Evaluation of approaches to identify associated SNPs that explain the linkage evidence in nuclear families with affected siblings

Human Heredity

Volumn 69, Issue 2, 2010, Pages 104-119

  Chen, Ming Huei ^a   Van Eerdewegh, Paul ^e   Vincent, Quentin B ^c,d   Alcais, Alexandre ^c,d   Abel, Laurent ^c,d   Dupuis, Josée ^b  

^a BOSTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b BOSTON UNIVERSITY SCHOOL OF PUBLIC HEALTH   (United States)

^c U550

^d UNIVERSITÉ PARIS DESCARTES   (France)

^e Genizon BioSciences Inc   (Canada)

Author keywords

Conditional allele sharing; Genetic linkage; Linkage disequilibrium

Indexed keywords

HLA DR ANTIGEN;

ARTICLE; CHROMOSOME 6; COMPUTER PROGRAM; COMPUTER SIMULATION; GENE FREQUENCY; GENE LOCUS; GENETIC LINKAGE; GENETIC MARKER; GENOTYPE; HOMOZYGOTE; HUMAN; INTERMETHOD COMPARISON; LINKAGE ANALYSIS; MAXIMUM LIKELIHOOD METHOD; NUCLEAR FAMILY; RHEUMATOID ARTHRITIS; SIBLING; SINGLE NUCLEOTIDE POLYMORPHISM;

GENETIC ASSOCIATION STUDIES; HOMOZYGOTE; HUMANS; LIKELIHOOD FUNCTIONS; MODELS, GENETIC; NUCLEAR FAMILY; POLYMORPHISM, SINGLE NUCLEOTIDE; SIBLINGS; SOFTWARE;

EID: 71249138456     PISSN: 00015652     EISSN: 14230062     Source Type: Journal    
DOI: 10.1159/000264448     Document Type: Article

References (27)

1. Zavattari P, et al: Conditional linkage disequilibrium analysis of a complex disease su-perlocus, IDDM1 in the HLA region, reveals the presence of independent modifying gene effects influencing the type 1 diabetes risk encoded by the major HLA-DQB1,-DRB1 disease loci. Hum Mol Genet 2001; 10: 881-889.
2. Horikawa Y, et al: Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genet 2000; 26: 16 3-175.
3. Sun L, Cox NJ, McPeek MS: A statistical method for identification of polymorphisms that explain a linkage result. Am J Hum Genet 2002; 70: 399-411.
4. Li C, Scott LJ, Boehnke M: Assessing whether an allele can account in part for a linkage signal: the Genotype-IBD Sharing Test (GIST). Am J Hum Genet 2004; 74: 418-431.
5. Biernacka JM, Cordell HJ: Exploring causality via identification of SNPs or haplotypes responsible for a linkage signal. Genet Epidemiol 2007; 31: 727-740.
6. Dupuis J, Van Eerdewegh P: Identification of polymorphisms that explain a linkage peak: conditioning on parental genotypes. Genet Epidemiol 2003; 25:247.
7. Chen MH, Van Eerdewegh P, Dupuis J: Identification of polymorphisms explaining a linkage signal: application to the GAW14 simulated data. BMC Genet 2005; 6(suppl 1): S88.
8. Abel L, Alcais A, Mallet A: Comparison of four sib-pair linkage methods for analyzing sibships with more than two affecteds: interest of the binomial maximum likelihood approach. Genet Epidemiol 1998; 15: 371-390.
9. Abel L, Muller-Myhsok B: Robustness and power of the maximum-likelihood- binomial and maximum-likelihood-score methods, in multipoint linkage analysis of affected-sibship data. Am J Hum Genet 1998; 63:638-647.
10. Li M, Boehnke M, Abecasis GR: Joint modeling of linkage and association: identifying SNPs responsible for a linkage signal. Am J Hum Genet 20 05; 76: 934-949.
11. Orozco G, Rueda B, Martin J: Genetic basis of rheumatoid arthritis. Biomed Pharmaco-ther 2006; 60: 656-6 62.
12. Jawaheer D, et al: A genomewide screen in multiplex rheumatoid arthritis families suggests genetic overlap with other autoimmune diseases. Am J Hum Genet 2001; 68: 927-936.
13. Jawaheer D, et al: Screening the genome for rheumatoid arthritis susceptibility genes: a replication study and combined analysis of 512 multicase families. Arthritis Rheum 2003; 48:906-916.
14. Amos CI, et al: High-density SNP analysis of 642 Caucasian families with rheumatoid arthritis identifies two new linkage regions on 11p12 and 2q33. Genes Immun 2006; 7:277-286.
15. Risch N: Linkage strategies for genetically complex traits. III. The effect of marker polymorphism on analysis of affected relative pairs. Am J Hum Genet 1990; 46: 242-253.
16. Robinson WP, et al: Homozygous parent affected sib pair method for detecting disease predisposing variants: application to insulin dependent diabetes mellitus. Genet Epidemiol 1993; 10:273-288.
17. Van Eerdewegh, et al: The importance of watching our weights: how the choice of weights for non-independent sib pairs can dramatically alter results. Genet Epidemiol 1999; 17(suppl 1):S373-S378.
18. R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org 2007.
19. Kruglyak L, Lander ES: Faster multipoint linkage analysis using Fourier transforms. J Comput Biol 1998; 5: 1-7.
20. Huang Q, et al: Examining the effect of linkage disequilibrium on multipoint linkage analysis. BMC Genet 2005; 6(suppl 1):S83.
21. Webb EL, Sellick GS, Houlston RS: SNP-LINK: multipoint linkage analysis of densely distributed SNP data incorporating automated linkage disequilibrium removal. Bio-informatics 2005; 21:3060-30361.
22. Abecasis GR, et al: Merlin-rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 20 02; 30: 97-101.
23. Kong A, Cox NJ: Allele-sharing models: LOD scores and accurate linkage tests. Am J Hu m Genet 1997; 61: 11 7 9-118 8.
24. Lake SL, Blacker D, Laird NM: Family-based tests of association in the presence of linkage. Am J Hum Genet 2000; 67: 1515-1525.
25. Suarez BK, Reich T, Trost J: Limits of the general two-allele single locus model with incomplete penetrance. Ann Hum Genet 1976; 40: 231-243.
26. Risch N: Linkage strategies for genetically complex traits. I. Multilocus models. Am J Hum Genet 1990; 46: 222-228.
27. Knapp M, Strauch K: Affected-sib-pair test for linkage based on constraints for identical-by-decent distributions corresponding to disease models with imprinting. Genet Epidemiol 2004; 26:273-285.