Haplotype-based approach to known MS-associated regions increases the amount of explained risk

Journal of Medical Genetics

Volumn 52, Issue 9, 2015, Pages 587-594

  Khankhanian, Pouya ^a,b   Gourraud, Pierre Antoine ^a,b   Lizee, Antoine ^a,b   Goodin, Douglas S ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UCSF Multiple Sclerosis Center   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; GENETIC ASSOCIATION; GENETIC MODEL; GENETIC RISK; GENOMICS; HAPLOTYPE; HERITABILITY; HUMAN; MULTIPLE SCLEROSIS; PRIORITY JOURNAL; SINGLE NUCLEOTIDE POLYMORPHISM; CHROMOSOMAL MAPPING; GENETIC PREDISPOSITION; GENETICS; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMAN GENOME; PROCEDURES; STATISTICAL MODEL;

CHROMOSOME MAPPING; GENETIC PREDISPOSITION TO DISEASE; GENOME, HUMAN; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HAPLOTYPES; HUMANS; LOGISTIC MODELS; MULTIPLE SCLEROSIS; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84945439678     PISSN: 00222593     EISSN: 14686244     Source Type: Journal    
DOI: 10.1136/jmedgenet-2015-103071     Document Type: Article

References (31)

1. Gourraud PA, Harbo HF, Hauser SL, Baranzini SE. The genetics of multiple sclerosis: an up-to-date review. Immunol Rev 2012;248:87-103.
2. Hofker MH, Fu J, Wijmenga C. The genome revolution and its role in understanding complex diseases. Biochim Biophys Acta 2014;1842:1889-95.
3. De Jager PL, Jia X, Wang J, et al. Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat Genet 2009;41:776-82.
4. Dyment DA, Herrera BM, Cader Z, et al. Complex interactions among MHC haplotypes in multiple sclerosis: susceptibility and resistance. Hum Mol Genet 2005;14:2019-26.
5. Hafler DA, Compston A, Sawcer S, et al. Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 2007;357:851-62.
6. Ramagopalan SV, Anderson C, Sadovnick AD, Ebers GC. Genomewide study of multiple sclerosis. N Engl J Med 2007;357:2199-200.
7. Link J, Kockum I, Lorentzen AR, et al. Importance of Human Leukocyte Antigen (HLA) class I and II alleles on the risk of multiple sclerosis. PLoS ONE 2012;7: e36779.
8. Patsopoulos NA, Barcellos LF, Hintzen RQ, et al. Fine-mapping the genetic association of the major histocompatibility complex in multiple sclerosis: HLA and Non-HLA effects. PLoS Genet 2014;9:e1003926.
9. GAMES, the Transatlantic Multiple Sclerosis Genetics Cooperative. A meta-analysis of whole genome linkage screens in multiple sclerosis. J Neuroimmunol 2003;143:39-46.
10. de Bakker PIW, Yelensky R, Pe'er I, et al. Efficiency and power in genetic association studies. Nat Genet 2005;37:1217-23.
11. The Wellcome Trust Case Control Consortium & The Australo-Anglo-American Spondylitis Consortium. Associations can of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants. Nat Genet 2007;39:1329-37.
12. The ANZgene Consortium. Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20. Nat Genet 2009;41:824-8.
13. Baranzini SE, Wang J, Gibson RA, et al. Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis. Hum Mol Genet 2009;18:767-78.
14. Sanna S, Pitzalis M, Zoledziewska M, et al. Variants within the immunoregulatory CBLB gene are associated with multiple sclerosis. Nat Genet 2010;42:495-7.
15. The International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 2011;476:214-19.
16. International Multiple Sclerosis Genetics Consortium (IMSGC). Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet 2014;45:1353-60.
17. Nagelkerke NJD. A note on the general definition of the coefficient of determination. Biometrica 1991;78:691-2.
18. Goodin DS. The genetic and environmental bases of complex human disease: extending the utility of twin-studies. PLoS ONE 2012;7:e47875.
19. Goodin DS. The nature of genetic susceptibility to multiple sclerosis: Constraining the possibilities. (submitted).
20. Dikson SP, Wang K, Krantz I, Hakonarson H, Goldstein DB. Rare variants create synthetic genome-wide associations. PLoS Biol 2010;8:e1000294.
21. Goodin DS, Khankhanian P. Single Nucleotide Polymorphism (SNP)-strings: an alternative method for assessing genetic associations. PLoS ONE 2014;9:e90034.
22. Zheng X, Shen J, Cox C, et al. HIBAG-HLA genotype imputation with attribute bagging. Pharmacogenom J 2014;14:192-200.
23. Gourraud PA, Lamiraux P, El-Kadhi N, et al. Inferred HLA haplotype information for donors from hematopoietic stem cells donor registries. Hum Immunol 2005;66: 563-70.
24. Gourraud PA, Khankhanian P Cereb N, et al. HLA diversity in the 1000 genomes dataset. PLoS One 2014;9:e9782.
25. Clark AG. Inference of haplotypes from PCR-amplified samples of diploid populations. Mol Biol Evol 1990;7:111-22.
26. The R core team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2014. http://www. R-project.org
27. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 1995;57:289-300.
28. Delaneau O, Zagury JF, Marchini J. Improved whole-chromosome phasing for disease and population genetic studies. Nat Methods 2012;10:5-6.
29. Delaneau O, Marchini J, Zagury JF. A linear complexity phasing method for thousands of genomes. Nat Methods 2011;9:179-81.
30. Howie B, Marchini J, Stephens M. Genotype imputation with thousands of genomes. G3 (Bethesda) 2011;1:457-70.
31. Kircher M, Kelso J. High-throughput DNA sequencing-concepts and limitations. Bioessays 2010;32:524-36.