A large-scale screen for coding variants in SERT/SLC6A4 in autism spectrum disorders.

In the current study we explored the hypothesis that rare variants in SLC6A4 contribute to autism susceptibility and to rigid-compulsive behaviors in autism. We made use of a large number of unrelated cases with autism spectrum disorders (approximately 350) and controls (approximately 420) and screened for rare exonic variants in SLC6A4 by a high-throughput method followed by sequencing. We observed no difference in the frequency of such variants in the two groups, irrespective of how we defined the rare variants. Furthermore, we did not observe an association of rare coding variants in SLC6A4 with rigid-compulsive traits scores in the cases. These results do not support a significant role for rare coding variants in SLC6A4 in autism spectrum disorders, nor do they support a significant role for SLC6A4 in rigid-compulsive traits in these disorders.

Pairwise linkage disequilibrium under disease models.

Many genetic studies of disease association rely heavily on linkage disequilibrium (LD) patterns between pairs of markers to detect susceptibility markers. This is true of large-scale positional mapping approaches as well as haplotype construction, selection of tagging single-nucleotide polymorphisms and population genetic analyses. Whereas the distribution of different LD measures has been investigated for randomly selected chromosomes from populations undergoing a variety of demographic effects, little is known about LD within disease-affected samples, and how various disease models influence the difference in LD between patients and the general population. As whole-genome efforts are now underway to characterize and utilize LD patterns in randomly sampled individuals, knowledge about the extent that LD differs between patients and the general population becomes crucial. Such information will allow investigators to design improved mapping experiments and better understand haplotype information arising from such experiments. In this paper, we explore two-site LD measures in the context of single gene disease models. Analytic expressions are presented for infinite populations and properties of sampling densities are reported for different disease models. Interestingly, results indicate that 'underdominant', some dominant, recessive and 'protective' disease models generate weaker LD levels in patients compared to the general population, whereas other models produce stronger LD among affected individuals. Analytic results are also presented for the ratio of LD in patients to the LD in the general population as a function of recombination fraction using a Haldane model. In addition, we explore the impact of various allele frequency combinations on LD differences.

Optimal genotype determination in highly multiplexed SNP data.

High-throughput genotyping technologies that enable large association studies are already available. Tools for genotype determination starting from raw signal intensities need to be automated, robust, and flexible to provide optimal genotype determination given the specific requirements of a study. The key metrics describing the performance of a custom genotyping study are assay conversion, call rate, and genotype accuracy. These three metrics can be traded off against each other. Using the highly multiplexed Molecular Inversion Probe technology as an example, we describe a methodology for identifying the optimal trade-off. The methodology comprises: a robust clustering algorithm and assessment of a large number of data filter sets. The clustering algorithm allows for automatic genotype determination. Many different sets of filters are then applied to the clustered data, and performance metrics resulting from each filter set are calculated. These performance metrics relate to the power of a study and provide a framework to choose the most suitable filter set to the particular study.

Population structure, differential bias and genomic control in a large-scale, case-control association study.

The main problems in drawing causal inferences from epidemiological case-control studies are confounding by unmeasured extraneous factors, selection bias and differential misclassification of exposure. In genetics the first of these, in the form of population structure, has dominated recent debate. Population structure explained part of the significant +11.2% inflation of test statistics we observed in an analysis of 6,322 nonsynonymous SNPs in 816 cases of type 1 diabetes and 877 population-based controls from Great Britain. The remainder of the inflation resulted from differential bias in genotype scoring between case and control DNA samples, which originated from two laboratories, causing false-positive associations. To avoid excluding SNPs and losing valuable information, we extended the genomic control method by applying a variable downweighting to each SNP.

Multiplexed variation scanning for 1,000 amplicons in hundreds of patients using mismatch repair detection (MRD) on tag arrays.

Identification of the genetic basis of common disease may require comprehensive sequence analysis of coding regions and regulatory elements in patients and controls to find genetic effects caused by rare or heterogeneous mutations. In this study, we demonstrate how mismatch repair detection on tag arrays can be applied in a case-control study. Mismatch repair detection allows >1,000 amplicons to be screened for variations in a single laboratory reaction. Variation scanning in 939 amplicons, mostly in coding regions within a linkage peak, was done for 372 patients and 404 controls. In total, >180 Mb of DNA was scanned. Several variants more prevalent in patients than in controls were identified. This study demonstrates an approach to the discovery of susceptibility genes for common disease: large-scale direct sequence comparison between patients and controls. We believe this approach can be scaled up to allow sequence comparison in the whole-genome coding regions among large sets of cases and controls at a reasonable cost in the near future.

Evidence and implications for multiplicative interactions among loci predisposing to human common disease.

OBJECTIVE: The aim of this study was to utilize information on monozygotic twin concordance rates and linkage studies results for common diseases to predict the likely mode of interaction between susceptibility loci. METHODS: We calculated combinations of allele frequency and genotypic relative risk (GRR) that would generate linkage results typically observed in common human diseases. Given these single locus effects, we calculated the expected monozygotic twin concordance assuming different numbers of loci under different interaction models. RESULTS: We demonstrate that, for disorders like schizophrenia, a purely additive model of interaction among loci is not consistent with the available evidence. Instead there are likely significant multiplicative or stronger interactions. Given these interactions, we show that in a diagnostic test based on a subset of predisposing loci, the marginal increase of predictive value rises with each additional locus that is discovered. Our model was consistent with susceptibility alleles being common or rare. CONCLUSIONS: Evidence from monozygotic twin concordance rates and linkage results point to a significant degree of multiplicative interaction among loci.

Highly multiplexed molecular inversion probe genotyping: over 10,000 targeted SNPs genotyped in a single tube assay.

Large-scale genetic studies are highly dependent on efficient and scalable multiplex SNP assays. In this study, we report the development of Molecular Inversion Probe technology with four-color, single array detection, applied to large-scale genotyping of up to 12,000 SNPs per reaction. While generating 38,429 SNP assays using this technology in a population of 30 trios from the Centre d'Etude Polymorphisme Humain family panel as part of the International HapMap project, we established SNP conversion rates of approximately 90% with concordance rates >99.6% and completeness levels >98% for assays multiplexed up to 12,000plex levels. Furthermore, these individual metrics can be "traded off" and, by sacrificing a small fraction of the conversion rate, the accuracy can be increased to very high levels. No loss of performance is seen when scaling from 6,000plex to 12,000plex assays, strongly validating the ability of the technology to suppress cross-reactivity at high multiplex levels. The results of this study demonstrate the suitability of this technology for comprehensive association studies that use targeted SNPs in indirect linkage disequilibrium studies or that directly screen for causative mutations.