In a recent study, Dr. Nianjun Liu, associate professor in the section on statistical genetics, department of biostatistics, at the University of Alabama at Birmingham, collaborating with Dr. Qi Yan, UAB alumna and current post-doctoral fellow at the University of Pittsburgh, wrote, “The recent development of sequencing technology allows identification of association between the whole spectrum of genetic variants and complex diseases. Over the past few years, a number of association tests for rare variants have been developed. Jointly testing for association between genetic variants and multiple correlated phenotypes may increase the power to detect causal genes in family-based studies, but familial correlation needs to be appropriately handled to avoid an inflated type I error rate.”
Their research proposed a novel approach to analyzing multivariate family data using kernel machine regression, referred to as MF-KM. MF-KM, based on a linear mixed model framework, can be applied to an extended variety of studies with different types of traits. The scientists observed, “In our simulation studies, the usual kernel machine test has inflated type I error rates when directly applied to familial data, while our proposed MF-KM method preserves the expected type I error rates. Moreover, the MF-KM method has increased power compared to methods that either analyze each phenotype separately while considering family structure or use only unrelated founders from the families. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.”
Co-investigators in the study include Dr. Hemant K. Tiwari, professor in the section on statistical genetics, department of biostatistics.
“Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method” was published online in October in the journal Genetics.