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Biomedical subjects

A Kraja

Publications and source records attributed to A Kraja.

2 recordsLinked to original sources
PubMed2006

QTL-specific genotype-by-smoking interaction and burden of calcified coronary atherosclerosis: the NHLBI Family Heart Study.

BACKGROUND: Calcified coronary plaque (CCP) is a complex trait influenced by both genes and environment, and plausibly an interaction between the two. Because the familial aggregation of CCP has been demonstrated and smoking is a significant, independent predictor of CCP, we assessed the evidence for genotype-by-smoking interaction and conducted linkage analysis of quantitative Agatston CCP scores in participants of the NHLBI Family Heart Study (FHS). METHODS: During standardized clinical exams smoking habits were ascertained and CCP was quantified with cardiac computed tomography (CT). Among 4387 relationship pairs from 2128 Caucasian examinees variance component analysis was implemented in SOLAR to examine: (1) additive genotype-by-smoking status interaction using a variance component approach; (2) linkage analysis in the full sample and among smoking subsets defined by individual smoking exposure; (3) QTL-specific genotype-by-smoking interaction in the regions that appeared to differentiate between smoking strata. RESULTS: The prevalence of CCP (and median Agatston score) was 75% (184.6) in men and 48% (51.0) in women. We detected four genome-wide significant logarithm of odds (LOD) scores in samples stratified by individual smoking exposure: chromosome 4 at 122cM (nearest marker D4S2297; robust adjusted LOD=3.1; q=0.053), chromosome 6 at 99cM (nearest marker D6S1056; robust adjusted LOD=3.3; q=0.053), chromosome 11 at 19cM (nearest marker D11S199; robust adjusted LOD=4.0; q=0.02) and chromosome 13 at 77cM (nearest marker D13S892; robust adjusted LOD=3.1; q=0.053). Additive and QTL-specific genotype-by-smoking interaction was detected on chromosomes 4, 6, 11 and 13; all P<0.05. Three of the four QTLs identified in this report have been previously linked to atherosclerosis and harbor interesting candidate genes. CONCLUSIONS: These findings demonstrate the importance of considering complex interactions in the search for genes that influence the pathogenesis of CCP.

Adult↗
PubMed2003

Multivariate and multilocus variance components method, based on structural relationships to assess quantitative trait linkage via SEGPATH.

A general-purpose modeling framework for performing path and segregation analysis jointly, called SEGPATH (Province and Rao [1995] Stat. Med. 7:185-198), has been extended to cover "model-free" robust, variance-components linkage analysis, based on identity-by-descent (IBD) sharing. These extended models can be used to analyze linkage to a single marker or to perform multipoint linkage analysis, with a single phenotype or multivariate vector of phenotypes, in pedigrees. Within a single, consistent approach, SEGPATH models can perform segregation analysis, path analysis, linkage analysis, or combinations thereof. SEGPATH models can incorporate environmental or other measured covariate fixed effects (including measured genotypes), genotype-specific covariate effects, population heterogeneity models, repeated-measures models, longitudinal models, autoregressive models, developmental models, gene-by-environment interaction models, etc., with or without linkage components. The data analyzed can have any missing value structure (assumed missing at random), with entire individuals missing, or missing on one or more measurements. Corrections for ascertainment can be made on a vector of phenotypes and/or other measures. Because of the flexibility of the class of models, the SEGPATH approach can also be used in nongenetic applications where there is a hierarchical structure, such as longitudinal, repeated-measures, time series, or nested models. A variety of specific models are provided, as well as some comparisons with other linkage analysis models. Particular applications demonstrate the importance of correctly accounting for the extraneous sources of familial resemblance, as can be done easily with these SEGPATH models, so as to give added power to detect linkage as well as to protect against spuriously inferring linkage.

Genetic Linkage↗