| Autor: |
Almeida M; South Texas Diabetes and Obesity Institute, University of Texas at Brownsville, 2700 East Jackson Street, Brownsville, TX 78520 USA., Blondell L; South Texas Diabetes and Obesity Institute, University of Texas at Brownsville, 2700 East Jackson Street, Brownsville, TX 78520 USA., Peralta JM; South Texas Diabetes and Obesity Institute, University of Texas at Brownsville, 2700 East Jackson Street, Brownsville, TX 78520 USA., Kent JW Jr; Department of Genetics, Texas Biomedical Research Institute, P.O. Box 760549, San Antonio, TX 78245-0549 USA., Jun G; Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX 77030 USA., Teslovich TM; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, SPH II, 1420 Washington Heights, Ann Arbor, MI 48109-2029 USA., Fuchsberger C; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, SPH II, 1420 Washington Heights, Ann Arbor, MI 48109-2029 USA., Wood AR; Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Mail Room, The Old Library, Prince of Wales Road, Exeter, Devon UK., Manning AK; Broad Institute, 415 Main Street, Cambridge, MA 02142 USA., Frayling TM; Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Mail Room, The Old Library, Prince of Wales Road, Exeter, Devon UK., Cingolani PE; School of Computer Science & Genome Quebec Innovation Centre, McIntyre Medical Building, 903 3655 Promenade Sir William Osler McGill University, Quebec, Canada., Sladek R; School of Computer Science & Genome Quebec Innovation Centre, McIntyre Medical Building, 903 3655 Promenade Sir William Osler McGill University, Quebec, Canada., Dyer TD; South Texas Diabetes and Obesity Institute, University of Texas at Brownsville, 2700 East Jackson Street, Brownsville, TX 78520 USA., Abecasis G; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, SPH II, 1420 Washington Heights, Ann Arbor, MI 48109-2029 USA., Duggirala R; South Texas Diabetes and Obesity Institute, University of Texas at Brownsville, 2700 East Jackson Street, Brownsville, TX 78520 USA., Blangero J; South Texas Diabetes and Obesity Institute, University of Texas at Brownsville, 2700 East Jackson Street, Brownsville, TX 78520 USA. |
| Abstrakt: |
The new generation of whole genome sequencing platforms offers great possibilities and challenges for dissecting the genetic basis of complex traits. With a very high number of sequence variants, a naïve multiple hypothesis threshold correction hinders the identification of reliable associations by the overreduction of statistical power. In this report, we examine 2 alternative approaches to improve the statistical power of a whole genome association study to detect reliable genetic associations. The approaches were tested using the Genetic Analysis Workshop 19 (GAW19) whole genome sequencing data. The first tested method estimates the real number of effective independent tests actually being performed in whole genome association project by the use of an extreme value distribution and a set of phenotype simulations. Given the familiar nature of the GAW19 data and the finite number of pedigree founders in the sample, the number of correlations between genotypes is greater than in a set of unrelated samples. Using our procedure, we estimate that the effective number represents only 15 % of the total number of independent tests performed. However, even using this corrected significance threshold, no genome-wide significant association could be detected for systolic and diastolic blood pressure traits. The second approach implements a biological relevance-driven hypothesis tested by exploiting prior computational predictions on the effect of nonsynonymous genetic variants detected in a whole genome sequencing association study. This guided testing approach was able to identify 2 promising single-nucleotide polymorphisms (SNPs), 1 for each trait, targeting biologically relevant genes that could help shed light on the genesis of the human hypertension. The first gene, PFH14 , associated with systolic blood pressure, interacts directly with genes involved in calcium-channel formation and the second gene, MAP4 , encodes a microtubule-associated protein and had already been detected by previous genome-wide association study experiments conducted in an Asian population. Our results highlight the necessity of the development of alternative approached to improve the efficiency on the detection of reasonable candidate associations in whole genome sequencing studies. |
