IBD Network Clustering for Heterogeneous Population Structure

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Solution Overview

Problem

Existing methods are inadequate for discovering historical populations within heterogeneous populations, such as those in the present-day US, using identity-by-descent (IBD) patterns, which are crucial for understanding recent demographic events and population structure.

Innovation Solution

The techniques involve analyzing phased genetic data to estimate shared IBD chromosomal segments, generating an IBD network, and applying network clustering algorithms to identify population structures, which are then annotated with historical data to characterize common ancestral origins and predict individual assignments to populations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If allele frequency modeling methods are used to study population structure, then historical migration patterns can be elucidated, but recent demographic events within heterogeneous populations cannot be detected

Engineering Contradiction:

Improvedetection of recent demographic eventsVSAvoidapplicability to heterogeneous populations

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent transitions from allele frequency-based parameters to IBD segment-based parameters, enabling detection of recent demographic events (within last 1000 years) that were invisible to traditional methods. This parameter change allows the system to resolve fine-scale population structure in heterogeneous populations like present-day US by measuring actual chromosomal inheritance patterns rather than statistical allele distributions

Inventive Principle:

Principle #35Parameter changes

2Loss of information

If IBD analysis is applied to heterogeneous populations, then fine-scale population structure can be discovered, but the complexity of analyzing every pair of individuals increases

Engineering Contradiction:

Improvediscovery of historical populationsVSAvoidcomputational complexity of IBD network

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent segments the population analysis into discrete IBD clusters based on chromosomal segment sharing patterns. By dividing the heterogeneous population into distinct genetic clusters (e.g., Northern European, Southern European, Ashkenazi Jewish populations), the system manages computational complexity while preserving fine-scale population structure information that would be lost in aggregate analysis

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an IBD network as an intermediary structure between raw genetic data and population conclusions. This network represents pairwise IBD relationships and enables efficient clustering algorithms to identify population structure without requiring exhaustive analysis of all individual pairs, thus reducing computational burden while maintaining detection accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11232854B2Characterizing heterogeneity with fine-scale population structure

Publication Date: 2022.01.25 ANCESTRY COM DNA LLC

AI summary

Described are techniques for determining population structure from identity-by-descent (IBD) of individuals. The techniques may be used to predict that an individual belongs to zero, one or more of a number of communities identified within an IBD network. Additional data may be used to annotate the communities with birth location, surname, and ethnicity information. In turn, these data may be used to provide to an individual a prediction of membership to zero, one or more communities, accompanied by a summary of the information annotated to those communities. Ethnicity heterogeneity and age information may be tabulated and provided based on community membership information.