A fast boosting-based screening method for large-scale association study in complex traits with genetic heterogeneity.
Genome-wide association study for complex diseases will generate massive amount of single nucleotide polymorphisms (SNPs) data. Univariate statistical test (i.e. Fisher exact test) was used to single out non-associated SNPs. However, the disease-susceptible SNPs may have little marginal effects in population and are unlikely to retain after the univariate tests. Also, model-based methods are impractical for large-scale dataset. Moreover, genetic heterogeneity makes the traditional methods harder to identify the genetic causes of diseases. A more recent random forest method provides a more robust method for screening the SNPs in thousands scale. However, for more large-scale data, i.e., Affymetrix Human Mapping 100K GeneChip data, a faster screening method is required to screening SNPs in whole-genome large scale association analysis with genetic heterogeneity. We propose a boosting-based method for rapid screening in large-scale analysis of complex traits in the presence of genetic heterogeneity. It provides a relatively fast and fairly good tool for screening and limiting the candidate SNPs for further more complex computational modeling task.
Wang
Lu-Yong
LY
Integrated Data Syst. Dept., Siemens Corp. Res. Inc., Princeton, NJ 08540, USA. luyong.wang@siemens.com
Fasulo
D
D
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D018740:Genetic Heterogeneity; D008040:Genetic Linkage; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011381:Programming Languages; D012984:Software