Identification of putative causal loci in whole-genome sequencing data via knockoff statistics.
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ABSTRACT:
SUBMITTER: He Z
PROVIDER: S-EPMC8149672 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Family-based designs can eliminate confounding due to population substructure and can distinguish direct from indirect genetic effects, but these designs are underpowered due to limited sample sizes. Here, we propose KnockoffTrio, a statistical method to identify putative causal genetic variants for father-mother-child trio design built upon a recently developed knockoff framework in statistics. KnockoffTrio controls the false discovery rate (FDR) in the presence of arbitrary correlations among tests and is less conservative and thus more powerful than the conventional methods that control the family-wise error rate via Bonferroni correction. Furthermore, KnockoffTrio is not restricted to family-based association tests and can be used in conjunction with more powerful, potentially nonlinear models to improve the power of standard family-based tests. We show, using empirical simulations, that KnockoffTrio can prioritize causal variants over associations due to linkage disequilibrium and can provide protection against confounding due to population stratification. In applications to 14,200 trios from three study cohorts for autism spectrum disorders (ASDs), including AGP, SPARK, and SSC, we show that KnockoffTrio can identify multiple significant associations that are missed by conventional tests applied to the same data. In particular, we replicate known ASD association signals with variants in several genes such as MACROD2, NRXN1, PRKAR1B, CADM2, PCDH9, and DOCK4 and identify additional associations with variants in other genes including ARHGEF10, SLC28A1, ZNF589, and HINT1 at FDR 10%.
| S-EPMC9606389 | biostudies-literature
Project description:Klebsiella pneumoniae is a growing cause of healthcare-associated infections for which multi-drug resistance is a concern. Its polysaccharide capsule is a major virulence determinant and epidemiological marker. However, little is known about capsule epidemiology since serological typing is not widely accessible and many isolates are serologically non-typeable. Molecular typing techniques provide useful insights, but existing methods fail to take full advantage of the information in whole genome sequences. We investigated the diversity of the capsule synthesis loci (K-loci) among 2503 K. pneumoniae genomes. We incorporated analyses of full-length K-locus nucleotide sequences and also clustered protein-encoding sequences to identify, annotate and compare K-locus structures. We propose a standardized nomenclature for K-loci and present a curated reference database. A total of 134 distinct K-loci were identified, including 31 novel types. Comparative analyses indicated 508 unique protein-encoding gene clusters that appear to reassort via homologous recombination. Extensive intra- and inter-locus nucleotide diversity was detected among the wzi and wzc genes, indicating that current molecular typing schemes based on these genes are inadequate. As a solution, we introduce Kaptive, a novel software tool that automates the process of identifying K-loci based on full locus information extracted from whole genome sequences (https://github.com/katholt/Kaptive). This work highlights the extensive diversity of Klebsiella K-loci and the proteins that they encode. The nomenclature, reference database and novel typing method presented here will become essential resources for genomic surveillance and epidemiological investigations of this pathogen.
| S-EPMC5359410 | biostudies-literature
Project description:BackgroundPremature ovarian failure (POF) is a highly heterogeneous disorder that occurs in 1% of women of reproductive age. Very few causative genes and variants contributing to POF have been detected, and the disease remains incompletely understood. In this study, we used whole exome sequencing (WES) to identify potential causal variants leading to POF.MethodsWES was conducted to identify variants in 34 Korean patients with POF, alongside 10 normal controls. Detected variants were filtered using a range of characterized bioinformatics analyses, and the machine learning tools, CADD and VEST, were used to predict pathogenic variants that could cause disease. VarSome was used for a comprehensive interpretation of the variants. Potential causal variants finally screened by these analyses were confirmed using Sanger sequencing.ResultsWe identified nine potential causative variants in genes previously associated with POF in 8 of 34 (24%) Korean patients by WES variant analysis. These potentially pathogenic variants included mutations in the MCM8, MCM9, and HFM1 genes, which are involved in homologous recombination, DNA repair, and meiosis, and are established as causing POF. Using a combination of CADD and VEST, 72 coding variants were also identified in 72 genes, including ADAMTSL1 and FER1L6, which have plausible functional links to POF.ConclusionsWES is a useful tool to detect genetic variants that cause POF. Accumulation and systematic management of data from a number of WES studies in specialized groups of patients with POF (family data, severe case populations) are needed to better comprehend the genetic landscape underlying POF.
| S-EPMC7590468 | biostudies-literature
Project description:Rheumatoid arthritis (RA) is a highly prevalent chronic autoimmune disease. However, genetic and environmental factors involved in RA pathogenesis are still remained largely unknown. To identify the genetic causal variants underlying pathogenesis and disease progression of RA patients, we undertook the first comprehensive whole-exome sequencing (WES) study in a total of 124 subjects including 58 RA cases and 66 healthy controls in Han Chinese population. We identified 378 novel genes that were enriched with deleterious variants in RA patients using a gene burden test. The further functional effects of associated genetic genes were classified and assessed, including 21 newly identified genes that were involved in the extracellular matrix (ECM)-receptor interaction, protein digestion and absorption, focal adhesion and glycerophospholipid metabolism pathways relevant to RA pathogenesis. Moreover, six pathogenic variants were investigated and structural analysis predicted their potentially functional alteration by homology modeling. Importantly, five novel and rare homozygous variants (NCR3LG1, RAP1GAP, CHCHD5, HIPK2 and DIAPH2) were identified, which may exhibit more functional impact on RA pathogenesis. Notably, 7 genes involved in the olfactory transduction pathway were enriched and associated with RA disease progression. Therefore, we performed an efficient and powerful technique WES in Chinese RA patients and identified novel, rare and common disease causing genes that alter innate immunity pathways and contribute to the risk of RA. Findings in this study may provide potential diagnostic tools and therapeutic strategies for RA patients.
| S-EPMC5762310 | biostudies-literature
Project description:MotivationTranscriptome-wide association study (TWAS) aims to identify trait-associated genes regulated by significant variants to explore the underlying biological mechanisms at a tissue-specific level. Despite the advancement of current TWAS methods to cover diverse traits, traditional approaches still face two main challenges: (i) the lack of methods that can guarantee finite-sample false discovery rate (FDR) control in identifying trait-associated genes; and (ii) the requirement for individual-level data, which is often inaccessible.ResultsTo address this challenge, we propose a powerful knockoff inference method termed TWAS-GKF to identify candidate trait-associated genes with a guaranteed finite-sample FDR control. TWAS-GKF introduces the main idea of Ghostknockoff inference to generate knockoff variables using only summary statistics instead of individual-level data. In extensive studies, we demonstrate that TWAS-GKF successfully controls the finite-sample FDR under a pre-specified FDR level across all settings. We further apply TWAS-GKF to identify genes in brain cerebellum tissue from the Genotype-Tissue Expression (GTEx) v8 project associated with schizophrenia (SCZ) from the Psychiatric Genomics Consortium (PGC), and genes in liver tissue related to low-density lipoprotein cholesterol (LDL-C) from the UK Biobank, respectively. The results reveal that the majority of the identified genes are validated by Open Targets Validation Platform.Availability and implementationThe R package TWAS.GKF is publicly available at https://github.com/AnqiWang2021/TWAS.GKF.
| S-EPMC11361808 | biostudies-literature
Project description:We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gene-based tests, BIGKnock produces smaller sets of significant genes that contain the causal gene(s) with high probability. We further illustrate its ability to pinpoint potential causal genes at [Formula: see text] of the associated loci.
| S-EPMC9926792 | biostudies-literature
Project description:Understanding the causal genetic architecture of complex phenotypes is essential for future research into disease mechanisms and potential therapies. Despite the widespread availability of genome-wide data, existing methods to analyze genetic data still primarily focus on marginal association models, which fall short of fully capturing the polygenic nature of complex traits and elucidating biological causal mechanisms. Here we present a computationally efficient causal inference framework for genome-wide detection of putative causal variants underlying genetic associations. Our approach utilizes summary statistics from potentially overlapping studies as input, constructs in silico knockoff copies of summary statistics as negative controls to attenuate confounding effects induced by linkage disequilibrium, and employs efficient ultrahigh-dimensional sparse regression to jointly model all genetic variants across the genome. Our method is computationally efficient, requiring less than 15 minutes on a single CPU to analyze genome-wide summary statistics. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD) we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline via marginal association testing. The identified putative causal variants achieve state-of-the-art agreement with massively parallel reporter assays and CRISPR-Cas9 experiments. Additionally, we applied the method to a retrospective analysis of large-scale genome-wide association studies (GWAS) summary statistics from 2013 to 2022. Results reveal the method's capacity to robustly discover additional loci for polygenic traits beyond conventional GWAS and pinpoint potential causal variants underpinning each locus (on average, 22.7% more loci and 78.7% fewer proxy variants), contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses. We are making the discoveries and software freely available to the community and anticipate that routine end-to-end in silico identification of putative causal genetic variants will become an important tool that will facilitate downstream functional experiments and future research into disease etiology, as well as the exploration of novel therapeutic avenues.
| S-EPMC10925326 | biostudies-literature
Project description:Copy number variants (CNVs) constitute an important class of genetic variants in human genome and are shown to be associated with complex diseases. Whole-genome sequencing provides an unbiased way of identifying all the CNVs that an individual carries. In this paper, we consider parametric modeling of the read depth (RD) data from whole-genome sequencing with the aim of identifying the CNVs, including both Poisson and negative-binomial modeling of such count data. We propose a unified approach of using a mean-matching variance stabilizing transformation to turn the relatively complicated problem of sparse segment identification for count data into a sparse segment identification problem for a sequence of Gaussian data. We apply the optimal sparse segment identification procedure to the transformed data in order to identify the CNV segments. This provides a computationally efficient approach for RD-based CNV identification. Simulation results show that this approach often results in a small number of false identifications of the CNVs and has similar or better performances in identifying the true CNVs when compared with other RD-based approaches. We demonstrate the methods using the trio data from the 1000 Genomes Project.
| S-EPMC4059462 | biostudies-literature
Project description:Scleroderma is a genetically complex autoimmune disease with substantial phenotypic heterogeneity. Previous genome-wide association studies have identified common genetic variants associated with disease risk, but these studies are not designed to capture rare or potential causal variants. Our goal was to identify rare as well as common genetic variants in patients with diffuse cutaneous systemic sclerosis (dcSSc) through whole-exome sequencing (WES) in order to identify potential causal variants.We generated WES data for 32 dcSSc patients with or without interstitial lung disease (ILD) and for 17 healthy "in-house" controls. Variants were annotated and filtered by quality, minor allele frequency, and deleterious effects on gene function. We applied a gene burden test to identify novel dcSSc and dcSSc-associated ILD candidate genes that were enriched with deleterious variants in cases compared to in-house controls as well as controls from the 1000 Genomes Project (n?=?130).We identified 70 genes that were enriched with deleterious variants in dcSSc patients. Two of them (BANK1 and TERT) were in pathways previously implicated in SSc or ILD pathogenesis or known susceptibility loci. Newly identified genes (COL4A3, COL4A4, COL5A2, COL13A1, and COL22A1) were significantly enriched in the extracellular matrix-related pathway, which is relevant to the fibrotic features of dcSSc, and in the DNA repair pathway (XRCC4).This study demonstrates the value of WES for the identification of novel gene variants and pathways that may contribute to scleroderma risk and/or severity. The candidate genes we discovered are potential targets for in-depth functional studies.
| S-EPMC5568050 | biostudies-literature
Project description:IntroductionGenome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci.MethodsWe performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases=2,184, N controls=2,383) and targeted analyses in sub-populations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants.ResultsSeventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses.DiscussionThis study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.
| S-EPMC10491364 | biostudies-literature