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Simulated data for genomic selection and genome-wide association studies using a combination of coalescent and gene drop methods.

ABSTRACT: An approach is described for simulating data sequence, genotype, and phenotype data to study genomic selection and genome-wide association studies (GWAS). The simulation method, implemented in a software package called AlphaDrop, can be used to simulate genomic data and phenotypes with flexibility in terms of the historical population structure, recent pedigree structure, distribution of quantitative trait loci effects, and with sequence and single nucleotide polymorphism-phased alleles and genotypes. Ten replicates of a representative scenario used to study genomic selection in livestock were generated and have been made publically available. The simulated data sets were structured to encompass a spectrum of additive quantitative trait loci effect distributions, relationship structures, and single nucleotide polymorphism chip densities.

SUBMITTER: Hickey JM

PROVIDER: S-EPMC3337470 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Simulated data for genomic selection and genome-wide association studies using a combination of coalescent and gene drop methods.

Hickey John M JM Gorjanc Gregor G

G3 (Bethesda, Md.) 20120401 4

An approach is described for simulating data sequence, genotype, and phenotype data to study genomic selection and genome-wide association studies (GWAS). The simulation method, implemented in a software package called AlphaDrop, can be used to simulate genomic data and phenotypes with flexibility in terms of the historical population structure, recent pedigree structure, distribution of quantitative trait loci effects, and with sequence and single nucleotide polymorphism-phased alleles and geno ...[more]

PMID: 22540033

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Project description:MotivationOne of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes which provide insight into the disease process. With rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of tens of thousands of genes and proteins resulting in enormous datasets where the number of genomic features is far greater than the number of subjects. Methods based on univariate Cox regression are often used to select genomic features related to survival outcome; however, the Cox model assumes proportional hazards (PH), which is unlikely to hold for each feature. When applied to genomic features exhibiting some form of non-proportional hazards (NPH), these methods could lead to an under- or over-estimation of the effects. We propose a broad array of marginal screening techniques that aid in feature ranking and selection by accommodating various forms of NPH. First, we develop an approach based on Kullback-Leibler information divergence and the Yang-Prentice model that includes methods for the PH and proportional odds (PO) models as special cases. Next, we propose R2 measures for the PH and PO models that can be interpreted in terms of explained randomness. Lastly, we propose a generalized pseudo-R2 index that includes PH, PO, crossing hazards and crossing odds models as special cases and can be interpreted as the percentage of separability between subjects experiencing the event and not experiencing the event according to feature measurements.ResultsWe evaluate the performance of our measures using extensive simulation studies and publicly available datasets in cancer genomics. We demonstrate that the proposed methods successfully address the issue of NPH in genomic feature selection and outperform existing methods.Availability and implementationR code for the proposed methods is available at github.com/lburns27/Feature-Selection.Contactkarthik.devarajan@fccc.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Simulated data for genomic selection and genome-wide association studies using a combination of coalescent and gene drop methods.

Publications

Simulated data for genomic selection and genome-wide association studies using a combination of coalescent and gene drop methods.

Similar Datasets

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