should be understood that each window (e.g., 0-145, 0-150, 0-155, etc.) will possess an associated fetal fraction that is distinct from the other windows, and this fetal fraction value can serve as the X-axis for a trajectory plot.

[0078] Regardless of how the allele balance data is visualized, it can be utilized to identify heterozygous and homozygous mutations or markers of interest within the cfDNA sequence library. For example, the allele balance could be converted to a banding pattern plot in which the y-axis displays the percentage of cfDNA in a sample with a given allele for a particular gene or nucleic acid sequence of interest (e.g., 0%, 10%, 20%, 30%, 40%, 50%, 60%), and the x-axis displays different alternatives for the gene or nucleic acid of interest that correspond to the wild-type sequence or a mutation / variant associated with a particular disease, condition, or trait-29- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[0O79| By way of example, in a sample or window that has 20% fetal fraction may show a band at 10% on the y-axis corresponds to a fetus that is a carrier from the biological father’s DNA (or, in some instances, it may represent a de novo mutation in the fetus). A band at 40% on the y-axis within this window corresponds to a fetus that is negative (i.e., homozygous reference) for the mutation / variant in the gene or sequence of interest. A band at 50% on the y-axis corresponds to a fetus that is a carrier from the biological mother’s DNA or, if both the mother and the father carry the same mutation / variant (i.e., alt allele), it is possible that the fetus has the father’s alt allele and the mother’s reference allele. Thus, the band at 50% may indicate that the fetus and the mother each have one alt allele. A band at 60% on the y-axis corresponds to a fetus that is homozygous alt (i.e., the fetus is positive) for the mutation / variant in the gene or sequence of interest. As such, analyzing the allele balance across multiple windows of the sequence library (i.e., multiple fetal fraction-enriched sequence libraries) provides a new and useful way to establish the presence of absence of a genetic variant / mutation from a maternal sample comprising cfDNA without the need for any additional samples. Moreover, as a result of the enrichment provided by moving window analysis, noise and background are significantly reduced, which allows robust detection even in samples with vanishingly small amounts of cffDNA (e.g., <5% of total cfDNA).Additionally, it should be noted that the foregoing bands may shift or move, and they may not be precisely at 10%, 40%, 50%, and 60%, respectively, if the window or sample does not have 20% fetal fraction.

[0080] While at least two windows are needed in order to determine an allele balance trajectory, the number of windows that can be assessed for the purposes of the disclosed methods in not particularly limited and may include multiple additional windows. Thus, in some embodiments, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 windows of the first sequence library are assessed, thereby obtaining, respectively, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 fetal fraction-enriched sequence libraries from which cffDNA sequences can be identified and isolated. In some embodiments, the at least two windows of the sequence library are selected from (i) sequences that are 0-145 nucleotides, (ii) sequences that are 0-150 nucleotides, (iii) 0-155 nucleotides, (iv) 0-160 nucleotides, (v) 0-165 nucleotides, (vi) 0-170 nucleotides, (vii) 0-175 nucleotides, (viii) 0-180 nucleotides, (ix) 0-190 nucleotides, (x) 0-195 nucleotides, (xi)-30- 4919-8854-3250.1Atty. Dkt. No.: 131588-16640-200 nucleotides, and (xii) ungated. In some embodiments, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 windows are selected from (i) sequences that are 0-145 nucleotides, (ii) sequences that are 0-150 nucleotides, (iii) 0-155 nucleotides, (iv) 0-160 nucleotides, (v) 0-165 nucleotides, (vi) 0-170 nucleotides, (vii) 0-175 nucleotides, (viii) 0-180 nucleotides, (ix) 0-190 nucleotides, (x) 0-195 nucleotides, (xi) 0-200 nucleotides, and (xii) ungated.(d) Sample Processing and Computational Pipeline

[0081] In general, the sample processing steps for performing the disclosed methods of parallel assessment of aneuploidies and genetic variants can be performed as described in Section II (“Sample Preparations”) above. Further features are expanded on here.

[0082] The disclosed methods can comprise a computational pipeline that transforms the sequencing data from the sequence library into a useful output, which includes a determination of whether aneuploidy or any genetic variants are present in the cffDNA. Additional useful outputs that can optionally be provided include, but are not limited to, determination of fetal sex and other basic fetal statistics.

[0083] The computation pipeline may comprise Binary Alignment Map (BAM) processing in which a collected DNA sample may be computationally reconstructed using short sequencing reads. The reconstruction of a genome can be facilitated if a reference genome is available to which the sequencing reads can be aligned. A sequence alignment tool can be used to map short reads stored in a file to the reference genome. This generates a BAM file wherein specific gene sequences may be dealt with in the next step.

[0084] The computation pipeline may also comprise depth and variant processing, during which specific gene sequences may be identified and isolated to inform follow-on analyses directed to specific aneuploidies and / or genetic variants. Based on the amount of initial DNA collected, specific portions of the collected DNA may be delineated and, optionally, assembled for use with analysis and detection of specific sequences of interest. Once delineated at the depth and variant processing step, specific callers and post processing may be used to identify and assemble output information regarding aneuploidy, genetic variants,-31- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664and any other outputs into a results report. The results are generally reported, delivered, or transmitted to the mother, the father, the physician overseeing the pregnancy (i.e., the mother’s OBGYN), or a combination thereof.

[0085] The depth of the DNA sample in the BAM file can be assessed using specific bioinformatic algorithms (i.e., “calling procedures”; described below). The callers used can determine the presence or absence of both aneuploidy and genetic variants of interest. That is, these two goals can be accomplished together (e.g., in a parallel manner) using the same prepared and processed BAM file. Thus, aneuploidies may be detected using an aneuploidy caller program, while other genetic variants using a dedicated caller program can be run in parallel. Specific aspects of these computational steps are discussed in more detail below.

[0086] It should be understood that the present disclosures as described above can be implemented in the form of control logic using computer software in a modular or integrated manner. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will know and appreciate other ways and / or methods to implement the present disclosure using hardware and a combination of hardware and software.

[0087] Any of the software components, processes or functions described in this application may be implemented as software code to be executed by a processor using any suitable computer language such as, for example, Python, R, Assembly language Java, JavaScript, C, C++ or Perl using, for example, conventional or object-oriented techniques. The software code may be stored as a series of instructions, or commands on a computer readable medium, such as a random-access memory (RAM), a read only memory (ROM), a magnetic medium such as a hard-drive or a floppy disk, or an optical medium such as a CD-ROM. Any such computer readable medium may reside on or within a single computational apparatus and may be present on or within different computational apparatuses within a system or network.(e) Detection of Aneuploidy

[0088] For the purposes of this disclosure, aneuploidies that may be assessed or detected using the disclosed methods include, but are not limited to, monosomy (e.g., Turner syndrome), trisomy (e.g., Down syndrome, Edwards syndrome, Patau syndrome, trisomy 13,-32- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664trisomy, 18, trisomy 21), tetrasomy, polysomy X and / or Y, microdeletions and micro duplications (such as Chromosome 22qll.2 deletion syndrome), and pentasomy.

[0089] The present disclosure provides methods for detecting aneuploidies, either alone or in parallel with genetic variants / mutations of interest that rely on sequencing depth to determine whether an aneuploidy is present or absent in a given sample. For the purposes of this disclosure, “depth” is defined as the ratio of the number of reads obtained by sequencing that overlap with a site of interest to the size of the library or the average number of times each base is measured in the library.

[0090] The observed depth in any given library that is prepared from a maternal cfDNA sample is a function of fetal fraction, maternal copy number, and fetal copy number. If an aneuploidy (e.g., trisomy) is present, the depth in target chromosome should be different from a sample with 23 chromosomes in a defined, predictable way. For instance, in a trisomy, the depth in a target chromosome (e.g., chromosome 21) will increase compared to the background.

[0091] In general, when detecting aneuploidies (whether it is a fetal aneuploidy or a maternal aneuploidy) within a maternal sample that includes some fraction of fetal cfDNA (e.g., the fetal fraction), the presence of an aneuploidy can be identified based on a shift in a detectable aneuploid region or aneuploid chromosome in comparison to known non-aneuploidy regions or chromosomes. That is, depending on the actual fetal fraction, an analysis (e.g., Formula 1, below) of each fragment will yield a plottable result of cfDNA pregnancy depth against cfDNA density. This shift can be calculated statistically or visualized.

[0092] In some embodiments, a depth calling plot can be used to visualize and quantify shifts. The depth of a given sample (i.e., the shaded area) may be determined to fit within one of four known copy number (CN) curves (e.g., CN=1, CN=2, CN=3, and CN=4. Various processing steps may be employed to enhance distribution plot results and quell noise in the data during analysis.-33- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[0093| In some embodiments, detecting the presence or absence of an aneuploidy may comprise calculating a depth trajectory. The depth trajectory is the change across observed windows of the read depth for any given genetic sequence of interest. The depth trajectory can be calculated as a slope of the depth versus fetal fraction across observed window, and it can be visualized in a number of ways, as shown, for example, in FIG. 2. A depth trajectory that decreases while fetal fraction increases would indicate the fetus has less copies of the gene (or chromosome) than the mother. A depth trajectory that stays constant as fetal fraction increases would indicate that the fetus and mother have the same copy number of the gene (or chromosome). And a depth trajectory that increases as fetal fraction increases would indicate that the fetus has more copies of the gene (or chromosome) than the mother. While depth trajectories and useful in determining chromosome number for the purposes of detecting the presence or absence of an aneuploidy, it should be noted that depth trajectories may also be used to detect the presence or absence of certain genetic variants, such as copy number abnormalities.[0094| In one aspect, the present disclosure provides methods of detecting maternal mosaic aneuploidy in a sample comprising cell-free fetal DNA (cffDNA) and cell-free maternal DNA (cfrnDNA), comprising: (a) extracting cffDNA and cfmDNA from a biological sample obtained from a pregnant subject; (b) sequencing the cffDNA and cfmDNA fragments to obtain a sequence library; (c) preparing at least two subsets of sequencing reads from the sequence library, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length in a range from about 145 nucleotides to about 200 nucleotides, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with different maximum fragment lengths; (d) calculating, for each subset of sequencing reads, an estimated fetal fraction and an estimated normalized read depth for each of one or more calling regions; (e) calculating, for each calling region, a depth trajectory, wherein the depth trajectory is a rate of change in normalized read depth as a function of fetal fraction; and (f) identifying the presence of maternal mosaic aneuploidy when: (i) a copy number greater than 2 is identified in the calling region and the depth trajectory is negative, or (ii) a copy number less than 2 is identified in the calling region and the depth trajectory is positive.-34- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664

[0095] During analysis of chromosome depth for any given sample, it may be necessary to account for and normalize GC-bias. GC-content (or guanine-cytosine content) is the percentage of nitrogenous bases on a DNA molecule that are either guanine or cytosine (from a possibility of four different ones, also including adenine and thymine). A high GC content can skew results and lead to high levels of noise. Correct normalization reduces variance in depth in high noise samples, thereby reducing effects of GC bias and improving aneuploidy calling.

[0096] Further, triple normalization controlling for variations caused by 1) GC bias, 2) sample background, and 3) hybridization probe capture (when appropriate; i.e., in embodiments utilizing hybrid probes) may be employed across sampled data to improve the distribution plots of the sampled data. Thus, triple normalization controlling can improve the distribution plots of sampled data and may be useful in certain disclosed embodiments or for certain samples. Once normalized, these distribution plots may be compared to model expectations to derive conclusions about the presence or absence aneuploidies.

[0097] Based on the predicted differences in depth that will be observed when an aneuploidy is present in a sample, an aneuploidy caller can be designed to select a set of maternal and fetal copy numbers that generates the highest likelihood of aneuploidy on a normal distribution. To this end, the following formula was developed for determining the mean normalized read depth of a given aneuploidy:[Formula 1]<&where:dPis plasma depthf is fetal fractionCm is maternal copy numberdb is background depthCf is fetal copy number-35- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[0098| This caller was shown to be both highly sensitive and specific for detecting autosomal and sex chromosome aneuploidies, as well as fetal sex calls. The Examples, below, provide further detail regarding the performance of the aneuploidy caller. The Examples also provide further detail for identifying false-positive calls, especially in the context of identifying maternal aneuploidies in a fetal sex chromosome aneuploidy screen.[0099| After completion of a screening as disclosed herein, a physician may choose to administer further assessments, such as an Expanded Aneuploidy Analysis (EAA) that analyzes even more numbered chromosome pairs to provide additional insights into the health of the pregnancy. Accordingly, in some embodiments, the disclosed methods of determining the presence or absence of an aneuploidy may further comprise an EAA.(f) Detection of Genetic Variants

[0100] In general, the genetic variants (e.g., genetic mutations) that are detected as part of the disclosed methods are genetic variants, markers, or mutations that are associated with specific genetic or inheritable diseases, conditions, or traits. Genetic variants may include single nucleotide variations (SNVs), pathogenic or non-pathogenic single nucleotide polymorphisms (SNPs), insertions and deletions (indels), substitution mutations, or single gene copy number variants.

[0101] A genetic variant can be associated with more than one disease, condition, or trait. Genetic variants can manifest as variations in a polynucleotide, such as at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50 or more sequence differences between a wild-type (i.e., nonmutated or unassociated with a disease or condition) gene or locus. Non-limiting examples of types of genetic variants that can be detected using the disclosed methods include, but are not limited to, single nucleotide polymorphisms (SNP), deletion / insertion polymorphisms (DIP), micro- copy number variants (CNV), short tandem repeats (STR), restriction fragment length polymorphisms (RFLP), single sequence repeats (SSR), variable number of tandem repeats (VNTR), randomly amplified polymorphic DNA (RAPD), amplified fragment length polymorphisms, retrotransposon-based insertion polymorphism, sequence specific amplified polymorphism, and heritable epigenetic modifications (for example, DNA methylation).-36- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[0102| For the purposes of the disclosed methods, the presence or absence of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, or 1000 or more different genetic variants may be detected is a single assay and in parallel with a detection of the presence or absence of aneuploidy. In some embodiments, the methods may detect in parallel the presence or absence of genetic variants that are associated with at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more diseases, conditions, or traits.[0103| In general, the presence of the types of genetic variants that are detected by the disclosed methods are associated with increased risk of having or developing the disease, condition, or trait by about, less than about, or more than about 1%, 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 200%, 300%, 400%, 500%, or more. In some embodiments, the presence of a genetic variant increases the risk of having or developing a disease, condition, or trait by about, less than about, or more than about 1-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 25-fold, 50-fold, 100-fold, 500-fold, 1000-fold, 10000-fold, or more. In some embodiments, the presence of a genetic variant increases the risk of having or developing a disease, condition, or trait by any statistically significant amount, such as an increase having a p-value of about or less than about 0.1, 0.05, IO’3, IO’4, 10’5, IO’6, IO’7, IO’8, IO’9, IO’10, 10’11, IO’12, IO’13, IO’14, 10’15, or smaller.10104] For the purposes of this disclosure, genetic diseases that may be assessed or detected by determining the presence or absence of a genetic variant include, but are not limited to, 21 -Hydroxylase Deficiency, ABCC8-Related Hyperinsulinism, ARSACS, Achondroplasia, Achromatopsia, Adenosine Monophosphate Deaminase 1, Agenesis of Corpus Callosum with Neuronopathy, Alkaptonuria, Alpha- 1 -Antitrypsin Deficiency, Alpha-Mannosidosis, Alpha-Sarcoglycanopathy, Alpha-Thalassemia, Alzheimers, Angiotensin II Receptor, Type I, Apolipoprotein E Genotyping, Argininosuccinicaciduria, Aspartylglycosaminuria, Ataxia with Vitamin E Deficiency, Ataxia-Telangiectasia, Autoimmune Polyendocrinopathy-37- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664Syndrome Type 1, BRCA1 Hereditary Breast / Ovarian Cancer, BRCA2 Hereditary Breast / Ovarian Cancer, Bardet-Biedl Syndrome, Best Vitelliform Macular Dystrophy, Beta-Sarcoglycanopathy, Beta- Thalassemia, Biotinidase Deficiency, Blau Syndrome, Bloom Syndrome, CFTR-Related Disorders, CLN3-Related Neuronal Ceroid-Lipofuscinosis, CLN5-Related Neuronal Ceroid-Lipofuscinosis, CLN8-Related Neuronal Ceroid-Lipofuscinosis, Canavan Disease, Carnitine Palmitoyltransferase IA Deficiency, Carnitine Palmitoyltransferase II Deficiency, Cartilage-Hair Hypoplasia, Cerebral Cavernous Malformation, Choroideremia, Cohen Syndrome, Congenital Cataracts, Facial Dysmorphism, and Neuropathy, Congenital Disorder of Glycosylationla, Congenital Disorder of Glycosylation lb, Congenital Finnish Nephrosis, Crohn Disease, Cystinosis, DFNA 9 (COCH), Diabetes and Hearing Loss, Early-Onset Primary Dystonia (DYTI), Epidermolysis Bullosa Junctional, Herlitz-Pearson Type, FANCC-Related Fanconi Anemia, FGFRl-Related Craniosynostosis, FGFR2 -Related Craniosynostosis, FGFR3-Related Craniosynostosis, Factor V Leiden Thrombophilia, Factor V R2 Mutation Thrombophilia, Factor XI Deficiency, Factor XIII Deficiency, Familial Adenomatous Polyposis, Familial Dysautonomia, Familial Hypercholesterolemia Type B, Familial Mediterranean Fever, Free Sialic Acid Storage Disorders, Frontotemporal Dementia with Parkinsonism- 17, Fumarase deficiency, GJB2-Related DFNA 3 Nonsyndromic Hearing Loss and Deafness, GJB2-Related DFNB 1 Nonsyndromic Hearing Loss and Deafness, GNE-Related Myopathies, Galactosemia, Gaucher Disease, Glucose-6-Phosphate Dehydrogenase Deficiency, Glutaricacidemia Type 1, Glycogen Storage Disease Type la, Glycogen Storage Disease Type lb, Glycogen Storage Disease Type II, Glycogen Storage Disease Type III, Glycogen Storage Disease Type V, Gracile Syndrome, HFE-Associated Hereditary Hemochromatosis, Halder AIMs, Hemoglobin S Beta-Thalassemia, Hereditary Fructose Intolerance, Hereditary Pancreatitis, Hereditary Thymine-Uraciluria, Hexosaminidase A Deficiency, Hidrotic Ectodermal Dysplasia 2, Homocystinuria Caused by Cystathionine Beta-Synthase Deficiency, Hyperkalemic Periodic Paralysis Type 1, Hyperornithinemia-Hyperammonemia-Homocitrullinuria Syndrome, Hyperoxaluria, Primary, Type 1, Hyperoxaluria, Primary, Type 2, Hypochondroplasia, Hypokalemic Periodic Paralysis Type 1, Hypokalemic Periodic Paralysis Type 2, Hypophosphatasia, Infantile Myopathy and Lactic Acidosis (Fatal and NonFatal Forms), Isovaleric Acidemias, Krabbe Disease, LGMD2I, Leber Hereditary Optic-38- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664Neuropathy, Leigh Syndrome, French-Canadian Type, Long Chain 3-Hydroxyacyl-CoA Dehydrogenase Deficiency, MELAS, MERRF, MTHFR Deficiency, MTHFR Thermolabile Variant, MTRNR1 -Related Hearing Loss and Deafness, MTTS1 -Related Hearing Loss and Deafness, MYH-Associated Polyposis, Maple Syrup Urine Disease Type 1 A, Maple Syrup Urine Disease Type IB, McCune- Albright Syndrome, Medium Chain Acyl-Coenzyme A Dehydrogenase Deficiency, Megalencephalic Leukoencephalopathy with Subcortical Cysts, Metachromatic Leukodystrophy, Mitochondrial Cardiomyopathy, Mitochondrial DNA-Associated Leigh Syndrome and NARP, Mucolipidosis IV, Mucopolysaccharidosis Type I, Mucopolysaccharidosis Type IIIA, Mucopolysaccharidosis Type VII, Multiple Endocrine Neoplasia Type 2, Muscle-Eye-Brain Disease, Nemaline Myopathy, Neurological phenotype, Niemann-Pick Disease Due to Sphingomyelinase Deficiency, Niemann-Pick Disease Type Cl, Nijmegen Breakage Syndrome, PPTl-Related Neuronal Ceroid-Lipofuscinosis, PROP1-related pituitary hormone deficiency, Pallister-Hall Syndrome, Paramyotonia Congenita, Pendred Syndrome, Peroxisomal Bifunctional Enzyme Deficiency, Pervasive Developmental Disorders, Phenylalanine Hydroxylase Deficiency, Plasminogen Activator Inhibitor I, Polycystic Kidney Disease, Autosomal Recessive, Prothrombin G20210A Thrombophilia, Pseudovitamin D Deficiency Rickets, Pycnodysostosis, Retinitis Pigmentosa, Autosomal Recessive, Bothnia Type, Rett Syndrome, Rhizomelic Chondrodysplasia Punctata Type 1, Short Chain Acyl-CoA Dehydrogenase Deficiency, Shwachman-Diamond Syndrome, Sjogren-Larsson Syndrome, Smith-Lemli-Opitz Syndrome, Spastic Paraplegia 13, Sulfate Transporter-Related Osteochondrodysplasia, TFR2 -Related Hereditary Hemochromatosis, TPP1 -Related Neuronal Ceroid-Lipofuscinosis, Thanatophoric Dysplasia, Transthyretin Amyloidosis, Trifunctional Protein Deficiency, Tyrosine Hydroxylase-Deficient DRD, Tyrosinemia Type I, Wilson Disease, X-Linked Juvenile Retinoschisis, cystic fibrosis, spinal muscular atrophy (SMA), a hemoglobinopathy, and Zellweger Syndrome Spectrum.

[0105] For the purposes of the disclosed methods, identification or detection of genetic variants can be performed using the in silico moving window analysis to establish a trajectory based on allele balance (when assessing genetic variants involving a SNP, Indel, or other point mutation) across the analyzed windows, as described herein, or based on depth (when assessing genetic variants involving a copy number change). This analysis may be particularly useful for detecting recessive conditions, traits, or diseases. As described above,-39- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664this process can comprise a read-length-based size exclusion of sequences in at least two windows of the sequence library, thereby obtaining at least two fetal fraction-enriched sequence libraries. Comparing the allele balance in each window allows for the calculation of an allele balance trajectory between the various fetal -fraction enriched sequence libraries. The allele balance trajectory is the change across the observed windows of the percentage of the allele balance for any given genetic sequence of interest. The allele balance trajectory can be calculated as a slope of the allele balance versus fetal fraction across observed window, and it can be visualized in a number of ways.

[0106] Allele balance trajectories can be utilized to identify heterozygous and homozygous mutations within the cfDNA library. For example, a single point in the trajectory is based on the allele balance in a given window and could be converted to a banding pattern plot in which the y-axis displays the percentage of cfDNA in a sample with a given allele for a particular gene or nucleic acid sequence of interest (e.g., 0%, 10%, 20%, 30%, 40%, 50%, 60%), and the x-axis displays different alleles (i.e., reference allele or alt allele) for the gene or nucleic acid of interest that correspond to the wild-type sequence or a mutation / variant associated with a particular disease, condition, or trait (e.g., different known mutations within the CFTR gene that are associated with cystic fibrosis). If the fetal fraction in the window or sample was, for example, 20%, then a band at 10% on the y-axis corresponds to a fetus that is a carrier from the biological father’s DNA or a de novo mutation in the fetus. A band at 40% on the y-axis corresponds to a fetus that is negative (i.e., homozygous reference) for the mutation / variant in the gene or sequence of interest and the mother is heterozygous (i.e., a carrier). A band at 50% on the y-axis corresponds to a fetus that is a carrier from the biological mother’s DNA or, in instances in which the mother are father are both carriers with the same alt allele, a carrier of the biological father’s DNA. A band at 60% on the y-axis corresponds to a fetus that is homozygous positive for the mutation / variant in the gene or sequence of interest. As noted above, the bands discussed above (i.e., at 10%, 40%, 50%, and 60%) are not fixed and their position will vary based on the fetal fraction. For example, if the fetal fraction were instead 10% (as opposed to 20% in the example above), the values of the bands change from 10%, 40%, 50%, and 60% to 5%, 45%, 50%, and 55%, respectively.-40- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[0107| An allele balance trajectory incorporates this static information from each of the observed window, which will necessarily have different fetal fractions. Thus, a trajectory could rely on a window with the 20% fetal fraction described above, a second window with a 10% fetal fraction, and, optionally, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 more windows with varying fetal fractions.[0108j Additionally or alternatively, specific callers for genetic variants of interest can rely on assessment of copy number, depth analysis (as described above with respect to aneuploidy), or other forms of detection known in the art. For example, depth trajectories can be used to detect the presence or absence of copy number variants, such as copy number variants of SMA1, RHD, HBA1 and HBA 2, which are all associated with particular genetic diseases. In some embodiments, a depth trajectory may have a negative slope (indicating fewer copies in the fetus), an approximately flat slope (indicating the same number of copies between the fetus and mother), or a positive slope (indicating more copies in the fetus, and such slopes may be based on 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 more windows with varying fetal fractions.[01001 In some embodiments, callers for certain conditions may rely on detecting the presence or absence of “diffbases.” In some embodiments, callers for certain conditions may rely on detecting the presence or absence of substitutions from a wild type sequence (e.g., SNVs). In some embodiments, callers for certain conditions may rely on detecting the presence or absence of single nucleotide polymorphisms (SNPs). In some embodiments, callers for certain conditions may rely on detecting the presence or absence of one or more insertions or deletions (INDELs). In instances when multiple SNVs, diffbases, SNPs, or a combination thereof are associated with a given condition, pooling or merging detection signals across even a small number of SNVs, diffbases, SNPs, INDELs, or a combination thereof (e.g., <3, <4, <5, <6, <7, <8, <9, <10, <11, <12, <13, <14, <15) can provide improved separation between genotypes. Accordingly, in some embodiments, a caller for a certain condition may rely on the detection of the presence or absence of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 or more SNVs, diffbases, SNPs, INDELs, or combinations thereof.-41- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[01101 For example, in detecting alpha thalassemia, the disclosed methods can utilize a caller that detects the presence or absence of double cis mutations of HBA1 and HBA2, which is the most common cause of the condition. Thus, a caller may detect, for example, a consensus copy number signal obtained from multiple probes in a region of interest, such as a double deletion region.[0111| As such, utilization of the disclosed methods allows for calling genetic variants of interest with a single sample and in parallel with detection of aneuploidy. Indeed, this method can even identify whether the fetus is a homozygous or heterozygous for a given genetic variant of interest. Further, in some embodiments in which a mother and father possess different alt alleles, it can be determined whether the fetus obtained a particular variant from the mother, the father, or both. This is a new and useful way to establish the presence of absence of a genetic variant / mutation from a maternal sample.(g) Reduction of Noise

[0112] As explained above, the disclosed methods and systems can significantly reduce noise in cfDNA data, which improves performance of assays used to detect genetic variants and aneuploidies. Due to low levels of cffDNA in most biological samples obtained from pregnant women, high levels of background noise from conventional processing and detection methods could render a sample unusable, uninterpretable, or both. Accordingly, the disclosed methods of noise reduction represent new and useful methods that improve conventional non-invasive pre-natal screening (NIPS).10113] Additionally, the present disclosure provides methods of reducing background noise from superfluous genetic material in non-invasive pre-natal screening (NIPS), comprising (i) obtaining a biological sample from a pregnant woman, wherein the biological sample comprises cell-free DNA (cfDNA); and (ii) processing the cfDNA used for NIPS, wherein processing comprises enriching the biological sample for cell-free fetal DNA (cffDNA), in silico processing of the cfDNA, or a combination thereof.-42- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[0114| In some embodiments, the methods of reducing noise will comprise both enriching the biological sample for cell-free fetal DNA (cffDNA) and in silico processing of the cfDNA.

[0115] For the purposes of reducing noise, the enriching of the biological sample for cffDNA may comprise any of the disclosed methods of physical isolation or enrichment of a fetal fraction. For example, in some embodiments, enriching the biological sample for cffDNA may comprise obtaining a biological sample comprising cell-free DNA (cfDNA) from a pregnant woman, wherein the cfDNA comprises cffDNA and cell-free DNA maternal (cfmDNA); extracting the cfDNA from the biological sample; and subjecting the extracted cfDNA to a size exclusion process, wherein the size exclusion process has a cutoff size of about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in lengths, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, or about 180 nucleotides in length, thereby producing a nucleic acid enriched for cffDNA.

[0116] Similarly, for the purposes of reducing noise, the in silico processing may comprise any of the disclosed methods of analysis of sequence libraries or sequence library data to focus any analysis of genetic variants or aneuploidies on the fetal fraction of a sample. For example, in some embodiments, in silico processing may comprise sequencing a cfDNA sample comprising cell-free fetal (cffDNA) and cell-free maternal DNA (cfmDNA) to prepare a sequence library; performing read-length-based analysis in which an allele balance for a nucleic acid sequence of interest is established in at least two windows of the sequence library; and establishing a trajectory based on the allele balance of the at least two windows, wherein the trajectory indicates the percentage of alleles present in the sample that comprise the nucleic acid sequence of interest.

[0117] Noise reduction may further comprise normalization to control for GC-bias, sample background, hybridization probe capture, or a combination thereof. In general, normalization can be a “median normalization.” In other words, probe read depths can be divided by the median across probes with similar GC content, then by the interquartile mean across samples and probes with putative copy number 2 in mother and fetus.-43- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[0118| Issues may arise with hybridization probe capture because DNA fragments that contain variants and overlap capture probes are captured less efficiently, decreasing allele balance of the alternate allele. However, capture bias is often reproducible, and in such cases it can be learned and corrected using the following formula:[Formula 2]<[0119 { Correction and normalization for hybridization probe capture is particularly useful for ensuring correct indel calling, though it can help with variant calling more generally.

[0120] The following examples are given to illustrate the disclosed methods. It should be understood, however, that the invention is not to be limited to the specific embodiments or details described in these examples.EXAMPLESExample 1. Detecting maternal mosaicism in fetal sex chromosome aneuploidy screening

[0121] Sex chromosome aneuploidies (SCA) are a common class of fetal aneuploidy, but cell-free DNA (cfDNA) screening assays have relatively low positive predictive value (PPV) for SCA calls. False positives are caused by factors including statistical error, confined placental mosaicism, and maternal mosaicism.1 122] To improve the clinical utility of fetal SCA screening, Applicant developed a “depth trajectory” analysis method that employs targeted sequencing to differentiate fetal from maternal aneuploidy by analyzing covariation in read depth and DNA fragment size.

[0123] Materials and Methods

[0124] Applicant screened 728 prenatal cfDNA (pcfDNA) samples for fetal SCA with a whole-genome sequencing (WGS) assay and a new targeted sequencing assay. The targeted assay uses in silico size selection to measure how chromosome dosage varies with fetal -44- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664fraction within each sample’s sequencing reads (FIG. 1). Maternal mosaic loss of X is identified when X chromosome dosage is depressed but increases as a function of fetal fraction.

[0125] The cohort included 25 patients screening positive for monosomy X and 36 patients screening positive for other SC As on the WGS assay. Fetal diagnosis via amniocentesis or postnatal karyotype was obtained for 12 monosomy X screen-positive patients.[0126| Results[0127J 59 of 61 patients screening positive for fetal SCA in the WGS assay were also positive in the targeted assay prior to maternal anomaly calling. Four samples were identified as maternal mosaic loss of X by depth trajectory analysis (FIG. 2). Maternal anomalies were not identified for any other SCA.

[0128] The positive predictive value (PPV) of monosomy X calls in the WGS assay was 42% (5 / 12; Table 1). Among 12 patients with diagnostic follow-up, one fetal mosaic true positive was not detected and 3 of 7 false positives were identified as maternal anomalies in the targeted assay (Table 1), improving PPV to 50% (4 / 8). The remaining 4 false positives had an average 61% degree of mosaicism in the targeted assay, consistent with placental mosaicism.Table 1. cffDNA calls and diagnostic results4919-8854-3250.1Atty. Dkt. No.: 131588-1664]0129| In general, high concordance was observed across WGS and targeted sequencing methods (FIG. 3).

[0130] Discussion[01311 Applicant’s studies showed that depth trajectory analysis can successfully identify maternal X chromosome mosaicism and can improves PPV in fetal sex chromosome anomaly screening. Applicant observed high confirmation rates in orthogonally re-tested samples, suggesting that confined placental mosaicism and maternal mosaicism explain most false positive SCA calls rather than statistical noise. Diagnostic follow-up focused on samples consistent with fetal mosaic X in the WGS assay, which may have reduced the rate of maternal anomalies identified. Identifying maternal anomalies in SCA screening may lead to fewer invasive follow-up tests and reduced anxiety for patients.Example 2. Depth trajectory analysis methodsDepth trajectory analysis

[0132] Applicant performed depth trajectory analyses of sequence libraries from biological samples obtained from pregnant human subjects. Briefly, Applicant obtained, for each subject, a set of sequencing reads with corresponding fragment size estimates. Applicant then prepared in silico multiple subsets of the full set of reads by filtering for progressively shorter molecules. In particular, Applicant prepared subsets with maximum fragment sizes of 190,-46- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664175, 170, 168, 165, 150, and 145 nucleotides. For each subset, Applicant obtained an estimate of the fetal fraction. This can be done via analysis of variant allele frequencies, region-specific depth-of-coverage anomalies, insert size mixtures, and by other approaches. In the studies described here, Applicant utilized variant allele frequencies to estimate the fetal fraction.[0133| For each subset, Applicant also obtained an estimate of the normalized read depth in each calling region (e.g. chromosome, chromosome arm, or microdeletion region).

[0134] For each calling region, Applicant estimated the depth trajectory - the rate of change in normalized read depths as a function of fetal fraction. Applicant estimated the depth trajectory by performing a linear regression analysis and extracting the slope.

[0135] Finally, Applicant applied certain decision rules to characterize an identified anomaly as maternal or fetal: if copy number greater than 2 is identified in the calling region and the depth trajectory is positive, identify the anomaly as fetal; if copy number greater than 2 is identified in the calling region and the depth trajectory is negative, identify the anomaly as maternal; if copy number less than 2 is identified in the calling region and the depth trajectory is negative, identify the anomaly as fetal; and if copy number less than 2 is identified in the calling region and depth trajectory is positive, identify the anomaly as maternal.Depth trajectory analysis computational frameworks(a) Fetal Fraction Inference(i) Expected Allele Fraction in Genotype Mixtures

[0136] For a typical singleton pregnancy with a euploid fetus there are 7 combinations of maternal and fetal genotypes (excluding do-novo mutations). These genotype combinations can be written in VCF style notation with a colon separating fetal from pregnant person genotypes. For example “0 / 0:0 / l” is a site with homozygous reference genotype in the fetus and a heterozygous genotype in the pregnant person. The set of genotype mixtures for a singleton pregnancy with a euploiud fetus is: 0 / 0:0 / 0, 0 / 1 :0 / 0, 0 / 0:0 / l, 0 / 1 :0 / l, 1 / 1 :0 / l, 0 / E1 / 1, l / El / l.-47- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[0137| The expected allele fraction at a variant with each of these genotype mixtures is a function of the fetal fraction f . The simplest case is a variant that is homozygous in the pregnant person and heterozygous in the fetus. These can be called “paternally inherited” sites because the ALT allele must be inherited from the father (assuming no de-novo mutation). In this case, the expected allele fraction is half the fetal fraction, because half the fetal molecules bear the ALT allele. Similar logic extends to all other genotype mixtures. In general, the expected allele fraction at a variant position in the plasmawith fetal allele fraction maternal allele fraction am, fetal copy number Cf and maternal copy number cmis [Formula 3]:

[0138] The two terms in the numerator give the number of ALT alleles expected from fetus and pregnant person, and the four terms in the denominator give the number of ALT and REF alleles expected from fetus and pregnant person. When maternal and fetal copy numbers are equal, this reduces a simple weighted average of fetal and maternal allele fractions [Formula 4]:FIG. 4 gives a visualization of the expected mixtures with simulated data.(ii) Reference Bias[0139J Applicant further corrected the expected allele fractions for bias in hybridization capture. Because the capture probes bear the reference allele, binding energy is slightly lower for molecules bearing an ALT than REF allele. This causes a small downward shift in allele fractions. Applicant modeled a bias parameter b reflecting the ratio of observed and expected allele fractions [Formula 5]:>-48- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664Rearranging, one can calculate the adjusted allele fraction as [Formula 6]:

[0140] This bias parameter is an average over all SNVs in the model. At finer scales, the amount of bias should differ for ALT alleles of different length or nucleotide composition. For example, long indels are more strongly biased towards the reference allele than SNVs because they have more mismatch between the capture probe and library molecule. We use separate allele-specific bias corrections for indels during single gene calling (see Supp Methods X for details), but these are not included in the fetal fraction model.[0141 j FIG. 5 shows the effect of reference bias at various levels in simulated data.(iii) Mixture Proportions

[0142] To make model fits more robust and reduce computational burden, Applicant used a simple population genetic model to fix the proportion of sites in each genotype mixture class rather than inferring them with expectation maximization.

[0143] The probability of a genotype combination is the probability of the maternal genotype g-m) times the probability of the fetal genotypegiven maternal and paternal genotypes (^d), marginalized over all possible paternal genotypes [Formula 7]:Maternal and paternal genotype probabilities are modeled with Hardy-Weinberg expectations given the site’s global minor allele frequency (MAF; this implicitly assumes random mating). If all sites have MAF 0.5, the mapping of expected genotype to mixture proportion is: 0 / 0:0 / 0 = 1 / 8, 0 / 1 :0 / 0 = 1 / 8, 0 / 0:0 / I = 1 / 8, 0 / 1 :0 / l = 1 / 4, 1 / 1 :0 / l = 1 / 8, 0 / 1: 1 / 1 = 1 / 8, 1 / 1: 1 / 1 = 1 / 8.(iv) Likelihood and Optimization-49- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664

[0144] Given an input bam file we first use Mutect2 (CITE) to extract the count of ALT and REF -bearing reads at each DBSNP position. Applicant modeled these allele counts as coming from a mixture of the set of possible genotypes listed above. Sites are indexed with z and genotype mixture states are indexed with k. The prior probability of a genotype mixture (or the proportion of sites in each mixture class k) is written nk. The binomial probability mass for a ALT allele counts given total depth n at expected allele fraction p, is written B(a\n,p). The full likelihood of the data A is [Formula 8]:Where pkis calculated for each genotype mixture & using Formulas 3 and 6 and n is calculated with Formula 7. For numerical stability, we fit the model in log space and use the “logsumexp” approximation for the sum over mixture classes.

[0145] Applicant maximized this likelihood with respect to fetal fraction ( / ) and bias (b) using the L-BFGS-B algorithm implemented in scipy, and rely on data structures and other numerical algorithms from numpy.(v) Handling High Fetal Fractions[0146| The full fetal fraction model has two equivalent solutions for any sample - one with fetal fraction 0.5 — x , and one with fetal fraction 0.5 + x. These solutions come from swapping the labels on the “0 / 0:0 / l” and “0 / 1 :0 / 0” peaks. Under 50% fetal fraction the “paternal inheritance” peak is closer to 0, and above 50% fetal fraction it’s closer to 0.5. Because samples with fetal fraction over 50% are very rare, Applicant set a maximum fetal fraction value of 0.5 when fitting to the initial data.

[0147] The fetal fraction model is also fit for separately to each in-silico-size-selected subset of reads, where exceeding 50% fetal fraction is relatively common. To handle this case, Applicant first identifies maternal heterozygous sites (i.e. sites with maximum likelihood class ending “ :0 / l”) in the model fit to the full data and then drop these sites when fitting to size-selected data.4919-8854-3250.1Atty. Dkt. No.: 131588-1664(vi) Comparison with an Alternate Fetal Fraction Measure[0148J Applicant validated the fetal fraction inference by comparison with an alternate fetal fraction measure based on normalized read depth on the Y chromosome in patients with an XY fetus. Normalized depth on the Y chromosome is expected to be half the fetal fraction because the Y is haploid in the fetus and absent in the pregnant person. Depth normalization followed the full procedure described in the aneuploidy depth normalization section.Applicant ran this comparison for 239 samples in the validation set with an XY fetal genotype call using each insert-size subset of reads. These measures were highly concordant (mean absolute error 0.86%, A2=0.9897 for reads with max insert size 150bp), suggesting that the method is comparable to an orthogonal signal available only for XY fetus patients.(b) Aneuploidy Depth Normalization(i) Sample-level GC Normalization

[0149] To minimize sample-level GC bias we use an approach similar to the Loess model described in Benjamini and Speed 2012 (academic. oup.com / nar / article / 40 / 10 / e72 / 2411059). Briefly, Applicant binned capture probes by GC content, calculated the median depth of all probes in a given GC bin, then divided the observed depth on each probe by its GC bin median depth. Sex chromosomes and other highly copy -number-variable regions were excluded from the set of probes used to calculate GC bin medians. This method minimizes GC bias and rescales depths to center at 1 for all samples, thus also removing sample-level background effects.(ii) Batch-level probe normalization

[0150] Probe bias p reflects variation in hybridization capture efficiency, library preparation, or cfDNA shedding specific to the genomic region covered by a capture probe. Applicant estimated this quantity by calculating the interquartile mean depth for each probe across all samples on a batch (up to 96) after sample-level GC normalization, while excluding any probes with copy numbers other than 2. In practice, it is not known which probes are non-CN2 prior to running the aneuploidy caller, so Applicant takes an iterative approach. In the first pass, Applicant called the presence of chrY, assumed samples with chrY detected have -51- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664an XY fetus, and excluded probes from the X chromosome of XY fetus samples from normalization. Applicant then ran a preliminary round of aneuploidy calling, mask any probes on preliminary positive chromosomes, and rerun normalization. Last, Applicant ran a more sensitive round of aneuploidy calling including mosaic classes, marked non-CN2 probes in this last round, and used this final set of probe exclusions to for the final probelevel normalization. Exemplary depth normalization plots are shown in FIG. 6.(c) Aneuploidy Trajectory Analysis(i) Equivalence of Fetal and Maternal Depth Anomalies

[0151] Both maternal and fetal copy number anomalies cause shifts in the distribution of read depths in cfDNA. When fetal fraction is low, it is relatively straightforward to identify maternal anomalies because they create a much larger depth signal. From Formula 1, assuming perfect normalization where a copy number 2 chromosome has mean depth 1, the expected depth for full fetal or maternal monosomy is:"f

[0152] A full fetal aneuploidy shifts depth by - and a full maternal aneuploidy shifts depth by

[0153] However maternal and fetal anomalies can produce identical depth signals if the maternal anomaly is mosaic or fetal fraction is high. For example, consider a sample with 20% fetal fraction and an observed (normalized) median depth 0.9 on the X chromosome. This is consistent either with a full fetal monosomy (c = 1) or with a mosaic maternal monosomy with approximately 25% of maternal cfDNA fragments derived from cells with one copy of the X chromosome (cm= 1.75).-52- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664[0154 { In general for any fetal copy number Cf an equivalent cfDNA depth signal can be created by a maternal copy number given by [Formula 9]:(ii) Degree of Mosaicism

[0155] It is sometimes useful to reparametrize the formula for expected depth in cfDNA (Formula 1) to account for “degree of mosaicism” (M) rather than maternal and fetal copy numbers. DOM is a metric that describes the proportion of aneuploid cells in a fetus (or in the case of maternal cfDNA, in placental cells of fetal origin). Applicant calculated DOM by comparing observed depth deviation and the depth signal expected from a full fetal trisomy [Formula 10]:dp- 1fetal2DOM is -1 for a full fetal monosomy and 1 for a full fetal trisomy.

[0156] Similarly, one can define a maternal degree of mosaicism as the ratio of observed depth signal to that expected from a full maternal anomaly [Formula 11]:

[0157] By rearranging, one can then write the observed depth as a function of f and DOM (Formula 12, Formula 13):(iii) Aneuploidy Depth Trajectory-53- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664

[0158] Fetal cfDNA fragments are slightly shorter than maternal fragments, which allows Applicant to computationally shift the fetal fraction of a sequencing library by selecting longer or shorter fragments. Analyzing the relationship between fetal fraction and read depth then lets us differentiate maternal from fetal anomalies.

[0159] For example, consider a sample with 20% fetal fraction and an observed normalized depth of 0.9 on the X chromosome. This is consistent with either fetal copy number 1 or maternal copy number 1.75. We subset the library to retain only reads with insert size under 150bp and observe that fetal fraction increases to 30% based on allele fractions at paternally inherited variant positions. If the anomaly is a full fetal monosomy, from Formula 1, a normalized depth 0.85 on chrX is expected. If the anomaly is maternal with effective copy number 1.75, a normalized depth 0.9125 is expected. That is, if the anomaly is fetal, then the depth signal gets stronger as fetal fraction increases; if the anomaly is maternal, then the depth signal gets weaker.

[0160] For a fetal anomaly, the slope s of the change in depth as a function of fetal fraction across two points where f2> can be written in terms of DOM as [Formula 14]:

[0161] The expected depth slope is half the degree of mosaicism of a fetal copy number anomaly. Because DOM is signed where > 0 reflects a fetal trisomy and < 0 indicates a fetal monosomy, the slope is positive for fetal trisomies and negative for fetal monosomies.

[0162] For a maternal anomaly the expected slope is [Formula 15]:_ maternal~ 24919-8854-3250.1Atty. Dkt. No.: 131588-1664[01631 By definition f2> so the depth slope in a maternal anomaly will have the opposite sign of Mmaternai- i.e. a maternal monosomy will cause depth to increase along with fetal fraction.[0164J For aneuploidy calls, in any sample with a depth anomaly identified using the “static” depth model described with Formula 1, Applicant fit a linear regression of depth as a function of fetal fraction across in-silico size selection bins to estimate s^etat. If the sign of the slope matches the sign of the fetal degree of mosaicism estimate of the target chromosome, the call is passed. Otherwise, the call is flagged as a suspected maternal anomaly.[0165| All patents and publications mentioned in the specification are indicative of the levels of those of ordinary skill in the art to which the disclosure pertains. All patents and publications are herein incorporated by reference to the same extent as if each individual publication was specifically and individually indicated to be incorporated by reference.

[0166] The present technology is not to be limited in terms of the particular embodiments described in this application, which are intended as single illustrations of individual aspects of the present technology. Many modifications and variations of this present technology can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the present technology, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the present technology. It is to be understood that this present technology is not limited to particular methods, reagents, compounds, compositions, or systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0167] Other embodiments are set forth in the claims.-55- 4919-8854-3250.1

Claims

Atty. Dkt. No.: 131588-1664WHAT IS CLAIMED IS:

1. A method of detecting maternal mosaic aneuploidy in a sample comprising cell-free fetal DNA (cffDNA) and cell-free maternal DNA (cfrnDNA), comprising:(a) extracting cffDNA and cfrnDNA from a biological sample obtained from a pregnant subject;(b) sequencing the cffDNA and cfrnDNA fragments to obtain a sequence library; (c) preparing at least two subsets of sequencing reads from the sequence library, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length in a range from about 145 nucleotides to about 200 nucleotides, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with different maximum fragment lengths;(d) calculating, for each subset of sequencing reads, an estimated fetal fraction and an estimated normalized read depth for each of one or more calling regions; (e) calculating, for each calling region, a depth trajectory, wherein the depth trajectory is a rate of change in normalized read depth as a function of fetal fraction; and(f) identifying the presence of maternal mosaic aneuploidy when:(i) a copy number greater than 2 is identified in the calling region and the depth trajectory is negative, or(ii) a copy number less than 2 is identified in the calling region and the depth trajectory is positive.

2. The method of claim 1, wherein the aneuploidy is selected from a monosomy, a trisomy, a tetrasomy, a pentasomy, a microdeletion, a microduplication, and mosaic versions of monosomy, trisomy, tetrasomy, and pentasomy.

3. The method of claim 1, wherein the aneuploidy is a sex chromosome aneuploidy.

4. The method of any one of claims 1-3, wherein the biological sample is blood or plasma.

5. The method of any one of claims 1-4, comprising preparing at least 3 subsets of sequencing reads, at least 4 subsets of sequencing reads, at least 5 subsets of sequencing reads, at least 6 subsets of sequencing reads, at least 7 subsets of-56- 4919-8854-3250.1Atty. Dkt. No.: 131588-1664sequencing reads, at least 8 subsets of sequencing reads, at least 9 subsets of sequencing reads, or at least 10 subsets of sequencing reads.

6. The method of any one of claims 1-5, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length of about 145 nucleotides, about 150 nucleotides, about 155 nucleotides, about 160 nucleotides, about 165 nucleotides, about 170 nucleotides, about 175 nucleotides, about 180 nucleotides, about 185 nucleotides, about 190 nucleotides, about 195 nucleotides, or about 200 nucleotides.

7. The method of claim 6, comprising preparing 7 subsets of sequencing reads, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length of 145 nucleotides, 150 nucleotides, 165 nucleotides, 168 nucleotides, 170 nucleotides, 175 nucleotides in length, or 190 nucleotides.

8. The method of any one of claims 1-7, wherein the estimated fetal fraction is calculated based on an analysis of variant allele frequencies, region-specific depth-of- coverage anomalies, insert size, or any combination thereof.

9. The method of claim 8, wherein the estimated fetal fraction is calculated based on an analysis of variant allele frequencies.

10. The method of any one of claims 1-9, wherein the calling region is selected from a chromosome, a chromosome arm, or microdeletion region.

11. The method of any one of claims 1-10, wherein the normalized read depth is normalized to control for GC-bias, sample background, hybridization probe capture, or a combination thereof.

12. The method of any one of claims 1-11, wherein the depth trajectory is calculated by linear regression analysis.

13. The method of any one of claims 1-12, further comprising (g) identifying fetal aneuploidy when:(i) a copy number greater than 2 is identified in the calling region and the depth trajectory is positive, or(ii) a copy number less than 2 is identified in the calling region and the depth trajectory is negative.-57- 4919-8854-3250.1Atty. Dkt. No.: 131588-166414. A method of screening for fetal sex chromosome aneuploidy (SCA) in a sample comprising cell-free fetal DNA (cffDNA) and cell-free maternal DNA (cfmDNA), comprising:(a) extracting cffDNA and cfmDNA from a biological sample obtained from a pregnant subject;(b) sequencing the cffDNA and cfmDNA fragments to obtain a sequence library; (c) preparing at least two subsets of sequencing reads from the sequence library, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length in a range from about 145 nucleotides to about 200 nucleotides, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with different maximum fragment lengths;(d) calculating, for each subset of sequencing reads, an estimated fetal fraction and an estimated normalized read depth for each of one or more calling regions;(e) calculating, for each calling region, a depth trajectory, wherein the depth trajectory is a rate of change in normalized read depth as a function of fetal fraction;(f) detecting maternal mosaic aneuploidy in the one or more calling region, wherein maternal mosaic aneuploidy is detected when:(i) a copy number greater than 2 is identified in the calling region and the depth trajectory is negative, or(ii) a copy number less than 2 is identified in the calling region and depth trajectory is positive; and(g) detecting fetal aneuploidy in the one or more calling region, wherein fetal aneuploidy is detected when:(iii) a copy number greater than 2 is identified in the calling region and the depth trajectory is positive, or(iv) a copy number less than 2 is identified in the calling region and the depth trajectory is negative.-58- 4919-8854-3250.1Atty. Dkt. No.: 131588-166415. The method of claim 14, wherein the aneuploidy is selected from a monosomy, a trisomy, a tetrasomy, a pentasomy, a microdeletion, a microduplication, and mosaic versions of monosomy, trisomy, tetrasomy, and pentasomy.

16. The method of claim 14, wherein the aneuploidy is a sex chromosome aneuploidy.

17. The method of any one of claims 14-16, wherein the biological sample is blood or plasma.

18. The method of any one of claims 14-17, comprising preparing at least 3 subsets of sequencing reads, at least 4 subsets of sequencing reads, at least 5 subsets of sequencing reads, at least 6 subsets of sequencing reads, at least 7 subsets of sequencing reads, at least 8 subsets of sequencing reads, at least 9 subsets of sequencing reads, or at least 10 subsets of sequencing reads.

19. The method of any one of claims 14-18, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length of about 145 nucleotides, about 150 nucleotides, about 155 nucleotides, about 160 nucleotides, about 165 nucleotides, about 170 nucleotides, about 175 nucleotides, about 180 nucleotides, about 185 nucleotides, about 190 nucleotides, about 195 nucleotides, or about 200 nucleotides.

20. The method of claim 19, comprising preparing 7 subsets of sequencing reads, wherein each of the subsets of sequencing reads comprises a plurality of sequencing reads with a maximum fragment length of 145 nucleotides, 150 nucleotides, 165 nucleotides, 168 nucleotides, 170 nucleotides, 175 nucleotides, or 190 nucleotides.

21. The method of any one of claims 14-20, wherein the estimated fetal fraction is calculated based on an analysis of variant allele frequencies, region-specific depth-of- coverage anomalies, insert size, or any combination thereof.

22. The method of claim 21, wherein the estimated fetal fraction is calculated based on an analysis of variant allele frequencies.

23. The method of any one of claims 14-22, wherein the calling region is selected from a chromosome, a chromosome arm, or microdeletion region.

24. The method of any one of claims 14-23, wherein the normalized read depth is normalized to control for GC-bias, sample background, hybridization probe capture, or a combination thereof.-59- 4919-8854-3250.1Atty. Dkt. No.: 131588-166425. The method of any one of claims 14-24, wherein the depth trajectory is calculated by linear regression analysis.

26. A method of in silico processing of cell-free DNA (cfDNA), comprising(a) sequencing cfDNA in a sample obtained from a pregnant subject to obtain a sequence library, wherein the biological sample comprises cell-free fetal DNA (cffDNA) and cell-free maternal DNA (cfmDNA);(b) determining an estimated fetal fraction and an estimated normalized read depth for each of one or more calling regions in at least two size selection windows of the sequence library in a read-length-based size analysis; and(c) determining, for each calling region, a depth trajectory, wherein the depth trajectory is a rate of change in normalized read depth as a function of fetal fraction.

27. The method of claim 26, wherein at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 size selection windows of the sequence library are assessed, thereby obtaining, respectively, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 fetal fraction-enriched subsets of sequencing reads.

28. The method of claim 26 or 27, wherein the at least two size selection windows of the sequence library are selected from (i) sequences that are 0-145 nucleotides, (ii) sequences that are 0-150 nucleotides, (iii) 0-155 nucleotides, (iv) 0-160 nucleotides, (v) 0-165 nucleotides, (vi) 0-168 nucleotides, (vii) 0-170 nucleotides, (viii) 0-175 nucleotides, (ix) 0-180 nucleotides, (x) 0-185 nucleotides, (xi) 0-190 nucleotides, (xii) 0-195 nucleotides, (xiii) 0-200 nucleotides, and (xiv) ungated.

29. The method of any one of claims 26-28, further comprising detecting aneuploidy.

30. The method of claim 29, wherein the aneuploidy is maternal mosaic aneuploidy.

31. The method of claim 30, wherein maternal mosaic aneuploidy is detected when:(i) a copy number greater than 2 is identified in the calling region and the depth trajectory is negative, or(ii) a copy number less than 2 is identified in the calling region and depth trajectory is positive.

32. The method of claim 29, wherein the aneuploidy is fetal aneuploidy.-60- 4919-8854-3250.1Atty. Dkt. No.: 131588-166433. The method of claim 32, wherein fetal aneuploidy is detected when:(iii) a copy number greater than 2 is identified in the calling region and the depth trajectory is positive, or(iv) a copy number less than 2 is identified in the calling region and depth trajectory is negative.

34. The method of any one of claims 26-33, wherein the estimated fetal fraction is determined based on an analysis of variant allele frequencies, region-specific depth- of-coverage anomalies, insert size, or any combination thereof.

35. The method of claim 34, wherein the estimated fetal fraction is determined based on an analysis of variant allele frequencies.

36. The method of any one of claims 26-35, wherein the calling region is selected from a chromosome, a chromosome arm, or microdeletion region.

37. The method of any one of claims 26-36, wherein the normalized read depth is normalized to control for GC-bias, sample background, hybridization probe capture, or a combination thereof.

38. The method of any one of claims 26-37, wherein the depth trajectory is determined by linear regression analysis.-61- 4919-8854-3250.1

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