The Power of Gene-Based Rare Variant Methods to Detect Disease-Associated Variation and Test Hypotheses About Complex Disease

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Re-sequencing technologies allow for a more complete interrogation of the role of human variation in complex disease. The inadequate power of single variant methods to assess the role of less common variation has led to the development of numerous statistical methods for testing aggregate groups of variants for association with disease. Such endeavors pose substantial analytical challenges, however, due to the diverse array of genetic hypotheses that need to be considered. In this work, we systematically quantify and compare the performance of a panel of commonly used gene-based association methods under a range of allelic architectures, significance thresholds, locus effect sizes, sample sizes, and filters for neutral variation. We find that MiST, SKAT-O, and KBAC have the highest mean power across simulated datasets. Across all methods, however, the power to detect even loci of relatively large effect is very low at exome-wide significance thresholds for sample sizes comparable with those of ongoing sequencing studies; as such, the absence of signal in studies of a few thousand individuals does not exclude a role for rare variation in complex traits. Finally, we directly compare the results reported by different gene-based methods in order to identify their comparative advantages and disadvantages under distinct locus architectures. Our findings have implications for meaningful interpretation of both positive and negative findings in ongoing and future sequencing studies.

Vyšlo v časopise: The Power of Gene-Based Rare Variant Methods to Detect Disease-Associated Variation and Test Hypotheses About Complex Disease. PLoS Genet 11(4): e32767. doi:10.1371/journal.pgen.1005165
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1005165

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Zdroje

1. Purcell S., Cherny S.S. & Sham P.C. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19, 149–50 (2003). 12499305

2. Kiezun A. et al. Exome sequencing and the genetic basis of complex traits. Nature Genetics 44, 623–30 (2012). doi: 10.1038/ng.2303 22641211

3. Asimit J. & Zeggini E. Rare variant association analysis methods for complex traits. Annual Review of Genetics 44, 293–308 (2010). doi: 10.1146/annurev-genet-102209-163421 21047260

4. Stitziel N.O., Kiezun A. & Sunyaev S. Computational and statistical approaches to analyzing variants identified by exome sequencing. Genome Biology 12, 227 (2011). doi: 10.1186/gb-2011-12-9-227 21920052

5. Rivas M. et al. Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nature Genetics 43, 1066–73 (2011). doi: 10.1038/ng.952 21983784

6. Cohen J.C., Boerwinkle E., Mosley T.H. & Hobbs H.H. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. New England Journal of Medicine 1264–1272 (2006). 16554528

7. Johansen C.T. et al. Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia. Nature Genetics 42, 684–7 (2010). doi: 10.1038/ng.628 20657596

8. Bonnefond A. et al. Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nature Genetics 44, 297–301 (2012). doi: 10.1038/ng.1053 22286214

9. Bansal V., Libiger O., Torkamani A.L.I. & Schork N.J. An application and empirical comparison of statistical analysis methods for associating rare variants to a complex phenotype. Pac Symp Biocomput 76–87 (2011). 21121035

10. Ladouceur M., Dastani Z., Aulchenko Y.S., Greenwood C.M.T. & Richards J.B. The empirical power of rare variant association methods: results from Sanger sequencing in 1,998 individuals. PLoS Genetics 8, e1002496 (2012). doi: 10.1371/journal.pgen.1002496 22319458

11. Basu S. & Pan W. Comparison of statistical tests for disease association with rare variants. Genetic Epidemiology 35, 606–619 (2011). doi: 10.1002/gepi.20609 21769936

12. Su Z., Marchini J. & Donnelly P. HAPGEN2: simulation of multiple disease SNPs. Bioinformatics 27, 2304–5 (2011). doi: 10.1093/bioinformatics/btr341 21653516

13. The 1000 Genomes Project Consortium A map of human genome variation from population-scale sequencing. Nature 467, 1061–73 (2010). doi: 10.1038/nature09534 20981092

14. Nelson M. et al. An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 People. Science 337, 100–104 (2012). doi: 10.1126/science.1217876 22604722

15. Agarwala V., Flannick J., Sunyaev S. & Altshuler D. Evaluating empirical bounds on complex disease genetic architecture. Nature Genetics 45, 1418–27 (2013). doi: 10.1038/ng.2804 24141362

16. Li B. & Leal S.M. Methods for detecting associations with rare variants for common diseases: Application to analysis of sequence data. The American Journal of Human Genetics 83(3)311–321 (2008).

17. Price A.L. et al. Pooled association tests for rare variants in exon-resequencing studies. American Journal of Human Genetics 86, 832–8 (2010). doi: 10.1016/j.ajhg.2010.04.005 20471002

18. PLINK/SEQ: A library for the analysis of genetic variation data. at <http://atgu.mgh.harvard.edu/plinkseq/>

19. Madsen B.E. & Browning S.R. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genetics 5, e1000384 (2009). doi: 10.1371/journal.pgen.1000384 19214210

20. Liu D.J. & Leal S.M. 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 Genetics 6, e1001156 (2010). doi: 10.1371/journal.pgen.1001156 20976247

21. Neale B.M. et al. Testing for an unusual distribution of rare variants. PLoS Genetics 7, e1001322 (2011). doi: 10.1371/journal.pgen.1001322 21408211

22. Wu S. et al. Rare variant association testing for sequencing data using the Sequence Kernel Association Test (SKAT). American Journal of Human Genetics 89, 82–93 (2011). doi: 10.1016/j.ajhg.2011.05.029 21737059

23. Lee S. et al. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. American Journal of Human Genetics 91, 224–37 (2012). doi: 10.1016/j.ajhg.2012.06.007 22863193

24. Sun J., Zheng Y. & Hsu L. A unified mixed-effects model for rare-variant association in sequencing studies. Genetic Epidemiology 37, 334–44 (2013). doi: 10.1002/gepi.21717 23483651

25. EPACTS: Efficient and Parallelizable Association Container Toolbox. <http://genome.sph.umich.edu/wiki/EPACTS>.

26. So H.-C., Gui A.H.S., Cherny S.S. & Sham P.C. Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases. Genetic Epidemiology 35, 310–7 (2011). doi: 10.1002/gepi.20579 21374718

27. Falconer D.S. The inheritance of liability to diseases with variable age of onset, with particular reference to diabetes mellitus. Annals of Human Genetics 31, 1–20 (1967). 6056557

28. Lohmueller KE, Sparso T et al. Whole-exome sequencing of 2,000 Danish individuals and the role of rare coding variants in type 2 diabetes. Am. J. Human Genetics. 93, 1072–86 (2013). doi: 10.1016/j.ajhg.2013.11.005 24290377

29. Fu W, O’Connor TD, et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature. 493, 216–220 (2013). doi: 10.1038/nature11690 23201682

30. Lee S., Abecasis G.R., Boehnke M., & Lin X. Rare-Variant Association Analysis: Study Designs and Statistical Tests. American Journal of Human Genetics 95, 5–23 (2014). doi: 10.1016/j.ajhg.2014.06.009 24995866

31. Li N & Stephens M. Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics 165 (4): 2213–33 (2003). 14704198