Comparison of methods and sampling designs to test for association between rare variants and quantitative traits

2011 ◽  
pp. n/a-n/a
Author(s):  
Silviu-Alin Bacanu ◽  
Matthew R. Nelson ◽  
John C. Whittaker
Keyword(s):  
Quantitative Traits ◽  
Rare Variants ◽  
Comparison Of Methods

scholarly journals Detecting Rare Variants for Quantitative Traits Using Nuclear Families

2012 ◽  
Vol 73 (3) ◽  
pp. 148-158 ◽  
Author(s):  
Wei Guo ◽  
Yin Yao Shugart
Keyword(s):  
Quantitative Traits ◽  
Rare Variants ◽  
Nuclear Families

scholarly journals A Flexible Approach for the Analysis of Rare Variants Allowing for a Mixture of Effects on Binary or Quantitative Traits

PLoS Genetics ◽  
2013 ◽  
Vol 9 (8) ◽  
pp. e1003694 ◽  
Author(s):  
Geraldine M. Clarke ◽  
Manuel A. Rivas ◽  
Andrew P. Morris
Keyword(s):  
Quantitative Traits ◽  
Rare Variants ◽  
Flexible Approach

scholarly journals Exome sequencing identifies high-impact trait-associated alleles enriched in Finns

2018 ◽  
Author(s):  
Adam E Locke ◽  
Karyn Meltz Steinberg ◽  
Charleston WK Chiang ◽  
Susan K Service ◽  
Aki S Havulinna ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Quantitative Traits ◽  
Rare Variants ◽  
Mendelian Disease ◽  
Finnish Population ◽  
Isolated Populations ◽  
Deleterious Alleles ◽  
Disease Mutations ◽  
Sequencing Studies ◽  
Novel Associations

ABSTRACTAs yet undiscovered rare variants are hypothesized to substantially influence an individual’s risk for common diseases and traits, but sequencing studies aiming to identify such variants have generally been underpowered. In isolated populations that have expanded rapidly after a population bottleneck, deleterious alleles that passed through the bottleneck may be maintained at much higher frequencies than in other populations. In an exome sequencing study of nearly 20,000 cohort participants from northern and eastern Finnish populations that exemplify this phenomenon, most novel trait-associated deleterious variants are seen only in Finland or display frequencies more than 20 times higher than in other European populations. These enriched alleles underlie 34 novel associations with 21 disease-related quantitative traits and demonstrate a geographical clustering equivalent to that of Mendelian disease mutations characteristic of the Finnish population. Sequencing studies in populations without this unique history would require hundreds of thousands to millions of participants for comparable power for these variants.

An Empirical Bayes risk prediction model using multiple traits for sequencing data

2015 ◽  
Vol 14 (6) ◽  
Author(s):  
Gengxin Li ◽  
Yuehua Cui ◽  
Hongyu Zhao
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Empirical Bayes ◽  
Quantitative Traits ◽  
Rare Variants ◽  
Disease Risk ◽  
Disease Status ◽  
Binary Outcomes ◽  
Sequencing Data ◽  
Multiple Traits

AbstractThe rapidly developing sequencing technologies have led to improved disease risk prediction through identifying many novel genes. Many prediction methods have been proposed to use rich genomic information to predict binary disease outcomes. It is intuitive that these methods can be further improved by making efficient use of the rich information in measured quantitative traits that are correlated with binary outcomes. In this article, we propose a novel Empirical Bayes prediction model that uses information from both quantitative traits and binary disease status to improve risk prediction. Our method is built on a new statistic that better infers the gene effect on multiple traits, and it also enjoys the good theoretical properties. We then consider using sequencing data by combining information from multiple rare variants in individual genes to strengthen the signals of causal genetic effects. In simulation study, we find that our proposed Empirical Bayes approach is superior to other existing methods in terms of feature selection and risk prediction. We further evaluate the effectiveness of our proposed method through its application to the sequencing data provided by the Genetic Analysis Workshop 18.

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