Novel method for combined linkage and genome-wide association analysis finds evidence of distinct genetic architecture for two subtypes of autism

  • Vieland V
  • Hallmayer J
  • Huang Y
 et al. 
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Abstract

The Autism Genome Project has assembled two large datasets originally designed for linkage analysis and genome-wide association analysis, respectively: 1,069 multiplex families genotyped on the Affymetrix 10 K platform, and 1,129 autism trios genotyped on the Illumina 1 M platform. We set out to exploit this unique pair of resources by analyzing the combined data with a novel statistical method, based on the PPL statistical framework, simultaneously searching for linkage and association to loci involved in autism spectrum disorders (ASD). Our analysis also allowed for potential differences in genetic architecture for ASD in the presence or absence of lower IQ, an important clinical indicator of ASD subtypes. We found strong evidence of multiple linked loci; however, association evidence implicating specific genes was low even under the linkage peaks. Distinct loci were found in the lower IQ families, and these families showed stronger and more numerous linkage peaks, while the normal IQ group yielded the strongest association evidence. It appears that presence/absence of lower IQ (LIQ) demarcates more genetically homogeneous subgroups of ASD patients, with not just different sets of loci acting in the two groups, but possibly distinct genetic architecture between them, such that the LIQ group involves more major gene effects (amenable to linkage mapping), while the normal IQ group potentially involves more common alleles with lower penetrances. The possibility of distinct genetic architecture across subtypes of ASD has implications for further research and perhaps for research approaches to other complex disorders as well.

Author-supplied keywords

  • Autism
  • Genome-wide association
  • IQ
  • Linkage analysis
  • PPL
  • PPLD

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Authors

  • Veronica J. Vieland

  • Joachim Hallmayer

  • Yungui Huang

  • Alistair T. Pagnamenta

  • Dalila Pinto

  • Hameed Khan

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