Chapter title |
Cross-Phenotype Association Analysis Using Summary Statistics from GWAS
|
---|---|
Chapter number | 22 |
Book title |
Statistical Human Genetics
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-7274-6_22 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7273-9, 978-1-4939-7274-6
|
Authors |
Xiaoyin Li, Xiaofeng Zhu |
Abstract |
For over a decade, genome-wide association studies (GWAS) have been a major tool for detecting genetic variants underlying complex traits. Recent studies have demonstrated that the same variant or gene can be associated with multiple traits, and such associations are termed cross-phenotype (CP) associations. CP association analysis can improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this chapter, we discuss existing statistical methods for analyzing association between a single marker and multivariate phenotypes, we introduce a general approach, CPASSOC, to detect the CP associations, and explain how to conduct the analysis in practice. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 35 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 31% |
Researcher | 4 | 11% |
Student > Doctoral Student | 3 | 9% |
Student > Master | 2 | 6% |
Student > Bachelor | 1 | 3% |
Other | 1 | 3% |
Unknown | 13 | 37% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 8 | 23% |
Agricultural and Biological Sciences | 4 | 11% |
Medicine and Dentistry | 4 | 11% |
Mathematics | 2 | 6% |
Social Sciences | 1 | 3% |
Other | 3 | 9% |
Unknown | 13 | 37% |