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Biology subjects

Pathak, G. A.

Publications and source records attributed to Pathak, G. A..

2 recordsLinked to original sources

Modeling the longitudinal changes of ancestry diversity in the Million Veteran Program

The Million Veteran Program (MVP) participants represent 100 years of US history, including significant social and demographic change over time. Our study assessed two aspects of the MVP: (i) longitudinal changes in population diversity and (ii) how these changes can be accounted for in genome-wide association studies (GWAS). The MVP was divided into five birth cohorts (N-range=123,888 [born from 1943-1947] to 136,699 [born from 1948-1953]). Groups of participants were defined by (i) HARE (harmonized ancestry and race/ethnicity) and (ii) a random-forest clustering approach using the 1000 Genomes Project and the Human Genome Diversity Project (1kGP+HGDP) reference panels (77 world populations representing six continental groups). In these groups, we performed GWASs of height, a trait potentially affected by population stratification. Birth cohorts demonstrate important trends in ancestry diversity over time. More recent HARE-assigned Europeans, Africans, and Hispanics had lower European ancestry proportions than older birth cohorts (0.010<Cohens d<0.259, p<7.80x10-4). Conversely, HARE-assigned East Asians showed an increase in European ancestry proportion over time. In GWAS of height using HARE assignments, genomic inflation due to population stratification was prevalent across all birth cohorts (linkage disequilibrium score regression intercept=1.08{+/-}0.042). The 1kGP+HGDP-based ancestry assignment significantly reduced the population stratification (mean intercept reduction=0.045{+/-}0.007, p<0.05) confounding in the GWAS statistics. This study provides a comprehensive characterization of ancestry diversity of the MVP cohort over time and highlights that more refined modeling of genetic diversity (e.g., the 1kGP+HGDP-based ancestry assignment) can more accurately capture the polygenic architecture of traits and diseases that could be affected by population stratification.

genomics↗

Natural selection influenced the genetic architecture of brain structure, behavioral and neuropsychiatric traits

Natural selection has shaped the phenotypic characteristics of human populations. Genome-wide association studies (GWAS) have elucidated contributions of thousands of common variants with small effects on an individuals predisposition to complex traits (polygenicity), as well as wide-spread sharing of risk alleles across traits in the human phenome (pleiotropy). It remains unclear how the pervasive effects of natural selection influence polygenicity in brain-related traits. We investigate these effects by annotating the genome with measures of background (BGS) and positive selection, indications of Neanderthal introgression, measures of functional significance including loss-of-function (LoF) intolerant and genic regions, and genotype networks in 75 brain-related traits. Evidence of natural selection was determined using binary annotations of top 2%, 1%, and 0.5% of selection scores genome-wide. We detected enrichment (q<0.05) of SNP-heritability at loci with elevated BGS (7 phenotypes) and in genic (34 phenotypes) and LoF-intolerant regions (67 phenotypes). BGS (top 2%) significantly predicted effect size variance for trait-associated loci ({sigma}2 parameter) in 75 brain-related traits ({beta}=4.39x10-5, p=1.43x10-5, model r2=0.548). By including the number of DSM-5 diagnostic combinations per psychiatric disorder, we substantially improved model fit ({sigma}2 ~ BTop2% x Genic x diagnostic combinations; model r2=0.661). We show that GWAS with larger variance in risk locus effect sizes are collectively predicted by the effects of loci under strong BGS and in regulatory regions of the genome. We further show that diagnostic complexity exacerbates this relationship and perhaps dampens the ability to detect psychiatric risk loci.

evolutionary biology↗