@article{mei_cuccaro_martin_2007, title={Multifactor dimensionality reduction-phenomics: A novel method to capture genetic heterogeneity with use of phenotypic variables}, volume={81}, ISSN={["0002-9297"]}, DOI={10.1086/522307}, abstractNote={Complex human diseases do not have a clear inheritance pattern, and it is expected that risk involves multiple genes with modest effects acting independently or interacting. Major challenges for the identification of genetic effects are genetic heterogeneity and difficulty in analyzing high-order interactions. To address these challenges, we present MDR-Phenomics, a novel approach based on the multifactor dimensionality reduction (MDR) method, to detect genetic effects in pedigree data by integration of phenotypic covariates (PCs) that may reflect genetic heterogeneity. The P value of the test is calculated using a permutation test adjusted for multiple tests. To validate MDR-Phenomics, we compared it with two MDR-based methods: (1) traditional MDR pedigree disequilibrium test (PDT) without consideration of PCs (MDR-PDT) and (2) stratified phenotype (SP) analysis based on PCs, with use of MDR-PDT with a Bonferroni adjustment (SP-MDR). Using computer simulations, we examined the statistical power and type I error of the different approaches under several genetic models and sampling scenarios. We conclude that MDR-Phenomics is more powerful than MDR-PDT and SP-MDR when there is genetic heterogeneity, and the statistical power is affected by sample size and the number of PC levels. We further compared MDR-Phenomics with conditional logistic regression (CLR) for testing interactions across single or multiple loci with consideration of PC. The results show that CLR with PC has only slightly smaller power than does MDR-Phenomics for single-locus analysis but has considerably smaller power for multiple loci. Finally, by applying MDR-Phenomics to autism, a complex disease in which multiple genes are believed to confer risk, we attempted to identify multiple gene effects in two candidate genes of interest--the serotonin transporter gene (SLC6A4) and the integrin beta 3 gene (ITGB3) on chromosome 17. Analyzing four markers in SLC6A4 and four markers in ITGB3 in 117 white family triads with autism and using sex of the proband as a PC, we found significant interaction between two markers--rs1042173 in SLC6A4 and rs3809865 in ITGB3.}, number={6}, journal={AMERICAN JOURNAL OF HUMAN GENETICS}, author={Mei, H. and Cuccaro, M. L. and Martin, E. R.}, year={2007}, month={Dec}, pages={1251–1261} }
 @article{mei_ma_ashley-koch_martin_2005, title={Extension of multifactor dimensionality reduction for identifying multilocus effects in the GAW14 simulated data}, volume={6}, journal={BMC Genetics}, author={Mei, H. and Ma, D. Q. and Ashley-Koch, A. and Martin, E. R.}, year={2005} }
 @article{ma_whitehead_menold_martin_ashley-koch_mei_ritchie_delong_abramson_wright_et al._2005, title={Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism}, volume={77}, ISSN={["1537-6605"]}, DOI={10.1086/433195}, abstractNote={Autism is a common neurodevelopmental disorder with a significant genetic component. Existing research suggests that multiple genes contribute to autism and that epigenetic effects or gene-gene interactions are likely contributors to autism risk. However, these effects have not yet been identified. Gamma-aminobutyric acid (GABA), the primary inhibitory neurotransmitter in the adult brain, has been implicated in autism etiology. Fourteen known autosomal GABA receptor subunit genes were studied to look for the genes associated with autism and their possible interactions. Single-nucleotide polymorphisms (SNPs) were screened in the following genes: GABRG1, GABRA2, GABRA4, and GABRB1 on chromosome 4p12; GABRB2, GABRA6, GABRA1, GABRG2, and GABRP on 5q34-q35.1; GABRR1 and GABRR2 on 6q15; and GABRA5, GABRB3, and GABRG3 on 15q12. Intronic and/or silent mutation SNPs within each gene were analyzed in 470 white families with autism. Initially, SNPs were used in a family-based study for allelic association analysis--with the pedigree disequilibrium test and the family-based association test--and for genotypic and haplotypic association analysis--with the genotype-pedigree disequilibrium test (geno-PDT), the association in the presence of linkage (APL) test, and the haplotype family-based association test. Next, with the use of five refined independent marker sets, extended multifactor-dimensionality reduction (EMDR) analysis was employed to identify the models with locus joint effects, and interaction was further verified by conditional logistic regression. Significant allelic association was found for markers RS1912960 (in GABRA4; P = .01) and HCV9866022 (in GABRR2; P = .04). The geno-PDT found significant genotypic association for HCV8262334 (in GABRA2), RS1912960 and RS2280073 (in GABRA4), and RS2617503 and RS12187676 (in GABRB2). Consistent with the allelic and genotypic association results, EMDR confirmed the main effect at RS1912960 (in GABRA4). EMDR also identified a significant two-locus gene-gene effect model involving RS1912960 in GABRA4 and RS2351299 in GABRB1. Further support for this two-locus model came from both the multilocus geno-PDT and the APL test, which indicated a common genotype and haplotype combination positively associated with disease. Finally, these results were also consistent with the results from the conditional logistic regression, which confirmed the interaction between GABRA4 and GABRB1 (odds ratio = 2.9 for interaction term; P = .002). Through the convergence of all analyses, we conclude that GABRA4 is involved in the etiology of autism and potentially increases autism risk through interaction with GABRB1. These results support the hypothesis that GABA receptor subunit genes are involved in autism, most likely via complex gene-gene interactions.}, number={3}, journal={AMERICAN JOURNAL OF HUMAN GENETICS}, author={Ma, DQ and Whitehead, PL and Menold, MM and Martin, ER and Ashley-Koch, AE and Mei, H and Ritchie, MD and DeLong, GR and Abramson, RK and Wright, HH and et al.}, year={2005}, month={Sep}, pages={377–388} }