Mapping quantitative traits in unselected families: algorithms and examples.
Linkage analysis has been widely used to identify from family data genetic variants influencing quantitative traits. Common approaches have both strengths and limitations. Likelihood ratio tests typically computed in variance component analysis can accommodate large families but are highly sensitive to departure from normality assumptions. Regression-based approaches are more robust but their use has primarily been restricted to nuclear families. In this paper, we develop methods for mapping quantitative traits in moderately large pedigrees. Our methods are based on the score statistic, which in contrast to the likelihood ratio statistic can use nonparametric estimators of variability to achieve robustness of the false-positive rate against departures from the hypothesized phenotypic model. Because the score statistic is easier to calculate than the likelihood ratio statistic, our basic mapping methods utilize relatively simple computer code that performs statistical analysis on output from any program that computes estimates of identity by descent. This simplicity also permits development and evaluation of methods to deal with multivariate and ordinal phenotypes, and with gene-gene and gene-environment interaction. We demonstrate our methods on simulated data and on fasting insulin, a quantitative trait measured in the Framingham Heart Study.
Dupuis
Josée
J
Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA. dupuis@bu.edu
Shi
Jianxin
J
Manning
Alisa K
AK
Benjamin
Emelia J
EJ
Meigs
James B
JB
Cupples
L Adrienne
LA
Siegmund
David
D
D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D004777:Environment; D005189:False Positive Reactions; D005192:Family Health; D005260:Female; D006801:Humans; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D017720:Molecular Epidemiology / Q000379:methods; D015999:Multivariate Analysis; D010375:Pedigree; D010641:Phenotype; D040641:Quantitative Trait Loci; D015203:Reproducibility of Results