The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
A General Test of Association for Quantitative Traits in Nuclear Families
Open accessAbstract
High-resolution mapping is an important step in the identification of complex disease genes. In outbred populations, linkage disequilibrium is expected to operate over short distances and could provide a powerful fine-mapping tool. Here we build on recently developed methods for linkage-disequilibrium mapping of quantitative traits to construct a general approach that can accommodate nuclear families of any size, with or without parental information. Variance components are used to construct a test that utilizes information from all available offspring but that is not biased in the presence of linkage or familiality. A permutation test is described for situations in which maximum-likelihood estimates of the variance components are biased. Simulation studies are used to investigate power and error rates of this approach and to highlight situations in which violations of multivariate normality assumptions warrant the permutation test. The relationship between power and the level of linkage disequilibrium for this test suggests that the method is well suited to the analysis of dense maps. The relationship between power and family structure is investigated, and these results are applicable to study design in complex disease, especially for late-onset conditions for which parents are usually not available. When parental genotypes are available, power does not depend greatly on the number of offspring in each family. Power decreases when parental genotypes are not available, but the loss in power is negligible when four or more offspring per family are genotyped. Finally, it is shown that, when siblings are available, the total number of genotypes required in order to achieve comparable power is smaller if parents are not genotyped.
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“Within-family genetic association estimates, such as those obtained from samples of siblings, can provide less biased estimates of direct genetic effects because they are unlikely to be affected by demographic and indirect genetic effects of parents7,17,30–34.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [31]
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PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709
“However, a comparison of statistical power is only meaningful if both procedures are anchored to the same Type 1 error rate with respect to the null hypothesis of no linkage with a causal variant, and Ewens et al. has shown that the QTDT is not robust against certain forms of confounding (population stratification) [3…”
MethodInfluentialGenetics of Food Intake Self-Regulation in Childhood: Literature Review and Research Opportunities
Faith, M. S., Carnell, S., & Kral, T. V. E. (2013) · Human Heredity · doi:10.1159/000353879 · Cited by 7,633
Yu, J., Pressoir, G., Briggs, W. H., Bi, I. V., Yamasaki, M., Doebley, J., McMullen, M. D., Gaut, B. S., Nielsen, D. M., Holland, J. B., et al. (2005) · Nature Genetics · doi:10.1038/ng1702 · Cited by 4,155
“Family-based samples (area II) have been exploited to avoid the effect of population structur”
MethodGenome-wide association studies for common diseases and complex traits
Hirschhorn, J. N., & Daly, M. J. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1521 · Cited by 2,923
Metadata
- Authors
- Abecasis, G. R., Cardon, L. R., & Cookson, W. O. C.
- Affiliations
- Centre for Human Genetics (GB)
- Corresponding author
- Abecasis, G.R.
- ORCID
- Abecasis, G.R.
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2000-01
- Volume
- 66
- Issue
- 1
- Pages
- 279-292
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC1288332
- OpenAlex
- W2078625752
- Cited by
- 1,126
- References
- 30
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 7 · 2023: 7 · 2022: 6 · 2021: 13 · 2020: 15 · 2019: 13 · 2018: 18 · 2017: 23 · 2016: 22 · 2015: 26 · 2014: 27 · 2013: 38 · 2012: 51
- FWCI
- 27.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- association mapping, quantitative traits, nuclear family, variance components, permutation tests, fine mapping, power analysis, family structure, parental genotypes, multivariate normality, research design
- MeSH terms
- Genetics, Population, Humans, Models, Genetic, Nuclear Family, Linkage Disequilibrium, Quantitative Trait, Heritable
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