  While studies show that autism is highly heritable, the nature of the genetic
basis of this disorder remains illusive. Based on the idea that highly
correlated genes are functionally interrelated and more likely to affect risk,
we develop a novel statistical tool to find more potentially autism risk genes
by combining the genetic association scores with gene co-expression in specific
brain regions and periods of development. The gene dependence network is
estimated using a novel partial neighborhood selection (PNS) algorithm, where
node specific properties are incorporated into network estimation for improved
statistical and computational efficiency. Then we adopt a hidden Markov random
field (HMRF) model to combine the estimated network and the genetic association
scores in a systematic manner. The proposed modeling framework can be naturally
extended to incorporate additional structural information concerning the
dependence between genes. Using currently available genetic association data
from whole exome sequencing studies and brain gene expression levels, the
proposed algorithm successfully identified 333 genes that plausibly affect
autism risk.
