scholarly journals Imputation of posterior linkage probability relations reveals a significant influence of structural 3D constraints on linkage disequilibrium

2018 ◽  
Author(s):  
Susanne Gerber ◽  
David Fournier ◽  
Charlotte Hewel ◽  
Illia Horenko
Keyword(s):  
Latent Variables ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Large Data ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Snp Data ◽  
Genome Wide

Genetic association studies have become increasingly important in unraveling the genetics of diseases or complex traits. Despite their value for modern genetics, conflicting conclusions often arise through the difficulty of confirming and replicating experimental results. We argue that this problem is largely based on the application of statistical relation measures that are not appropriate for genomic data analysis and demonstrate that the standard measures used for Genome-wide association studies or genomics linkage analysis bear a statistic bias. This may come from the violation of underlying assumptions (such as independence or stationarity) as well as from other conceptual limitations in the measures or relations, such as missing invariance with respect to coding or the inability to reflect latent factors. Attempts to introduce unbiased relation measures that avoid these limitations are usually computationally expensive and do not scale for large data sizes being typical for genomics applications.To tackle these problems, we propose a straightforwardly computable relation measure called Linkage Probability (LP). This measure provides the posterior probability of a relation between two categorical data sets and considers potential biases from latent variables. We compare several aspects of popular relation measures through an illustrative example and human genomics data. We demonstrate that the application of LP to the analysis of Single Nucleotide Polymorphisms (SNP) reveals latent 3D steric effects within 1D SNP data, that approximate to chromatin loops captured by high resolution Hi-C maps.

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

scholarly journals Meta-Analysis of Allergy Genome-Wide Association Studies

2020 ◽  
Author(s):  
Ronin Sharma
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Genetic Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Significance Level ◽  
Genome Wide ◽  
Linear Regressions

AbstractAllergies are complex conditions involving both environmental and genetic factors. The genetic basis underlying allergic disease is investigated through genetic association studies. Genome-wide association studies (GWAS) leverage sequenced data to identify genetic mutations, such as single-nucleotide polymorphisms (SNPs), associated with phenotypes of interest. Machine learning can be used to analyze large datasets and generate predictive models. In this study, several classification models were created to predict the significance level of SNPs associated with allergies. Summary statistics were obtained from the GWAS Catalog and combined from several studies. Biological features such as chromosomal location, base pair location, effect allele, and odds ratio were used to train the models. The models ranged from simple linear regressions to multi-layer neural networks. The final models reached accuracies of 80% and reflect the features that have the largest impact on a SNP’s association level.

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 Performing post-genome-wide association study analysis: overview, challenges and recommendations

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1002
Author(s):  
Yagoub Adam ◽  
Chaimae Samtal ◽  
Jean-tristan Brandenburg ◽  
Oluwadamilare Falola ◽  
Ezekiel Adebiyi
Keyword(s):  
Complex Traits ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Single Phenotype ◽  
Insight Into

Genome-wide association studies (GWAS) provide  huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to  millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis.  Finally, we include a custom pGWAS pipeline to guide new users when performing their research.

scholarly journals Embracing polygenicity: a review of methods and tools for psychiatric genetics research

2017 ◽  
Vol 48 (7) ◽  
pp. 1055-1067 ◽  
Author(s):  
R. M. Maier ◽  
P. M. Visscher ◽  
M. R. Robinson ◽  
N. R. Wray
Keyword(s):  
Psychiatric Disorders ◽  
Genetic Risk ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genetics Research ◽  
Genome Wide ◽  
Analyse Data ◽  
Shared Genetic

AbstractThe availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.

scholarly journals VarGoats project: a dataset of 1159 whole-genome sequences to dissect Capra hircus global diversity

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Laure Denoyelle ◽  
Estelle Talouarn ◽  
Philippe Bardou ◽  
Licia Colli ◽  
Adriana Alberti ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Geographic Origin ◽  
Genetic Distances ◽  
Capra Hircus ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Selection Processes ◽  
Genome Wide ◽  
Broad Variety

Abstract Background Since their domestication 10,500 years ago, goat populations with distinctive genetic backgrounds have adapted to a broad variety of environments and breeding conditions. The VarGoats project is an international 1000-genome resequencing program designed to understand the consequences of domestication and breeding on the genetic diversity of domestic goats and to elucidate how speciation and hybridization have modeled the genomes of a set of species representative of the genus Capra. Findings A dataset comprising 652 sequenced goats and 507 public goat sequences, including 35 animals representing eight wild species, has been collected worldwide. We identified 74,274,427 single nucleotide polymorphisms (SNPs) and 13,607,850 insertion-deletions (InDels) by aligning these sequences to the latest version of the goat reference genome (ARS1). A Neighbor-joining tree based on Reynolds genetic distances showed that goats from Africa, Asia and Europe tend to group into independent clusters. Because goat breeds from Oceania and Caribbean (Creole) all derive from imported animals, they are distributed along the tree according to their ancestral geographic origin. Conclusions We report on an unprecedented international effort to characterize the genome-wide diversity of domestic goats. This large range of sequenced individuals represents a unique opportunity to ascertain how the demographic and selection processes associated with post-domestication history have shaped the diversity of this species. Data generated for the project will also be extremely useful to identify deleterious mutations and polymorphisms with causal effects on complex traits, and thus will contribute to new knowledge that could be used in genomic prediction and genome-wide association studies.

scholarly journals Large Scale Association Analysis for Drug Addiction: Results from SNP to Gene

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Xiaobo Guo ◽  
Zhifa Liu ◽  
Xueqin Wang ◽  
Heping Zhang
Keyword(s):  
Association Analysis ◽  
Animal Studies ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Complex Diseases ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide

Many genetic association studies used single nucleotide polymorphisms (SNPs) data to identify genetic variants for complex diseases. Although SNP-based associations are most common in genome-wide association studies (GWAS), gene-based association analysis has received increasing attention in understanding genetic etiologies for complex diseases. While both methods have been used to analyze the same data, few genome-wide association studies compare the results or observe the connection between them. We performed a comprehensive analysis of the data from the Study of Addiction: Genetics and Environment (SAGE) and compared the results from the SNP-based and gene-based analyses. Our results suggest that the gene-based method complements the individual SNP-based analysis, and conceptually they are closely related. In terms of gene findings, our results validate many genes that were either reported from the analysis of the same dataset or based on animal studies for substance dependence.

scholarly journals Multi-ethnic genome-wide association analyses of white blood cell and platelet traits in the Population Architecture using Genomics and Epidemiology (PAGE) study

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yao Hu ◽  
Stephanie A. Bien ◽  
Katherine K. Nishimura ◽  
Jeffrey Haessler ◽  
Chani J. Hodonsky ◽  
...  
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Association Analyses ◽  
Genome Wide

Abstract Background Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented. Results We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance (P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance (P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations. Conclusions Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.

scholarly journals Genetic Etiologies for Phenotypic Diversity in Sickle Cell Anemia

2009 ◽  
Vol 9 ◽  
pp. 46-67 ◽  
Author(s):  
Martin H. Steinberg
Keyword(s):  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Phenotypic Diversity ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
General Measure ◽  
Genome Wide ◽  
Genetic Modulators

The clinical course of patients with sickle cell anemia, a Mendelian trait, is characteristically highly variable. HbF concentration and the presence of a thalassemia are established modulators of the disease, but cannot account for all of its clinical heterogeneity. To find additional genetic modulators of disease, genotype-phenotype association studies, where single nucleotide polymorphisms (SNPs) in candidate genes are linked with a particular phenotype, have been informative. SNPs in several genes of the TGF-ß/MP superfamily, and some other genes linked to the endothelial function, and nitric oxide biology are associated with the subphenotypes of stroke, osteonecrosis, priapism, leg ulcers, pulmonary hypertension, and a more general measure of overall disease severity. Genome-wide association studies should help to confirm these observations and also to find hitherto unsuspected genetic modulators. Genetic association studies can have immediate prognostic value; they might also help to identify new pathophysiological pathways that could be susceptible to modulation.

scholarly journals Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review

Pathogens ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1604
Author(s):  
Nelisiwe Mkize ◽  
Azwihangwisi Maiwashe ◽  
Kennedy Dzama ◽  
Bekezela Dube ◽  
Ntanganedzeni Mapholi
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Cattle Tick ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genetic Determinants ◽  
Genome Wide ◽  
Minimum Number ◽  
The Cost ◽  
Tick Resistance

Understanding the biological mechanisms underlying tick resistance in cattle holds the potential to facilitate genetic improvement through selective breeding. Genome wide association studies (GWAS) are popular in research on unraveling genetic determinants underlying complex traits such as tick resistance. To date, various studies have been published on single nucleotide polymorphisms (SNPs) associated with tick resistance in cattle. The discovery of SNPs related to tick resistance has led to the mapping of associated candidate genes. Despite the success of these studies, information on genetic determinants associated with tick resistance in cattle is still limited. This warrants the need for more studies to be conducted. In Africa, the cost of genotyping is still relatively expensive; thus, conducting GWAS is a challenge, as the minimum number of animals recommended cannot be genotyped. These population size and genotype cost challenges may be overcome through the establishment of collaborations. Thus, the current review discusses GWAS as a tool to uncover SNPs associated with tick resistance, by focusing on the study design, association analysis, factors influencing the success of GWAS, and the progress on cattle tick resistance studies.

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