@article {133, title = {RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID.}, journal = {PLoS Genet}, volume = {17}, year = {2021}, month = {2021 01}, pages = {e1009315}, abstract = {

Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.

}, keywords = {Biological Specimen Banks, Genome, Human, Genome-Wide Association Study, Genotyping Techniques, Haplotypes, Humans, Models, Genetic, Pedigree, Polymorphism, Single Nucleotide}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1009315}, author = {Naseri, Ardalan and Shi, Junjie and Lin, Xihong and Zhang, Shaojie and Zhi, Degui} } @article {87, title = {Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts.}, journal = {Am J Hum Genet}, volume = {106}, year = {2020}, month = {2020 01 02}, pages = {112-120}, abstract = {

Whole-genome sequencing (WGS) can improve assessment of low-frequency and rare variants, particularly in non-European populations that have been underrepresented in existing genomic studies. The genetic determinants of C-reactive protein (CRP), a biomarker of chronic inflammation, have been extensively studied, with existing genome-wide association studies (GWASs) conducted in >200,000 individuals of European ancestry. In order to discover novel loci associated with CRP levels, we examined a multi-ancestry population (n = 23,279) with WGS (\~{}38{\texttimes} coverage) from the Trans-Omics for Precision Medicine (TOPMed) program. We found evidence for eight distinct associations at the CRP locus, including two variants that have not been identified previously (rs11265259 and rs181704186), both of which are non-coding and more common in individuals of African ancestry (\~{}10\% and \~{}1\% minor allele frequency, respectively, and rare or monomorphic in 1000 Genomes populations of East Asian, South Asian, and European ancestry). We show that the minor (G) allele of rs181704186 is associated with lower CRP levels and decreased transcriptional activity and protein binding in~vitro, providing a plausible molecular mechanism for this African ancestry-specific signal. The individuals homozygous for rs181704186-G have a mean CRP level of 0.23~mg/L, in contrast to individuals heterozygous for rs181704186 with mean CRP of 2.97~mg/L and major allele homozygotes with mean CRP of 4.11~mg/L. This study demonstrates the utility of WGS in multi-ethnic populations to drive discovery of complex trait associations of large effect and to identify functional alleles in noncoding regulatory regions.

}, keywords = {African Continental Ancestry Group, Asian Continental Ancestry Group, C-Reactive Protein, Cohort Studies, European Continental Ancestry Group, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Whole Genome Sequencing}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.12.002}, author = {Raffield, Laura M and Iyengar, Apoorva K and Wang, Biqi and Gaynor, Sheila M and Spracklen, Cassandra N and Zhong, Xue and Kowalski, Madeline H and Salimi, Shabnam and Polfus, Linda M and Benjamin, Emelia J and Bis, Joshua C and Bowler, Russell and Cade, Brian E and Choi, Won Jung and Comellas, Alejandro P and Correa, Adolfo and Cruz, Pedro and Doddapaneni, Harsha and Durda, Peter and Gogarten, Stephanie M and Jain, Deepti and Kim, Ryan W and Kral, Brian G and Lange, Leslie A and Larson, Martin G and Laurie, Cecelia and Lee, Jiwon and Lee, Seonwook and Lewis, Joshua P and Metcalf, Ginger A and Mitchell, Braxton D and Momin, Zeineen and Muzny, Donna M and Pankratz, Nathan and Park, Cheol Joo and Rich, Stephen S and Rotter, Jerome I and Ryan, Kathleen and Seo, Daekwan and Tracy, Russell P and Viaud-Martinez, Karine A and Yanek, Lisa R and Zhao, Lue Ping and Lin, Xihong and Li, Bingshan and Li, Yun and Dupuis, Jos{\'e}e and Reiner, Alexander P and Mohlke, Karen L and Auer, Paul L} } @article {47, title = {Weighted pseudolikelihood for SNP set analysis with multiple secondary outcomes in case-control genetic association studies.}, journal = {Biometrics}, volume = {73}, year = {2017}, month = {2017 Dec}, pages = {1210-1220}, abstract = {

We propose a weighted pseudolikelihood method for analyzing the association of a SNP set, example, SNPs in a gene or a genetic pathway or network, with multiple secondary phenotypes in case-control genetic association studies. To boost analysis power, we assume that the SNP-specific effects are shared across all secondary phenotypes using a scaled mean model. We estimate regression parameters using Inverse Probability Weighted (IPW) estimating equations obtained from the weighted pseudolikelihood, which accounts for case-control sampling to prevent potential ascertainment bias. To test the effect of a SNP set, we propose a weighted variance component pseudo-score test. We also propose a penalized IPW pseudolikelihood method for selecting a subset of SNPs that are associated with the multiple secondary phenotypes. We show that the proposed variable selection procedure has the oracle properties and is robust to misspecification of the correlation structure among secondary phenotypes. We select the tuning parameter using a weighted Bayesian Information-like Criterion (wBIC). We evaluate the finite sample performance of the proposed methods via simulations, and illustrate the methods by the analysis of the multiple secondary smoking behavior outcomes in a lung cancer case-control genetic association study.

}, keywords = {Case-Control Studies, Computer Simulation, Genetic Association Studies, Humans, Likelihood Functions, Lung Neoplasms, Phenotype, Polymorphism, Single Nucleotide, Smoking}, issn = {1541-0420}, doi = {10.1111/biom.12680}, author = {Sofer, Tamar and Schifano, Elizabeth D and Christiani, David C and Lin, Xihong} }