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A novel method for testing association of multiple genetic markers with a multinomial trait.

ABSTRACT: We developed a multinomial probit model with singular value decomposition for testing a large number of single nucleotide polymorphisms (SNPs) simultaneously, using maximum likelihood estimation and permutation. The method was validated by simulation. We simulated 1000 SNPs, including 9 associated with disease states, and 8 of the 9 were successfully identified. Applying the method to study 32 genes in our Mexican-American samples for association with prediabetes through either impaired glucose tolerance (IGT) or impaired fasting glucose (IFG), we found 3 genes (SORCS1, AMPD1, PPAR) associated with both IGT and IFG, while 5 genes (AMPD2, PRKAA2, C5, TCF7L2, ITR) with the IGT mechanism only and 6 genes (CAPN10, IL4,NOS3, CD14, GCG, SORT1) with the IFG mechanism only. These data suggest that IGT and IFG may indicate different physiological mechanism to prediabetes, via different genetic determinants.

SUBMITTER: Kwon S

PROVIDER: S-EPMC4439253 | biostudies-literature | 2010 Jul-Aug

REPOSITORIES: biostudies-literature

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A novel method for testing association of multiple genetic markers with a multinomial trait.

Kwon Soonil S Goodarzi Mark O MO Taylor Kent D KD Cui Jinrui J Chen Y-D Ida YD Rotter Jerome I JI Hsueh Willa W Guo Xiuqing X

Proceedings. American Statistical Association. Annual Meeting 20100701

We developed a multinomial probit model with singular value decomposition for testing a large number of single nucleotide polymorphisms (SNPs) simultaneously, using maximum likelihood estimation and permutation. The method was validated by simulation. We simulated 1000 SNPs, including 9 associated with disease states, and 8 of the 9 were successfully identified. Applying the method to study 32 genes in our Mexican-American samples for association with prediabetes through either impaired glucose ...[more]

PMID: 26005397

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Project description:BACKGROUND:Characterizing the genetic determinants of complex diseases can be further augmented by incorporating knowledge of underlying structure or classifications of the genome, such as newly developed mappings of protein-coding genes, epigenetic marks, enhancer elements and non-coding RNAs. METHODS:We apply a simple class-level testing framework, termed Genetic Class Association Testing (GenCAT), to identify protein-coding gene association with 14 cardiometabolic (CMD) related traits across 6 publicly available genome wide association (GWA) meta-analysis data resources. GenCAT uses SNP-level meta-analysis test statistics across all SNPs within a class of elements, as well as the size of the class and its unique correlation structure, to determine if the class is statistically meaningful. The novelty of findings is evaluated through investigation of regional signals. A subset of findings are validated using recently updated, larger meta-analysis resources. A simulation study is presented to characterize overall performance with respect to power, control of family-wise error and computational efficiency. All analysis is performed using the GenCAT package, R version 3.2.1. RESULTS:We demonstrate that class-level testing complements the common first stage minP approach that involves individual SNP-level testing followed by post-hoc ascribing of statistically significant SNPs to genes and loci. GenCAT suggests 54 protein-coding genes at 41 distinct loci for the 13 CMD traits investigated in the discovery analysis, that are beyond the discoveries of minP alone. An additional application to biological pathways demonstrates flexibility in defining genetic classes. CONCLUSIONS:We conclude that it would be prudent to include class-level testing as standard practice in GWA analysis. GenCAT, for example, can be used as a simple, complementary and efficient strategy for class-level testing that leverages existing data resources, requires only summary level data in the form of test statistics, and adds significant value with respect to its potential for identifying multiple novel and clinically relevant trait associations.

Dataset Information

A novel method for testing association of multiple genetic markers with a multinomial trait.

Publications

A novel method for testing association of multiple genetic markers with a multinomial trait.

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