Evolutionarily significant selective sweeps may result in long stretches of homozygous polymorphisms in individuals from outbred populations. We developed whole-genome homozygosity association (WGHA) methodology to characterize this phenomenon in healthy individuals and to use this genomic feature to identify genetic risk loci for schizophrenia (SCZ). Applying WGHA to 178 SCZ cases and 144 healthy controls genotyped at 500,000 markers, we found that runs of homozygosity (ROHs), ranging in size from 200 kb to 15 mb, were common in unrelated Caucasians. Properties of common ROHs in healthy subjects, including chromosomal location and presence of nonancestral haplotypes, converged with prior reports identifying regions under selective pressure. This interpretation was further supported by analysis of multiethnic HapMap samples genotyped with the same markers. ROHs were significantly more common in SCZ cases, and a set of nine ROHs significantly differentiated cases from controls. Four of these 9 “risk ROHs” contained or neighbored genes associated with SCZ ( NOS1AP , ATF2 , NSF , and PIK3C3 ). Several of these risk ROHs were very rare in healthy subjects, suggesting that recessive effects of relatively high penetrance may explain a proportion of the genetic liability for SCZ. Other risk ROHs feature haplotypes that are also common in healthy individuals, possibly indicating a source of balancing selection.

During the last several years, high-density genotyping SNP arrays have facilitated genome-wide association studies (GWAS) that successfully identified common genetic variants associated with a variety of phenotypes. However, each of the identified genetic variants only explains a very small fraction of the underlying genetic contribution to the studied phenotypic trait. Moreover, discordance observed in results between independent GWAS indicates the potential for Type I and II errors. High reliability of genotyping technology is needed to have confidence in using SNP data and interpreting GWAS results. Therefore, reproducibility of two widely genotyping technology platforms from Affymetrix and Illumina was assessed by analyzing four technical replicates from each of the six individuals in five laboratories. Genotype concordance of 99.40% to 99.87% within a laboratory for the sample platform, 98.59% to 99.86% across laboratories for the same platform, and 98.80% across genotyping platforms was observed. Moreover, arrays with low quality data were detected when comparing genotyping data from technical replicates, but they could not be detected according to venders’ quality control (QC) suggestions. Our results demonstrated the technical reliability of currently available genotyping platforms but also indicated the importance of incorporating some technical replicates for genotyping QC in order to improve the reliability of GWAS results. The impact of discordant genotypes on association analysis results was simulated and could explain, at least in part, the irreproducibility of some GWAS findings when the effect size (i.e. the odds ratio) and the minor allele frequencies are low.

Structural variation is thought to play a major etiological role in the development of autism spectrum disorders (ASDs), and numerous studies documenting the relevance of copy number variants (CNVs) in ASD have been published since 2006. To determine if large ASD families harbor high-impact CNVs that may have broader impact in the general ASD population, we used the Affymetrix genome-wide human SNP array 6.0 to identify 153 putative autism-specific CNVs present in 55 individuals with ASD from 9 multiplex ASD pedigrees. To evaluate the actual prevalence of these CNVs as well as 185 CNVs reportedly associated with ASD from published studies many of which are insufficiently powered, we designed a custom Illumina array and used it to interrogate these CNVs in 3,000 ASD cases and 6,000 controls. Additional single nucleotide variants (SNVs) on the array identified 25 CNVs that we did not detect in our family studies at the standard SNP array resolution. After molecular validation, our results demonstrated that 15 CNVs identified in high-risk ASD families also were found in two or more ASD cases with odds ratios greater than 2.0, strengthening their support as ASD risk variants. In addition, of the 25 CNVs identified using SNV probes on our custom array, 9 also had odds ratios greater than 2.0, suggesting that these CNVs also are ASD risk variants. Eighteen of the validated CNVs have not been reported previously in individuals with ASD and three have only been observed once. Finally, we confirmed the association of 31 of 185 published ASD-associated CNVs in our dataset with odds ratios greater than 2.0, suggesting they may be of clinical relevance in the evaluation of children with ASDs. Taken together, these data provide strong support for the existence and application of high-impact CNVs in the clinical genetic evaluation of children with ASD.