A genealogical estimate of genetic relationships

Fan, Caoqi; Mancuso, Nicholas; Chiang, Charleston W. K.

doi:10.1016/j.ajhg.2022.03.016

Cited by 19 publications

(40 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our GWAS pipeline, we corrected for population stratification using only the top 10 Principal Components (PCs) of the genetic relationship matrix (GRM), which may not adequately capture the more recent demographic history reflected by rare variants [87,88]. This residual confounding effect may be addressed by increasing the number PCs used in the GWAS analysis [75] or utilizing more genealogically-informed estimates of the GRM [89].…”

Section: Discussionmentioning

confidence: 99%

Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference

Zabad

Gravel

2022

Preprint

View full text Add to dashboard Cite

The recent proliferation of large scale genome-wide association studies (GWASs) has motivated the development of statistical methods for phenotype prediction using single nucleotide polymorphism (SNP) array data. These polygenic risk score (PRS) methods formulate the task of polygenic prediction in terms of a multiple linear regression framework, where the goal is to infer the joint effect sizes of all genetic variants on the trait. Among the subset of PRS methods that operate on GWAS summary statistics, sparse Bayesian methods have shown competitive predictive ability. However, existing Bayesian approaches employ Markov Chain Monte Carlo (MCMC) algorithms for posterior inference, which are computationally inefficient and do not scale favorably with the number of SNPs included in the analysis. Here, we introduce Variational Inference of Polygenic Risk Scores (VIPRS), a Bayesian summary statistics-based PRS method that utilizes Variational Inference (VI) techniques to efficiently approximate the posterior distribution for the effect sizes. Our experiments with genome-wide simulations and real phenotypes from the UK Biobank (UKB) dataset demonstrated that variational approximations to the posterior are competitively accurate and highly efficient. When compared to state-of-the-art PRS methods, VIPRS consistently achieves the best or second best predictive accuracy in our analyses of 18 simulation configurations as well as 12 real phenotypes measured among the UKB participants of “White British” background. This performance advantage was higher among individuals from other ethnic groups, with an increase in R2 of up to 1.7-fold among participants of Nigerian ancestry for Low-Density Lipoprotein (LDL) cholesterol. Furthermore, given its computational efficiency, we applied VIPRS to a dataset of up to 10 million genetic markers, an order of magnitude greater than the standard HapMap3 subset used to train existing PRS methods. Modeling this expanded set of variants conferred modest improvements in prediction accuracy for a number of highly polygenic traits, such as standing height.

show abstract

Section: Discussionmentioning

confidence: 99%

Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference

Zabad

Gravel

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The clade marked with the dotted circles is identical at loci 1 and 2, and the mutation at locus 2 is informative about the branch length at locus 1. One or more marginal trees are used to calculate a local eGRM using the method described in Fan et al (2022). This matrix is then tested for association with the phenotypes using Restricted Maximum Likelihood (REML).…”

Section: Characterizing Local Relatednessmentioning

confidence: 99%

“…B) Computing the eGRM. This panel is redrawn from Fan et al (2022). A genome-wide genetic relatedness matrix (GRM) can be viewed as an average of single-locus GRMs for every genotyped locus.…”

Section: Characterizing Local Relatednessmentioning

confidence: 99%

“…Figure 1B describes the method we use to calculate the pairwise expected relatedness matrix (eGRM), developed by Fan et al (2022), and also how the genetic relatedness matrix is conventionally calculated. Figure 1C shows an example of allelic heterogeneity: multiple causal alleles are in close linkage (e.g.…”

Section: Characterizing Local Relatednessmentioning

confidence: 99%

“…Here, we present a tree-based approach to QTL mapping (Figure 1). We build on a recently proposed representation of tree-based relatedness, the expectated genetic relatedness matrix, or eGRM (Fan et al, 2022, see Figure 1B here), independently identified by Zhang and colleagues (2021) as the ARG-GRM and in a phylogenetic context by Wang and colleagues (2021) as the expected genetic similarity matrix. (And see McVean (2009, eq.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tree-based QTL mapping with expected local genetic relatedness matrices

Link

Schraiber

Fan

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide Association Studies (GWAS) are a powerful way to find genetic loci associated with phenotypes. GWAS are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix given the ARG (local eGRM). Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to identify a large-effect BMI locus, the \textit{CREBRF} gene, in a sample of Native Hawaiians in which it was not previously detectable by GWAS because of a lack of population-specific imputation resources. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

show abstract

Fast and accurate Bayesian polygenic risk modeling with variational inference

Zabad

Gravel

2023

The American Journal of Human Genetics

View full text Add to dashboard Cite

A genealogical estimate of genetic relationships

Cited by 19 publications

References 56 publications

Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference

Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference

Tree-based QTL mapping with expected local genetic relatedness matrices

Fast and accurate Bayesian polygenic risk modeling with variational inference

Contact Info

Product

Resources

About