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. 2004 Jul;167(3):1445–1459. doi: 10.1534/genetics.103.021600

Identifying the susceptibility gene(s) in a set of trait-linked genes using genotype data.

Ao Yuan 1, Guanjie Chen 1, Yuanxiu Chen 1, Charles Rotimi 1, George E Bonney 1
PMCID: PMC1470967  PMID: 15280254

Abstract

There are generally three steps to isolate a disease linkage-susceptibility gene: genome-wide scan, fine mapping, and, last, positional cloning. The last step is time consuming and involves intensive laboratory work. In some cases, fine mapping cannot proceed further on a set of markers because they are tightly linked. For years, genetic statisticians have been trying different ways to narrow the fine-mapping results to provide some guidance for the next step of laboratory work. Although these methods are practical and efficient, most of them are based on IBD data, which usually can be inferred only from the genotype data with some uncertainty. The corresponding methods thus have no greater power than one using genotype data directly. Also, IBD-based methods apply only to relative pair data. Here, using genotype data, we have developed a statistical hypothesis-testing method to pinpoint a SNP, or SNPs, suspected of responsibility for a disease trait linkage among a set of SNPs tightly linked in a region. Our method uses genotype data of affected individuals or case-control studies, which are widely available in the laboratory. The testing statistic can be constructed using any genotype-based disease-marker disequilibrium measure and is asymptotically distributed as a chi-square mixture. This method can be used for singleton data, relative pair data, or general pedigree data. We have applied the method to simulated data as well as a real data set; it gives satisfactory results.

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Selected References

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  1. Bengtsson B. O., Thomson G. Measuring the strength of associations between HLA antigens and diseases. Tissue Antigens. 1981 Nov;18(5):356–363. doi: 10.1111/j.1399-0039.1981.tb01404.x. [DOI] [PubMed] [Google Scholar]
  2. Cardon L. R., Abecasis G. R. Some properties of a variance components model for fine-mapping quantitative trait loci. Behav Genet. 2000 May;30(3):235–243. doi: 10.1023/a:1001970425822. [DOI] [PubMed] [Google Scholar]
  3. Devlin B., Roeder K. Genomic control for association studies. Biometrics. 1999 Dec;55(4):997–1004. doi: 10.1111/j.0006-341x.1999.00997.x. [DOI] [PubMed] [Google Scholar]
  4. Feder J. N., Gnirke A., Thomas W., Tsuchihashi Z., Ruddy D. A., Basava A., Dormishian F., Domingo R., Jr, Ellis M. C., Fullan A. A novel MHC class I-like gene is mutated in patients with hereditary haemochromatosis. Nat Genet. 1996 Aug;13(4):399–408. doi: 10.1038/ng0896-399. [DOI] [PubMed] [Google Scholar]
  5. Fulker D. W., Cherny S. S., Sham P. C., Hewitt J. K. Combined linkage and association sib-pair analysis for quantitative traits. Am J Hum Genet. 1999 Jan;64(1):259–267. doi: 10.1086/302193. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Hodge S. E. Linkage analysis versus association analysis: distinguishing between two models that explain disease-marker associations. Am J Hum Genet. 1993 Aug;53(2):367–384. [PMC free article] [PubMed] [Google Scholar]
  7. Lazzeroni L. C., Lange K. A conditional inference framework for extending the transmission/disequilibrium test. Hum Hered. 1998 Mar-Apr;48(2):67–81. doi: 10.1159/000022784. [DOI] [PubMed] [Google Scholar]
  8. Lehesjoki A. E., Koskiniemi M., Norio R., Tirrito S., Sistonen P., Lander E., de la Chapelle A. Localization of the EPM1 gene for progressive myoclonus epilepsy on chromosome 21: linkage disequilibrium allows high resolution mapping. Hum Mol Genet. 1993 Aug;2(8):1229–1234. doi: 10.1093/hmg/2.8.1229. [DOI] [PubMed] [Google Scholar]
  9. Lin Shin, Cutler David J., Zwick Michael E., Chakravarti Aravinda. Haplotype inference in random population samples. Am J Hum Genet. 2002 Oct 17;71(5):1129–1137. doi: 10.1086/344347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Nielsen D. M., Ehm M. G., Weir B. S. Detecting marker-disease association by testing for Hardy-Weinberg disequilibrium at a marker locus. Am J Hum Genet. 1998 Nov;63(5):1531–1540. doi: 10.1086/302114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Siegmund K. D., Vora H., Gauderman W. J. Combined linkage and association analysis in pedigrees. Genet Epidemiol. 2001;21 (Suppl 1):S358–S363. doi: 10.1002/gepi.2001.21.s1.s358. [DOI] [PubMed] [Google Scholar]
  12. Soria J. M., Almasy L., Souto J. C., Tirado I., Borell M., Mateo J., Slifer S., Stone W., Blangero J., Fontcuberta J. Linkage analysis demonstrates that the prothrombin G20210A mutation jointly influences plasma prothrombin levels and risk of thrombosis. Blood. 2000 May 1;95(9):2780–2785. [PubMed] [Google Scholar]
  13. Sun Lei, Cox Nancy J., McPeek Mary Sara. A statistical method for identification of polymorphisms that explain a linkage result. Am J Hum Genet. 2002 Jan 8;70(2):399–411. doi: 10.1086/338660. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Tait B. D., Harrison L. C. Overview: the major histocompatibility complex and insulin dependent diabetes mellitus. Baillieres Clin Endocrinol Metab. 1991 Jun;5(2):211–228. doi: 10.1016/s0950-351x(05)80124-7. [DOI] [PubMed] [Google Scholar]
  15. Thomson G. HLA population genetics. Baillieres Clin Endocrinol Metab. 1991 Jun;5(2):247–260. doi: 10.1016/s0950-351x(05)80126-0. [DOI] [PubMed] [Google Scholar]
  16. Valdes A. M., Thomson G. Detecting disease-predisposing variants: the haplotype method. Am J Hum Genet. 1997 Mar;60(3):703–716. [PMC free article] [PubMed] [Google Scholar]

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