Systematic Detection of Epistatic Interactions Based on Allele Pair Frequencies

被引:11
作者
Ackermann, Marit [1 ]
Beyer, Andreas [1 ]
机构
[1] Tech Univ Dresden, Ctr Biotechnol, D-01062 Dresden, Germany
来源
PLOS GENETICS | 2012年 / 8卷 / 02期
关键词
LINKAGE DISEQUILIBRIUM; GENE INTERACTIONS; EXPRESSION DATA; ASSOCIATIONS; INHERITANCE; POPULATION; GENOMES; TESTS; LOCI; MAPS;
D O I
10.1371/journal.pgen.1002463
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Epistatic genetic interactions are key for understanding the genetic contribution to complex traits. Epistasis is always defined with respect to some trait such as growth rate or fitness. Whereas most existing epistasis screens explicitly test for a trait, it is also possible to implicitly test for fitness traits by searching for the over-or under-representation of allele pairs in a given population. Such analysis of imbalanced allele pair frequencies of distant loci has not been exploited yet on a genome-wide scale, mostly due to statistical difficulties such as the multiple testing problem. We propose a new approach called Imbalanced Allele Pair frequencies (ImAP) for inferring epistatic interactions that is exclusively based on DNA sequence information. Our approach is based on genome-wide SNP data sampled from a population with known family structure. We make use of genotype information of parent-child trios and inspect 363 contingency tables for detecting pairs of alleles from different genomic positions that are over-or under-represented in the population. We also developed a simulation setup which mimics the pedigree structure by simultaneously assuming independence of the markers. When applied to mouse SNP data, our method detected 168 imbalanced allele pairs, which is substantially more than in simulations assuming no interactions. We could validate a significant number of the interactions with external data, and we found that interacting loci are enriched for genes involved in developmental processes.
引用
收藏
页数:11
相关论文
共 60 条
[1]  
Agresti A, 2013, Categorical data analysis, V3rd
[2]   Improved scoring of functional groups from gene expression data by decorrelating GO graph structure [J].
Alexa, Adrian ;
Rahnenfuehrer, Joerg ;
Lengauer, Thomas .
BIOINFORMATICS, 2006, 22 (13) :1600-1607
[3]   A map of human genome variation from population-scale sequencing [J].
Altshuler, David ;
Durbin, Richard M. ;
Abecasis, Goncalo R. ;
Bentley, David R. ;
Chakravarti, Aravinda ;
Clark, Andrew G. ;
Collins, Francis S. ;
De la Vega, Francisco M. ;
Donnelly, Peter ;
Egholm, Michael ;
Flicek, Paul ;
Gabriel, Stacey B. ;
Gibbs, Richard A. ;
Knoppers, Bartha M. ;
Lander, Eric S. ;
Lehrach, Hans ;
Mardis, Elaine R. ;
McVean, Gil A. ;
Nickerson, DebbieA. ;
Peltonen, Leena ;
Schafer, Alan J. ;
Sherry, Stephen T. ;
Wang, Jun ;
Wilson, Richard K. ;
Gibbs, Richard A. ;
Deiros, David ;
Metzker, Mike ;
Muzny, Donna ;
Reid, Jeff ;
Wheeler, David ;
Wang, Jun ;
Li, Jingxiang ;
Jian, Min ;
Li, Guoqing ;
Li, Ruiqiang ;
Liang, Huiqing ;
Tian, Geng ;
Wang, Bo ;
Wang, Jian ;
Wang, Wei ;
Yang, Huanming ;
Zhang, Xiuqing ;
Zheng, Huisong ;
Lander, Eric S. ;
Altshuler, David L. ;
Ambrogio, Lauren ;
Bloom, Toby ;
Cibulskis, Kristian ;
Fennell, Tim J. ;
Gabriel, Stacey B. .
NATURE, 2010, 467 (7319) :1061-1073
[4]   The Challenge of Detecting Epistasis (G x G Interactions): Genetic Analysis Workshop 16 [J].
An, Ping ;
Mukherjee, Odity ;
Chanda, Pritam ;
Yao, Li ;
Engelman, Corinne D. ;
Huang, Chien-Hsun ;
Zheng, Tian ;
Kovac, Ilija P. ;
Dube, Marie-Pierre ;
Liang, Xueying ;
Li, Jia ;
de Andrade, Mariza ;
Culverhouse, Robert ;
Malzahn, Doerthe ;
Manning, Alisa K. ;
Clarke, Geraldine M. ;
Jung, Jeesun ;
Province, Michael A. .
GENETIC EPIDEMIOLOGY, 2009, 33 :S58-S67
[5]   Determinants of Divergent Adaptation and Dobzhansky-Muller Interaction in Experimental Yeast Populations [J].
Anderson, James B. ;
Funt, Jason ;
Thompson, Dawn Anne ;
Prabhu, Snehit ;
Socha, Amanda ;
Sirjusingh, Caroline ;
Dettman, Jeremy R. ;
Parreiras, Lucas ;
Guttman, David S. ;
Regev, Aviv ;
Kohn, Linda M. .
CURRENT BIOLOGY, 2010, 20 (15) :1383-1388
[6]   Gene Ontology: tool for the unification of biology [J].
Ashburner, M ;
Ball, CA ;
Blake, JA ;
Botstein, D ;
Butler, H ;
Cherry, JM ;
Davis, AP ;
Dolinski, K ;
Dwight, SS ;
Eppig, JT ;
Harris, MA ;
Hill, DP ;
Issel-Tarver, L ;
Kasarskis, A ;
Lewis, S ;
Matese, JC ;
Richardson, JE ;
Ringwald, M ;
Rubin, GM ;
Sherlock, G .
NATURE GENETICS, 2000, 25 (01) :25-29
[7]   Quantitative Genetic Interactions Reveal Biological Modularity [J].
Beltrao, Pedro ;
Cagney, Gerard ;
Krogan, Nevan J. .
CELL, 2010, 141 (05) :739-745
[8]   CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING [J].
BENJAMINI, Y ;
HOCHBERG, Y .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) :289-300
[9]   Integrating physical and genetic maps: from genomes to interaction networks [J].
Beyer, Andreas ;
Bandyopadhyay, Sourav ;
Ideker, Trey .
NATURE REVIEWS GENETICS, 2007, 8 (09) :699-710
[10]   Hybrid necrosis: autoimmunity as a potential gene-flow barrier in plant species [J].
Bomblies, Kirsten ;
Weigel, Detlef .
NATURE REVIEWS GENETICS, 2007, 8 (05) :382-393