The effect of phenotypic outliers and non-normality on rare-variant association testing

European Journal of Human Genetics

Volumn 24, Issue 8, 2016, Pages 1188-1194

  Auer, Paul L ^a   Reiner, Alex P ^b   Leal, Suzanne M ^c  

^a UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

^c BAYLOR COLLEGE OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ERROR; GENETIC ASSOCIATION; GENETIC ASSOCIATION STUDY; GENETIC MODEL; GENETIC PARAMETERS; GENETIC VARIABILITY; HUMAN; NON NORMALITY; PHENOTYPE; PHENOTYPIC OUTLIER; PRIORITY JOURNAL; QUANTITATIVE TRAIT; RARE VARIANT ASSOCIATION STUDY; SECONDARY ANALYSIS; SIMULATION; ALLELE; BIOLOGICAL MODEL; GENOME-WIDE ASSOCIATION STUDY; MUTATION; MUTATION RATE; PROCEDURES; STANDARDS; STATISTICAL BIAS;

ALLELES; BIAS (EPIDEMIOLOGY); GENOME-WIDE ASSOCIATION STUDY; HUMANS; MODELS, GENETIC; MUTATION; MUTATION RATE; PHENOTYPE;

EID: 84953258516     PISSN: 10184813     EISSN: 14765438     Source Type: Journal    
DOI: 10.1038/ejhg.2015.270     Document Type: Article

References (30)

1. Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature 2009; 461: 747-753.
2. Auer PL, Lettre G. Rare variant association studies: considerations, challenges and opportunities. Genome Med 2015; 7: 16.
3. Auer PL, Teumer A, Schick U, et al. Rare and low-frequency coding variants in CXCR2 and other genes are associated with hematological traits. Nat Genet 2014; 46: 629-634.
4. Do R, Stitziel NO, Won HH, et al. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature 2014; 518: 102-106.
5. Emond MJ, Louie T, Emerson J, et al. Exome sequencing of extreme phenotypes identifies DCTN4 as a modifier of chronic Pseudomonas aeruginosa infection in cystic fibrosis. Nat Genet 2012; 44: 886-889.
6. Huyghe JR, Jackson AU, Fogarty MP, et al. Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat Genet 2013; 45: 197-201.
7. Lange LA, Hu Y, Zhang H, et al. Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol. Am J Hum Genet 2014; 94: 233-245.
8. Peloso GM, Auer PL, Bis JC, et al. Association of low-frequency and rare codingsequence variants with blood lipids and coronary heart disease in 56, 000 whites and blacks. Am J Hum Genet 2014; 94: 223-232.
9. Tg, Hdl Working Group of the Exome Sequencing Project NHL, Blood I, Crosby J, Peloso GM, et al. Loss-of-function mutations in APOC3, triglycerides, and coronary disease. N Engl J Med 2014; 371: 22-31.
10. Lee S, Abecasis GR, Boehnke M, et al. Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet 2014; 95: 5-23.
11. Lee S, Emond MJ, Bamshad MJ, et al. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet 2012; 91: 224-237.
12. Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet 2008; 83: 311-321.
13. Lin DY, Tang ZZ: A general framework for detecting disease associations with rare variants in sequencing studies. Am J Hum Genet 2011; 89: 354-367.
14. Logsdon BA, Dai JY, Auer PL, et al. A variational Bayes discrete mixture test for rare variant association. Genet Epidemiol 2014; 38: 21-30.
15. Price AL, Kryukov GV, de Bakker PI, et al. Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet 2010; 86: 832-838.
16. Wu MC, Lee S, Cai T, et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 2011; 89: 82-93.
17. Reiner AP, Lettre G, Nalls MA, et al. Genome-wide association study of white blood cell count in 16, 388 African Americans: the continental origins and genetic epidemiology network (COGENT). PLoS Genet 2011; 7: e1002108.
18. Schick UM, Auer PL, Bis JC, et al. Association of exome sequences with plasma C-reactive protein levels in 49000 participants. Hum Mol Genet 2015; 24: 559-571.
19. Huber PJ Robust Statistics. Hoboken, N.J 2009.
20. Auer PL, Wang G, Leal SM. Testing for rare variant associations in the presence of missing data. Genet Epidemiol 2013; 37: 529-538.
21. Morris AP, Zeggini E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 2010; 34: 188-193.
22. Design of the Women's Health Initiative clinical trial and observational study. The Women's Health Initiative Study Group. Control Clin Trials 1998; 19: 61-109.
23. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559-575.
24. Beasley TM, Erickson S, Allison DB. Rank-based inverse normal transformations are increasingly used, but are they merited?. Behav Genet 2009; 39: 580-595.
25. Locke AE, Kahali B, Berndt SI, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 2015; 518: 197-206.
26. Buzkova P. Linear regression in genetic association studies. PLoS One 2013; 8: e56976.
27. Kutner MH, Nachtsheim CJ, Neter J, et al. Applied Linear Statistical Models. McGraw-Hill/Irwin: New York, NY, 2005.
28. Che R, Motsinger-Reif AA, Brown CC. Loss of power in two-stage residual-outcome regression analysis in genetic association studies. Genet Epidemiol 2012; 36: 890-894.
29. Demissie S, Cupples LA. Bias due to two-stage residual-outcome regression analysis in genetic association studies. Genet Epidemiol 2011; 35: 592-596.
30. Shapiro SS, Wilk MB. An Analysis of Variance Test for Normality. Biometrika 1965; 52: 591-611.