Methods for genetic linkage analysis using trisomies
- Emory Univ., Atlanta, GA (United States)
Certain genetic disorders (e.g. congenital cataracts, duodenal atresia) are rare in the general population, but more common in people with Down`s syndrome. We present a method for using individuals with trisomy 21 to map genes for such traits. Our methods are analogous to methods for mapping autosomal dominant traits using affected relative pairs by looking for markers with greater than expected identity-by-descent. In the trisomy case, one would take trisomic individuals and look for markers with greater than expected reduction to homozygosity in the chromosomes inherited form the non-disjoining parent. We present statistical methods for performing such a linkage analysis, including a test for linkage to a marker, a method for estimating the distance from the marker to the gene, a confidence interval for that distance, and methods for computing power and sample sizes. The methods are described in the context of gene-dosage model for the etiology of the disorder, but can be extended to other models. We also resolve some practical issues involved in implementing the methods, including how to use partially informative markers, how to test candidate genes, and how to handle the effect of reduced recombination associated with maternal meiosis I non-disjunction.
- OSTI ID:
- 133363
- Report Number(s):
- CONF-941009-; ISSN 0002-9297; TRN: 95:005313-0091
- Journal Information:
- American Journal of Human Genetics, Vol. 55, Issue Suppl.3; Conference: 44. annual meeting of the American Society of Human Genetics, Montreal (Canada), 18-22 Oct 1994; Other Information: PBD: Sep 1994
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
BASIC STUDIES
99 MATHEMATICS
COMPUTERS
INFORMATION SCIENCE
MANAGEMENT
LAW
MISCELLANEOUS
GENES
GENETIC MAPPING
GENE RECOMBINATION
DOWNS SYNDROME
ETIOLOGY
PATIENTS
CONGENITAL MALFORMATIONS
HUMAN CHROMOSOME 21
NON-DISJUNCTION
CHROMOSOMAL ABERRATIONS
GENETICS
BIOLOGICAL MODELS
COMPUTER CALCULATIONS
STATISTICS
BIOLOGICAL MARKERS
DOMINANT MUTATIONS