To improve our understanding of the role(s) that genes and environmental factors play in a complex disease, we need statistical approaches that model multiple factors simultaneously in a hierarchical manner that aims to reflect the underlying biological system(s). We present an approach that models genes as latent constructs, defined by multiple variants (single nucleotide polymorphisms, SNPs) within each gene, using the multivariate statistical framework of structural equation modeling (SEM) to model multiple, putative genetic and environmental factors involved in energy imbalance ('obesity') using subjects from a colon polyp case-control study. We found that modeling constructs for the leptin receptor (LEPR) gene (defined by SNPs rs1137100, rs1137101, rs1805096, rs6588147) and the fat mass-and-obesity-associated (FTO) gene (defined by SNPs rs9939609, rs1421085, rs8044769) together with demographic (age, race, gender), physical activity, diet and sleep variables increased the strength of the association (β(std)=-0.13 ± 0.06; p=0.03) between the FTO and obesity constructs compared to that observed in a reduced model with only the FTO and LEPR constructs and demographic variables (β(std)=-0.05 ± 0.03; p=0.08). Several indirect paths, including an association between the LEPR and physical activity constructs (β(std)=-0.15 ± 0.04; p=0.01), were found. Interestingly, removing FTO revealed a marginal association between the LEPR and obesity constructs (β(std)=0.24 ± 0.14; p=0.09), which was not present when FTO was in the model. These results illustrate the importance of modeling multiple relevant genes and other factors in the same model, which is a major strength of this approach. Moreover, our latent gene construct approach exploits the correlation structure between SNPs while capturing overall effects of variation in that gene, which will enable better utilization of candidate gene and genome-wide SNP array data.
{"title":"Modeling Genetic and Environmental Factors in Biological Systems Using Structural Equation Modeling: An Application to Energy Balance.","authors":"Nora L Nock, Li Li, Robert C Elston","doi":"10.1109/OCCBIO.2009.18","DOIUrl":"https://doi.org/10.1109/OCCBIO.2009.18","url":null,"abstract":"<p><p>To improve our understanding of the role(s) that genes and environmental factors play in a complex disease, we need statistical approaches that model multiple factors simultaneously in a hierarchical manner that aims to reflect the underlying biological system(s). We present an approach that models genes as latent constructs, defined by multiple variants (single nucleotide polymorphisms, SNPs) within each gene, using the multivariate statistical framework of structural equation modeling (SEM) to model multiple, putative genetic and environmental factors involved in energy imbalance ('obesity') using subjects from a colon polyp case-control study. We found that modeling constructs for the leptin receptor (LEPR) gene (defined by SNPs rs1137100, rs1137101, rs1805096, rs6588147) and the fat mass-and-obesity-associated (FTO) gene (defined by SNPs rs9939609, rs1421085, rs8044769) together with demographic (age, race, gender), physical activity, diet and sleep variables increased the strength of the association (β(std)=-0.13 ± 0.06; p=0.03) between the FTO and obesity constructs compared to that observed in a reduced model with only the FTO and LEPR constructs and demographic variables (β(std)=-0.05 ± 0.03; p=0.08). Several indirect paths, including an association between the LEPR and physical activity constructs (β(std)=-0.15 ± 0.04; p=0.01), were found. Interestingly, removing FTO revealed a marginal association between the LEPR and obesity constructs (β(std)=0.24 ± 0.14; p=0.09), which was not present when FTO was in the model. These results illustrate the importance of modeling multiple relevant genes and other factors in the same model, which is a major strength of this approach. Moreover, our latent gene construct approach exploits the correlation structure between SNPs while capturing overall effects of variation in that gene, which will enable better utilization of candidate gene and genome-wide SNP array data.</p>","PeriodicalId":89470,"journal":{"name":"Proceedings. Ohio Collaborative Conference on Bioinformatics","volume":" ","pages":"3-8"},"PeriodicalIF":0.0,"publicationDate":"2009-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/OCCBIO.2009.18","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40104260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The dNTP supply system genes RRM1, DCTD, TYMS, TK1 and DCK balance dNTP pools to avoid incorrect insertions of bases (i.e. DNA mismatches) and the DNA mismatch repair system genes MLH1 and MSH2 are involved in removing such mismatches. The objective of this study is to explore the possibility of interactions between these two systems, since greater mismatch production rates are expected to be more detrimental in cells that also have compromised mismatch removal rates. This conjecture was explored here specifically with respect to the development of breast cancer. More than 2400 breast cancer cases and controls are included in the Cancer Genetic Markers of Susceptibility (CGEMS) single nucleotide polymorphism (SNP) dataset. For each of these individuals, a total of 99 SNPs (69 dNTP supply SNPs and 30 mismatch repair SNPs) and 2070 SNP-SNP interactions between these two groups were evaluated for their effect on breast cancer using logistic regression to compute odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Of these, 12 SNPs had found statistically significant associations with breast cancer individually (Four of them to decrease risk and eight of them to increase risk) and 697 of 2070 two-way interactions were significant associated with the risk of breast cancer. Thus, our study suggests that mismatches contribute to the formation of breast cancer.
{"title":"SNP-SNP interactions between dNTP supply enzymes and mismatch DNA repair in breast cancer.","authors":"I Jung Feng, Tomas Radivoyevitch","doi":"10.1109/OCCBIO.2009.25","DOIUrl":"https://doi.org/10.1109/OCCBIO.2009.25","url":null,"abstract":"<p><p>The dNTP supply system genes RRM1, DCTD, TYMS, TK1 and DCK balance dNTP pools to avoid incorrect insertions of bases (i.e. DNA mismatches) and the DNA mismatch repair system genes MLH1 and MSH2 are involved in removing such mismatches. The objective of this study is to explore the possibility of interactions between these two systems, since greater mismatch production rates are expected to be more detrimental in cells that also have compromised mismatch removal rates. This conjecture was explored here specifically with respect to the development of breast cancer. More than 2400 breast cancer cases and controls are included in the Cancer Genetic Markers of Susceptibility (CGEMS) single nucleotide polymorphism (SNP) dataset. For each of these individuals, a total of 99 SNPs (69 dNTP supply SNPs and 30 mismatch repair SNPs) and 2070 SNP-SNP interactions between these two groups were evaluated for their effect on breast cancer using logistic regression to compute odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Of these, 12 SNPs had found statistically significant associations with breast cancer individually (Four of them to decrease risk and eight of them to increase risk) and 697 of 2070 two-way interactions were significant associated with the risk of breast cancer. Thus, our study suggests that mismatches contribute to the formation of breast cancer.</p>","PeriodicalId":89470,"journal":{"name":"Proceedings. Ohio Collaborative Conference on Bioinformatics","volume":"2009 ","pages":"123-128"},"PeriodicalIF":0.0,"publicationDate":"2009-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/OCCBIO.2009.25","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30181127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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