Abstract
Informing missing heritability for complex disease will likely require leveraging information across multiple SNPs within a gene region simultaneously to characterize gene and locus-level contributions to disease phenotypes. To this aim, we introduce a novel strategy, termed Mixed modeling of Meta-Analysis P-values (MixMAP), that draws on a principled statistical modeling framework and the vast array of summary data now available from genetic association studies, to test formally for locus level association. The primary inputs to this approach are: (a) single SNP level p-values for tests of association; and (b) the mapping of SNPs to genomic regions. The output of MixMAP is comprised of locus level estimates and tests of association. In application of MixMAP to summary data from the Global Lipids Gene Consortium, we suggest twelve new loci (PKN, FN1, UGT1A1, PPARG, DMDGH, PPARD, CDK6, VPS13B, GAD2, GAB2, APOH and NPC1) for low-density lipoprotein cholesterol (LDL-C), a causal risk factor for cardiovascular disease and we also demonstrate the potential utility of MixMAP in small data settings. Overall, MixMAP offers novel and complementary information as compared to SNP-based analysis approaches and is straightforward to implement with existing open-source statistical software tools.
Highlights
Serum lipid levels are established determinants of cardiovascular disease morbidity with well-described heritability
Meta-analysis of data arising from recent genome-wide association studies has identified common genetic variants in at least 95 loci associated with low-density lipoprotein cholesterol (LDL-C), highdensity lipoprotein cholesterol (HDL-C), triglycerides (TG) and total cholesterol (TC) [1]
Summary approach This section reports the results of applying MixMAP to data arising from two independent sources: the Global Lipids Gene Consortium (GLGC) and the Penn Coronary Artery Calcification (PennCAC)
Summary
Serum lipid levels are established determinants of cardiovascular disease morbidity with well-described heritability. Selection of statistically relevant genes or loci, subsequently referred to collectively as loci, is often based on simple linear regression of single-nucleotide polymorphisms (SNPs); that is, loci within which there is at least one SNP that reaches genome-wide significance, defined according to a Bonferroni level correction for multiple testing, are regarded as significantly associated with the trait under study. While this approach is valid, we conjecture that substantial, complementary knowledge about association can be acquired by considering available information on all SNPs within a locus simultaneously in characterizing association. By incorporating all available information about SNPs within a locus, MixMAP results in increased sensitivity for identifying loci with multiple SNPs of moderate significance, as evidenced in the applications described below
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