A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Pare, Guillaume ^a,b,c,d   Mao, Shihong ^c   Deng, Wei Q ^e  

^a MCMASTER UNIVERSITY   (Canada)

^b MCMASTER UNIVERSITY   (Canada)

^c MCMASTER UNIVERSITY   (Canada)

^d MCMASTER UNIVERSITY   (Canada)

^e UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BODY MASS; GENE LINKAGE DISEQUILIBRIUM; GENETIC ASSOCIATION; GENETIC VARIABILITY; GENOME; HAPLOTYPE; HEIGHT; MAJOR CLINICAL STUDY; MODEL; MULTIPLE LINEAR REGRESSION ANALYSIS; RETIREMENT; STATISTICS; ALGORITHM; BIOLOGICAL MODEL; GENETIC ASSOCIATION STUDY; GENETIC LINKAGE; GENETIC VARIATION; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMAN; MULTIFACTORIAL INHERITANCE; PHENOTYPE; PROCEDURES; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL MODEL;

ALGORITHMS; GENETIC ASSOCIATION STUDIES; GENETIC LINKAGE; GENETIC VARIATION; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMANS; LINKAGE DISEQUILIBRIUM; MODELS, GENETIC; MODELS, STATISTICAL; MULTIFACTORIAL INHERITANCE; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84974577269     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep27644     Document Type: Article

References (27)

1. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nature Genetics 42, 565-569, doi: 10.1038/ng.608 (2010).
2. Davies, G. et al. Genetic contributions to variation in general cognitive function: A meta-analysis of genome-wide association studies in the CHARGE consortium (N = 53949). Mol Psychiatry 20, 183-192, doi: 10.1038/mp.2014.188 (2015).
3. Stahl, E. A. et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nature genetics 44, 483-489, doi: 10.1038/ng.2232 (2012).
4. Pare, G., Asma, S. & Deng, W. Q. Contribution of large region joint associations to complex traits genetics. PLoS Genet 11, e1005103, doi: 10.1371/journal.pgen.1005103 (2015).
5. Beyene, J., Tritchler, D., Asimit, J. L. & Hamid, J. S. Gene-or region-based analysis of genome-wide association studies. Genet Epidemiol 33 Suppl 1, S105-110, doi: 10.1002/gepi.20481 (2009).
6. Gusev, A. et al. Quantifying missing heritability at known GWAS loci. PLoS Genet 9, e1003993, doi: 10.1371/journal.pgen.1003993 (2013).
7. Cheung, V. G. & Spielman, R. S. Genetics of human gene expression: Mapping DNA variants that influence gene expression. 10, 595-604, doi: 10.1038/nrg2630 (2009).
8. Consortium, T. E. P. An integrated encyclopedia of DNA elements in the human genome. 489, 57-74, doi: 10.1038/nature11247 (2012).
9. Speed, D., Hemani, G., Johnson, M. R. & Balding, D. J. Improved heritability estimation from genome-wide SNPs. Am J Hum Genet 91, 1011-1021, doi: 10.1016/j.ajhg.2012.10.010 (2012).
10. Loh, P.-R. et al. Contrasting regional architectures of schizophrenia and other complex diseases using fast variance components analysis. bioRxiv (2015).
11. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47, 291-295, doi: 10.1038/ng.3211 (2015).
12. Bulik-Sullivan, B. Relationship between LD Score and Haseman-Elston Regression. bioRxiv (2015).
13. Ehret, G. B. et al. 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 (2012).
14. Palla, L. & Dudbridge, F. A Fast Method that Uses Polygenic Scores to Estimate the Variance Explained by Genome-wide Marker Panels and the Proportion of Variants Affecting a Trait. Am J Hum Genet 97, 250-259, doi: 10.1016/j.ajhg.2015.06.005 (2015).
15. So, H. C., Li, M. & Sham, P. C. Uncovering the total heritability explained by all true susceptibility variants in a genome-wide association study. Genet Epidemiol 35, 447-456, doi: 10.1002/gepi.20593 (2011).
16. Genomes Project, C. et al. A global reference for human genetic variation. Nature 526, 68-74, doi: 10.1038/nature15393 (2015).
17. Sonnega, A. et al. Cohort Profile: The Health and Retirement Study (HRS). Int J Epidemiol 43, 576-585, doi: 10.1093/ije/dyu067 (2014).
18. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: A tool for genome-wide complex trait analysis. Am J Hum Genet 88, 76-82, doi: 10.1016/j.ajhg.2010.11.011 (2011).
19. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197-206, doi: 10.1038/nature14177 (2015).
20. Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nature genetics 46, 1173-1186, doi: 10.1038/ng.3097 (2014).
21. Sammalisto, S. et al. Genome-wide linkage screen for stature and body mass index in 3.032 families: Evidence for sex-and population-specific genetic effects. Eur J Hum Genet 17, 258-266, doi: 10.1038/ejhg.2008.152 (2009).
22. Perola, M. et al. Combined genome scans for body stature in 6,602 European twins: Evidence for common Caucasian loci. PLoS Genet 3, e97, doi: 10.1371/journal.pgen.0030097 (2007).
23. Vilhjalmsson, B. et al. Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. bioRxiv (2015).
24. Ohtani, K. & Tanizaki, H. Exact Distributions of R2 and Adjusted R2 in a Linear Regression Model with Multivariate Error Terms. Journal Of The Japan Statistical Society 34, 101-109, doi: 10.14490/jjss.34.101 (2004).
25. Yang, J. et al. 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 (2012).
26. Berndt, S. I. et al. 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 (2013).
27. Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832-838, doi: 10.1038/nature09410 (2010).