title: Filtering genetic variants and placing informative priors based on putative biological function.
abstract: High-density genetic marker data, especially sequence data, imply an immense multiple testing burden. This can be ameliorated by filtering genetic variants, exploiting or accounting for correlations between variants, jointly testing variants, and by incorporating informative priors. Priors can be based on biological knowledge or predicted variant function, or even be used to integrate gene expression or other omics data. Based on Genetic Analysis Workshop (GAW) 19 data, this article discusses diversity and usefulness of functional variant scores provided, for example, by PolyPhen2, SIFT, or RegulomeDB annotations. Incorporating functional scores into variant filters or weights and adjusting the significance level for correlations between variants yielded significant associations with blood pressure traits in a large family study of Mexican Americans (GAW19 data set). Marker rs218966 in gene PHF14 and rs9836027 in MAP4 significantly associated with hypertension; additionally, rare variants in SNUPN significantly associated with systolic blood pressure. Variant weights strongly influenced the power of kernel methods and burden tests. Apart from variant weights in test statistics, prior weights may also be used when combining test statistics or to informatively weight p values while controlling false discovery rate (FDR). Indeed, power improved when gene expression data for FDR-controlled informative weighting of association test p values of genes was used. Finally, approaches exploiting variant correlations included identity-by-descent mapping and the optimal strategy for joint testing rare and common variants, which was observed to depend on linkage disequilibrium structure.
authors: Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Göttingen, Germany. stefanie.friedrichs@med.uni-goettingen.de.
authors: Stefanie
authors: S
authors: Friedrichs
authors: Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Göttingen, Germany. dmalzah@gwdg.de.
authors: Dörthe
authors: D
authors: Malzahn
authors: Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. epugh1@jhmi.edu.
authors: Elizabeth W
authors: EW
authors: Pugh
authors: South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA. Marcio.Almeida@utrgv.edu.
authors: Marcio
authors: M
authors: Almeida
authors: Department of Obstetrics, Gynecology, and Reproductive Sciences, Department of Biochemistry and Medical Genetics, Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada. liu@cc.umanitoba.ca.|Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada. liu@cc.umanitoba.ca.
authors: Xiao Qing
authors: XQ
authors: Liu
authors: Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA. JBailey@mednet.ucla.edu.|Epilepsy Genetics/Genomics Laboratory, West Los Angeles Veterans Administration, Los Angeles, CA, USA. JBailey@mednet.ucla.edu.
authors: Julia N
authors: JN
authors: Bailey
mesh_terms: D001794:Blood Pressure / Q000235:genetics; D005819:Genetic Markers / Q000235:genetics; D014644:Genetic Variation; D006801:Humans; D006973:Hypertension / Q000235:genetics; D017058:Mexican Americans / Q000235:genetics*; D008869:Microtubule-Associated Proteins / Q000235:genetics*; D039381:RNA Cap-Binding Proteins / Q000235:genetics*; D018160:Receptors, Cytoplasmic and Nuclear / Q000235:genetics*; D012984:Software; D014157:Transcription Factors / Q000235:genetics*
chemical_list: D005819:Genetic Markers; C000601103:MAP4 protein, human; D008869:Microtubule-Associated Proteins; D039381:RNA Cap-Binding Proteins; D018160:Receptors, Cytoplasmic and Nuclear; C114090:SNUPN protein, human; D014157:Transcription Factors