A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes.
BACKGROUND
Current Genome-Wide Association Studies (GWAS) are performed in a single trait framework without considering genetic correlations between important disease traits. Hence, the GWAS have limitations in discovering genetic risk factors affecting pleiotropic effects.


RESULTS
This work reports a novel data mining approach to discover patterns of multiple phenotypic associations over 52 anthropometric and biochemical traits in KARE and a new analytical scheme for GWAS of multivariate phenotypes defined by the discovered patterns. This methodology applied to the GWAS for multivariate phenotype highLDLhighTG derived from the predicted patterns of the phenotypic associations. The patterns of the phenotypic associations were informative to draw relations between plasma lipid levels with bone mineral density and a cluster of common traits (Obesity, hypertension, insulin resistance) related to Metabolic Syndrome (MS). A total of 15 SNPs in six genes (PAK7, C20orf103, NRIP1, BCL2, TRPM3, and NAV1) were identified for significant associations with highLDLhighTG. Noteworthy findings were that the significant associations included a mis-sense mutation (PAK7:R335P), a frame shift mutation (C20orf103) and SNPs in splicing sites (TRPM3).


CONCLUSIONS
The six genes corresponded to rat and mouse quantitative trait loci (QTLs) that had shown associations with the common traits such as the well characterized MS and even tumor susceptibility. Our findings suggest that the six genes may play important roles in the pleiotropic effects on lipid metabolism and the MS, which increase the risk of Type 2 Diabetes and cardiovascular disease. The use of the multivariate phenotypes can be advantageous in identifying genetic risk factors, accounting for the pleiotropic effects when the multivariate phenotypes have a common etiological pathway.
Department of Bioinformatics and Life Science, Soongsil University, Seoul, RO Korea.
Sung Hee
SH
Park
Ji Young
JY
Lee
Sangsoo
S
Kim
D000328:Adult; D000465:Algorithms; D000818:Animals; D015519:Bone Density; D002318:Cardiovascular Diseases / Q000235:genetics; D002784:Cholesterol / Q000097:blood; D015331:Cohort Studies; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D005260:Female; D005819:Genetic Markers; D058685:Genetic Pleiotropy; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D007328:Insulin / Q000097:blood; D008297:Male; D024821:Metabolic Syndrome / Q000235:genetics; D051379:Mice; D008875:Middle Aged; D015999:Multivariate Analysis; D009765:Obesity / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D060066:Protein Interaction Maps; D040641:Quantitative Trait Loci; D051381:Rats; D012307:Risk Factors; D014280:Triglycerides / Q000097:blood
D005819:Genetic Markers; D007328:Insulin; D014280:Triglycerides; D002784:Cholesterol