#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Contribution of Large Region Joint Associations to Complex Traits Genetics


It is widely accepted that genetics influences a broad range of human traits and diseases, yet only a few genetic variants are known to determine these traits and their impact is modest. In this report, we made the hypothesis that combining information from a large number of genetic variants would help better explain how they together contribute to traits such as height. To do so, we first had to select a proper method to integrate large numbers of genetic variants in a single test, here named “large region joint association”. Next, we tested our method on height in 3,740 European participants from the Health and Retirement Study. We showed that the contribution of regional associations to variation in height was 17.2%, as compared to the 12.9% explained by known genetic determinants of height. In other words, the joint effect of multiple genetic variants integrated together contributed to a substantial fraction of the genetics of height. These results are significant because they can help identify new genes or genetic regions associated with human traits or diseases. Conversely, these results can be used to better understand genes that we already know are associated. Furthermore, our results provide insights on how traits are genetically determined.


Vyšlo v časopise: Contribution of Large Region Joint Associations to Complex Traits Genetics. PLoS Genet 11(4): e32767. doi:10.1371/journal.pgen.1005103
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1005103

Souhrn

It is widely accepted that genetics influences a broad range of human traits and diseases, yet only a few genetic variants are known to determine these traits and their impact is modest. In this report, we made the hypothesis that combining information from a large number of genetic variants would help better explain how they together contribute to traits such as height. To do so, we first had to select a proper method to integrate large numbers of genetic variants in a single test, here named “large region joint association”. Next, we tested our method on height in 3,740 European participants from the Health and Retirement Study. We showed that the contribution of regional associations to variation in height was 17.2%, as compared to the 12.9% explained by known genetic determinants of height. In other words, the joint effect of multiple genetic variants integrated together contributed to a substantial fraction of the genetics of height. These results are significant because they can help identify new genes or genetic regions associated with human traits or diseases. Conversely, these results can be used to better understand genes that we already know are associated. Furthermore, our results provide insights on how traits are genetically determined.


Zdroje

1. Visscher PM, Hill WG, Wray NR (2008) Heritability in the genomics era—concepts and misconceptions. Nature reviews Genetics 9: 255–266. doi: 10.1038/nrg2322 18319743

2. Yang J, Benyamin B, Mcevoy BP, Gordon S, Henders AK, et al. (2010) Common SNPs explain a large proportion of the heritability for human height. Nature Genetics 42: 565–569. doi: 10.1038/ng.608 20562875

3. Eichler EE, Flint J, Gibson G, Kong A, Leal SM, et al. (2010) Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 11: 446–450. doi: 10.1038/nrg2809 20479774

4. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Lucia A, et al. (2010) Finding the missing heritability of complex diseases. 461: 747–753. doi: 10.1038/nature08494 19812666

5. Beyene J, Tritchler D, Asimit JL, Hamid JS (2009) Gene- or region-based analysis of genome-wide association studies. Genet Epidemiol 33 Suppl 1: S105–110. doi: 10.1002/gepi.20481 19924708

6. Gusev A, Bhatia G, Zaitlen N, Vilhjalmsson BJ, Diogo D, et al. (2013) Quantifying missing heritability at known GWAS loci. PLoS Genet 9: e1003993. doi: 10.1371/journal.pgen.1003993 24385918

7. Yang J, Ferreira T, Morris AP, Medland SE, Madden PA, et al. (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 44: 369–375, S361–363. doi: 10.1038/ng.2213 22426310

8. Lappalainen T, Montgomery SB, Nica AC, Dermitzakis ET (2011) Epistatic selection between coding and regulatory variation in human evolution and disease. American journal of human genetics 89: 459–463. doi: 10.1016/j.ajhg.2011.08.004 21907014

9. Cheung VG, Spielman RS (2009) Genetics of human gene expression: mapping DNA variants that influence gene expression. 10: 595–604. doi: 10.1038/nrg2630 19636342

10. Consortium TEP (2012) An integrated encyclopedia of DNA elements in the human genome. 489: 57–74. doi: 10.1038/nature11247 22955616

11. Ionita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin X (2013) Sequence kernel association tests for the combined effect of rare and common variants. Am J Hum Genet 92: 841–853. doi: 10.1016/j.ajhg.2013.04.015 23684009

12. Tregouet DA, Konig IR, Erdmann J, Munteanu A, Braund PS, et al. (2009) Genome-wide haplotype association study identifies the SLC22A3-LPAL2-LPA gene cluster as a risk locus for coronary artery disease. Nat Genet 41: 283–285. doi: 10.1038/ng.314 19198611

13. Ehret GB, Lamparter D, Hoggart CJ, Genetic Investigation of Anthropometric Traits C, Whittaker JC, et al. (2012) A multi-SNP locus-association method reveals a substantial fraction of the missing heritability. Am J Hum Genet 91: 863–871. doi: 10.1016/j.ajhg.2012.09.013 23122585

14. Visscher PM (2008) Sizing up human height variation. Nature genetics 40: 489–490. doi: 10.1038/ng0508-489 18443579

15. Berndt SI, Gustafsson S, Magi R, Ganna A, Wheeler E, et al. (2013) Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet 45: 501–512. doi: 10.1038/ng.2606 23563607

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

17. Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, et al. (2010) Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467: 832–838. doi: 10.1038/nature09410 20881960

18. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, et al. (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42: 937–948. doi: 10.1038/ng.686 20935630

19. Global Lipids Genetics C, Willer CJ, Schmidt EM, Sengupta S, Peloso GM, et al. (2013) Discovery and refinement of loci associated with lipid levels. Nat Genet 45: 1274–1283. doi: 10.1038/ng.2797 24097068

20. Dehghan A, Dupuis J, Barbalic M, Bis JC, Eiriksdottir G, et al. (2011) Meta-analysis of genome-wide association studies in >80 000 subjects identifies multiple loci for C-reactive protein levels. Circulation 123: 731–738. doi: 10.1161/CIRCULATIONAHA.110.948570 21300955

21. International Schizophrenia C, Purcell SM, Wray NR, Stone JL, Visscher PM, et al. (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460: 748–752. doi: 10.1038/nature08185 19571811

22. Speed D, Hemani G, Johnson MR, Balding DJ (2012) Improved heritability estimation from genome-wide SNPs. Am J Hum Genet 91: 1011–1021. doi: 10.1016/j.ajhg.2012.10.010 23217325

23. Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet 2: e190. 17194218

24. Zou F, Lee S, Knowles MR, Wright FA (2010) Quantification of population structure using correlated SNPs by shrinkage principal components. Hum Hered 70: 9–22. doi: 10.1159/000288706 20413978

25. Wu MC, Lee S, Cai T, Li Y, Boehnke M, et al. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. American journal of human genetics 89: 82–93. doi: 10.1016/j.ajhg.2011.05.029 21737059

26. Presence of multiple independent effects in risk loci of common complex human diseases. Am J Hum Genet 91: 185–192. doi: 10.1016/j.ajhg.2012.05.020 22770979

27. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575. 17701901

28. Erdfelder E, Faul F, Buchner A (1996) GPOWER: A general power analysis program. Behavior Research Methods, Instruments, Computers 28: 1–11.

29. Duchesne P, Lafaye De Micheaux P (2010) Computing the distribution of quadratic forms: Further comparisons between the Liu—Tang—Zhang approximation and exact methods. Computational Statistics Data Analysis 54: 858–862.

30. Davies RB (1980) Algorithm AS 155: The Distribution of a Linear Combination of χ2 Random Variables. Journal of the Royal Statistical Society Series C (Applied Statistics) 29: 323–333.

Štítky
Genetika Reprodukčná medicína

Článok vyšiel v časopise

PLOS Genetics


2015 Číslo 4
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Aktuální možnosti diagnostiky a léčby litiáz
nový kurz
Autori: MUDr. Tomáš Ürge, PhD.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#