Genetic Inference on Quantitative Traits Through Linkage and Association Studies

2018 ◽  
pp. 88-112
Author(s):  
Seth C Murray
Keyword(s):  
Quantitative Traits ◽  
Association Studies ◽  
Genetic Inference

scholarly journals Estimating the effective sample size in association studies of quantitative traits

2021 ◽  
Author(s):  
Andrey Ziyatdinov ◽  
Jihye Kim ◽  
Dmitry Prokopenko ◽  
Florian Privé ◽  
Fabien Laporte ◽  
...  
Keyword(s):  
Statistical Power ◽  
Quantitative Traits ◽  
Mixed Model ◽  
Association Studies ◽  
Effective Sample Size ◽  
Environment Interaction ◽  
Uk Biobank ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
The Uk

Abstract The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.

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 Identification of a Cis-Acting Regulatory Polymorphism in a Eucalypt COBRA-Like Gene Affecting Cellulose Content

Genetics ◽  
2009 ◽  
Vol 183 (3) ◽  
pp. 1153-1164 ◽  
Author(s):  
Bala R. Thumma ◽  
Bronwyn A. Matheson ◽  
Deqiang Zhang ◽  
Christian Meeske ◽  
Roger Meder ◽  
...  
Keyword(s):  
Quantitative Traits ◽  
Association Studies ◽  
Allelic Expression ◽  
Cellulose Content ◽  
Nucleotide Polymorphisms ◽  
Cis Acting ◽  
Functional Polymorphisms ◽  
Functional Variants ◽  
Regulatory Polymorphism ◽  
Trait Locus

Populations with low linkage disequilibrium (LD) offer unique opportunities to study functional variants influencing quantitative traits. We exploited the low LD in forest trees to identify functional polymorphisms in a Eucalyptus nitens COBRA-like gene (EniCOBL4A), whose Arabidopsis homolog has been implicated in cellulose deposition. Linkage analysis in a full-sib family revealed that EniCOBL4A is the most strongly associated marker in a quantitative trait locus (QTL) region for cellulose content. Analysis of LD by genotyping 11 common single-nucleotide polymorphisms (SNPs) and a simple sequence repeat (SSR) in an association population revealed that LD declines within the length of the gene. Using association studies we fine mapped the effect of the gene to SNP7, a synonymous SNP in exon 5, which occurs between two small haplotype blocks. We observed patterns of allelic expression imbalance (AEI) and differential binding of nuclear proteins to the SNP7 region that indicate that SNP7 is a cis-acting regulatory polymorphism affecting allelic expression. We also observed AEI in SNP7 heterozygotes in a full-sib family that is linked to heritable allele-specific methylation near SNP7. This study demonstrates the potential to reveal functional polymorphisms underlying quantitative traits in low LD populations.

Sequential sib-pair and association studies to detect genes in quantitative traits

1997 ◽  
Vol 14 (6) ◽  
pp. 885-890
Author(s):  
Amanda Savage ◽  
Fengzhu Sun ◽  
Dana C. Crawford ◽  
Allison E. Ashley ◽  
Quanhe Yang ◽  
...  
Keyword(s):  
Quantitative Traits ◽  
Association Studies

scholarly journals Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies

2011 ◽  
Vol 35 (8) ◽  
pp. 790-799 ◽  
Author(s):  
Dalin Li ◽  
Juan Pablo Lewinger ◽  
William J. Gauderman ◽  
Cassandra Elizabeth Murcray ◽  
David Conti
Keyword(s):  
Quantitative Traits ◽  
Association Studies ◽  
Extreme Phenotype ◽  
Causal Variants
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