Systematic Cell-Based Phenotyping of Missense Alleles Empowers Rare Variant Association Studies: A Case for and Myocardial Infarction

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Exome sequencing has proven powerful to identify protein-coding variation across the human genome, unravel the basis of monogenic diseases and discover rare alleles that confer risk for complex disease. Nevertheless, two key challenges limit its application to complex phenotypes: first, most alleles identified in a population are extremely rare; and second, most alleles are neutral on protein activities. Consequently, association tests that rely on enumerating rare alleles in cases and controls (termed rare variant association studies, RVAS) are typically underpowered, as the many neutral alleles dampen signals that arise from the few alleles that disrupt protein functions. Strategies to securely discriminate disruptive from neutral variants are immature, in particular for missense variants. Here we show that the statistical power of RVAS improves dramatically if variants are stratified according to their in vitro ascertained functions. We establish scalable technology to objectively profile the biological effects of exome-identified missense variants in the low-density lipoprotein receptor (LDLR) through systematic overexpression and complementation experiments in cells. We demonstrate that carriers of LDLR alleles, which our experiments identify as “disruptive-missense”, have higher plasma LDL-C, and that considering in vitro data may make it possible to reduce RVAS sample sizes by more than 2-fold.

Vyšlo v časopise: Systematic Cell-Based Phenotyping of Missense Alleles Empowers Rare Variant Association Studies: A Case for and Myocardial Infarction. PLoS Genet 11(2): e32767. doi:10.1371/journal.pgen.1004855
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004855

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Zdroje

1. Kiezun A, Garimella K, Do R, Stitziel NO, Neale BM, et al. (2012) Exome sequencing and the genetic basis of complex traits. Nat Genet 10 : 623–630. doi: 10.1038/ng.2303

2. Goldstein DB, Allen A, Keebler J, Margulies EH, Petrou S, et al. (2013) Sequencing studies in human genetics: design and interpretation. Nat Rev Genet 14 : 460–470. doi: 10.1038/nrg3455 23752795

3. Rehm HL (2013) Disease-targeted sequencing: a cornerstone in the clinic. Nat Rev Genet 14 : 295–300. doi: 10.1038/nrg3463 23478348

4. Cutting GR (2014) Annotating DNA variants is the next major goal for human genetics. Am J Hum Genet 94 : 5–10. doi: 10.1016/j.ajhg.2013.12.008 24387988

5. Cotton RG, Scriver CR (1998) Proof of “disease causing” mutation. Hum Mutat 12 : 1–3. doi: 10.1002/(SICI)1098-1004(1998)12 : 1%3C1::AID-HUMU1%3E3.0.CO;2-M 9633813

6. Duzkale H, Shen J, McLaughlin H, Alfares A, Kelly MA, et al. (2013) A systematic approach to assessing the clinical significance of genetic variants. Clin Genet 84 : 453–463. doi: 10.1111/cge.12257 24033266

7. Flannick J, Beer NL, Bick AG, Agarwala V, Molnes J, et al. (2013) Assessing the phenotypic effects in the general population of rare variants in genes for a dominant Mendelian form of diabetes. Nat Genet 45 : 1380–1385. doi: 10.1038/ng.2794 24097065

8. Ormond KE (2008) Medical ethics for the genome world: a paper from the 2007 William Beaumont hospital symposium on molecular pathology. J Mol Diag 10 : 377–382. doi: 10.2353/jmoldx.2008.070162

9. Cooper GM, Shendure J (2011) Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nat Rev Genet 12 : 628–640. doi: 10.1038/nrg3046 21850043

10. Zuk O, Schaffner SF, Samocha K, Do R, Hechter E, et al. (2014) Searching for missing heritability: Designing rare variant association studies. Proc Natl Acad Sci U S A 111: E455–464. doi: 10.1073/pnas.1322563111 24443550

11. Liu DJ, Peloso GM, Zhan X, Holmen OL, Zawistowski M, et al. (2014) Meta-analysis of gene-level tests for rare variant association. Nat Genet 46 : 200–204. doi: 10.1038/ng.2852 24336170

12. Do R, Stitziel NO, Won HH, Berg Jørgensen A, Duga S, et al. (2014) Multiple rare alleles at LDLR and APOA5 confer risk for early-onset myocardial infarction. Nature. In press. doi: 10.1038/nature13917 25487149

13. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491 : 56–65. doi: 10.1038/nature11632 23128226

14. MacArthur DG, Balasubramanian S, Frankish A, Huang N, Morris J, et al. (2012) A systematic survey of loss-of-function variants in human protein-coding genes. Science 335 : 823–828. doi: 10.1126/science.1215040 22344438

15. Richards CS, Bale S, Bellissimo DB, Das S, Grody WW, et al. (2008) ACMG recommendations for standards for interpretation and reporting of sequence variations: Revisions 2007. Genet Med 10 : 294–300. doi: 10.1097/GIM.0b013e31816b5cae 18414213

16. Brown MS, Goldstein JL (1986) A receptor-mediated pathway for cholesterol homeostasis. Science 232 : 34–47. doi: 10.1126/science.3513311 3513311

17. Goldstein JL, Hazzard WR, Schrott HG, Bierman EL, Motulsky AG (1973) Hyperlipidemia in coronary heart disease. I. Lipid levels in 500 survivors of myocardial infarction. J Clin Invest 52 : 1533–1543. doi: 10.1172/JCI107331 4718952

18. Hobbs HH, Russell DW, Brown MS, Goldstein JL (1990) The LDL receptor locus in familial hypercholesterolemia: mutational analysis of a membrane protein. Ann Rev Genet 24 : 133–170. doi: 10.1146/annurev.ge.24.120190.001025 2088165

19. Marks D, Thorogood M, Neil HA, Humphries SE (2003) A review on the diagnosis, natural history, and treatment of familial hypercholesterolaemia. Atherosclerosis 168 : 1–14. doi: 10.1016/S0021-9150(02)00330-1 12732381

20. Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, et al. (2013) ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med 15 : 565–574. doi: 10.1038/gim.2013.73 23788249

21. Bartz F, Kern L, Erz D, Zhu M, Gilbert D, et al. (2009) Identification of cholesterol-regulating genes by targeted RNAi screening. Cell Metab 10 : 63–75. doi: 10.1016/j.cmet.2009.05.009 19583955

22. Blattmann P, Schuberth C, Pepperkok R, Runz H (2013) RNAi–Based Functional Profiling of Loci from Blood Lipid Genome-Wide Association Studies Identifies Genes with Cholesterol-Regulatory Function. PLoS Genet 9: e1003338. doi: 10.1371/journal.pgen.1003338 23468663

23. Davis CG, Lehrman MA, Russell DW, Anderson RGW, Brown MS, et al. (1986) The J. D. mutation in familial hypercholesterolemia: Amino acid substitution in cytoplasmic domain impedes internalization of LDL receptors. Cell 45 : 15–24. doi: 10.1016/0092-8674(86)90533-7 3955657

24. Bonnefond A, Clement N, Fawcett K, Yengo L, Vaillant E, et al. (2012) Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nat Genet 44 : 297–301. doi: 10.1038/ng.1053 22286214

25. Peloso GM, Auer PL, Bis JC, Voorman A, Morrison AC, 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 : 223–232. doi: 10.1016/j.ajhg.2014.01.009 24507774

26. Sosnay PR, Siklosi KR, Van Goor F, Kaniecki K, Yu H, et al. (2013) Defining the disease liability of variants in the cystic fibrosis transmembrane conductance regulator gene. Nat Genet 45 : 1160–1167. doi: 10.1038/ng.2745 23974870

27. Majithia AR, Flannick J, Shahinian P, Guo M, Bray MA, et al. (2014) Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc Natl Acad Sci U S A 111 : 13127–13132. doi: 10.1073/pnas.1410428111 25157153

28. Cohen JC, Stender S, Hobbs HH (2014) APOC3, coronary disease, and complexities of mendelian randomization studies. Cell Metab 20 : 387–389. doi: 10.1016/j.cmet.2014.08.007 25185943

29. Dorschner MO, Amendola LM, Turner EH, Robertson PD, Shirts BH, et al. (2013) Actionable, Pathogenic Incidental Findings in 1,000 Participants Exomes. Am J Hum Genet 93 : 631–640. doi: 10.1016/j.ajhg.2013.08.006 24055113

30. Yang Y, Muzny DM, Reid JG, Bainbridge MN, Willis A, et al. (2013) Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N Engl J Med 369 : 1502. doi: 10.1056/NEJMoa1306555 24088041

31. Atherosclerosis-Thrombosis-and-Vascular-Biology-Italian-Study-Group (2003) No evidence of association between prothrombotic gene polymorphisms and the development of acute myocardial infarction at a young age. Circulation 107 : 1117–1122. doi: 10.1161/01.CIR.0000051465.94572.D0 12615788

32. Ardissino D, Berzuini C, Merlini PA, Mannuccio P, Surti A, et al. (2011) Influence of 9p21.3 genetic variants on clinical and angiographic outcomes in early-onset myocardial infarction. J Am Coll Card 58 : 426–434. doi: 10.1016/j.jacc.2010.11.075

33. DeMott K, Nherera L, Humphries SE, Minhas R, Shaw EJ, et al. (2008) Clinical Guidelines and Evidence Review for Familial hypercholesterolaemia: the identification and management of adults and children with familial hypercholesterolaemia. National Collaborating Centre for Primary Care and Royal College of General Practitioners (London).

34. Tennessen JA, Bigham AW, O’Connor TD, Fu W, Kenny EE, et al. (2012) Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337 : 64–69. doi: 10.1126/science.1219240 22604720

35. Anderson RG, Goldstein JL, Brown MS (1977) A mutation that impairs the ability of lipoprotein receptors to localise in coated pits on the cell surface of human fibroblasts. Nature 270 : 695–699. doi: 10.1038/270695a0 201867

36. Villéger L, Abifadel M, Allard D, Rabès JP, Thiart R, et al. (2002) The UMD-LDLR database: additions to the software and 490 new entries to the database. Hum Mutat 20 : 81–87. doi: 10.1002/humu.10102 12124988

37. Leigh SE, Foster AH, Whittall RA, Hubbart CS, Humphries SE (2008) Update and analysis of the University College London low density lipoprotein receptor familial hypercholesterolemia database. Ann Hum Genet 72 : 485–498. doi: 10.1111/j.1469-1809.2008.00436.x 18325082

38. Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, et al. (2014) ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res 42: D980–985. doi: 10.1093/nar/gkt1113 24234437

39. Cooper DN, Krawczak M (1996) Human Gene Mutation Database. Hum Genet 98 : 629. doi: 10.1007/s004390050272 8882888

40. Bertolini S, Cassanelli S, Garuti R, Ghisellini M, Simone ML, et al. (1999) Analysis of LDL Receptor Gene Mutations in Italian Patients With Homozygous Familial Hypercholesterolemia. Arterioscler Thromb Vasc Biol 19 : 408–418. doi: 10.1161/01.ATV.19.2.408 9974426

41. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, et al. (2010) A method and server for predicting damaging missense mutations. Nat Methods 7 : 248–249. doi: 10.1038/nmeth0410-248 20354512

42. Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4 : 1073–1081. doi: 10.1038/nprot.2009.86 19561590

43. Reva B, Antipin Y, Sander C (2011) Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res 39: e118. doi: 10.1093/nar/gkr407 21727090

44. Schwarz JM, Cooper DN, Schuelke M, Seelow D (2014) MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods 11 : 361–362. doi: 10.1038/nmeth.2890 24681721

45. Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, et al. (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7: R100–R100. doi: 10.1186/gb-2006-7-10-r100 17076895

46. Goldstein JL, Brown MS (1989) Familial hypercholesterolemia in The Metabolic and Molecular Basis of Inherited Disease (eds. Scriver C.R., Beaudet A.L., Sly W.S. & Valle D.) 1215–1250, McGraw Hill (New York).

47. Talmud PJ, Shah S, Whittall R, Futema M, Howard P, et al. (2013) Use of low-density lipoprotein cholesterol gene score to distinguish patients with polygenic and monogenic familial hypercholesterolaemia: a case-control study. Lancet 381 : 1293–1301. doi: 10.1016/S0140-6736(12)62127-8 23433573

48. 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. doi: 10.1038/nature09270 20686565

49. Erdmann J, Stark K, Esslinger UB, Rumpf PM, Koesling D, et al. (2013) Dysfunctional nitric oxide signalling increases risk of myocardial infarction. Nature 504 : 432–436. doi: 10.1038/nature12722 24213632