Age-Stratified QTL Genome Scan Analyses for Anthropometric Measures
Author Information
Author(s): Beck Stephanie R, Brown W Mark, Williams Adrienne H, Pierce June, Rich Stephen S, Langefeld Carl D
Primary Institution: Wake Forest University School of Medicine
Hypothesis
How does age influence the genetic analysis of traits related to cardiovascular disease?
Conclusion
The study suggests that age-stratified analyses can help identify genetic loci influencing traits like height, weight, and BMI over time.
Supporting Evidence
- Linkage signals for height were detected on chromosome 14q11.2.
- Evidence of linkage to BMI was found on chromosome 3q22 in the 31–49 age group.
- Linkage was also supported on chromosome 1p22.1 for BMI and weight in the 31–49 age group.
Takeaway
This study looked at how our genes might affect things like height and weight differently as we get older.
Methodology
Genome-wide QTL analyses were performed using SOLAR on data from the Framingham Heart Study, focusing on height, weight, BMI, and systolic blood pressure across different age groups.
Potential Biases
Censoring of data due to morbidity and mortality may have influenced the findings.
Limitations
The study may have excluded subjects with severe traits, potentially affecting the results.
Participant Demographics
Participants were from the Framingham Heart Study, with a mean age of 71.8 at the last exam attended before death.
Statistical Information
P-Value
0.0003, 0.0002, 0.0014
Statistical Significance
p=0.0003, p=0.0002, p=0.0014
Digital Object Identifier (DOI)
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