Enriching targeted sequencing experiments for rare disease alleles

Discovery of rare variants implicated in schizophrenia using next-generation sequencing

Schizophrenia is a highly heritable psychiatric disorder that affects 1% of the population. Genome-wide association studies have identified common variants in candidate genes associated with schizophrenia, but the genetics mechanisms of this disorder have not yet been elucidated. The discovery of rare genetic variants that contribute to schizophrenia symptoms promises to help explain the missing heritability of the disease. Next generation sequencing techniques are revolutionizing the field of psychiatric genetics. Various statistical approaches have been developed for rare variant association testing in case-control and family studies. Targeted resequencing, whole exome sequencing and whole genome sequencing combined with these computational tools are used for the discovery of rare genetic variations in schizophrenia. The findings provide useful information for characterizing the rare mutations and elucidating the genetic mechanisms by which the variants cause schizophrenia.

GWAS to Sequencing: Divergence in Study Design and Analysis

The success of genome-wide association studies (GWAS) in uncovering genetic risk factors for complex traits has generated great promise for the complete data generated by sequencing. The bumpy transition from GWAS to whole-exome or whole-genome association studies (WGAS) based on sequencing investigations has highlighted important differences in analysis and interpretation. We show how the loss in power due to the allele frequency spectrum targeted by sequencing is difficult to compensate for with realistic effect sizes and point to study designs that may help. We discuss several issues in interpreting the results, including a special case of the winner's curse. Extrapolation and prediction using rare SNPs is complex, because of the selective ascertainment of SNPs in case-control studies and the low amount of information at each SNP, and naive procedures are biased under the alternative. We also discuss the challenges in tuning gene-based tests and accounting for multiple testing when genes have very different sets of SNPs. The examples we emphasize in this paper highlight the difficult road we must travel for a two-letter switch.

SeqSIMLA: a sequence and phenotype simulation tool for complex disease studies

Background

Association studies based on next-generation sequencing (NGS) technology have become popular, and statistical association tests for NGS data have been developed rapidly. A flexible tool for simulating sequence data in either unrelated case–control or family samples with different disease and quantitative trait models would be useful for evaluating the statistical power for planning a study design and for comparing power among statistical methods based on NGS data.

Results

We developed a simulation tool, SeqSIMLA, which can simulate sequence data with user-specified disease and quantitative trait models. We implemented two disease models, in which the user can flexibly specify the number of disease loci, effect sizes or population attributable risk, disease prevalence, and risk or protective loci. We also implemented a quantitative trait model, in which the user can specify the number of quantitative trait loci (QTL), proportions of variance explained by the QTL, and genetic models. We compiled recombination rates from the HapMap project so that genomic structures similar to the real data can be simulated.

Conclusions

SeqSIMLA can efficiently simulate sequence data with disease or quantitative trait models specified by the user. SeqSIMLA will be very useful for evaluating statistical properties for new study designs and new statistical methods using NGS. SeqSIMLA can be downloaded for free at http://seqsimla.sourceforge.net.

Single Nucleotide Polymorphism (SNP) Detection and Genotype Calling from Massively Parallel Sequencing (MPS) Data

Massively parallel sequencing (MPS), since its debut in 2005, has transformed the field of genomic studies. These new sequencing technologies have resulted in the successful identification of causal variants for several rare Mendelian disorders. They have also begun to deliver on their promise to explain some of the missing heritability from genome-wide association studies (GWAS) of complex traits. We anticipate a rapidly growing number of MPS-based studies for a diverse range of applications in the near future. One crucial and nearly inevitable step is to detect SNPs and call genotypes at the detected polymorphic sites from the sequencing data. Here, we review statistical methods that have been proposed in the past five years for this purpose. In addition, we discuss emerging issues and future directions related to SNP detection and genotype calling from MPS data.

The impact of genomics on pediatric research and medicine

In this review, we discuss some of the most recent developments in genomics research and their relevance to the field of pediatrics. In particular, we examine 3 major approaches that are being used to identify genetic correlates of disease: genome-wide association studies, copy number variation studies, and next-generation sequencing. In the past few years, these approaches have yielded major insights into the causes and pathophysiology of a wide range of diseases but are also constrained by certain limitations. This review provides an overview of the genomic landscape in complex pediatric disorders and sets the stage for translating new discoveries into clinical practice, the future of genomic medicine.

Optimized selection of unrelated subjects for whole-genome sequencing studies of rare high-penetrance alleles

Sequencing studies using whole-genome or exome scans are still more expensive than genome-wide association studies on a per-subject basis. As a result, only a subset of subjects from a larger study will be selected for sequencing. To perform an agnostic investigation of the entire genome, subjects may be selected that capture independent ancestral lineages, i.e., founder genomes, and thus avoid redundant information from regions that were inherited identical by descent (IBD) from a common ancestor. We present SampleSeq2 that can be used to select a subset of optimally unrelated subjects with minimal IBD sharing. It also can be used to estimate the number, G(T), of founder chromosomes in a sample or select the minimum number of subjects that will carry a target G(T). We evaluated SampleSeq2 compared to a random draw of a small number of subjects both by simulation and using the Anabaptist genealogy. SampleSeq2 provided an increase in G(T) relative to a random draw across a range of small sample sizes. This increase in founder chromosomes improves the power of association tests, mitigates the effect of cryptic relatedness on parameter estimates, increases the total yield of alleles from sequencing, and minimizes the average size of regions shared IBD around disease alleles in cases.

SEQCHIP: a powerful method to integrate sequence and genotype data for the detection of rare variant associations

MOTIVATION

Next-generation sequencing greatly increases the capacity to detect rare-variant complex-trait associations. However, it is still expensive to sequence a large number of samples and therefore often small datasets are used. Given cost constraints, a potentially more powerful two-step strategy is to sequence a subset of the sample to discover variants, and genotype the identified variants in the remaining sample. If only cases are sequenced, directly combining sequence and genotype data will lead to inflated type-I errors in rare-variant association analysis. Although several methods have been developed to correct for the bias, they are either underpowered or theoretically invalid. We proposed a new method SEQCHIP to integrate genotype and sequence data, which can be used with most existing rare-variant tests.

RESULTS

It is demonstrated using both simulated and real datasets that the SEQCHIP method has controlled type-I errors, and is substantially more powerful than all other currently available methods.

AVAILABILITY

SEQCHIP is implemented in an R-Package and is available at http://linkage.rockefeller.edu/suzanne/seqchip/Seqchip.html.