A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits

Gui, Jiang; Moore, Jason H.; Williams, Scott M.; Andrews, Peter C.; Hillege, Hans L.; Harst, Pim van der; Navis, Gerjan; Gilst, Wiek H. van; Asselbergs, Folkert W.; Gilbert‐Diamond, Diane

doi:10.1371/journal.pone.0066545

Cited by 86 publications

(80 citation statements)

References 27 publications

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“…In order to perform analyses using dimensions we used quantitative MDR (QMDR) approach for quantitative traits (Gui et al, 2013). Its implementation is available for R environment for statistical computing (Team, 2013) by mbmdr package (Calle et al, 2010).…”

Section: Quantitative Trait Definition and Statistical Analysesmentioning

confidence: 99%

Analysis of genetic association and epistasis interactions between circadian clock genes and symptom dimensions of bipolar affective disorder

Maciukiewicz

Dmitrzak‐Węglarz

Pawlak

et al. 2014

Chronobiology International

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Bipolar affective disorder (BD) is a severe psychiatric disorder characterized by periodic changes in mood from depression to mania. Disruptions of biological rhythms increase risk of mood disorders. Because clinical representation of disease is heterogeneous, homogenous sets of patients are suggested to use in the association analyses. In our study, we aimed to apply previously computed structure of bipolar disorder symptom dimension for analyses of genetic association. We based quantitative trait on: main depression, sleep disturbances, appetite disturbances, excitement and psychotic dimensions consisted of OPCRIT checklist items. We genotyped 42 polymorphisms from circadian clock genes: PER3, ARNTL, CLOCK and TIMELSSS from 511 patients BD (n = 292 women and n = 219 men). As quantitative trait we used clinical dimensions, described above. Genetic associations between alleles and quantitative trait were performed using applied regression models applied in PLINK. In addition, we used the Kruskal-Wallis test to look for associations between genotypes and quantitative trait. During second stage of our analyses, we used multidimensional scaling (multifactor dimensionality reduction) for quantitative trait to compute pairwise epistatic interactions between circadian gene variants. We found association between ARNTL variant rs11022778 main depression (p = 0.00047) and appetite disturbances (p = 0.004). In epistatic interaction analyses, we observed two locus interactions between sleep disturbances (p = 0.007; rs11824092 of ARNTL and rs11932595 of CLOCK) as well as interactions of subdimension in main depression and ARNTL variants (p = 0.0011; rs3789327, rs10766075) and appetite disturbances in depression and ARNTL polymorphism (p = 7 × 10(-4); rs11022778, rs156243).

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Section: Quantitative Trait Definition and Statistical Analysesmentioning

confidence: 99%

Analysis of genetic association and epistasis interactions between circadian clock genes and symptom dimensions of bipolar affective disorder

Maciukiewicz

Dmitrzak‐Węglarz

Pawlak

et al. 2014

Chronobiology International

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“…Recognizing the complexity of both HDL-C and TG, we performed quantitative multifactor dimensionality reduction (qMDR) analysis in order to reveal the presence of multi-locus interactions, or epistasis in our phenotypes (Gui et al, 2013). Multifactor dimensionality reduction (MDR) is a well-known nonparametric method for detecting epistatic interactions with or without the presence of a significant single locus effect in dichotomous data (Ritchie et al, 2001), and the quantitative version (qMDR) is optimized to use the same principles for continuous outcomes.…”

Section: Multi-locus Analysis For Low Hdl-c and High Tgmentioning

confidence: 99%

Multi-Locus Candidate Gene Analyses of Lipid Levels in a Pediatric Turkish Cohort: Lessons Learned onLPL, CETP, LIPC, ABCA1,andSHBG

Ağırbaşlı

Eren

Ağırbaşlı

et al. 2013

OMICS: A Journal of Integrative Biology

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Cardiovascular risk factors and atherosclerosis precursors were examined in 365 Turkish children and adolescents. Study participants were recruited at five different state schools. We tested single and multi-locus effects of six polymorphisms from five candidate genes, chosen based on prior known association with lipid levels in adults, for association with low ( £ 10 th percentile) high density lipoprotein cholesterol (HDL-C) and high ( ‡ 90 th percentile) triglycerides (TG), and the related continuous outcomes. We observed an association between CETP variant rs708272 and low HDL-C (allelic p = 0.020, genotypic p = 0.046), which was supported by an independent analysis, PRAT (PRAT control p = 0.027). Sex-stratified logistic regression analysis showed that the B2 allele of rs708272 decreased odds of being in the lower tenth percentile of HDL-C measurements (OR = 0.36, p = 0.02) in girls; this direction of effect was also seen in boys but was not significant (OR = 0.64, p = 0.21). Logistic regression analysis also revealed that the T allele of rs6257 (SHBG) decreased odds of being in the top tenth percentile of TG measurements in boys (OR = 0.43, p = 0.03). Analysis of lipid levels as a continuous trait revealed a significant association between rs708272 (CETP) and LDL-C levels in males ( p = 0.02) with the B2B2 genotype group having the lowest mean LDL-C; the same direction of effect was also seen in females ( p = 0.05). An effect was also seen between rs708272 and HDL-C levels in girls ( p = 0.01), with the B2B2 genotype having the highest mean HDL-C levels. Multi-locus analysis, using quantitative multifactor dimensionality reduction (qMDR) identified the previously mentioned CETP variant as the best single locus model, and overall model, for predicting HDL-C levels in children. This study provides evidence for association between CETP and low HDL-C phenotype in children, but the results appear to be weaker in children than previous results in adults and may also be subject to gender effects.

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“…To alleviate this problem, the number of hypotheses being tested are usually reduced by focusing on pairs of loci that are functionally associated through regulatory elements, pathways, protein interactions, and other functional annotations [11,18]. Some algorithms also prune out certain pairs of loci based on their allelic distributions in the case and control populations, but without explicitly testing them for epistasis (e.g., TEAM [27], QMDR [9], SNPHarvester [24]). These methods perform reasonably well for certain models of epistasis; however, they usually test a very large number of hypotheses.…”

Section: Introductionmentioning

confidence: 99%

Prioritization of genomic locus pairs for testing epistasis

Ayati

Koyutürk

2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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In recent years, genome-wide association studies (GWAS) have successfully identified loci that harbor genetic variants associated with complex diseases. However, susceptibility loci identified by GWAS so far generally account for a limited fraction of heritability in patient populations. More recently, there has been considerable attention on identifying epistatic interactions. However, the large number of pairs to be tested for epistasis poses significant challenges, in terms of both computational (run-time) and statistical (multiple hypothesis testing) considerations.In this paper, we propose a new method to reduce the number of tests required to identify epistatic pairs of genomic loci. The key idea of the proposed algorithm is to reduce the data by identifying sets of loci that may be complementary in their association with the disease. Namely, we identify population covering locus sets (PoCos), i.e., sets of loci that harbor at least one susceptibility allele in samples with the phenotype of interest. Then we compute representative genotypes for PoCos, and assess the significance of the interactions between pairs of PoCos. We use the results of this assessment to prioritize pairs of loci to be tested for epistasis. We test the proposed method on two independent GWAS data sets of Type 2 Diabetes (T2D). Our experimental results show that the proposed method reduces the number of hypotheses to be tested drastically, enabling efficient identification of more epistatic loci that are statistically significant. Moreover, some of the identified epistatic pairs of loci are reproducible between the two datasets. We also show that the proposed method outperforms an existing method for prioritization of locus pairs.

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A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits

Cited by 86 publications

References 27 publications

Analysis of genetic association and epistasis interactions between circadian clock genes and symptom dimensions of bipolar affective disorder

Analysis of genetic association and epistasis interactions between circadian clock genes and symptom dimensions of bipolar affective disorder

Multi-Locus Candidate Gene Analyses of Lipid Levels in a Pediatric Turkish Cohort: Lessons Learned onLPL, CETP, LIPC, ABCA1,andSHBG

Prioritization of genomic locus pairs for testing epistasis

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