Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci

English version České info

Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20–30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5′ and 3′ untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants.

Vyšlo v časopise: Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci. PLoS Genet 10(1): e32767. doi:10.1371/journal.pgen.1004147
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004147

Souhrn

Zdroje

1. MartinI, DawsonVL, DawsonTM (2011) Recent advances in the genetics of Parkinson's disease. Annu Rev Genomics Hum Genet 12: 301–325.

2. KathiresanS, SrivastavaD (2012) Genetics of human cardiovascular disease. Cell 148: 1242–1257.

3. LusisAJ, AttieAD, ReueK (2008) Metabolic syndrome: from epidemiology to systems biology. Nat Rev Genet 9: 819–830.

4. RantakallioP (1969) Groups at risk in low birth weight infants and perinatal mortality. Acta Paediatr Scand 193: Suppl 193: 191.

5. ValleT, TuomilehtoJ, BergmanRN, GhoshS, HauserER, et al. (1998) Mapping genes for NIDDM. Design of the Finland-United States Investigation of NIDDM Genetics (FUSION) Study. Diab Care 21: 949–958.

6. ScottLJ, MohlkeKL, BonnycastleLL, WillerCJ, LiY, et al. (2007) A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316: 1341–1345.

7. SabattiC, ServiceSK, HartikainenAL, PoutaA, RipattiS, et al. (2009) Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat Genet 41: 35–46.

8. TeslovichTM, MusunuruK, SmithAV, EdmondsonAC, StylianouIM, et al. (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466: 707–713.

9. NelsonMR, WegmannD, EhmMG, KessnerD, St JeanP, et al. (2012) An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people. Science 337: 100–104.

10. JakkulaE, RehnstromK, VariloT, PietilainenOP, PaunioT, et al. (2008) The genome-wide patterns of variation expose significant substructure in a founder population. Am J Hum Genet 83: 787–794.

11. PeltonenL, PalotieA, LangeK (2000) Use of population isolates for mapping complex traits. Nat Rev Genet 1: 182–190.

12. MathiesonI, McVeanG (2012) Differential confounding of rare and common variants in spatially structured populations. Nat Genet 44: 243–246.

13. KathiresanS, MelanderO, GuiducciC, SurtiA, BurttNP, et al. (2008) Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet 40: 189–197.

14. WillerCJ, SannaS, JacksonAU, ScuteriA, BonnycastleLL, et al. (2008) Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet 40: 161–169.

15. DupuisJ, LangenbergC, ProkopenkoI, SaxenaR, SoranzoN, et al. (2010) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42: 105–116.

16. HuygheJR, JacksonAU, FogartyMP, BuchkovichML, StancakovaA, et al. (2013) Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat Genet 45: 197–201.

17. AdzhubeiIA, SchmidtS, PeshkinL, RamenskyVE, GerasimovaA, et al. (2010) A method and server for predicting damaging missense mutations. Nat Methods 7: 248–249.

18. FuW, O'ConnorTD, JunG, KangHM, AbecasisG, et al. (2013) Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493: 216–220.

19. TennessenJA, BighamAW, O'ConnorTD, FuW, KennyEE, et al. (2012) Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337: 64–69.

20. NielsenR, BustamanteC, ClarkAG, GlanowskiS, SacktonTB, et al. (2005) A scan for positively selected genes in the genomes of humans and chimpanzees. PLoS Biol 3: e170.

21. CohenJC, KissRS, PertsemlidisA, MarcelYL, McPhersonR, et al. (2004) Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science 305: 869–872.

22. LiB, LealSM (2008) Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet 83: 311–321.

23. WuMC, LeeS, CaiT, LiY, BoehnkeM, et al. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89: 82–93.

24. BenjaminiY, HochbergY (1995) Controlling the false discovery rate - a practical and powerful approach to multiple testing. J Roy Stat Soc B Met 57: 289–300.

25. HegeleRA, VezinaC, MoorjaniS, LupienPJ, GagneC, et al. (1991) A hepatic lipase gene mutation associated with heritable lipolytic deficiency. J Clin Endocrinol Metab 72: 730–732.

26. HuntKA, MistryV, BockettNA, AhmadT, BanM, et al. (2013) Negligible impact of rare autoimmune-locus coding-region variants on missing heritability. Nature 498: 232–235.

27. BodziochM, OrsoE, KluckenJ, LangmannT, BottcherA, et al. (1999) The gene encoding ATP-binding cassette transporter 1 is mutated in Tangier disease. Nat Genet 22: 347–351.

28. Frikke-SchmidtR, NordestgaardBG, JensenGB, Tybjaerg-HansenA (2004) Genetic variation in ABC transporter A1 contributes to HDL cholesterol in the general population. J Clin Invest 114: 1343–1353.

29. O'BrienRM (2013) Moving on from GWAS: Functional Studies on the G6PC2 Gene Implicated in the Regulation of Fasting Blood Glucose. Curr Diab Rep 13: 768–777.

30. StollG, PietilainenOP, LinderB, SuvisaariJ, BrosiC, et al. (2013) Deletion of TOP3B, a component of FMRP-containing mRNPs, contributes to neurodevelopemental disorders. Nat Neurosci 16: 1228–37.

31. LiH, DurbinR (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754–1760.

32. PurcellS, NealeB, Todd-BrownK, ThomasL, FerreiraMA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575.

33. PriceAL, PattersonNJ, PlengeRM, WeinblattME, ShadickNA, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38: 904–909.