The application of genetic relationships among individuals, characterized by a genetic relationship matrix (GRM), has far-reaching effects in human genetics. However, the current standard to calculate the GRM generally does not take advantage of linkage information and does not reflect the underlying genealogical history of the study sample. Here, we propose a coalescent-informed framework to infer the expected relatedness between pairs of individuals given an ancestral recombination graph (ARG) of the sample. Through extensive simulations we show that the eGRM is an unbiased estimate of latent pairwise genome-wide relatedness and is robust when computed using genealogies inferred from incomplete genetic data. As a result, the eGRM better captures the structure of a population than the canonical GRM, even when using the same genetic information. More importantly, our framework allows a principled approach to estimate the eGRM at different time depths of the ARG, thereby revealing the time-varying nature of population structure in a sample. When applied to genotyping data from a population sample from Northern and Eastern Finland, we find that clustering analysis using the eGRM reveals population structure driven by subpopulations that would not be apparent using the canonical GRM, and that temporally the population model is consistent with recent divergence and expansion. Taken together, our proposed eGRM provides a robust tree-centric estimate of relatedness with wide application to genetic studies.

/pdf/a-genealogical-estimate-of-genetic-relationships-6xlm2ne1aa.pdf

A genealogical estimate of genetic relationships

Genome-wide association studies rely on the statistical inference of untyped variants, called imputation, to increase the coverage of genotyping arrays However, the results are often suboptimal in populations underrepresented in existing reference panels and array designs, since the selected single nucleotide polymorphisms (SNPs) may fail to capture population-specific haplotype structures, hence the full extent of common genetic variation Here, we propose to sequence the full genome of a small subset of an underrepresented study cohort to inform the selection of population-specific add-on SNPs, such that the remaining array-genotyped cohort could be more accurately imputed Using a Tanzania-based cohort as a proof-of-concept, we demonstrate the validity of our approach by showing improvements in imputation accuracy after the addition of our designed add-on SNPs to the base H3Africa array

https://biorxiv.org/content/10.1101/2021.02.03.429542v1.full.pdf

Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations

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Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations