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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
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Longitudinal variance-components analysis of the Framingham Heart Study data.

Stuart Macgregor1, Sara A Knott, Ian White

  • 1Biostatistics and Bioinformatics Unit, University of Wales College of Medicine, Heath Hospital, Cardiff, United Kingdom. smacgreg@hgmp.mrc.ac.uk

BMC Genetics
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This study used a random-regression approach to analyze genetic effects on health traits over time in the Framingham Heart Study. A quantitative trait locus (QTL) influencing Body Mass Index (BMI) was found to be most active during early adulthood.

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Area of Science:

  • Genetics
  • Biostatistics
  • Longitudinal Studies

Background:

  • The Framingham Heart Study offspring cohort provides complex, longitudinal phenotype data.
  • Analyzing such data requires methods that can handle irregular spacing and simultaneous analysis.

Purpose of the Study:

  • To apply a mixed-model-based random-regression (RR) approach for analyzing longitudinal phenotype data.
  • To perform a quantitative trait locus (QTL) analysis on adult phenotype data, accounting for age-related genetic effects.
  • To characterize the changes in QTL effects over time and identify specific QTLs for traits like BMI, HDL cholesterol, total cholesterol, and height.

Main Methods:

  • Utilized a mixed-model-based random-regression (RR) approach to fit age-dependent genetic effects using polynomial functions.
  • Incorporated fixed effects (e.g., sex) and random effects (e.g., familial environment) within the mixed model.
  • Performed QTL analysis on 26,106 phenotypic records, comparing RR with conventional univariate variance component methods.

Main Results:

  • A QTL influencing Body Mass Index (BMI) was identified, demonstrating a significant effect primarily in early adulthood.
  • The RR method successfully characterized the dynamic changes in QTL effects across the aging process.
  • Analysis included key cardiovascular risk factors: BMI, HDL cholesterol (HDLC), total cholesterol, and height.

Conclusions:

  • The random-regression approach is effective for analyzing complex longitudinal genetic data.
  • Genetic influences on traits like BMI can change significantly with age.
  • This methodology provides insights into the developmental trajectory of genetic effects on health phenotypes.