scholarly journals Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits

2019 ◽  
Author(s):  
Sebastian Akle ◽  
Sung Chun ◽  
Athanasios Teodosiadis ◽  
Brian E. Cade ◽  
Heming Wang ◽  
...  
Keyword(s):  
Statistical Power ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Obstructive Sleep ◽  
A Chain ◽  
The Cost ◽  
Underlying Pathophysiology ◽  
Insight Into

AbstractGenetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and invasiveness of the required phenotyping. This reduces statistical power to discover multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology, using obstructive sleep apnea (OSA) as an exemplar. OSA is a common disorder diagnosed via overnight physiological testing (polysomnography). Here, we leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example we find links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of desmoplakin (DSP) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases.

scholarly journals Optimal Genomic Control in Large-scale Genetic Associations for Binary Diseases

2021 ◽  
Author(s):  
Runqing Yang ◽  
Yuxin Song ◽  
Li Jiang ◽  
Zhiyu Hao ◽  
Runqing Yang
Keyword(s):  
Multiple Testing ◽  
Statistical Power ◽  
Large Scale ◽  
Association Studies ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genomic Heritability ◽  
Large Scale Data ◽  
Genome Wide

Abstract Complex computation and approximate solution hinder the application of generalized linear mixed models (GLMM) into genome-wide association studies. We extended GRAMMAR to handle binary diseases by considering genomic breeding values (GBVs) estimated in advance as a known predictor in genomic logit regression, and then controlled polygenic effects by regulating downward genomic heritability. Using simulations and case analyses, we showed in optimizing GRAMMAR, polygenic effects and genomic controls could be evaluated using the fewer sampling markers, which extremely simplified GLMM-based association analysis in large-scale data. In addition, joint analysis for quantitative trait nucleotide (QTN) candidates chosen by multiple testing offered significant improved statistical power to detect QTNs over existing methods.

scholarly journals An atlas of genetic associations in UK Biobank

2017 ◽  
Author(s):  
Oriol Canela-Xandri ◽  
Konrad Rawlik ◽  
Albert Tenesa
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Genetic Associations ◽  
Genome Wide ◽  
Related Individuals ◽  
Sufficient Statistical Power

ABSTRACTGenome-wide association studies have revealed many loci contributing to the variation of complex traits, yet the majority of loci that contribute to the heritability of complex traits remain elusive. Large study populations with sufficient statistical power are required to detect the small effect sizes of the yet unidentified genetic variants. However, the analysis of huge cohorts, like UK Biobank, is complicated by incidental structure present when collecting such large cohorts. For instance, UK Biobank comprises 107,162 third degree or closer related participants. Traditionally, GWAS have removed related individuals because they comprised an insignificant proportion of the overall sample size, however, removing related individuals in UK Biobank would entail a substantial loss of power. Furthermore, modelling such structure using linear mixed models is computationally expensive, which requires a computational infrastructure that may not be accessible to all researchers. Here we present an atlas of genetic associations for 118 non-binary and 599 binary traits of 408,455 related and unrelated UK Biobank participants of White-British descent. Results are compiled in a publicly accessible database that allows querying genome-wide association summary results for 623,944 genotyped and HapMap2 imputed SNPs, as well downloading whole GWAS summary statistics for over 30 million imputed SNPs from the Haplotype Reference Consortium panel. Our atlas of associations (GeneATLAS,http://geneatlas.roslin.ed.ac.uk) will help researchers to query UK Biobank results in an easy way without the need to incur in high computational costs.

scholarly journals Case-Base-Control designs

2019 ◽  
Author(s):  
Najla Saad Elhezzani ◽  
Wicher Bergsma ◽  
Mike Weale
Keyword(s):  
Statistical Power ◽  
Association Studies ◽  
Likelihood Method ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Three Samples ◽  
The Em Algorithm ◽  
Tests Of Association ◽  
Control Designs

AbstractMost genome-wide association studies (GWASs) use randomly selected samples from the population (hereafter bases) as the control set. This approach is successful when the trait of interest is rare; otherwise, a loss in the statistical power to detect disease-associated variants is expected. To address this, a proposal to combine the three sample types, cases, controls and bases is introduced, for instances when the disease under study is prevalent. This is done by modelling the bases as a mixture of multinomial logistic functions of cases and controls, according to the disease prevalence. The maximum likelihood method is used to estimate the underlying parameters using the EM algorithm. Three classical tests of association; score, Walds, and likelihood ratio tests are derived and their power of detecting genetic associations under different designs is compared. Simulations show that combining the three samples can increase the power to detect disease-associated variants, though a very large base sample set can compensate for the lack of controls.

scholarly journals XGMix: Local-Ancestry Inference with Stacked XGBoost

2020 ◽  
Author(s):  
Arvind Kumar ◽  
Daniel Mas Montserrat ◽  
Carlos Bustamante ◽  
Alexander Ioannidis
Keyword(s):  
Association Studies ◽  
Genomic Medicine ◽  
Genetic Association Studies ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Local Ancestry ◽  
Genome Wide ◽  
Health Burdens ◽  
Local Ancestry Inference

AbstractGenomic medicine promises increased resolution for accurate diagnosis, for personalized treatment, and for identification of population-wide health burdens at rapidly decreasing cost (with a genotype now cheaper than an MRI and dropping). The benefits of this emerging form of affordable, data-driven medicine will accrue predominantly to those populations whose genetic associations have been mapped, so it is of increasing concern that over 80% of such genome-wide association studies (GWAS) have been conducted solely within individuals of European ancestry [1]. The severe under-representation of the majority of the world’s populations in genetic association studies stems in part from an addressable algorithmic weakness: lack of simple, accurate, and easily trained methods for identifying and annotating ancestry along the genome (local ancestry). Here we present such a method (XGMix) based on gradient boosted trees, which, while being accurate, is also simple to use, and fast to train, taking minutes on consumer-level laptops.

scholarly journals Systematic meta-analyses, field synopsis and global assessment of the evidence of genetic association studies in colorectal cancer

Gut ◽  
2019 ◽  
Vol 69 (8) ◽  
pp. 1460-1471 ◽  
Author(s):  
Zahra Montazeri ◽  
Xue Li ◽  
Christine Nyiraneza ◽  
Xiangyu Ma ◽  
Maria Timofeeva ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genetic Associations ◽  
Genome Wide ◽  
Discovery Probability ◽  
Assessment Of Evidence ◽  
Meta Analyses

ObjectiveTo provide an understanding of the role of common genetic variations in colorectal cancer (CRC) risk, we report an updated field synopsis and comprehensive assessment of evidence to catalogue all genetic markers for CRC (CRCgene2).DesignWe included 869 publications after parallel literature review and extracted data for 1063 polymorphisms in 303 different genes. Meta-analyses were performed for 308 single nucleotide polymorphisms (SNPs) in 158 different genes with at least three independent studies available for analysis. Scottish, Canadian and Spanish data from genome-wide association studies (GWASs) were incorporated for the meta-analyses of 132 SNPs. To assess and classify the credibility of the associations, we applied the Venice criteria and Bayesian False-Discovery Probability (BFDP). Genetic associations classified as ‘positive’ and ‘less-credible positive’ were further validated in three large GWAS consortia conducted in populations of European origin.ResultsWe initially identified 18 independent variants at 16 loci that were classified as ‘positive’ polymorphisms for their highly credible associations with CRC risk and 59 variants at 49 loci that were classified as ‘less-credible positive’ SNPs; 72.2% of the ‘positive’ SNPs were successfully replicated in three large GWASs and the ones that were not replicated were downgraded to ‘less-credible’ positive (reducing the ‘positive’ variants to 14 at 11 loci). For the remaining 231 variants, which were previously reported, our meta-analyses found no evidence to support their associations with CRC risk.ConclusionThe CRCgene2 database provides an updated list of genetic variants related to CRC risk by using harmonised methods to assess their credibility.

scholarly journals Best practices of multi-ancestry, meta-analytic transcriptome-wide association studies: lessons from the Global Biobank Meta-analysis Initiative

2021 ◽  
Author(s):  
Arjun Bhattacharya ◽  
Jibril B Hirbo ◽  
Dan Zhou ◽  
Wei Zhou ◽  
Jie Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Meta Analysis ◽  
Genomic Medicine ◽  
Genetic Association Studies ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Study Population

The Global Biobank Meta-analysis Initiative (GBMI), through its genetic and demographic diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ethnic study populations across biobanks, the GBMI provides a distinct set of challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWAS) are a popular tool to boost detection power for and provide biological context to genetic associations by integrating single nucleotide polymorphism to trait (SNP-trait) associations from genome-wide association studies (GWAS) with SNP-based predictive models of gene expression. TWAS presents unique challenges beyond GWAS, especially in a multi-biobank and meta-analytic setting like the GBMI. In this work, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity and meta-analytic strategies, as well as open challenges at every step of the framework. Our work provides a strong foundation for adding tissue-specific gene expression context to biobank-linked genetic association studies, allowing for ancestry-aware discovery to accelerate genomic medicine.

scholarly journals Optimal Genomic Control in Large-scale Genetic Associations for Binary Diseases

2021 ◽  
Author(s):  
Runqing Yang ◽  
Yuxin Song ◽  
Li Jiang ◽  
Zhiyu Hao ◽  
Runqing Yang
Keyword(s):  
Multiple Testing ◽  
Statistical Power ◽  
Large Scale ◽  
Association Studies ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genomic Heritability ◽  
Large Scale Data ◽  
Genome Wide

Abstract Complex computation and approximate solution hinder the application of generalized linear mixed models (GLMM) into genome-wide association studies. We extended GRAMMAR to handle binary diseases by considering genomic breeding values (GBVs) estimated in advance as a known predictor in genomic logit regression, and then controlled polygenic effects by regulating downward genomic heritability. Using simulations and case analyses, we showed in optimizing GRAMMAR, polygenic effects and genomic controls could be evaluated using the fewer sampling markers, which extremely simplified GLMM-based association analysis in large-scale data. In addition, joint analysis for quantitative trait nucleotide (QTN) candidates chosen by multiple testing offered significant improved statistical power to detect QTNs over existing methods.

scholarly journals Common genetic variants with fetal effects on birth weight are enriched for proximity to genes implicated in rare developmental disorders

2021 ◽  
Author(s):  
Robin N Beaumont ◽  
Isabelle K Mayne ◽  
Rachel M Freathy ◽  
Caroline F Wright
Keyword(s):  
Birth Weight ◽  
Statistical Power ◽  
Developmental Disorders ◽  
Association Studies ◽  
Later Life ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
Causal Genes

Abstract Birth weight is an important factor in newborn survival; both low and high birth weights are associated with adverse later-life health outcomes. Genome-wide association studies (GWAS) have identified 190 loci associated with maternal or fetal effects on birth weight. Knowledge of the underlying causal genes is crucial to understand how these loci influence birth weight and the links between infant and adult morbidity. Numerous monogenic developmental syndromes are associated with birth weights at the extreme ends of the distribution. Genes implicated in those syndromes may provide valuable information to prioritize candidate genes at the GWAS loci. We examined the proximity of genes implicated in developmental disorders (DDs) to birth weight GWAS loci using simulations to test whether they fall disproportionately close to the GWAS loci. We found birth weight GWAS single nucleotide polymorphisms (SNPs) fall closer to such genes than expected both when the DD gene is the nearest gene to the birth weight SNP and also when examining all genes within 258 kb of the SNP. This enrichment was driven by genes causing monogenic DDs with dominant modes of inheritance. We found examples of SNPs in the intron of one gene marking plausible effects via different nearby genes, highlighting the closest gene to the SNP not necessarily being the functionally relevant gene. This is the first application of this approach to birth weight, which has helped identify GWAS loci likely to have direct fetal effects on birth weight, which could not previously be classified as fetal or maternal owing to insufficient statistical power.

scholarly journals Evaluation of genome-wide power of genetic association studies based on empirical data from the HapMap project

2007 ◽  
Vol 16 (20) ◽  
pp. 2494-2505 ◽  
Author(s):  
Yasuhito Nannya ◽  
Kenjiro Taura ◽  
Mineo Kurokawa ◽  
Shigeru Chiba ◽  
Seishi Ogawa
Keyword(s):  
Genetic Association ◽  
Empirical Data ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Hapmap Project ◽  
Genome Wide
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