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基于家系的关联分析：一种针对多个变异体进行多变量关联分析的快速有效方法。

Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants.

作者信息

Won Sungho, Kim Wonji, Lee Sungyoung, Lee Young, Sung Joohon, Park Taesung

机构信息

Department of Public Health Science, Seoul National University, Seoul, Korea.

Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea.

出版信息

BMC Bioinformatics. 2015 Feb 15;16:46. doi: 10.1186/s12859-015-0484-5.

DOI:10.1186/s12859-015-0484-5
PMID:25887481
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4339744/
Abstract

BACKGROUND

Many disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes. Therefore, joint analysis of multiple phenotypes and multiple markers has been considered as an efficient strategy for genome-wide association analysis, and in this work we propose an omnibus family-based association test for the joint analysis of multiple genotypes and multiple phenotypes.

RESULTS

The proposed test can be applied for both quantitative and dichotomous phenotypes, and it is robust under the presence of population substructure, as long as large-scale genomic data is available. Using simulated data, we showed that our method is statistically more efficient than the existing methods, and the practical relevance is illustrated by application of the approach to obesity-related phenotypes.

CONCLUSIONS

The proposed method may be more statistically efficient than the existing methods. The application was developed in C++ and is available at the following URL: http://healthstat.snu.ac.kr/software/mfqls/ .

摘要

背景

许多疾病表型是多个基因之间复杂相互作用的结果，并且单个或多个基因型会影响多种表型。因此，多表型和多标记的联合分析被认为是全基因组关联分析的一种有效策略，在本研究中，我们提出了一种基于家系的综合关联检验，用于多基因型和多表型的联合分析。

结果

所提出的检验可应用于定量和二分表型，并且只要有大规模基因组数据，在存在群体亚结构的情况下它也具有稳健性。使用模拟数据，我们表明我们的方法在统计学上比现有方法更有效，并且通过将该方法应用于肥胖相关表型说明了其实际相关性。

结论

所提出的方法在统计学上可能比现有方法更有效。该应用程序是用C++开发的，可通过以下网址获取：http://healthstat.snu.ac.kr/software/mfqls/ 。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/81143da6f52a/12859_2015_484_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/6ad6fcd69389/12859_2015_484_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/89330be43397/12859_2015_484_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/8e55c2f9ae00/12859_2015_484_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/3aaf9244d923/12859_2015_484_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/cb98a738efd5/12859_2015_484_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/a307b8bb8325/12859_2015_484_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/81143da6f52a/12859_2015_484_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/6ad6fcd69389/12859_2015_484_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/89330be43397/12859_2015_484_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/8e55c2f9ae00/12859_2015_484_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/3aaf9244d923/12859_2015_484_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/cb98a738efd5/12859_2015_484_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/a307b8bb8325/12859_2015_484_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4294/4339744/81143da6f52a/12859_2015_484_Fig7_HTML.jpg

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