Screening large numbers of target regions in multiple DNA samples for sequence variation is an important application of next-generation sequencing but an efficient method to enrich the samples in parallel has yet to be reported. We describe an advanced method that combines DNA samples using indexes or barcodes prior to target enrichment to facilitate this type of experiment. Sequencing libraries for multiple individual DNA samples, each incorporating a unique 6-bp index, are combined in equal quantities, enriched using a single in-solution target enrichment assay and sequenced in a single reaction. Sequence reads are parsed based on the index, allowing sequence analysis of individual samples. We show that the use of indexed samples does not impact on the efficiency of the enrichment reaction. For three- and nine-indexed HapMap DNA samples, the method was found to be highly accurate for SNP identification. Even with sequence coverage as low as 8x, 99% of sequence SNP calls were concordant with known genotypes. Within a single experiment, this method can sequence the exonic regions of hundreds of genes in tens of samples for sequence and structural variation using as little as 1 μg of input DNA per sample.

There are conflicting reports suggesting that the parental origin of transmitted risk alleles may play a role in the etiology of attention deficit/hyperactivity disorder (ADHD). A recent report by Hawi and colleagues observed a generalized paternal over-transmission of alleles associated with ADHD. This was not replicated in more recent studies. Using data from a large multicenter study we examined the overall and gene-specific parent of origin effect in 554 independent SNPs across 47 genes. Transmission disequilibrium and explicit parent of origin test were performed using PLINK. Overall parent of origin effect was tested by Chi-square. There was no overall parent of origin effect in the IMAGE sample (chi(1)(2) = 1.82, P = 0.117). Five markers in three genes, DDC, TPH2, and SLC6A2 showed nominal association (P < 0.01) with ADHD combined subtype when restricted to maternal or paternal transmission only. Following the initial report by Hawi and co-workers three studies, including this one, found no evidence to support an overall parent of origin effect for markers associated with ADHD. We cannot however, exclude gene-specific parent of origin effect in the etiology ADHD.

No abstract

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case–control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available.In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.