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4 results on '"Ying-Chao Lin"'

1. Power of linkage analysis using traits generated from simulated longitudinal data of the Framingham Heart Study

Author

Ying-Chao Lin, Huiying Yang, Kai Yang, Dai Wang, Xiuqing Guo, and Xiaohui Li

Subjects
Male, lcsh:QH426-470, Genetic Linkage, Longitudinal data, Genome Scan, Biology, Cohort Studies, 03 medical and health sciences, Quantitative Trait, Heritable, Framingham Heart Study, Genetic linkage analysis, Genetic linkage, Genetics, Humans, Computer Simulation, Genetics(clinical), Longitudinal Studies, Genetics (clinical), 030304 developmental biology, Linkage (software), 0303 health sciences, Framingham Risk Score, 030305 genetics & heredity, lcsh:Genetics, Proceedings, Phenotype, Data Interpretation, Statistical, Adult Children, Female, Cohort study
Abstract

The Framingham Heart Study is a very successful longitudinal research for cardiovascular diseases. The completion of a 10-cM genome scan in Framingham families provided an opportunity to evaluate linkage using longitudinal data. Several descriptive traits based on simulated longitudinal data from the Genetic Analysis Workshop 13 (GAW13) were generated, and linkage analyses were performed for these traits. We compared the power of detecting linkage for baseline and slope genes in the simulated data of GAW13 using these traits. We found that using longitudinal traits based on multiple follow-ups may not be more powerful than using cross-sectional traits for genetic linkage analysis.

Published
2003

2. Genome-wide linkage analysis using cross-sectional and longitudinal traits for body mass index in a subsample of the Framingham Heart Study

Author

Carlos Samayoa, Ying-Chao Lin, Xiaohui Li, Huiying Yang, Kai Yang, Dai Wang, and Xiuqing Guo

Subjects
Adult, Male, lcsh:QH426-470, Genetic Linkage, Cross-sectional study, Quantitative Trait Loci, Locus (genetics), Quantitative trait locus, Biology, Body Mass Index, Time, Cohort Studies, 03 medical and health sciences, Quantitative Trait, Heritable, 0302 clinical medicine, Framingham Heart Study, Genetic linkage, Genetics, Humans, Genetics(clinical), Longitudinal Studies, Time point, Genetics (clinical), Aged, 030304 developmental biology, Aged, 80 and over, 2. Zero hunger, Analysis of Variance, 0303 health sciences, Genome, Human, Body Weight, Chromosome Mapping, Middle Aged, lcsh:Genetics, Proceedings, Cross-Sectional Studies, Adult Children, Female, Body mass index, Chromosomes, Human, Pair 16, 030217 neurology & neurosurgery, Cohort study
Abstract

To evaluate linkage evidence for body mass index (BMI) using both cross-sectional and longitudinal data, we performed genome-wide multipoint linkage analyses on subjects who had complete data at four selected time points (initial, 8th, 12th, and 16th year following the initial visit) from the Framingham Heart Study. The cross-sectional measures included BMI at each of the four selected time points and the longitudinal measure was the within-subject mean of BMI at the above four time points. Using the variance components method, we consistently observed the maximum LOD score out of the genome scan using BMI at each time point and the mean of BMI between 049xd2 and GATA71H05 on chromosome 16. The highest LOD score (3.0) was at time point 1, while the lowest (1.9) was at time point 4. We also observed other suggestive linkages on chromosome 6, 10, and 18 at time point 1 only. The longitudinal measure we studied (mean of BMI) did not provide greater power to identify a positive linkage than some of the cross-sectional measures (e.g., time point 1). The changing of linkage evidence over time provided some insights on the variation of genetic effect on BMI with aging. There may be a QTL on chromosome 16 that contributes to BMI and this locus, and maybe others, is more likely to affect BMI during early adulthood.

Published
2003

3. Genome-wide linkage analysis using cross-sectional and longitudinal traits for body mass index in a subsample of the Framingham Heart Study.

Author

Xiaohui Li, Dai Wang, Kai Yang, Xiuqing Guo, Ying-chao Lin, Samayoa, Carlos G., and Huiying Yang

Subjects
LINKAGE (Genetics), BODY mass index, GENOMES, LOCUS (Genetics)
Abstract

To evaluate linkage evidence for body mass index (BMI) using both cross-sectional and longitudinal data, we performed genome-wide multipoint linkage analyses on subjects who had complete data at four selected time points (initial, 8th, 12th, and 16th year following the initial visit) from the Framingham Heart Study. The cross-sectional measures included BMI at each of the four selected time points and the longitudinal measure was the within-subject mean of BMI at the above four time points. Using the variance components method, we consistently observed the maximum LOD score out of the genome scan using BMI at each time point and the mean of BMI between 049xd2 and GATA71H05 on chromosome 16. The highest LOD score (3.0) was at time point 1, while the lowest (1.9) was at time point 4. We also observed other suggestive linkages on chromosome 6, 10, and 18 at time point 1 only. The longitudinal measure we studied (mean of BMI) did not provide greater power to identify a positive linkage than some of the cross-sectional measures (e.g., time point 1). The changing of linkage evidence over time provided some insights on the variation of genetic effect on BMI with aging. There may be a QTL on chromosome 16 that contributes to BMI and this locus, and maybe others, is more likely to affect BMI during early adulthood. [ABSTRACT FROM AUTHOR]

Published
2003
Full Text
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4. Power of linkage analysis using traits generated from simulated longitudinal data of the Framingham Heart Study.

Author

Dai Wang, Xiaohui Li, Ying-Chao Lin, Kai Yang, Xiuqing Guo, and Huiying Yang

Subjects
CARDIOVASCULAR diseases, PATIENTS, LINKAGE (Genetics), GENETICS
Abstract

The Framingham Heart Study is a very successful longitudinal research for cardiovascular diseases. The completion of a 10-cM genome scan in Framingham families provided an opportunity to evaluate linkage using longitudinal data. Several descriptive traits based on simulated longitudinal data from the Genetic Analysis Workshop 13 (GAW13) were generated, and linkage analyses were performed for these traits. We compared the power of detecting linkage for baseline and slope genes in the simulated data of GAW13 using these traits. We found that using longitudinal traits based on multiple follow-ups may not be more powerful than using cross-sectional traits for genetic linkage analysis. [ABSTRACT FROM AUTHOR]

Published
2003
Full Text
View/download PDF

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