SpringerLink
Log in

Assessing Rare Variation in Complex Traits

  • Protocol
  • First Online:
Genetic Epidemiology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1793))

Abstract

While genome-wide association studies have been very successful in identifying associations of common genetic variants with many different traits, the rarer frequency spectrum of the genome has not yet been comprehensively explored. Technological developments increasingly lift restrictions to access rare genetic variation. Dense reference panels enable improved genotype imputation for rarer variants in studies using DNA microarrays. Moreover, the decreasing cost of next generation sequencing makes whole exome and genome sequencing increasingly affordable for large samples. Large-scale efforts based on sequencing, such as ExAC, 100,000 Genomes, and TopMed, are likely to significantly advance this field.

The main challenge in evaluating complex trait associations of rare variants is statistical power. The choice of population should be considered carefully because allele frequencies and linkage disequilibrium structure differ between populations. Genetically isolated populations can have favorable genomic characteristics for the study of rare variants.

One strategy to increase power is to assess the combined effect of multiple rare variants within a region, known as aggregate testing. A  range of methods have been developed for this. Model performance depends on the genetic architecture of the region of interest.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
USD 49.95
Price excludes VAT (Brazil)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (Brazil)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (Brazil)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (Brazil)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Cohen J, Pertsemlidis A, Kotowski IK et al (2005) Low LDL cholesterol in African Americans resulting from frequent nonsense mutations in PCSK9. Nat Genet 37(3):328–328. https://doi.org/10.1038/ng0305-328c

    Article  CAS  Google Scholar 

  2. Cohen JC, Boerwinkle E, Mosley TH (2006) Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. New Engl J Med 354(12):1264–1272. https://doi.org/10.1056/NEJMoa054013

    Article  PubMed  CAS  Google Scholar 

  3. Roth EM, McKenney JM, Hanotin C, Asset G, Stein EA (2012) Atorvastatin with or without an antibody to PCSK9 in primary hypercholesterolemia. New Engl J Med 367(20):1891–1900. https://doi.org/10.1056/NEJMoa1201832

    Article  PubMed  CAS  Google Scholar 

  4. Koren MJ, Scott R, Kim JB et al (2012) Efficacy, safety, and tolerability of a monoclonal antibody to proprotein convertase subtilisin/kexin type 9 as monotherapy in patients with hypercholesterolaemia (MENDEL): a randomised, double-blind, placebo-controlled, phase 2 study. Lancet 380(9858):1995–2006. https://doi.org/10.1016/S0140-6736(12)61771-1

    Article  PubMed  CAS  Google Scholar 

  5. Timpson NJ, Walter K, Min JL et al (2014) A rare variant in APOC3 is associated with plasma triglyceride and VLDL levels in Europeans. Nat Commun 5:4871. https://doi.org/10.1038/ncomms5871

    Article  PubMed  CAS  Google Scholar 

  6. Gilly A, Ritchie GR, Southam L (2016) Very low-depth sequencing in a founder population identifies a cardioprotective APOC3 signal missed by genome-wide imputation. Hum Mol Genet 25(11):2360–2365. https://doi.org/10.1093/hmg/ddw088

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Tachmazidou I, Dedoussis G, Southam L et al (2013) A rare functional cardioprotective APOC3 variant has risen in frequency in distinct population isolates. Nat Commun 4:2872. https://doi.org/10.1038/ncomms3872

    Article  PubMed  CAS  Google Scholar 

  8. Pollin TI, Damcott CM, Shen HQ et al (2008) A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent Cardioprotection. Science 322(5908):1702–1705. https://doi.org/10.1126/science.1161524

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Jorgensen A, Frikke-Schmidt R, Nordestgaard BG, Tybjaerg-Hansen A (2014) Loss-of-function mutations in Apoc3 and reduced risk of ischemic vascular disease. Atherosclerosis 235(2):E18–E18

    Article  Google Scholar 

  10. Crosby J, Peloso GM, Auer PL et al (2014) Loss-of-function mutations in APOC3, triglycerides, and coronary disease. New Engl J Med 371(1):22–31. https://doi.org/10.1056/NEJMoa1307095

    Article  PubMed  CAS  Google Scholar 

  11. Gaudet D, Alexander VJ, Baker BF et al (2015) Antisense inhibition of apolipoprotein C-III in patients with hypertriglyceridemia. New Engl J Med 373(5):438–447. https://doi.org/10.1056/NEJMoa1400283

    Article  PubMed  CAS  Google Scholar 

  12. Altshuler DM, Durbin RM, Abecasis GR et al (2015) A global reference for human genetic variation. Nature 526(7571):68–74. https://doi.org/10.1038/nature15393

    Article  CAS  Google Scholar 

  13. UK10K Consortium, Walter K, Min JL et al (2015) The UK10K project identifies rare variants in health and disease. Nature 526(7571):82–90. https://doi.org/10.1038/nature14962

    Article  CAS  Google Scholar 

  14. National Heart Lung and Blood Institute (2016) Trans-Omics for Precision Medicine (TOPMed) Program. https://www.nhlbi.nih.gov/research/resources/nhlbi-precision-medicine-initiative/topmed. Accessed 8 Nov 2016

  15. Genomics England (2016) The 100,000 Genomes Project. https://www.genomicsengland.co.uk/the-100000-genomes-project/. Accessed 8 Nov 2016

  16. Peplow M (2016) The 100 000 genomes project. BMJ 353. ARTN i1757. https://doi.org/10.1136/bmj.i1757

  17. ExAC project pins down rare gene variants (2016). Nature 536(7616):249. https://doi.org/10.1038/536249a

  18. Wang H, Liu L, Zhao J et al (2013) Large scale meta-analyses of fasting plasma glucose raising variants in GCK, GCKR, MTNR1B and G6PC2 and their impacts on type 2 diabetes mellitus risk. PLoS One 8(6):e67665. https://doi.org/10.1371/journal.pone.0067665

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Gillespie JH (2010) Population genetics: a concise guide. Johns Hopkins University Press, Baltimore

    Google Scholar 

  20. Campbell MC, Tishkoff SA (2008) African genetic diversity: implications for human demographic history, modern human origins, and complex disease map**. Annu Rev Genomics Hum Genet 9:403–433. https://doi.org/10.1146/annurev.genom.9.081307.164258

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Varilo T, Peltonen L (2004) Isolates and their potential use in complex gene map** efforts - commentary. Curr Opin Genet Dev 14(3):316–323. https://doi.org/10.1016/j.gde.2004.04.008

    Article  PubMed  CAS  Google Scholar 

  22. Minster RL, Hawley NL, Su CT et al (2016) A thrifty variant in CREBRF strongly influences body mass index in Samoans. Nat Genet 48(9):1049–1054. https://doi.org/10.1038/ng.3620

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Steinthorsdottir V, Thorleifsson G, Reynisdottir I et al (2007) A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet 39(6):770–775. https://doi.org/10.1038/ng2043

    Article  PubMed  CAS  Google Scholar 

  24. Holm H, Gudbjartsson DF, Sulem P et al (2011) A rare variant in MYH6 is associated with high risk of sick sinus syndrome. Nat Genet 43(4):316–320. https://doi.org/10.1038/ng.781

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Huyghe JR, Jackson AU, Fogarty MP et al (2013) Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat Genet 45(2):197–201. https://doi.org/10.1038/ng.2507

    Article  PubMed  CAS  Google Scholar 

  26. Moltke I, Fumagalli M, Korneliussen TS et al (2015) Uncovering the genetic history of the present-day greenlandic population. Am J Hum Genet 96(1):54–69. https://doi.org/10.1016/j.ajhg.2014.11.012

    Article  PubMed  CAS  Google Scholar 

  27. Moltke I, Grarup N, Jorgensen ME et al (2014) A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature 512(7513):190–193. https://doi.org/10.1038/nature13425

    Article  PubMed  CAS  Google Scholar 

  28. Wetterstrand K (2016) DNA Sequencing Costs: Data from NHGRI Genome Sequencing Program (GSP). http://www.genome.gov/sequencingcostsdata. Accessed 28 Oct 2016

  29. Yang J, Bakshi A, Zhu Z et al (2015) Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet 47(10):1114–1120. https://doi.org/10.1038/ng.3390

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. McCarthy S, Das S, Kretzschmar W et al (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 48(10):1279–1283. https://doi.org/10.1038/ng.3643

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Abecasis G, Altshuler D, Boehnke M, et al (2016) Exome Chip. http://genome.sph.umich.edu/wiki/Exome_Chip_Design. Accessed 31 Oct 2016

  32. Wessel J, Chu AY, Willems SM et al (2015) Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat Commun 6:5897. https://doi.org/10.1038/ncomms6897

    Article  PubMed  CAS  Google Scholar 

  33. Peloso GM, Auer PL, Bis JC et al (2014) Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am J Hum Genet 94(2):223–232. https://doi.org/10.1016/j.ajhg.2014.01.009

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Visscher PM, Brown MA, McCarthy MI et al (2012) Five years of GWAS discovery. Am J Hum Genet 90(1):7–24. https://doi.org/10.1016/j.ajhg.2011.11.029

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Ionita-Laza I, McCallum K, Xu B et al (2016) A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat Genet 48(2):214–220. https://doi.org/10.1038/ng.3477

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Ritchie GRS, Dunham I, Zeggini E et al (2014) Functional annotation of noncoding sequence variants. Nat Methods 11(3):294–U351. https://doi.org/10.1038/nmeth.2832

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Kircher M, Witten DM, Jain P et al (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46(3):310–315. https://doi.org/10.1038/ng.2892

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Ma C, Blackwell T, Boehnke M et al (2013) Recommended joint and meta-analysis strategies for case-control association testing of single low-count variants. Genet Epidemiol 37(6):539–550. https://doi.org/10.1002/gepi.21742

    Article  PubMed  PubMed Central  Google Scholar 

  39. Bigdeli TB, Neale BM, Neale MC (2014) Statistical properties of single-marker tests for rare variants. Twin Res Hum Genet 17(3):143–150. https://doi.org/10.1017/thg.2014.17

    Article  PubMed  Google Scholar 

  40. Fisher RA (1922) On the interpretation of chi-squared from contingency tables, and the calculation of P. J R Stat Soc 85(1):87–94. https://doi.org/10.2307/2340521

    Article  Google Scholar 

  41. Wang X (2014) Firth logistic regression for rare variant association tests. Front Genet 5:187. https://doi.org/10.3389/fgene.2014.00187

    Article  PubMed  PubMed Central  Google Scholar 

  42. Lee S, Fuchsberger C, Kim S et al (2016) An efficient resampling method for calibrating single and gene-based rare variant association analysis in case-control studies. Biostatistics 17(1):1–15. https://doi.org/10.1093/biostatistics/kxv033

    Article  PubMed  CAS  Google Scholar 

  43. Auer PL, Reiner AP, Leal SM (2016) The effect of phenotypic outliers and non-normality on rare-variant association testing. Eur J Hum Genet 24(8):1188–1194. https://doi.org/10.1038/ejhg.2015.270

    Article  PubMed  PubMed Central  Google Scholar 

  44. O'Connor TD, Kiezun A, Bamshad M et al (2013) Fine-scale patterns of population stratification confound rare variant association tests. PLoS One 8(7):e65834. https://doi.org/10.1371/journal.pone.0065834

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Zhang Y, Shen X, Pan W (2013) Adjusting for population stratification in a fine scale with principal components and sequencing data. Genet Epidemiol 37(8):787–801. https://doi.org/10.1002/gepi.21764

    Article  PubMed  Google Scholar 

  46. Babron MC, de Tayrac M, Rutledge DN et al (2012) Rare and low frequency variant stratification in the UK population: description and impact on association tests. PLoS One 7(10):e46519. https://doi.org/10.1371/journal.pone.0046519

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Liu Q, Nicolae DL, Chen LS (2013) Marbled inflation from population structure in gene-based association studies with rare variants. Genet Epidemiol 37(3):286–292. https://doi.org/10.1002/gepi.21714

    Article  PubMed  Google Scholar 

  48. Mathieson I, McVean G (2012) Differential confounding of rare and common variants in spatially structured populations. Nat Genet 44(3):243–246. https://doi.org/10.1038/ng.1074

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Tintle N, Aschard H, Hu I et al (2011) Inflated type I error rates when using aggregation methods to analyze rare variants in the 1000 genomes project exon sequencing data in unrelated individuals: summary results from group 7 at genetic analysis workshop 17. Genet Epidemiol 35 Suppl 1:S56–S60. https://doi.org/10.1002/gepi.20650

    Article  PubMed  Google Scholar 

  50. International HapMap Consortium (2005) A haplotype map of the human genome. Nature 437(7063):1299–1320. https://doi.org/10.1038/nature04226

    Article  CAS  Google Scholar 

  51. Pe’er I, Yelensky R, Altshuler D et al (2008) Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol 32(4):381–385. https://doi.org/10.1002/gepi.20303

    Article  PubMed  Google Scholar 

  52. Dudbridge F, Gusnanto A (2008) Estimation of significance thresholds for genomewide association scans. Genet Epidemiol 32(3):227–234. https://doi.org/10.1002/gepi.20297

    Article  PubMed  PubMed Central  Google Scholar 

  53. Fadista J, Manning AK, Florez JC et al (2016) The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants. Eur J Hum Genet 24(8):1202–1205. https://doi.org/10.1038/ejhg.2015.269

    Article  PubMed  PubMed Central  Google Scholar 

  54. Sham PC, Purcell SM (2014) Statistical power and significance testing in large-scale genetic studies. Nat Rev Genet 15(5):335–346. https://doi.org/10.1038/nrg3706

    Article  PubMed  CAS  Google Scholar 

  55. Kryukov GV, Pennacchio LA, Sunyaev SR (2007) Most rare missense alleles are deleterious in humans: implications for complex disease and association studies. Am J Hum Genet 80(4):727–739. https://doi.org/10.1086/513473

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Nelson MR, Wegmann D, Ehm MG et al (2012) An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people. Science 337(6090):100–104. https://doi.org/10.1126/science.1217876

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Fu W, O'Connor TD, Jun G et al (2013) Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493(7431):216–220. https://doi.org/10.1038/nature11690

    Article  PubMed  CAS  Google Scholar 

  58. Bansal V, Libiger O, Torkamani A et al (2010) Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet 11(11):773–785. https://doi.org/10.1038/nrg2867

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Wu G, Zhi D (2013) Pathway-based approaches for sequencing-based genome-wide association studies. Genet Epidemiol 37(5):478–494. https://doi.org/10.1002/gepi.21728

    Article  PubMed  Google Scholar 

  60. Morris AP, Zeggini E (2010) An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 34(2):188–193. https://doi.org/10.1002/gepi.20450

    Article  PubMed  Google Scholar 

  61. Morgenthaler S, Thilly WG (2007) A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutat Res 615(1–2):28–56. https://doi.org/10.1016/j.mrfmmm.2006.09.003

    Article  CAS  PubMed  Google Scholar 

  62. 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(3):311–321. https://doi.org/10.1016/j.ajhg.2008.06.024

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Madsen BE, Browning SR (2009) A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet 5(2):e1000384. https://doi.org/10.1371/journal.pgen.1000384

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Han F, Pan W (2010) A data-adaptive sum test for disease association with multiple common or rare variants. Hum Hered 70(1):42–54. https://doi.org/10.1159/000288704

    Article  PubMed  PubMed Central  Google Scholar 

  65. Magi R, Asimit JL, Day-Williams AG et al (2012) Genome-wide association analysis of imputed rare variants: application to seven common complex diseases. Genet Epidemiol 36(8):785–796. https://doi.org/10.1002/gepi.21675

    Article  PubMed  PubMed Central  Google Scholar 

  66. Zawistowski M, Gopalakrishnan S, Ding J et al (2010) Extending rare-variant testing strategies: analysis of noncoding sequence and imputed genotypes. Am J Hum Genet 87(5):604–617. https://doi.org/10.1016/j.ajhg.2010.10.012

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Magi R, Kumar A, Morris AP (2011) Assessing the impact of missing genotype data in rare variant association analysis. BMC Proc 5(Suppl 9):S107. https://doi.org/10.1186/1753-6561-5-S9-S107

    Article  PubMed  PubMed Central  Google Scholar 

  68. Asimit JL, Day-Williams AG, Morris AP et al (2012) ARIEL and AMELIA: testing for an accumulation of rare variants using next-generation sequencing data. Hum Hered 73(2):84–94. https://doi.org/10.1159/000336982

    Article  PubMed  CAS  Google Scholar 

  69. Hoffmann TJ, Marini NJ, Witte JS (2010) Comprehensive approach to analyzing rare genetic variants. PLoS One 5(11):e13584. https://doi.org/10.1371/journal.pone.0013584

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Lin DY, Tang ZZ (2011) A general framework for detecting disease associations with rare variants in sequencing studies. Am J Hum Genet 89(3):354–367. https://doi.org/10.1016/j.ajhg.2011.07.015

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. 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(10):e1001156. https://doi.org/10.1371/journal.pgen.1001156

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Price AL, Kryukov GV, de Bakker PI et al (2010) Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet 86(6):832–838. https://doi.org/10.1016/j.ajhg.2010.04.005

    Article  PubMed  PubMed Central  Google Scholar 

  73. Neale BM, Rivas MA, Voight BF et al (2011) Testing for an unusual distribution of rare variants. PLoS Genet 7(3):e1001322. https://doi.org/10.1371/journal.pgen.1001322

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Basu S, Pan W (2011) Comparison of statistical tests for disease association with rare variants. Genet Epidemiol 35(7):606–619. https://doi.org/10.1002/gepi.20609

    Article  PubMed  PubMed Central  Google Scholar 

  75. Lee S, Wu MC, Lin X (2012) Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13(4):762–775. https://doi.org/10.1093/biostatistics/kxs014

    Article  PubMed  PubMed Central  Google Scholar 

  76. Ladouceur M, Dastani Z, Aulchenko YS et al (2012) The empirical power of rare variant association methods: results from sanger sequencing in 1,998 individuals. PLoS Genet 8(2):e1002496. https://doi.org/10.1371/journal.pgen.1002496

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Wu MC, Lee S, Cai T et al (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89(1):82–93. https://doi.org/10.1016/j.ajhg.2011.05.029

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Pan W (2009) Asymptotic tests of association with multiple SNPs in linkage disequilibrium. Genet Epidemiol 33(6):497–507. https://doi.org/10.1002/gepi.20402

    Article  PubMed  PubMed Central  Google Scholar 

  79. Ionita-Laza I, Lee S, Makarov V et al (2013) Sequence kernel association tests for the combined effect of rare and common variants. Am J Hum Genet 92(6):841–853. https://doi.org/10.1016/j.ajhg.2013.04.015

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Hasegawa T, Kojima K, Kawai Y et al (2016) AP-SKAT: highly-efficient genome-wide rare variant association test. BMC Genomics 17(1):745. https://doi.org/10.1186/s12864-016-3094-3

    Article  PubMed  PubMed Central  Google Scholar 

  81. Lee S, Emond MJ, Bamshad MJ et al (2012) Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet 91(2):224–237. https://doi.org/10.1016/j.ajhg.2012.06.007

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Sun J, Zheng Y, Hsu L (2013) A unified mixed-effects model for rare-variant association in sequencing studies. Genet Epidemiol 37(4):334–344. https://doi.org/10.1002/gepi.21717

    Article  PubMed  PubMed Central  Google Scholar 

  83. Li H, Chen J (2016) Efficient unified rare variant association test by modeling the population genetic distribution in case-control studies. Genet Epidemiol 40(7):579–590. https://doi.org/10.1002/gepi.21995

    Article  PubMed  PubMed Central  Google Scholar 

  84. Moutsianas L, Agarwala V, Fuchsberger C et al (2015) The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease. PLoS Genet 11(4):e1005165. https://doi.org/10.1371/journal.pgen.1005165

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Greco B, Hainline A, Arbet J et al (2016) A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures. Eur J Hum Genet 24(5):767–773. https://doi.org/10.1038/ejhg.2015.194

    Article  PubMed  Google Scholar 

  86. Dering C, Konig IR, Ramsey LB et al (2014) A comprehensive evaluation of collapsing methods using simulated and real data: excellent annotation of functionality and large sample sizes required. Front Genet 5:323. https://doi.org/10.3389/fgene.2014.00323

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Yi N, Zhi D (2011) Bayesian analysis of rare variants in genetic association studies. Genet Epidemiol 35(1):57–69. https://doi.org/10.1002/gepi.20554

    Article  PubMed  PubMed Central  Google Scholar 

  88. Quintana MA, Berstein JL, Thomas DC et al (2011) Incorporating model uncertainty in detecting rare variants: the Bayesian risk index. Genet Epidemiol 35(7):638–649. https://doi.org/10.1002/gepi.20613

    Article  PubMed  PubMed Central  Google Scholar 

  89. Chen LS, Hsu L, Gamazon ER et al (2012) An exponential combination procedure for set-based association tests in sequencing studies. Am J Hum Genet 91(6):977–986. https://doi.org/10.1016/j.ajhg.2012.09.017

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Logsdon BA, Dai JY, Auer PL et al (2014) A variational Bayes discrete mixture test for rare variant association. Genet Epidemiol 38(1):21–30

    Article  PubMed  PubMed Central  Google Scholar 

  91. Fan R, Wang Y, Mills JL et al (2013) Functional linear models for association analysis of quantitative traits. Genet Epidemiol 37(7):726–742. https://doi.org/10.1002/gepi.21757

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Luo L, Zhu Y, **ong M (2012) Quantitative trait locus analysis for next-generation sequencing with the functional linear models. J Med Genet 49(8):513–524. https://doi.org/10.1136/jmedgenet-2012-100798

    Article  PubMed  Google Scholar 

  93. Luo L, Boerwinkle E, **ong M (2011) Association studies for next-generation sequencing. Genome Res 21(7):1099–1108. https://doi.org/10.1101/gr.115998.110

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Fan R, Wang Y, Mills JL et al (2014) Generalized functional linear models for gene-based case-control association studies. Genet Epidemiol 38(7):622–637. https://doi.org/10.1002/gepi.21840

    Article  PubMed  PubMed Central  Google Scholar 

  95. Vsevolozhskaya OA, Zaykin DV, Greenwood MC et al (2014) Functional analysis of variance for association studies. PLoS One 9(9):e105074. https://doi.org/10.1371/journal.pone.0105074

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Fan R, Wang Y, Boehnke M et al (2015) Gene level meta-analysis of quantitative traits by functional linear models. Genetics 200(4):1089–1104. https://doi.org/10.1534/genetics.115.178343

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Wang Y, Liu A, Mills JL et al (2015) Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models. Genet Epidemiol 39(4):259–275. https://doi.org/10.1002/gepi.21895

    Article  PubMed  PubMed Central  Google Scholar 

  98. Wijsman EM (2012) The role of large pedigrees in an era of high-throughput sequencing. Hum Genet 131(10):1555–1563. https://doi.org/10.1007/s00439-012-1190-2

    Article  PubMed  PubMed Central  Google Scholar 

  99. De G, Yip WK, Ionita-Laza I et al (2013) Rare variant analysis for family-based design. PLoS One 8(1):e48495. https://doi.org/10.1371/journal.pone.0048495

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Ionita-Laza I, Lee S, Makarov V et al (2013) Family-based association tests for sequence data, and comparisons with population-based association tests. Eur J Hum Genet 21(10):1158–1162. https://doi.org/10.1038/ejhg.2012.308

    Article  PubMed  PubMed Central  Google Scholar 

  101. Chen H, Meigs JB, Dupuis J (2013) Sequence kernel association test for quantitative traits in family samples. Genet Epidemiol 37(2):196–204. https://doi.org/10.1002/gepi.21703

    Article  PubMed  Google Scholar 

  102. Schaid DJ, McDonnell SK, Sinnwell JP et al (2013) Multiple genetic variant association testing by collapsing and kernel methods with pedigree or population structured data. Genet Epidemiol 37(5):409–418. https://doi.org/10.1002/gepi.21727

    Article  PubMed  Google Scholar 

  103. Feng S, Pistis G, Zhang H et al (2015) Methods for association analysis and meta-analysis of rare variants in families. Genet Epidemiol 39(4):227–238. https://doi.org/10.1002/gepi.21892

    Article  PubMed  PubMed Central  Google Scholar 

  104. Svishcheva GR, Belonogova NM, Axenovich TI (2014) FFBSKAT: fast family-based sequence kernel association test. PLoS One 9(6):e99407. https://doi.org/10.1371/journal.pone.0099407

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. Oualkacha K, Dastani Z, Li R et al (2013) Adjusted sequence kernel association test for rare variants controlling for cryptic and family relatedness. Genet Epidemiol 37(4):366–376. https://doi.org/10.1002/gepi.21725

    Article  PubMed  Google Scholar 

  106. Jiang D, McPeek MS (2014) Robust rare variant association testing for quantitative traits in samples with related individuals. Genet Epidemiol 38(1):10–20. https://doi.org/10.1002/gepi.21775

    Article  PubMed  Google Scholar 

  107. Svishcheva GR, Belonogova NM, Axenovich TI (2015) Region-based association test for familial data under functional linear models. PLoS One 10(6):e0128999. https://doi.org/10.1371/journal.pone.0128999

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Chen H, Lumley T, Brody J et al (2014) Sequence kernel association test for survival traits. Genet Epidemiol 38(3):191–197. https://doi.org/10.1002/gepi.21791

    Article  PubMed  PubMed Central  Google Scholar 

  109. Cai T, Tonini G, Lin X (2011) Kernel machine approach to testing the significance of multiple genetic markers for risk prediction. Biometrics 67(3):975–986. https://doi.org/10.1111/j.1541-0420.2010.01544.x

    Article  PubMed  PubMed Central  Google Scholar 

  110. Lin X, Cai T, Wu MC et al (2011) Kernel machine SNP-set analysis for censored survival outcomes in genome-wide association studies. Genet Epidemiol 35(7):620–631. https://doi.org/10.1002/gepi.20610

    Article  PubMed  PubMed Central  Google Scholar 

  111. Fan R, Wang Y, Yan Q et al (2016) Gene-based association analysis for censored traits via fixed effect functional regressions. Genet Epidemiol 40(2):133–143. https://doi.org/10.1002/gepi.21947

    Article  PubMed  PubMed Central  Google Scholar 

  112. Liu DJ, Leal SM (2015) Replicating sequencing-based association studies of rare variants. In: Zeggini E, Morris A (eds) Assessing rare variation in complex traits: design and analysis of genetic studies. Springer, New York, NY, pp 201–213. https://doi.org/10.1007/978-1-4939-2824-8_14

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Wellcome Trust Sanger Institute, Cambridge, UK

    Karoline Kuchenbaecker

  2. University College London, London, UK

    Karoline Kuchenbaecker

  3. Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Genetics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark

    Emil Vincent Rosenbaum Appel

Authors
  1. Karoline Kuchenbaecker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emil Vincent Rosenbaum Appel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karoline Kuchenbaecker .

Editor information

Editors and Affiliations

  1. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece

    Evangelos Evangelou

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Kuchenbaecker, K., Appel, E.V.R. (2018). Assessing Rare Variation in Complex Traits. In: Evangelou, E. (eds) Genetic Epidemiology. Methods in Molecular Biology, vol 1793. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7868-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7868-7_5

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7867-0

  • Online ISBN: 978-1-4939-7868-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation