Evidence map›Paper›PMID 26635803›Full record

ArticleFrontiers in immunology2015

A Comparison of Statistical Methods for the Discovery of Genetic Risk Factors Using Longitudinal Family Study Designs.

Kelly M Burkett, Marie-Hélène Roy-Gagnon, Jean-François Lefebvre, Cheng Wang, Bénédicte Fontaine-Bisson, Lise Dubois

Abstract read
In one paragraph

Article in Frontiers in immunology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Kelly M BurkettDepartment of Mathematics and Statistics, University of Ottawa , Ottawa, ON , Canada.
Marie-Hélène Roy-GagnonSchool of Epidemiology, Public Health and Preventive Medicine, University of Ottawa , Ottawa, ON , Canada.
Jean-François LefebvreSchool of Epidemiology, Public Health and Preventive Medicine, University of Ottawa , Ottawa, ON , Canada.
Cheng WangSchool of Epidemiology, Public Health and Preventive Medicine, University of Ottawa , Ottawa, ON , Canada.
Bénédicte Fontaine-BissonNutrition Sciences Program, University of Ottawa , Ottawa, ON , Canada.
Lise DuboisSchool of Epidemiology, Public Health and Preventive Medicine, University of Ottawa , Ottawa, ON , Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The etiology of immune-related diseases or traits is often complex, involving many genetic and environmental factors and their interactions. While methodological approaches focusing on an outcome measured at one time point have succeeded in identifying genetic factors involved in immune-related traits, they fail to capture complex disease mechanisms that fluctuate over time. It is increasingly recognized that longitudinal studies, where an outcome is measured at multiple time points, have great potential to shed light on complex disease mechanisms involving genetic factors. However, longitudinal data require specialized statistical methods, especially in family studies where multiple sources of correlation in the data must be modeled. Using simulated data with known genetic effects, we examined the performance of different analytical methods for investigating associations between genetic factors and longitudinal phenotypes in twin data. The simulations were modeled on data from the Québec Newborn Twin Study, an ongoing population-based longitudinal study of twin births with multiple phenotypes, such as cortisol levels and body mass index, collected multiple times in infancy and early childhood and with sequencing data on immune-related genes and pathways. We compared approaches that we classify as (1) family-based methods applied to summaries of the observations over time, (2) longitudinal-based methods with simplifications of the familial correlation, and (3) Bayesian family-based method with simplifications of the temporal correlation. We found that for estimation of the genetic main and interaction effects, all methods gave estimates close to the true values and had similar power. If heritability estimation is desired, approaches of type (1) also provide heritability estimates close to the true value. Our work shows that the simpler approaches are likely adequate to detect genetic effects; however, interpretation of these effects is more challenging.

Indexed as

family designgeneralized estimating equationsgenetic associationlinear mixed modelslongitudinal studies

Identifiers

PMID26635803
PMCPMC4652172

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