A stochastic inference of de novo CNV detection and association test in multiplex schizophrenia families.
The copy number variation (CNV) is a type of genetic variation in the genome. It is measured based on signal intensity measures and can be assessed repeatedly to reduce the uncertainty in PCR-based typing. Studies have shown that CNVs may lead to phenotypic variation and modification of disease expression. Various challenges exist, however, in the exploration of CNV-disease association. Here we construct latent variables to infer the discrete CNV values and to estimate the probability of mutations. In addition, we propose to pool rare variants to increase the statistical power and we conduct family studies to mitigate the computational burden in determining the composition of CNVs on each chromosome. To explore in a stochastic sense the association between the collapsing CNV variants and disease status, we utilize a Bayesian hierarchical model incorporating the mutation parameters. This model assigns integers in a probabilistic sense to the quantitatively measured copy numbers, and is able to test simultaneously the association for all variants of interest in a regression framework. This integrative model can account for the uncertainty in copy number assignment and differentiate if the variation was de novo or inherited on the basis of posterior probabilities. For family studies, this model can accommodate the dependence within family members and among repeated CNV data. Moreover, the Mendelian rule can be assumed under this model and yet the genetic variation, including de novo and inherited variation, can still be included and quantified directly for each individual. Finally, simulation studies show that this model has high true positive and low false positive rates in the detection of de novo mutation.
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University Taipei, Taiwan.
Shi-Heng
SH
Wang
Wei J
WJ
Chen
Yu-Chin
YC
Tsai
Yung-Hsiang
YH
Huang
Hai-Gwo
HG
Hwu
Chuhsing K
CK
Hsiao
Bayesian model; CNV association test; de novo CNV detection; random mutation parameter; schizophrenia multiplex family