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1.  Prediction of genetic contributions to complex traits using whole genome sequencing data 
BMC Proceedings  2014;8(Suppl 1):S68.
Although markers identified by genome-wide association studies have individually strong statistical significance, their performance in prediction remains limited. Our goal was to use animal breeding genomic prediction models to predict additive genetic contributions for systolic blood pressure (SBP) using whole genome sequencing data with different validation designs.
The additive genetic contributions of SBP were estimated via linear mixed model. Rare variants (MAF<0.05) were collapsed through the k-means method to create a "collapsed single-nucleotide polymorphisms." Prediction of the additive genomic contributions of SBP was conducted using genomic Best Linear Unbiased Predictor (GBLUP) and BayesCπ. Estimates of predictive accuracy were compared using common single-nucleotide polymorphisms (SNPs) versus common and collapsed SNPs, and for prediction within and across families.
The additive genetic variance of SBP contributed to 18% of the phenotypic variance (h2 = 0.18). BayesCπ had slightly better prediction accuracies than GBLUP. In both models, within-family predictions had higher accuracies both in the training and testing set than didacross-family design. Collapsing rare variants via the k-means method and adding to the common SNPs did not improve prediction accuracies. The prediction model, including both pedigree and genomic information, achieved a slightly higher accuracy than using either source of information alone.
Prediction of genetic contributions to complex traits is feasible using whole genome sequencing and statistical methods borrowed from animal breeding. The relatedness of individuals between the training and testing set strongly affected the performance of prediction models. Methods for inclusion of rare variants in these models need more development.
doi:10.1186/1753-6561-8-S1-S68
PMCID: PMC4143683  PMID: 25519339
2.  Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model 
BMC Proceedings  2011;5(Suppl 9):S93.
Next-generation sequencing technologies are rapidly changing the field of genetic epidemiology and enabling exploration of the full allele frequency spectrum underlying complex diseases. Although sequencing technologies have shifted our focus toward rare genetic variants, statistical methods traditionally used in genetic association studies are inadequate for estimating effects of low minor allele frequency variants. Four our study we use the Genetic Analysis Workshop 17 data from 697 unrelated individuals (genotypes for 24,487 autosomal variants from 3,205 genes). We apply a Bayesian hierarchical mixture model to identify genes associated with a simulated binary phenotype using a transformed genotype design matrix weighted by allele frequencies. A Metropolis Hasting algorithm is used to jointly sample each indicator variable and additive genetic effect pair from its conditional posterior distribution, and remaining parameters are sampled by Gibbs sampling. This method identified 58 genes with a posterior probability greater than 0.8 for being associated with the phenotype. One of these 58 genes, PIK3C2B was correctly identified as being associated with affected status based on the simulation process. This project demonstrates the utility of Bayesian hierarchical mixture models using a transformed genotype matrix to detect genes containing rare and common variants associated with a binary phenotype.
doi:10.1186/1753-6561-5-S9-S93
PMCID: PMC3287935  PMID: 22373180
3.  Detecting gene-by-smoking interactions in a genome-wide association study of early-onset coronary heart disease using random forests 
BMC Proceedings  2009;3(Suppl 7):S88.
Background
Genome-wide association studies are often limited in their ability to attain their full potential due to the sheer volume of information created. We sought to use the random forest algorithm to identify single-nucleotide polymorphisms (SNPs) that may be involved in gene-by-smoking interactions related to the early-onset of coronary heart disease.
Methods
Using data from the Framingham Heart Study, our analysis used a case-only design in which the outcome of interest was age of onset of early coronary heart disease.
Results
Smoking status was dichotomized as ever versus never. The single SNP with the highest importance score assigned by random forests was rs2011345. This SNP was not associated with age alone in the control subjects. Using generalized estimating equations to adjust for sex and account for familial correlation, there was evidence of an interaction between rs2011345 and smoking status.
Conclusion
The results of this analysis suggest that random forests may be a useful tool for identifying SNPs taking part in gene-by-environment interactions in genome-wide association studies.
PMCID: PMC2795991  PMID: 20018084

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