Next-generation sequencing has made possible the detection of rare variant (RV) associations with quantitative traits (QT). Due to high sequencing cost, many studies can only sequence a modest number of selected samples with extreme QT. Therefore association testing in individual studies can be underpowered. Besides the primary trait, many clinically important secondary traits are often measured. It is highly beneficial if multiple studies can be jointly analyzed for detecting associations with commonly measured traits. However, analyzing secondary traits in selected samples can be biased if sample ascertainment is not properly modeled. Some methods exist for analyzing secondary traits in selected samples, where some burden tests can be implemented. However p-values can only be evaluated analytically via asymptotic approximations, which may not be accurate. Additionally, potentially more powerful sequence kernel association tests, variable selection-based methods, and burden tests that require permutations cannot be incorporated. To overcome these limitations, we developed a unified method for analyzing secondary trait associations with RVs (STAR) in selected samples, incorporating all RV tests. Statistical significance can be evaluated either through permutations or analytically. STAR makes it possible to apply more powerful RV tests to analyze secondary trait associations. It also enables jointly analyzing multiple cohorts ascertained under different study designs, which greatly boosts power. The performance of STAR and commonly used RV association tests were comprehensively evaluated using simulation studies. STAR was also implemented to analyze a dataset from the SardiNIA project where samples with extreme low-density lipoprotein levels were sequenced. A significant association between LDLR and systolic blood pressure was identified, which is supported by pharmacogenetic studies. In summary, for sequencing studies, STAR is an important tool for detecting secondary-trait RV associations.
References
[1]
Sanna S, Li B, Mulas A, Sidore C, Kang HM, et al. (2011) Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. PLoS Genet 7: e1002198 doi:10.1371/journal.pgen.1002198.
[2]
Raychaudhuri S, Iartchouk O, Chin K, Tan PL, Tai AK, et al. (2011) A rare penetrant mutation in CFH confers high risk of age-related macular degeneration. Nat Genet 43: 1232–1236. doi: 10.1038/ng.976
[3]
Rivas MA, Beaudoin M, Gardet A, Stevens C, Sharma Y, et al. (2011) Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nat Genet 43: 1066–1073. doi: 10.1038/ng.952
[4]
Ji W, Foo JN, O'Roak BJ, Zhao H, Larson MG, et al. (2008) Rare independent mutations in renal salt handling genes contribute to blood pressure variation. Nat Genet 40: 592–599. doi: 10.1038/ng.118
[5]
Harismendy O, Bansal V, Bhatia G, Nakano M, Scott M, et al. (2011) Population sequencing of two endocannabinoid metabolic genes identifies rare and common regulatory variants associated with extreme obesity and metabolite level. Genome Biol 11: R118. doi: 10.1186/gb-2010-11-11-r118
[6]
Darvasi A (2006) Closing in on complex traits. Nat Genet 38: 861–862. doi: 10.1038/ng0806-861
[7]
Plomin R, Haworth CM, Davis OS (2009) Common disorders are quantitative traits. Nat Rev Genet 10: 872–878. doi: 10.1038/nrg2670
[8]
Cauchi S, Nead KT, Choquet H, Horber F, Potoczna N, et al. (2008) The genetic susceptibility to type 2 diabetes may be modulated by obesity status: implications for association studies. BMC Med Genet 9: 45. doi: 10.1186/1471-2350-9-45
[9]
Ioannidis JP, Thomas G, Daly MJ (2009) Validating, augmenting and refining genome-wide association signals. Nat Rev Genet 10: 318–329. doi: 10.1038/nrg2544
[10]
McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, et al. (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9: 356–369. doi: 10.1038/nrg2344
[11]
Bouatia-Naji N, Rocheleau G, Van Lommel L, Lemaire K, Schuit F, et al. (2008) A polymorphism within the G6PC2 gene is associated with fasting plasma glucose levels. Science 320: 1085–1088. doi: 10.1126/science.1156849
[12]
Elliott P, Chambers JC, Zhang W, Clarke R, Hopewell JC, et al. (2009) Genetic Loci associated with C-reactive protein levels and risk of coronary heart disease. JAMA 302: 37–48. doi: 10.1001/jama.2009.954
[13]
Han F, Pan W (2010) A data-adaptive sum test for disease association with multiple common or rare variants. Hum Hered 70: 42–54. doi: 10.1159/000288704
[14]
Madsen BE, Browning SR (2009) A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet 5: e1000384 doi:10.1371/journal.pgen.1000384.
[15]
Liu DJ, Leal SM (2010) A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions. PLoS Genet 6: e1001156 doi:10.1371/journal.pgen.1001156.
[16]
Li B, Leal SM (2008) Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet 83: 311–321. doi: 10.1016/j.ajhg.2008.06.024
[17]
Wu MC, Lee S, Cai T, Li Y, Boehnke M, et al. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89: 82–93. doi: 10.1016/j.ajhg.2011.05.029
[18]
Morris AP, Zeggini E (2009) An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol doi: 10.1002/gepi.20450
[19]
Bhatia G, Bansal V, Harismendy O, Schork NJ, Topol EJ, et al. (2010) A covering method for detecting genetic associations between rare variants and common phenotypes. PLoS Comput Biol 6: e1000954 doi:10.1371/journal.pcbi.1000954.
[20]
Lin DY, Zeng D (2009) Proper analysis of secondary phenotype data in case-control association studies. Genet Epidemiol 33: 256–265. doi: 10.1002/gepi.20377
[21]
Liu DJ, Leal SM (2011) A flexible likelihood framework for detecting associations with secondary phenotypes in genetic studies using selected samples: application to sequence data. Eur J Hum Genet doi: 10.1038/ejhg.2011.211
[22]
Liu DJ, Leal SM (2010) Replication Strategies for Rare Variant Complex Trait Association Studies via Next-Generation Sequencing. Am J Hum Genet 87: 790–801. doi: 10.1016/j.ajhg.2010.10.025
[23]
Lin DY, Tang ZZ (2011) A general framework for detecting disease associations with rare variants in sequencing studies. Am J Hum Genet 89: 354–367. doi: 10.1016/j.ajhg.2011.07.015
[24]
Price AL, Kryukov GV, de Bakker PI, Purcell SM, Staples J, et al. (2010) Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet 86: 832–838. doi: 10.1016/j.ajhg.2010.04.005
[25]
Kryukov GV, Shpunt A, Stamatoyannopoulos JA, Sunyaev SR (2009) Power of deep, all-exon resequencing for discovery of human trait genes. Proc Natl Acad Sci U S A 106: 3871–3876. doi: 10.1073/pnas.0812824106
[26]
Liljedahl U, Lind L, Kurland L, Berglund L, Kahan T, et al. (2004) Single nucleotide polymorphisms in the apolipoprotein B and low density lipoprotein receptor genes affect response to antihypertensive treatment. BMC Cardiovasc Disord 4: 16.
[27]
Adams AM, Hudson RR (2004) Maximum-likelihood estimation of demographic parameters using the frequency spectrum of unlinked single-nucleotide polymorphisms. Genetics 168: 1699–1712. doi: 10.1534/genetics.104.030171
[28]
Munafo MR, Flint J (2004) Meta-analysis of genetic association studies. Trends Genet 20: 439–444. doi: 10.1016/j.tig.2004.06.014
[29]
Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, et al. (2009) Genome-wide association study of blood pressure and hypertension. Nat Genet 41: 677–687. doi: 10.1038/ng.384
[30]
Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, et al. (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466: 707–713.
[31]
Wolfe KH, Sharp PM, Li WH (1989) Mutation rates differ among regions of the mammalian genome. Nature 337: 283–285. doi: 10.1038/337283a0
[32]
Basu S, Pan W (2011) Comparison of statistical tests for disease association with rare variants. Genet Epidemiol 35: 606–619. doi: 10.1002/gepi.20609
[33]
Ladouceur M, Dastani Z, Aulchenko YS, Greenwood CM, Richards JB (2012) The empirical power of rare variant association methods: results from sanger sequencing in 1,998 individuals. PLoS Genet 8: e1002496 doi:10.1371/journal.pgen.1002496.
[34]
Newman DL, Hoffjan S, Bourgain C, Abney M, Nicolae RI, et al. (2004) Are common disease susceptibility alleles the same in outbred and founder populations? Eur J Hum Genet 12: 584–590. doi: 10.1038/sj.ejhg.5201191
