Posts Tagged ‘human-genetics’

Statistical Approach for Calculating Environmental Influences in Genome-Wide Association Study (GWAS) Results

The approach fills a gap in current analyses. Complex diseases like cancer usually arise from complex interactions among genetic and environmental factors. When many such combinations are studied, identifying the relevant interactions versus those that reflect chance combinations among affected individuals becomes difficult. In this study, the investigators developed a novel approach for evaluating the relevance of interactions using a Bayesian hierarchal mixture framework. The approach is applicable for the study of interactions among genes or between genetic and environmental factors.

Chris Amos, PhD, senior author of the paper said, “These findings can be used to develop models that include only those interactions that are relevant to disease causation, allowing the researcher to remove false positive findings that plague modern research when many dozens of factors and their interactions are suggested to play a role in causing complex diseases.”

The model evaluates “gene by gene” and “gene by environment” factors by looking at specific DNA sequencing variations. Complex diseases are caused by multiple factors. In some cases a genetic predisposition or abnormality may be a factor. A person’s healthy lifestyle and environment, however, may help him or her overcome a genetic vulnerability and avoid a chronic disease like cancer. In other situations, a person whose DNA does not have an abnormality may develop one when exposed to known carcinogens like tobacco smoke or sunburn.

“Understanding the combinations of genetic and environmental factors that cause complex diseases is important,” said Amos, associate director of population sciences and deputy director of Norris Cotton Cancer Center, “because understanding the genetic architecture underlying complex disease may help us to identify specific targets for prevention or therapy upon which interventions may appropriately reduce the risk of cancer development or progression.”

The study applied the model in cutaneous melanoma and lung cancer genetic sequences using previously identified abnormalities (known as single nucleotide polymorphisms or SNPs) with environmental factors introduced as independent variables. The Bayesian mixture model was compared with the traditional logistic regression model. The hierarchal model successfully controlled the probability of false positive discovery and identified significant interactions. It also showed good performance on parameter estimation and variable selection. The model cannot be applied to a complete GWAS because if its reliance on other probability models (MCMC ). It is most effective when applied to a group of SNPs.

“The method was effective for the study of melanoma and lung cancer risk because these cancers develop from a complex interaction between genetic and environmental factors but understanding how these factors interact has been difficult to achieve without the sophisticated modeling that has been developed in this study,” said Amos.

source : http://www.sciencedaily.com/releases/2014/08/140827111811.htm

More powerful approach to analyze melanoma’s genetic causes

The gene-gene interactions underlying CM had not been fully explored. The usual functional model uses substitution of alleles for estimating genetic effects but the estimators are confounded. The NOIA model estimates population effects of alleles and the resulting estimators are orthogonal and no longer confounded. In simulation studies, the NOIA model had higher power for finding interactions and main effects than the usual model.

"We confirmed the previously identified significant associated genes HERC2, MC1R, and CDKN2A using a NOIA one-locus statistical model," said Christopher I. Amos, PhD, associate director for Population Sciences, Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, a corresponding author of the study. "When compared to the usual one-locus model we found that the HERC2 signal was detected more clearly by the NOIA model" The NOIA model also identified an additional potential interaction between the rs1129038 of HERC2 gene and a region at chromosome 5. The SNPs that interact with HERC2 to increase melanoma risk are located in the IL31RA gene, which is involved in STAT3 signaling and upregulated in activated monocytes.

The first author Feifei Xiao, a postdoctoral associate of Yale University, concluded that the power of the NOIA model was better for detecting genetic effects when interactions are tested. When main and interaction effects between two loci were modeled, the usual functional model was less powerful.

CM is highly aggressive and accounts for the majority of deaths from skin cancer. Prior genome-wide association studies have identified multiple genetic factors for the illness, including MC1R, HERC2, and CDKN2A. This study provides new insights for understanding the influence of gene-gene interactions on melanoma risk.

The NOIA framework was developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. The NOIA statistical model can be used for additive, dominant, and recessive genetic models as well as for a binary environmental exposures. It is an easily implemented approach that improves estimation of genetic effects that include interactions.

source : http://www.sciencedaily.com/releases/2013/12/131211132551.htm