Dataset Information

A variance component based multi-marker association test using family and unrelated data.

ABSTRACT:

Background

Incorporating family data in genetic association studies has become increasingly appreciated, especially for its potential value in testing rare variants. We introduce here a variance-component based association test that can test multiple common or rare variants jointly using both family and unrelated samples.

Results

The proposed approach implemented in our R package aggregates or collapses the information across a region based on genetic similarity instead of genotype scores, which avoids the power loss when the effects are in different directions or have different association strengths. The method is also able to effectively leverage the LD information in a region and it can produce a test statistic with an adaptively estimated number of degrees of freedom. Our method can readily allow for the adjustment of non-genetic contributions to the familial similarity, as well as multiple covariates.

Conclusions

We demonstrate through simulations that the proposed method achieves good performance in terms of Type I error control and statistical power. The method is implemented in the R package "fassoc", which provides a useful tool for data analysis and exploration.

SUBMITTER: Wang X

PROVIDER: S-EPMC3614458 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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Publications

A variance component based multi-marker association test using family and unrelated data.

Wang Xuefeng X Morris Nathan J NJ Zhu Xiaofeng X Elston Robert C RC

BMC genetics 20130304

<h4>Background</h4>Incorporating family data in genetic association studies has become increasingly appreciated, especially for its potential value in testing rare variants. We introduce here a variance-component based association test that can test multiple common or rare variants jointly using both family and unrelated samples.<h4>Results</h4>The proposed approach implemented in our R package aggregates or collapses the information across a region based on genetic similarity instead of genotyp ...[more]

PMID: 23497289

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Project description:Bovine tuberculosis (bTB) is an infectious disease of cattle generally caused by Mycobacterium bovis, a bacterium that can elicit disease humans. Since the 1950s, the objective of the national bTB eradication program in Republic of Ireland was the biological extinction of bTB; that purpose has yet to be achieved. Objectives of the present study were to develop the statistical methodology and variance components to undertake routine genetic evaluations for resistance to bTB; also of interest was the detection of regions of the bovine genome putatively associated with bTB infection in dairy and beef breeds. The novelty of the present study, in terms of research on bTB infection, was the use of beef breeds in the genome-wide association and the utilization of imputed whole genome sequence data. Phenotypic bTB data on 781,270 animals together with imputed whole genome sequence data on 7,346 of these animals' sires were available. Linear mixed models were used to quantify variance components for bTB and EBVs were validated. Within-breed and multi-breed genome-wide associations were undertaken using a single-SNP regression approach. The estimated genetic standard deviation (0.09), heritability (0.12), and repeatability (0.30) substantiate that genetic selection help to eradicate bTB. The multi-breed genome-wide association analysis identified 38 SNPs and 64 QTL regions associated with bTB infection; two QTL regions (both on BTA23) identified in the multi-breed analysis overlapped with the within-breed analyses of Charolais, Limousin, and Holstein-Friesian. Results from the association analysis, coupled with previous studies, suggest bTB is controlled by an infinitely large number of loci, each having a small effect. The methodology and results from the present study will be used to develop national genetic evaluations for bTB in the Republic of Ireland. In addition, results can also be used to help uncover the biological architecture underlying resistance to bTB infection in cattle.

Dataset Information

A variance component based multi-marker association test using family and unrelated data.

Background

Results

Conclusions

Publications

A variance component based multi-marker association test using family and unrelated data.

Similar Datasets

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