Abstract
The maximum-likelihood-binomial (MLB) method, based on the binomial distribution of parental marker alleles among affected offspring, recently was shown to provide promising results by two-point linkage analysis of affected-sibship data. In this article, we extend the MLB method to multipoint linkage analysis, using the general framework of hidden Markov models. Furthermore, we perform a large simulation study to investigate the robustness and power of the MLB method, compared with those of the maximum-likelihood-score (MLS) method as implemented in MAPMAKER/SIBS, in the multipoint analysis of different affected-sibship samples. Analyses of multiple-affected sibships by means of the MLS were conducted by consideration of all possible sib pairs, with (weighted MLS [MLSw]) or without (unweighted MLS [MLSu]) application of a classic weighting procedure. In simulations under the null hypothesis, the MLB provided very consistent type I errors regardless of the type of family sample (sib pairs or multiple-affected sibships), as did the MLS for samples with sib pairs only. When samples included multiple-affected sibships, the MLSu led to inflation of low type I errors, whereas the MLSw yielded very conservative tests. Power comparisons showed that the MLB generally was more powerful than the MLS, except in recessive models with allele frequencies <.3. Missing parental marker data did not strongly influence type I error and power results in these multipoint analyses. The MLB approach, which in a natural way accounts for multiple-affected sibships and which provides a simple likelihood-ratio test for linkage, is an interesting alternative for multipoint analysis of sibships.
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Selected References
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- 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(4):371–390. doi: 10.1002/(SICI)1098-2272(1998)15:4<371::AID-GEPI4>3.0.CO;2-5. [DOI] [PubMed] [Google Scholar]
- Badner J. A., Chakravarti A., Wagener D. K. A test of nonrandom segregation. Genet Epidemiol. 1984;1(4):329–340. doi: 10.1002/gepi.1370010405. [DOI] [PubMed] [Google Scholar]
- Daly M. J., Lander E. S. The importance of being independent: sib pair analysis in diabetes. Nat Genet. 1996 Oct;14(2):131–132. doi: 10.1038/ng1096-131. [DOI] [PubMed] [Google Scholar]
- Hanis C. L., Boerwinkle E., Chakraborty R., Ellsworth D. L., Concannon P., Stirling B., Morrison V. A., Wapelhorst B., Spielman R. S., Gogolin-Ewens K. J. A genome-wide search for human non-insulin-dependent (type 2) diabetes genes reveals a major susceptibility locus on chromosome 2. Nat Genet. 1996 Jun;13(2):161–166. doi: 10.1038/ng0696-161. [DOI] [PubMed] [Google Scholar]
- Holmans P. Asymptotic properties of affected-sib-pair linkage analysis. Am J Hum Genet. 1993 Feb;52(2):362–374. [PMC free article] [PubMed] [Google Scholar]
- Knapp M., Seuchter S. A., Baur M. P. Linkage analysis in nuclear families. 2: Relationship between affected sib-pair tests and lod score analysis. Hum Hered. 1994 Jan-Feb;44(1):44–51. doi: 10.1159/000154188. [DOI] [PubMed] [Google Scholar]
- Kong A., Frigge M., Bell G. I., Lander E. S., Daly M. J., Cox N. J. Diabetes, dependence, asymptotics, selection and significance. Nat Genet. 1997 Oct;17(2):148–148. doi: 10.1038/ng1097-148. [DOI] [PubMed] [Google Scholar]
- Kruglyak L., Daly M. J., Reeve-Daly M. P., Lander E. S. Parametric and nonparametric linkage analysis: a unified multipoint approach. Am J Hum Genet. 1996 Jun;58(6):1347–1363. [PMC free article] [PubMed] [Google Scholar]
- Kruglyak L., Lander E. S. Complete multipoint sib-pair analysis of qualitative and quantitative traits. Am J Hum Genet. 1995 Aug;57(2):439–454. [PMC free article] [PubMed] [Google Scholar]
- Lander E. S., Green P. Construction of multilocus genetic linkage maps in humans. Proc Natl Acad Sci U S A. 1987 Apr;84(8):2363–2367. doi: 10.1073/pnas.84.8.2363. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Majumder P. P., Pal N. Nonrandom segregation: uniformly most powerful test and related considerations. Genet Epidemiol. 1987;4(4):277–287. doi: 10.1002/gepi.1370040406. [DOI] [PubMed] [Google Scholar]
- Meunier F., Philippi A., Martinez M., Demenais F. Affected sib-pair tests for linkage: type I errors with dependent sib-pairs. Genet Epidemiol. 1997;14(6):1107–1111. doi: 10.1002/(SICI)1098-2272(1997)14:6<1107::AID-GEPI91>3.0.CO;2-K. [DOI] [PubMed] [Google Scholar]
- Risch N. Linkage strategies for genetically complex traits. I. Multilocus models. Am J Hum Genet. 1990 Feb;46(2):222–228. [PMC free article] [PubMed] [Google Scholar]
- Speer M. C., Terwilliger J. D., Ott J. Data simulation for GAW9 problems 1 and 2. Genet Epidemiol. 1995;12(6):561–564. doi: 10.1002/gepi.1370120606. [DOI] [PubMed] [Google Scholar]
- Suarez B. K., Van Eerdewegh P. A comparison of three affected-sib-pair scoring methods to detect HLA-linked disease susceptibility genes. Am J Med Genet. 1984 May;18(1):135–146. doi: 10.1002/ajmg.1320180117. [DOI] [PubMed] [Google Scholar]