scholarly journals Local joint testing improves power and identifies missing heritability in association studies

2016 ◽  
Author(s):  
Brielin C. Brown ◽  
Alkes L. Price ◽  
Nikolaos A. Patsopoulos ◽  
Noah Zaitlen
Keyword(s):  
Multiple Testing ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Testing Procedure ◽  
Missing Heritability ◽  
Multiple Testing Correction ◽  
Testing Framework ◽  
Genome Wide ◽  
Disease Associations ◽  
Causal Variants

AbstractThere is mounting evidence that complex human phenotypes are highly polygenic, with many loci harboring multiple causal variants, yet most genetic association studies examine each SNP in isolation. While this has lead to the discovery of thousands of disease associations, discovered variants account for only a small fraction of disease heritability. Alternative multi-SNP methods have been proposed, but issues such as multiple testing correction, sensitivity to genotyping error, and optimization for the underlying genetic architectures remain. Here we describe a local joint testing procedure, complete with multiple testing correction, that leverages a genetic phenomenon we call linkage masking wherein linkage disequilibrium between SNPs hides their signal under standard association methods. We show that local joint testing on the original Wellcome Trust Case Control Consortium dataset leads to the discovery of 29% more associated loci that were later found in followup studies containing thousands of additional individuals. These loci double the heritability explained by genome-wide significant associations in the WTCCC dataset, implicating linkage masking as a novel source of missing heritability. Furthermore, we show that local joint testing in a cis-eQTL study of the gEUVADIS dataset increases the number of genes discovered by 10.7% over marginal analyses. Our multiple hypothesis correction and joint testing framework are available in a python software package called jester, available at github.com/brielin/Jester.

scholarly journals Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

2018 ◽  
Author(s):  
David M. Howard ◽  
Mark J. Adams ◽  
Toni-Kim Clarke ◽  
Jonathan D. Hafferty ◽  
Jude Gibson ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Drug Repositioning ◽  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Synaptic Structure ◽  
Genome Wide

AbstractMajor depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

scholarly journals False discovery rates for genome-wide association tests in biobanks with thousands of phenotypes

2021 ◽  
Author(s):  
Aubrey Annis ◽  
Anita Pandit ◽  
Jonathon LeFaive ◽  
Sarah Gagliano Taliun ◽  
Lars Fritsche ◽  
...  
Keyword(s):  
Genetic Association ◽  
Multiple Testing ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Minor Allele ◽  
Type I ◽  
Allele Carrier ◽  
Multiple Testing Correction ◽  
Phenotypic Data ◽  
False Discovery

Abstract Biobanks housing genetic and phenotypic data for thousands of individuals introduce new opportunities and challenges for genetic association studies. Association testing across many phenotypes increases the multiple-testing burden and correlation between phenotypes makes appropriate multiple-testing correction uncertain. Moreover, analysis including low-frequency variants results in inflated type I error due to the much larger number of tests and the elevated importance of each individual minor allele carrier in those tests. Here we demonstrate that standard Bonferroni and permutation-based methods for multiple testing correction are inadequate for a holistic analysis of biobank data because ideal significance thresholds vary across datasets and minor allele frequencies. We propose a single-iteration permutation method that is computationally feasible and provides false discovery rate (FDR) estimates tailored to individual datasets and variant frequencies. Each dataset’s unique FDR estimates provide customized levels of confidence for association results and enable informed interpretation of genetic association studies across the phenome.

Genomic Approaches Identify Novel Gene Associations with Out of Season Lambing in Sheep

2019 ◽  
Vol 110 (5) ◽  
pp. 577-586 ◽  
Author(s):  
Christian J Posbergh ◽  
Michael L Thonney ◽  
Heather J Huson
Keyword(s):  
Candidate Genes ◽  
Multiple Testing ◽  
Association Studies ◽  
Reproductive Hormones ◽  
Day Length ◽  
Fixation Index ◽  
Multiple Testing Correction ◽  
Genetic Progress ◽  
Genome Wide ◽  
Gene Associations

Abstract Sheep are seasonally polyestrous, traditionally breeding when the day length shortens in the autumn. The changing photoperiod stimulates reproductive hormones through a series of chemical pathways, ultimately leading to cyclicity. Some breeds of sheep, such as the Polypay and Dorset, have been selected for reduced seasonality and can lamb year-round. Despite this selection, there is still variation within these breeds in the ability to lamb out of season. The identification of out of season lambing quantitative trait loci has the potential to improve genetic progress using genomic selection schemes. Association studies, fixation index (FST), and runs of homozygosity (ROH) were evaluated to identify regions of the genome that influence the ability of ewes to lamb out of season. All analyses used genotypic data from the Illumina Ovine HD beadchip. Genome-wide associations were tested both across breeds in 257 ewes and within the Dorset and Polypay breeds. FST was measured across breeds and between UK and US Dorsets to assess population differences. ROH were estimated in ewes to identify homozygous regions contributing to out of season lambing. Significant associations after multiple testing correction were found through these approaches, leading to the identification of several candidate genes for further study. Genes involved with eye development, reproductive hormones, and neuronal changes were identified as the most promising for influencing the ewe’s ability to lamb year-round. These candidate genes could be advantageous for selection for improved year-round lamb production and provide better insight into the complex regulation of seasonal reproduction.

scholarly journals networkGWAS: A network-based approach for genome-wide association studies in structured populations

2021 ◽  
Author(s):  
Giulia Muzio ◽  
Leslie O'Bray ◽  
Laetitia Meng-Papaxanthos ◽  
Juliane Klatt ◽  
Karsten Borgwardt
Keyword(s):  
Genetic Markers ◽  
Complex Traits ◽  
Multiple Testing ◽  
Association Studies ◽  
Search Space ◽  
Structured Populations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Genome Wide

While the search for associations between genetic markers and complex traits has discovered tens of thousands of trait-related genetic variants, the vast majority of these only explain a tiny fraction of observed phenotypic variation. One possible strategy to detect stronger associations is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffers from a huge search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, and/or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose <tt>networkGWAS</tt>, a computationally efficient and statistically sound approach to gene-based genome-wide association studies based on mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated p-values, which we obtain through a block permutation scheme. <tt>networkGWAS</tt> successfully detects known or plausible associations on simulated rare variants from H. sapiens data as well as semi-simulated and real data with common variants from A. thaliana and enables the systematic combination of gene-based genome-wide association studies with biological network information.

scholarly journals Novel bioinformatics approach to investigate quantitative phenotype-genotype associations in neuroimaging studies

2015 ◽  
Author(s):  
Sejal Patel ◽  
Min Tae M Park ◽  
Mallar M Chakravarty ◽  
Jo Knight
Keyword(s):  
Multiple Testing ◽  
Association Studies ◽  
Genetic Research ◽  
Hippocampal Volume ◽  
Imaging Genetics ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Novel Approach ◽  
Genome Wide ◽  
Novel Method

Imaging genetics is an emerging field in which the association between genes and neuroimaging-based quantitative phenotypes are used to explore the functional role of genes in neuroanatomy and neurophysiology in the context of healthy function and neuropsychiatric disorders. The main obstacle for researchers in the field is the high dimensionality of the data in both the imaging phenotypes and the genetic variants commonly typed. In this article, we develop a novel method that utilizes Gene Ontology, an online database, to select and prioritize certain genes, employing a stratified false discovery rate (sFDR) approach to investigate their associations with imaging phenotypes. sFDR has the potential to increase power in genome wide association studies (GWAS), and is quickly gaining traction as a method for multiple testing correction. Our novel approach addresses both the pressing need in genetic research to move beyond candidate gene studies, while not being overburdened with a loss of power due to multiple testing. As an example of our methodology, we perform a GWAS of hippocampal volume using the Alzheimer's Disease Neuroimaging Initiative sample.

scholarly journals Genome-wide associations for udder and teat conformational risk factors for mastitis in Holstein cows

2019 ◽  
Author(s):  
Asha M. Miles ◽  
Christian Posbergh ◽  
Heather Jay Huson
Keyword(s):  
Cell Proliferation ◽  
Multiple Testing ◽  
Association Studies ◽  
Principal Component ◽  
Special Focus ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Genome Wide ◽  
Length Width ◽  
Mastitis Susceptibility

Abstract BACKGROUND The objective of our study was to conduct high-density genome-wide association studies of dairy cow udder and teat conformation with direct phenotyping. We identified and compared quantitative trait loci ( QTL ) for a novel composite mastitis risk trait and considered environmental impact of milking by comparing primiparous cows only. Cows (N = 471) were genotyped on the Illumina BovineHD 777K beadchip and scored for front and rear teat length, width, end shape, and placement, fore udder attachment, udder cleft, udder depth, rear udder height, and rear udder width. Principal component analysis was performed on fore udder attachment, rear teat end shape, rear teat width, and rear udder height, to create a single new phenotype describing mastitis susceptibility based on these high-risk traits.RESULTS Over all 14 traits of interest, a total of 56 genome-wide associations were performed and 28 significantly associated (Bonferroni multiple testing correction < 0.05) QTL were identified. The linkage disequilibrium ( LD ) block surrounding the associated QTL or a 1 Mb window in the absence of LD was interrogated for candidate genes, resulting in the identification of genes with functions related to both cell proliferation and immune signaling, including ZNF683, DHX9, CUX1, TNNT1 , and SPRY1 . We assessed a primiparous only subset of cows (n = 144) to account for the possibility that the genetic variance component of the phenotype is greater for cows who have had less exposure to the environment, and observed different associated QTL and inheritance patterns for udder depth in primiparous cows compared to the total cohort.CONCLUSION Special focus was given to the aforementioned mastitis risk traits, and candidate gene investigation revealed both immune function and cell proliferation related genes in the areas surrounding significantly associated QTL, suggesting that selecting for mastitis resistant cows based on these traits would be an effective method for increasing mastitis resiliency in a herd.

scholarly journals Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shuquan Rao ◽  
Yao Yao ◽  
Daniel E. Bauer
Keyword(s):  
Genome Editing ◽  
Genetic Variants ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Functional Studies ◽  
Functional Genetics ◽  
Genome Wide ◽  
Causal Variants ◽  
Experimental Approaches

AbstractGenome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

scholarly journals 2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sangyoon Yi ◽  
Xianyang Zhang ◽  
Lu Yang ◽  
Jinyan Huang ◽  
Yuanhang Liu ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Statistical Power ◽  
Association Studies ◽  
Control Procedure ◽  
Multiple Testing Correction ◽  
New Approach ◽  
False Discovery ◽  
Traditional Procedure ◽  
Extensive Evaluation ◽  
Confounder Adjustment

AbstractOne challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.

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