Biology subjects

NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium,

Publications and source records attributed to NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium,.

9 recordsLinked to original sources

bioRxiv2024

Genetic Architecture and Analysis Practices of Circulating Metabolites in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

Circulating metabolite levels partly reflect the state of human health and diseases and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single-study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally diverse samples. We provided a set of reasonable strategies for outlier and imputation handling to process metabolite data. Following the practical analysis framework, we further performed a genome-wide association analysis on 1,135 selected metabolites using whole genome sequencing data from 16,359 individuals passing the quality control filters, and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus-metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2, D2HGDH, SUGP1, UTG2B17, strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

bioinformatics↗

bioRxiv2021

Expanding the pool of public controls for GWAS via a method for combining genotypes from arrays and sequencing

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing case-only cohorts with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control type I error and recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery.

bioinformatics↗

bioRxiv2020

eSCAN: Scan Regulatory Regions for Aggregate Association Testing using Whole Genome Sequencing Data

Multiple statistical methods for aggregate association testing have been developed for whole genome sequencing (WGS) data. Many aggregate variants in a given genomic window and ignore existing knowledge to define test regions, resulting in many identified regions not clearly linked to genes and thus limiting biological understanding. Functional information from new technologies (such as Hi-C and its derivatives), which can help link enhancers to their effector genes, can be leveraged to predefine variant sets for aggregate testing in WGS data. Here we propose the eSCAN (Scan the Enhancers) method for genome-wide assessment of enhancer regions in sequencing studies, combining the advantages of dynamic window selection in SCANG, a previously developed method, with the advantages of incorporating putative regulatory regions from annotation. eSCAN, by searching in putative enhancer, increases statistical power and aids mechanistic interpretation, as demonstrated by extensive simulation studies. We also apply eSCAN for blood cell traits using TOPMed WGS data. Results from real data analysis show that eSCAN is able to capture more significant signals, and these signals are of shorter length (indicating higher resolution fine-mapping capability) and drive association of larger regions detected by other methods.

genetics↗

bioRxiv2020

find-tfbs: a tool to identify functional non-coding variants associated with complex human traits using open chromatin maps and phased whole-genome sequences

MotivationWhole-genome DNA sequencing (WGS) enables the discovery of non-coding variants, but tools are lacking to prioritize the subset that functionally impacts human phenotypes. DNA sequence variants that disrupt or create transcription factor binding sites (TFBS) can modulate gene expression. find-tfbs efficiently scans phased WGS in large cohorts to identify and count TFBSs in regulatory sequences. This information can then be used in association testing to find putatively functional non-coding variants associated with complex human diseases or traits. ResultsWe applied find-tfbs to discover functional non-coding variants associated with hematological traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) WGS dataset (Nmax=44,709). We identified >2000 associations at P<1x10-9, implicating specific blood cell-types, transcription factors and causal genes. The vast majority of these associations are captured by variants identified in large genome-wide association studies (GWAS) for blood-cell traits. find-tfbs is computationally efficient and robust, allowing for the rapid identification of non-coding variants associated with multiple human phenotypes in very large sample size. Availabilityhttps://github.com/Helkafen/find-tfbs and https://github.com/Helkafen/find-tfbs-demo Contactssebastian.meric.de.bellefon@umontreal.ca and guillaume.lettre@umontreal.ca Supplementary informationSupplementary data are available.

genomics↗

bioRxiv2020

Robust, flexible, and scalable tests for Hardy-Weinberg Equilibrium across diverse ancestries

Traditional Hardy-Weinberg equilibrium (HWE) tests (the {chi}2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in datasets comprised of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence datasets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently amongst the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

bioinformatics↗

bioRxiv2020

Rare variant enriched identity-by-descent enables the detection of distant relatedness and older divergence between populations

MotivationThe global human population has experienced an explosive growth from a few million to roughly 7 billion people in the last 10,000 years. Accompanying this growth has been the accumulation of rare variants that can inform our understanding of human evolutionary history. Common variants have primarily been used to infer the structure of the human population and relatedness between two individuals. However, with the increasing abundance of rare variants observed in large-scale projects, such as Trans-Omics for Precision Medicine (TOPMed), the use of rare variants to decipher cryptic relatedness and fine-scale population structure can be beneficial to the study of population demographics and association studies. Identity-by-descent (IBD) is an important framework used for identifying these relationships. IBD segments are broken down by recombination over time, such that longer shared haplotypes give strong evidence of recent relatedness while shorter shared haplotypes are indicative of more distant relationships. Current methods to identify IBD accurately detect only long segments (> 2cM) found in related individuals. AlgorithmWe describe a metric that leverages rare-variants shared between individuals to improve the detection of short IBD segments. We computed IBD segments using existing methods implemented in Refined IBD where we enrich the signal using our metric that facilitates the detection of short IBD segments (<2cM) by explicitly incorporating rare variants. ResultsTo test our new metric, we simulated datasets involving populations with varying divergent time-scales. We show that rare-variant IBD identifies shorter segments with greater confidence and enables the detection of older divergence between populations. As an example, we applied our metric to the Old-Order Amish cohort with known genealogies dating 14 generations back to validate its ability to detect genetic relatedness between distant relatives. This analysis shows that our method increases the accuracy of identifying shorter segments that in turn capture distant relationships. ConclusionsWe describe a method to enrich the detection of short IBD segments using rare-variant sharing within IBD segments. Leveraging rare-variant sharing improves the information content of short IBD segments better than common variants alone. We validated the method in both simulated and empirical datasets. This method can benefit association analyses, IBD mapping analyses, and demographic inferences.

genomics↗

bioRxiv2019

Novel genetic determinants of telomere length from a multi-ethnic analysis of 75,000 whole genome sequences in TOPMed

Telomeres shorten in replicating somatic cells, and telomere length (TL) is associated with age-related diseases 1,2. To date, 17 genome-wide association studies (GWAS) have identified 25 loci for leukocyte TL 3-19, but were limited to European and Asian ancestry individuals and relied on laboratory assays of TL. In this study from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, we used whole genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of TL in n=109,122 trans-ethnic (European, African, Asian and Hispanic/Latino) individuals. We identified 59 sentinel variants (p-value <5x10-9) from 36 loci (20 novel, 13 replicated in external datasets). There was little evidence of effect heterogeneity across populations, and 10 loci had >1 independent signal. Fine-mapping at OBFC1 indicated the independent signals colocalized with cell-type specific eQTLs for OBFC1 (STN1). We further identified two novel genes, DCLRE1B (SNM1B) and PARN, using a multi-variant gene-based approach.

genetics↗

bioRxiv2018

Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole genome sequencing studies

With advances in Whole Genome Sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and Sequence Kernel Association Test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally-efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-Set Mixed Model Association Tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institutes Trans-Omics for Precision Medicine (TOPMed) program. SMMAT tests share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be only fit once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMAT tests correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

genetics↗

bioRxiv2018

A Fully-Adjusted Two-Stage Procedure for Rank Normalization in Genetic Association Studies

When testing genotype-phenotype associations using linear regression, departure of the trait distribution from normality can impact both Type I error rate control and statistical power, with worse consequences for rarer variants. While it has been shown that applying a rank-normalization transformation to trait values before testing may improve these statistical properties, the factor driving them is not the trait distribution itself, but its residual distribution after regression on both covariates and genotype. Because genotype is expected to have a small effect (if any) investigators now routinely use a two-stage method, in which they first regress the trait on covariates, obtain residuals, rank-normalize them, and then secondly use the rank-normalized residuals in association analysis with the genotypes. Potential confounding signals are assumed to be removed at the first stage, so in practice no further adjustment is done in the second stage. Here, we show that this widely-used approach can lead to tests with undesirable statistical properties, due to both a combination of a mis-specified mean-variance relationship, and remaining covariate associations between the rank-normalized residuals and genotypes. We demonstrate these properties theoretically, and also in applications to genome-wide and whole-genome sequencing association studies. We further propose and evaluate an alternative fully-adjusted two-stage approach that adjusts for covariates both when residuals are obtained, and in the subsequent association test. This method can reduce excess Type I errors and improve statistical power.

genetics↗