Biology subjects

Ziyatdinov, A.

Publications and source records attributed to Ziyatdinov, A..

7 recordsLinked to original sources

bioRxiv2023

Joint testing of rare variant burden scores using non-negative least squares

Gene-based burden tests are a popular and powerful approach for analysis of exome-wide association studies. These approaches combine sets of variants within a gene into a single burden score that is then tested for association. Typically, a range of burden scores are calculated and tested across a range of annotation classes and frequency bins. Correlation between these tests can complicate the multiple testing correction and hamper interpretation of the results. We introduce a new method called the Sparse Burden Association Test (SBAT) that tests the joint set of burden scores under the assumption that causal burden scores act in the same effect direction. The method simultaneously assesses the significance of the model fit and selects the set of burden scores that best explain the association at the same time. Using simulated data, we show that the method is well calibrated and highlight some scenarios where the test outperforms existing gene-based tests. We apply the method to 73 quantitative traits from the UK Biobank which further illustrates the power of the method. This test is implemented in the REGENIE software.

genetics↗

bioRxiv2022

Genotyping, sequencing and analysis of 140,000 adults from the Mexico City Prospective Study

The Mexico City Prospective Study (MCPS) is a prospective cohort of over 150,000 adults recruited two decades ago from the urban districts of Coyoacan and Iztapalapa in Mexico City. We generated genotype and exome sequencing data for all individuals, and whole genome sequencing for 10,000 selected individuals. We uncovered high levels of relatedness and substantial heterogeneity in ancestry composition across individuals. Most sequenced individuals had admixed Native American, European and African ancestry, with extensive admixture from indigenous groups in Central, Southern and South Eastern Mexico. Native Mexican segments of the genome had lower levels of coding variation, but an excess of homozygous loss of function variants compared with segments of African and European origin. We estimated population specific allele frequencies at 142 million genomic variants, with an effective sample size of 91,856 for Native Mexico at exome variants, all available via a public browser. Using whole genome sequencing, we developed an imputation reference panel which outperforms existing panels at common variants in individuals with high proportions of Central, South and South Eastern Native Mexican ancestry. Our work illustrates the value of genetic studies in populations with diverse ancestry and provides foundational imputation and allele frequency resources for future genetic studies in Mexico and in the United States where the Hispanic/Latino population is predominantly of Mexican descent.

genetics↗

bioRxiv2020

Multitrait genetic-phenotype associations to connect disease variants and biological mechanisms

BackgroundGenome-wide association studies (GWAS) uncovered a wealth of associations between common variants and human phenotypes. These results, widely shared across the scientific community as summary statistics, fostered a flurry of secondary analysis: heritability and genetic correlation assessment, pleiotropy characterization and multitrait association test. Amongst these secondary analyses, a rising new field is the decomposition of multitrait genetic effects into distinct profiles of pleiotropy. ResultsWe conducted an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link to biological mechanisms. We started by benchmarking multitrait association tests on a large panel of phenotype sets and established the Omnibus test as the most powerful in practice. We detected 322 new associations that were not previously reported by univariate screening. Using independent significant associations, we investigated the breakdown of genetic association into clusters of variants harboring similar multitrait association profile. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how SNPs within clusters can be mapped to biological pathways and disease mechanisms, providing a putative insight for numerous SNPs with unknown biological function. Finally, for the metabolism set, we investigate the link between gene cluster assignment and success of drug targets in random control trials. We report additional uninvestigated drug targets classified by clusters. ConclusionsMultitrait genetic signals can be decomposed into distinct pleiotropy profiles that reveal consistent with pathways databases and random control trials. We propose this method for the mapping of unannotated SNPs to putative pathways.

genetics↗

bioRxiv2020

Computationally efficient whole genome regression for quantitative and binary traits

Genome-wide association analysis of cohorts with thousands of phenotypes is computationally expensive, particularly when accounting for sample relatedness or population structure. Here we present a novel machine learning method called REGENIE for fitting a whole genome regression model that is orders of magnitude faster than alternatives, while maintaining statistical efficiency. The method naturally accommodates parallel analysis of multiple phenotypes, and only requires local segments of the genotype matrix to be loaded in memory, in contrast to existing alternatives which must load genomewide matrices into memory. This results in substantial savings in compute time and memory usage. The method is applicable to both quantitative and binary phenotypes, including rare variant analysis of binary traits with unbalanced case-control ratios where we introduce a fast, approximate Firth logistic regression test. The method is ideally suited to take advantage of distributed computing frameworks. We demonstrate the accuracy and computational benefits of this approach compared to several existing methods using quantitative and binary traits from the UK Biobank dataset with up to 407,746 individuals.

genetics↗

bioRxiv2019

Estimating the effective sample size in association studies of quantitative traits

The effective sample size (ESS) is a quantity estimated in genome-wide association studies (GWAS) with related individuals and/or linear mixed models used in analysis. ESS originally measured relative power in family-based GWAS and has recently become important for correcting GWAS summary statistics in post-GWAS analyses. However, existing ESS approaches have been overlooked and based on empirical estimation. This work presents an analytical form of ESS in mixed-model GWAS of quantitative traits, which is derived using the expectation of quadratic form and validated in extensive simulations. We illustrate the performance and relevance of our ESS estimator in common GWAS scenarios and analytically show that (i) family-based studies are consistently underpowered compared to studies of unrelated individuals of the same sample size; (ii) conditioning on polygenic genetic effect by linear mixed models boosts power; and (iii) power of detecting gene-environment interaction can be substantially gained or lost in family-based designs depending on exposure distribution. We further analyze UK Biobank dataset in two samples of 336,347 unrelated and 68,910 related individuals. Analysis in unrelated individuals reveals a high accuracy of our ESS estimator compared to the existing empirical approach; and analysis of related individuals suggests that the loss in effective sample size due to relatedness is at most 0.94x. Overall, we provide an analytical form of ESS for guiding GWAS designs and processing summary statistics in post-GWAS analyses.

genetics↗

bioRxiv2019

Mixed-model admixture mapping identifies smoking-dependent loci of lung function in African Americans

Admixture mapping has led to the discovery of many genes associated with differential disease risk by ancestry, highlighting the importance of ancestry-based approaches to association studies. However, the potential of admixture mapping in deciphering the interplay between genes and environment exposures has been seldom explored. Here, we performed a genome-wide screening of local ancestry-smoking interactions for five spirometric lung function phenotypes in 3,300 African Americans from the COPDGene study. To account for population structure and outcome heterogeneity across exposure groups, we developed a multi-component linear mixed model for mapping gene-environment interactions, and empirically showed its robustness and increased power. When applied to the COPDGene study, our approach identified two 11p15.2-3 and 2q37 loci, exhibiting local ancestry-smoking interactions at genome-wide significant level, that would have been missed by standard singlenucleotide polymorphism analyses. These two loci harbor the PARVA and RAB17 genes previously recognized to be involved in smoking behavior. Overall, our study provides the first evidence for potential synergistic effects between African ancestry and smoking on pulmonary function and underlines the importance of ethnic diversity in genetic studies.

genetics↗

bioRxiv2017

lme4qtl: Linear Mixed Models With Flexible Covariance Structure For Genetic Studies Of Related Individuals

BackgroundQuantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software.\n\nResultsTo address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project.\n\nConclusionsOur software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl.

genetics↗