Genetics Research
rs1464443
Population Frequencies12
African
C 0.78934A 0.21066CC 0.628064CA/AC 0.322557AA 0.049379pop=50,912
African American
C 0.78554A 0.21446CC 0.62179CA/AC 0.3275AA 0.05071pop=49,142
African Others
C 0.8949A 0.1051CC 0.80226CA/AC 0.185311AA 0.012429pop=1,770
Asian
C 0.0985A 0.9015CC 0.010483CA/AC 0.176035AA 0.813483pop=11,066
East Asian
C 0.0984A 0.9016CC 0.009314CA/AC 0.178101AA 0.812585pop=8,804
European
C 0.30102A 0.69898CC 0.092078CA/AC 0.417885AA 0.490037pop=518,932
Latin American 1
C 0.4381A 0.5619CC 0.195337CA/AC 0.485606AA 0.319058pop=8,406
Latin American 2
C 0.26031A 0.73969CC 0.071155CA/AC 0.378316AA 0.550529pop=13,042
Other
C 0.32504A 0.67496CC 0.126817CA/AC 0.396439AA 0.476744pop=16,512
Other Asian
C 0.099A 0.901CC 0.015031CA/AC 0.167993AA 0.816976pop=2,262
South Asian
C 0.2383A 0.7617CC 0.061127CA/AC 0.354382AA 0.584491pop=7,918
Studies3
Unread Studies3 ▼
1
2
The goal of genetic epidemiology is to find DNA sequence variants in the human genome that are involved in the development of diseases. Genome-wide association studies have led to the identification of hundreds of regions in the genome that are associated with complex diseases. Nevertheless, a large part of heritability remains unexplained. Interaction between genetic variants could, among other things, explain the case of "missing heritability" [Maher, 2008]. However, genome-wide interaction analysis (GWIA) of all SNP pairs (SNP, English. Single Nucleotide Polymorphism) from a standard marker panel is computationally impossible without massive parallelization. Furthermore, a GWIA with all SNP triples would be utopian.
3
Genome-wide association studies (GWAS) have lead to the identification of hundreds of genomic regions associated with complex diseases. Nevertheless, a large fraction of their heritability remains unexplained. Interaction between genetic variants is one of several putative explanations for the 'case of missing heritability' and, therefore, a compelling next analysis step. However, genome-wide interaction analysis (GWIA) of all pairs of SNPs from a standard marker panel is computationally unfeasible without massive parallelization. Furthermore, GWIA of all SNP triples is utopian. In order to overcome these computational constraints, we present a GWIA approach that selects combinations of SNPs for interaction analysis based on a priori information. Sources of information are statistical evidence (single marker association at a moderate level), genetic relevance (genomic location) and biologic relevance (SNP function class and pathway information). We introduce the software package INTERSNP that implements a logistic regression framework as well as log-linear models for joint analysis of multiple SNPs. Automatic handling of SNP annotation and pathways from the KEGG database is provided. In addition, Monte Carlo simulations to judge genome-wide significance are implemented. We introduce various meaningful GWIA strategies that can be conducted using INTERSNP. Typical examples are, for instance, the analysis of all pairs of non-synonymous SNPs, or, the analysis of all ...
Curated Studies0 ▼
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