Identification of causal genes for complex traits

Bioinformatics

Volumn 31, Issue 12, 2015, Pages i206-i213

  Hormozdiari, Farhad ^a   Kichaev, Gleb ^b   Yang, Wen Yun ^a   Pasaniuc, Bogdan ^b,c   Eskin, Eleazar ^a,c  

^a University of California   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MUS;

APOLIPOPROTEIN A2;

ALGORITHM; ANIMAL; GENE; GENE LINKAGE DISEQUILIBRIUM; GENETIC ASSOCIATION; GENETICS; HUMAN; MOUSE; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL ANALYSIS;

ALGORITHMS; ANIMALS; APOLIPOPROTEIN A-II; DATA INTERPRETATION, STATISTICAL; GENES; GENOME-WIDE ASSOCIATION STUDY; HUMANS; LINKAGE DISEQUILIBRIUM; MICE; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84931076807     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv240     Document Type: Conference Paper

References (29)

1. Altshuler D., et al. (2008). Genetic mapping in human disease. Science, 322, 881-888
2. Bennett B.J., et al. (2010). A high-resolution association mapping panel for the dissection of complex traits in mice. Genome Res., 20, 281-290
3. International Multiple Sclerosis Genetics Consortium, et al. (2013). Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat. Genet., 45, 1353-1360
4. Darnell G., et al. (2012). Incorporating prior information into association studies. Bioinformatics, 28, i147-i153
5. Eskin E. (2008). Increasing power in association studies by using linkage disequilibrium structure and molecular function as prior information. Genome Res., 18, 653-660
6. Flint J. and Eskin E. (2012). Genome-wide association studies in mice. Nat. Rev. Genet., 13, 807-817
7. Hakonarson H., et al. (2007). A genome-wide association study identifies kiaa0350 as a type 1 diabetes gene. Nature, 448, 591-594
8. Han B., et al. (2009). Rapid and accurate multiple testing correction and power estimation for millions of correlated markers. PLoS Genet., 5, e1000456
9. Hormozdiari F., et al. (2014). Identifying causal variants at loci with multiple signals of association. Genetics, 198, 497-508
10. Jul J.H. and Eskin E. (2011). Increasing power of groupwise association test with likelihood ratio test. J. Comput. Biol., 18, 1611-1624
11. Kang H.M., et al. (2008). Efficient control of population structure in model organism association mapping. Genetics, 5, e1000456
12. Kichaev G., et al. (2014). Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet., 10, e1004722
13. Kottgen A., et al. (2013). Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nat. Genet., 45, 145-154
14. Lee D., et al. (2013). DIST: direct imputation of summary statistics for unmeasured SNPs. Bioinformatics, 29, 2925-2927
15. Lippert C., et al. (2011). FaST linear mixed models for genome-wide association studies. Nat. Methods, 8, 833-835
16. Listgarten J., et al. (2012). Improved linear mixed models for genome-wide association studies. Nat. Methods, 9, 525-526
17. Maller J.B., et al. (2012). Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet., 44, 1294-1301
18. Pasaniuc B., et al. (2014). Fast and accurate imputation of summary statistics enhances evidence of functional enrichment. Bioinformatics, 30, 2906-2914
19. Pearl J. (2000). Causality: Models, Reasoning and Inference. Vol. 29. Cambridge University Press, New York, NY
20. Price A.L., et al. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet., 38, 904-909
21. Pritchard J.K. and Przeworski M. (2001). Linkage disequilibrium in humans: models and data. Am. J. Hum. Genet., 69, 1-14
22. Reich D.E., et al. (2001). Linkage disequilibrium in the human genome. Nature, 411, 199-204
23. Ripke S., et al. (2013). Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat. Genet., 45, 1150-1159
24. Spirtes P., et al. (2000). . Causation, Prediction, and Search. Vol. 81. MIT press, Cambridge, MA, USA
25. van Nas A., et al. (2009). Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks. Endocrinology, 150, 1235-1249
26. Yang J., et al. (2012). Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet., 44, 369-375
27. Zaitlen N., et al. (2010). Leveraging genetic variability across populations for the identification of causal variants. Am. J. Hum. Genet., 86, 23-33
28. Zhang W., et al. (2012). Genome-wide association mapping of quantitative traits in outbred mice. G3 (Bethesda) , 2, 167-174
29. Zhou X. and Stephens M. (2012). Genome-wide efficient mixed model analysis for association studies. Nat. Genet., 44, 821-824