Parametric proportional hazards model for mapping genomic imprinting of survival traits

Genome-wide random regression analysis for parent-of-origin effects of body composition allometries in mouse

Genomic imprinting underlying growth and development traits has been recognized, with a focus on the form of absolute or pure growth. However, little is known about the effect of genomic imprinting on relative growth. In this study, we proposed a random regression model to estimate genome-wide imprinting effects on the relative growth of multiple tissues and organs to body weight in mice. Joint static allometry scaling equation as sub-model is nested within the genetic effects of markers and polygenic effects caused by a pedigree. Both chromosome-wide and genome-wide statistical tests were conducted to identify imprinted quantitative trait nucleotides (QTNs) associated with relative growth of individual tissues and organs to body weight. Real data analysis showed that three of six analysed tissues and organs are significantly associated with body weight in terms of phenotypic relative growth. At the chromosome-wide level, a total 122 QTNs were associated with allometries of kidney, spleen and liver weights to body weight, 36 of which were imprinted with different imprinting fashions. Further, only two imprinted QTNs responsible for relative growth of spleen and liver were verified by genome-wide test. Our approach provides a general framework for statistical inference of genomic imprinting underlying allometry scaling in animals.

Cox regression model for dissecting genetic architecture of survival time

Common quantitative trait locus (QTL) mapping methods fail to analyze survival traits of skewed normal distributions. As a result, some mapping methods for survival traits have been proposed based on survival analysis. Under a single QTL model, however, those methods perform poorly in detecting multiple QTLs and provide biased estimates of QTL parameters. For sparse oversaturated model used to map survival time loci, the least absolute shrinkage and selection operator (LASSO) for Cox regression model can be employed to efficiently shrink most of genetic effects to zero. Then, a few non-zero genetic effects are re-estimated and statistically tested using the standard maximum Cox partial likelihood method. Simulation shows that the proposed method has higher statistic power for QTL detection than that of the LASSO for logarithmic linear model or the interval mapping based on Cox model, although it somewhat underestimates QTL effects. Especially, computational speed of the method is very fast. An application of this method illustrates mapping main effect and interacting QTLs for heading time in the North American Barley Genome Mapping Project.