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Parametric Linkage Analysis (parametric + linkage_analysis)

Distribution by Scientific Domains

Life Sciences	80%

Selected Abstracts

MQScore_SNP Software for Multipoint Parametric Linkage Analysis of Quantitative Traits in Large Pedigrees

ANNALS OF HUMAN GENETICS, Issue 3 2010
Tatiana I. Axenovich
Summary We describe software for multipoint parametric linkage analysis of quantitative traits using information about SNP genotypes. A mixed model of major gene and polygene inheritance is implemented in this software. Implementation of several algorithms to avoid computational underflow and decrease running time permits application of our software to the analysis of very large pedigrees collected in human genetically isolated populations. We tested our software by performing linkage analysis of adult height in a large pedigree from a Dutch isolated population. Three significant and four suggestive loci were identified with the help of our programs, whereas variance-component-based linkage analysis, which requires the pedigree fragmentation, demonstrated only three suggestive peaks. The software package MQScore_SNP is available at http://mga.bionet.nsc.ru/soft/index.html. [source]

Characterization of a family with dominant hypophosphatasia

EUROPEAN JOURNAL OF ORAL SCIENCES, Issue 3 2000
Jan C.-C.
A kindred with dominant hypophosphatasia resulting from an alanine to threonine substitution at position 99 of the alkaline phosphatase protein is described. The clinical findings of individual members of the kindred were assessed by oral and physical examinations, or from the descriptions of multiple family members. The proband displayed enamel hypoplasia and premature loss of fully rooted primary anterior teeth, which were shown by histological examination to lack cementum. Serum alkaline phosphatase (ALP) and a vitamin B6 panel, and urine phosphoethanolamine (PEA) were measured on 21 family members. Based upon the clinical and laboratory tests, affected and unaffected status was assigned. Parametric linkage analysis of the kindred using different dominant models and frequency distributions for the disease allele and the mutation gave lodscores >4.2 and confirmed the strong linkage between the disease and the mutation. Assuming the defined mutation causes the disease, the reliability of clinical and laboratory tests is assessed. [source]

MCMC-based linkage analysis for complex traits on general pedigrees: multipoint analysis with a two-locus model and a polygenic component

GENETIC EPIDEMIOLOGY, Issue 2 2007
Yun Ju Sung
Abstract We describe a new program lm_twoqtl, part of the MORGAN package, for parametric linkage analysis with a quantitative trait locus (QTL) model having one or two QTLs and a polygenic component, which models additional familial correlation from other unlinked QTLs. The program has no restriction on number of markers or complexity of pedigrees, facilitating use of more complex models with general pedigrees. This is the first available program that can handle a model with both two QTLs and a polygenic component. Competing programs use only simpler models: one QTL, one QTL plus a polygenic component, or variance components (VC). Use of simple models when they are incorrect, as for complex traits that are influenced by multiple genes, can bias estimates of QTL location or reduce power to detect linkage. We compute the likelihood with Markov Chain Monte Carlo (MCMC) realization of segregation indicators at the hypothesized QTL locations conditional on marker data, summation over phased multilocus genotypes of founders, and peeling of the polygenic component. Simulated examples, with various sized pedigrees, show that two-QTL analysis correctly identifies the location of both QTLs, even when they are closely linked, whereas other analyses, including the VC approach, fail to identify the location of QTLs with modest contribution. Our examples illustrate the advantage of parametric linkage analysis with two QTLs, which provides higher power for linkage detection and better localization than use of simpler models. Genet. Epidemiol. © 2006 Wiley-Liss, Inc. [source]

Approaches to detecting gene × gene interaction in Genetic Analysis Workshop 14 pedigrees

GENETIC EPIDEMIOLOGY, Issue S1 2005
Brion S. Maher
Abstract Whether driven by the general lack of success in finding single-gene contributions to complex disease, by increased knowledge about the potential involvement of specific biological interactions in complex disease, or by recent dramatic increases in computational power, a large number of approaches to detect locus × locus interactions were recently proposed and implemented. The six Genetic Analysis Workshop 14 (GAW14) papers summarized here each applied either existing or refined approaches with the goal of detecting gene × gene, or locus × locus, interactions in the GAW14 data. Five of six papers analyzed the simulated data; the other analyzed the Collaborative Study on the Genetics of Alcoholism data. The analytic strategies implemented for detecting interactions included multifactor dimensionality reduction, conditional linkage analysis, nonparametric linkage correlation, two-locus parametric linkage analysis, and a joint test of linkage and association. Overall, most of the groups found limited success in consistently detecting all of the simulated interactions due, in large part, to the nature of the generating model. Genet. Epidemiol. 29(Suppl. 1):S116,S119, 2005. © 2005 Wiley-Liss, Inc. [source]