A genome-wide screen for variants influencing certolizumab pegol response in a moderate to severe rheumatoid arthritis population.

Certolizumab pegol (CZP) is a PEGylated Fc-free tumor necrosis factor (TNF) inhibitor antibody approved for use in the treatment of rheumatoid arthritis (RA), Crohn's disease, psoriatic arthritis, axial spondyloarthritis and psoriasis. In a clinical trial of patients with severe RA, CZP improved disease symptoms in approximately half of patients. However, variability in CZP efficacy remains a problem for clinicians, thus, the aim of this study was to identify genetic variants predictive of CZP response. We performed a genome-wide association study (GWAS) of 302 RA patients treated with CZP in the REALISTIC trial to identify common single nucleotide polymorphisms (SNPs) associated with treatment response. Whole-exome sequencing was also performed for 74 CZP extreme responders and non-responders within the same population, as well as 1546 population controls. No common SNPs or rare functional variants were significantly associated with CZP response, though a non-significant enrichment in the RA-implicated KCNK5 gene was observed. Two SNPs near spondin-1 and semaphorin-4G approached genome-wide significance. The results of the current study did not provide an unambiguous predictor of CZP response.

Evaluating whole genome sequence data from the Genetic Absence Epilepsy Rat from Strasbourg and its related non-epileptic strain.

OBJECTIVE: The Genetic Absence Epilepsy Rats from Strasbourg (GAERS) are an inbreed Wistar rat strain widely used as a model of genetic generalised epilepsy with absence seizures. As in humans, the genetic architecture that results in genetic generalized epilepsy in GAERS is poorly understood. Here we present the strain-specific variants found among the epileptic GAERS and their related Non-Epileptic Control (NEC) strain. The GAERS and NEC represent a powerful opportunity to identify neurobiological factors that are associated with the genetic generalised epilepsy phenotype. METHODS: We performed whole genome sequencing on adult epileptic GAERS and adult NEC rats, a strain derived from the same original Wistar colony. We also generated whole genome sequencing on four double-crossed (GAERS with NEC) F2 selected for high-seizing (n = 2) and non-seizing (n = 2) phenotypes. RESULTS: Specific to the GAERS genome, we identified 1.12 million single nucleotide variants, 296.5K short insertion-deletions, and 354 putative copy number variants that result in complete or partial loss/duplication of 41 genes. Of the GAERS-specific variants that met high quality criteria, 25 are annotated as stop codon gain/loss, 56 as putative essential splice sites, and 56 indels are predicted to result in a frameshift. Subsequent screening against the two F2 progeny sequenced for having the highest and two F2 progeny for having the lowest seizure burden identified only the selected Cacna1h GAERS-private protein-coding variant as exclusively co-segregating with the two high-seizing F2 rats. SIGNIFICANCE: This study highlights an approach for using whole genome sequencing to narrow down to a manageable candidate list of genetic variants in a complex genetic epilepsy animal model, and suggests utility of this sequencing design to investigate other spontaneously occurring animal models of human disease.

Leveraging prior information to detect causal variants via multi-variant regression.

Although many methods are available to test sequence variants for association with complex diseases and traits, methods that specifically seek to identify causal variants are less developed. Here we develop and evaluate a Bayesian hierarchical regression method that incorporates prior information on the likelihood of variant causality through weighting of variant effects. By simulation studies using both simulated and real sequence variants, we compared a standard single variant test for analyzing variant-disease association with the proposed method using different weighting schemes. We found that by leveraging linkage disequilibrium of variants with known GWAS signals and sequence conservation (phastCons), the proposed method provides a powerful approach for detecting causal variants while controlling false positives.

Using ERDS to infer copy-number variants in high-coverage genomes.

Although there are many methods available for inferring copy-number variants (CNVs) from next-generation sequence data, there remains a need for a system that is computationally efficient but that retains good sensitivity and specificity across all types of CNVs. Here, we introduce a new method, estimation by read depth with single-nucleotide variants (ERDS), and use various approaches to compare its performance to other methods. We found that for common CNVs and high-coverage genomes, ERDS performs as well as the best method currently available (Genome STRiP), whereas for rare CNVs and high-coverage genomes, ERDS performs better than any available method. Importantly, ERDS accommodates both unique and highly amplified regions of the genome and does so without requiring separate alignments for calling CNVs and other variants. These comparisons show that for genomes sequenced at high coverage, ERDS provides a computationally convenient method that calls CNVs as well as or better than any currently available method.

Prioritizing genetic variants for causality on the basis of preferential linkage disequilibrium.

To date, the widely used genome-wide association studies (GWASs) of the human genome have reported thousands of variants that are significantly associated with various human traits. However, in the vast majority of these cases, the causal variants responsible for the observed associations remain unknown. In order to facilitate the identification of causal variants, we designed a simple computational method called the "preferential linkage disequilibrium (LD)" approach, which follows the variants discovered by GWASs to pinpoint the causal variants, even if they are rare compared with the discovery variants. The approach is based on the hypothesis that the GWAS-discovered variant is better at tagging the causal variants than are most other variants evaluated in the original GWAS. Applying the preferential LD approach to the GWAS signals of five human traits for which the causal variants are already known, we successfully placed the known causal variants among the top ten candidates in the majority of these cases. Application of this method to additional GWASs, including those of hepatitis C virus treatment response, plasma levels of clotting factors, and late-onset Alzheimer disease, has led to the identification of a number of promising candidate causal variants. This method represents a useful tool for delineating causal variants by bringing together GWAS signals and the rapidly accumulating variant data from next-generation sequencing.

Exome sequencing followed by large-scale genotyping fails to identify single rare variants of large effect in idiopathic generalized epilepsy.

Idiopathic generalized epilepsy (IGE) is a complex disease with high heritability, but little is known about its genetic architecture. Rare copy-number variants have been found to explain nearly 3% of individuals with IGE; however, it remains unclear whether variants with moderate effect size and frequencies below what are reliably detected with genome-wide association studies contribute significantly to disease risk. In this study, we compare the exome sequences of 118 individuals with IGE and 242 controls of European ancestry by using next-generation sequencing. The exome-sequenced epilepsy cases include study subjects with two forms of IGE, including juvenile myoclonic epilepsy (n = 93) and absence epilepsy (n = 25). However, our discovery strategy did not assume common genetic control between the subtypes of IGE considered. In the sequence data, as expected, no variants were significantly associated with the IGE phenotype or more specific IGE diagnoses. We then selected 3,897 candidate epilepsy-susceptibility variants from the sequence data and genotyped them in a larger set of 878 individuals with IGE and 1,830 controls. Again, no variant achieved statistical significance. However, 1,935 variants were observed exclusively in cases either as heterozygous or homozygous genotypes. It is likely that this set of variants includes real risk factors. The lack of significant association evidence of single variants with disease in this two-stage approach emphasizes the high genetic heterogeneity of epilepsy disorders, suggests that the impact of any individual single-nucleotide variant in this disease is small, and indicates that gene-based approaches might be more successful for future sequencing studies of epilepsy predisposition.

Exome sequencing followed by large-scale genotyping suggests a limited role for moderately rare risk factors of strong effect in schizophrenia.

Schizophrenia is a severe psychiatric disorder with strong heritability and marked heterogeneity in symptoms, course, and treatment response. There is strong interest in identifying genetic risk factors that can help to elucidate the pathophysiology and that might result in the development of improved treatments. Linkage and genome-wide association studies (GWASs) suggest that the genetic basis of schizophrenia is heterogeneous. However, it remains unclear whether the underlying genetic variants are mostly moderately rare and can be identified by the genotyping of variants observed in sequenced cases in large follow-up cohorts or whether they will typically be much rarer and therefore more effectively identified by gene-based methods that seek to combine candidate variants. Here, we consider 166 persons who have schizophrenia or schizoaffective disorder and who have had either their genomes or their exomes sequenced to high coverage. From these data, we selected 5,155 variants that were further evaluated in an independent cohort of 2,617 cases and 1,800 controls. No single variant showed a study-wide significant association in the initial or follow-up cohorts. However, we identified a number of case-specific variants, some of which might be real risk factors for schizophrenia, and these can be readily interrogated in other data sets. Our results indicate that schizophrenia risk is unlikely to be predominantly influenced by variants just outside the range detectable by GWASs. Rather, multiple rarer genetic variants must contribute substantially to the predisposition to schizophrenia, suggesting that both very large sample sizes and gene-based association tests will be required for securely identifying genetic risk factors.

A whole-genome analysis of premature termination codons.

We sequenced the genomes of ten unrelated individuals and identified heterozygous stop codon-gain variants in protein-coding genes: we then sequenced their transcriptomes and assessed the expression levels of the stop codon-gain alleles. An ANOVA showed statistically significant differences between their expression levels (p=4×10(-16)). This difference was almost entirely accounted for by whether the stop codon-gain variant had a second, non-protein-truncating function in or near an alternate transcript: stop codon-gains without alternate functions were generally not found in the cDNA (p=3×10(-5)). Additionally, stop codon-gain variants in two intronless genes were not expressed, an unexpected outcome given previous studies. In this study, stop codon-gain variants were either well expressed in all individuals or were never expressed. Our finding that stop codon-gain variants were generally expressed only when they had an alternate function suggests that most naturally occurring stop codon-gain variants in protein-coding genes are either not transcribed or have their transcripts destroyed.

SVA: software for annotating and visualizing sequenced human genomes.

SUMMARY: Here we present Sequence Variant Analyzer (SVA), a software tool that assigns a predicted biological function to variants identified in next-generation sequencing studies and provides a browser to visualize the variants in their genomic contexts. SVA also provides for flexible interaction with software implementing variant association tests allowing users to consider both the bioinformatic annotation of identified variants and the strength of their associations with studied traits. We illustrate the annotation features of SVA using two simple examples of sequenced genomes that harbor Mendelian mutations. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at http://www.svaproject.org.

A genome-wide comparison of the functional properties of rare and common genetic variants in humans.

One of the longest running debates in evolutionary biology concerns the kind of genetic variation that is primarily responsible for phenotypic variation in species. Here, we address this question for humans specifically from the perspective of population allele frequency of variants across the complete genome, including both coding and noncoding regions. We establish simple criteria to assess the likelihood that variants are functional based on their genomic locations and then use whole-genome sequence data from 29 subjects of European origin to assess the relationship between the functional properties of variants and their population allele frequencies. We find that for all criteria used to assess the likelihood that a variant is functional, the rarer variants are significantly more likely to be functional than the more common variants. Strikingly, these patterns disappear when we focus on only those variants in which the major alleles are derived. These analyses indicate that the majority of the genetic variation in terms of phenotypic consequence may result from a mutation-selection balance, as opposed to balancing selection, and have direct relevance to the study of human disease.