[en] Due to the recent gains in the availability of single-nucleotide polymorphism data, genome-wide association testing has become feasible. It is hoped that this additional data may confirm the presence of disease susceptibility loci, and identify new genetic determinants of disease. However, the problem of multiple comparisons threatens to diminish any potential gains from this newly available data. To circumvent the multiple comparisons issue, we utilize a recently developed screening technique using family-based association testing. This screening methodology allows for the identification of the most promising single-nucleotide polymorphisms for testing without biasing the nominal significance level of our test statistic. We compare the results of our screening technique across univariate and multivariate family-based association tests. From our analyses, we observe that the screening technique, applied to different settings, is fairly consistent in identifying optimal markers for testing. One of the identified markers, TSC0047225, was significantly associated with both the ttth1 (p=0.004) and ttth1-ttth4 (p=0.004) phenotype(s). We find that both univariate- and multivariate-based screening techniques are powerful tools for detecting an association.
Disciplines :
Genetics & genetic processes
Author, co-author :
Murphy, A.
McGueen, M. B.
Su, J.
Kraft, P.
Lazarus, R.
Laird, N. M.
Lange, C.
Van Steen, Kristel ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique
Language :
English
Title :
Genomic screening in family-based association testing
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Bibliography
Van Steen K, McQueen MB, Herbert A, Raby B, Lyon H, DeMeo DL, Murphy AJ, Su J, Datta S, Rosenow C, Christman M, Silverman EK, Laird NM, Weiss ST, Lange C: Genomic screening and replication using the same data set in family-based association testing. Nat Genet 2005, 37:683-691.
Lange C, DeMeo D, Silverman E, Weiss S, Laird NM: Using the noninformative families in family-based association tests: A powerful new testing strategy. Am J Hum Genet 2003, 73:801-811.
Lange C, Lyon H, DeMeo D, Raby BA, Silverman E, Weiss S: A new powerful non-parametric two-stage approach for testing multiple phenotypes in family-based association studies. Hum Hered 2003, 56:10-17.
Tange C, DeMeo D, Silverman EK, Weiss ST, Laird NM: PBAT: tools for family-based association studies. Am J Hum Genet 2004, 74:367-369.
Reich T, Edenberg HJ, Goate A, Williams JT, Rice JP, Van Eerdewegh P, Foroud T, Hesselbrock V, Schuckit MA, Bucholz K, Porjesz B, Li TK, Conneally PM, Nurnberger JI Jr, Tischfield JA, Crowe RR, Cloninger CR, Wu W, Shears S, Carr K, Crose C, Willig C, Begleiter H: Genome-wide search for genes affecting the risk for alcohol dependence. Am J Med Genet 1998, 81:207-215.
Porjesz B, Begleiter H, Wang K, Almasy L, Chorlian DB, Stimus AT, Kuperman S, O'Connor SJ, Rohrbaugh J, Bauer LO, Edenberg HJ, Goate A, Rice JP, Reich T: Linkage and linkage disequilibrium mapping of ERP and EEG phenotypes. Biol Psych 2002, 61:229-248.
Porjesz B, Almasy L, Edenberg HJ, Wang K, Chorlian DB, Foroud T, Goate A, Rice JP, O'Connor SJ, Rohrbaugh J, Kuperman S, Bauer LO, Crowe RR, Schuckit MA, Hesselbrock V, Conneally PM, Tischfield JA, Li TK, Reich T, Begleiter H: Linkage disequilibrium between the beta frequency of the human EEG and a GABAA receptor gene locus. Proc Natl Acad Sci U S A 2002, 99:3729-3733.
Laird N, Horvath S, Xu X: Implementing a unified approach to family based tests of association. Genetic Epidemiol 2000, 19(Suppl 1):S36-S42.
Rabinowitz D, Laird NM: A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information. Hum Hered 2000, 504:227-233.
Lange C, Laird NM: On a general class of conditional tests for family-based association studies in genetics: The asymptotic distribution, the conditional power, and optimality considerations. Genet Epidemiol 2002, 23:165-180.
Lange C, Van Steen K, Andrew T, Lyon H, DeMeo DL, Raby B, Murphy A, Silverman EK, MacGregor A, Weiss ST, Laird NM: A familybased association test for repeatedly measured quantitative traits and/or polygenic effects. Stat Appl Genet Mol Biol 2004, 3:1-29.
Lange C, Silverman EK, Xu X, Weiss ST, Laird NM: A multivariate family-based association test using generalized estimating equations: FBAT-GEE. Biostatistics 2003, 4:195-206.
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