Identifying rare variants associated with complex traits via sequencing

Whole-exome sequencing in 415,422 individuals identifies rare variants associated with mitochondrial DNA copy number

Inter-individual variation in the number of copies of the mitochondrial genome, called mitochondrial DNA copy number (mtDNA-CN), reflects mitochondrial function and has been associated with various aging-related diseases. We examined 415,422 exomes of self-reported White ancestry individuals from the UK Biobank and tested the impact of rare variants, at the level of single variants and through aggregate variant-set tests, on mtDNA-CN. A survey across nine variant sets tested enrichment of putatively causal variants and identified 14 genes at experiment-wide significance and three genes at marginal significance. These included associations at known mtDNA depletion syndrome genes (mtDNA helicase TWNK, p = 1.1 × 10-30; mitochondrial transcription factor TFAM, p = 4.3 × 10-15; mtDNA maintenance exonuclease MGME1, p = 2.0 × 10-6) and the V617F dominant gain-of-function mutation in the tyrosine kinase JAK2 (p = 2.7 × 10-17), associated with myeloproliferative disease. Novel genes included the ATP-dependent protease CLPX (p = 8.4 × 10-9), involved in mitochondrial proteome quality, and the mitochondrial adenylate kinase AK2 (p = 4.7 × 10-8), involved in hematopoiesis. The most significant association was a missense variant in SAMHD1 (p = 4.2 × 10-28), found on a rare, 1.2-Mb shared ancestral haplotype on chromosome 20. SAMHD1 encodes a cytoplasmic host restriction factor involved in viral defense response and the mitochondrial nucleotide salvage pathway, and is associated with Aicardi-Goutières syndrome 5, a childhood encephalopathy and chronic inflammatory response disorder. Rare variants were enriched in Mendelian mtDNA depletion syndrome loci, and these variants implicated core processes in mtDNA replication, nucleoid structure formation, and maintenance. These data indicate that strong-effect mutations from the nuclear genome contribute to the genetic architecture of mtDNA-CN.

TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8

Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we developed Transcriptome-Integrated Genetic Association Resource V2 (TIGAR-V2), which directly reads Variant Call Format (VCF) files, enables parallel computation, and reduces up to 90% of computation cost (mainly due to loading genotype data) compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet process regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWASs using either individual-level or summary-level genome-wide association study (GWAS) data, and implement both burden and variance-component statistics for gene-based association tests. We trained gene expression prediction models by DPR for 49 tissues using Genotype-Tissue Expression (GTEx) V8 by TIGAR-V2 and illustrated the usefulness of these Bayesian cis-expression quantitative trait locus (eQTL) weights through TWASs of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes, respectively, for breast and ovarian cancer, most of which are either known or near previously identified GWAS (∼95%) or TWAS (∼40%) risk genes and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWASs can provide biological insight into the transcriptional regulation of complex diseases. The TIGAR-V2 tool, trained Bayesian cis-eQTL weights, and linkage disequilibrium (LD) information from GTEx V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases.

Emerging roles of rare and low-frequency genetic variants in type 1 diabetes mellitus

Type 1 diabetes mellitus (T1DM) is defined as an autoimmune disorder and has enormous complexity and heterogeneity. Although its precise pathogenic mechanisms are obscure, this disease is widely acknowledged to be precipitated by environmental factors in individuals with genetic susceptibility. To date, the known susceptibility loci, which have mostly been identified by genome-wide association studies, can explain 80%–85% of the heritability of T1DM. Researchers believe that at least a part of its missing genetic component is caused by undetected rare and low-frequency variants. Most common variants have only small to modest effect sizes, which increases the difficulty of dissecting their functions and restricts their potential clinical application. Intriguingly, many studies have indicated that rare and low-frequency variants have larger effect sizes and play more significant roles in susceptibility to common diseases, including T1DM, than common variants do. Therefore, better recognition of rare and low-frequency variants is beneficial for revealing the genetic architecture of T1DM and for providing new and potent therapeutic targets for this disease. Here, we will discuss existing challenges as well as the great significance of this field and review current knowledge of the contributions of rare and low-frequency variants to T1DM.

Whole-Exome Sequencing in Patients Affected by Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis Reveals New Variants Potentially Contributing to the Phenotype

Background

Adverse drug reactions (ADRs) are frequent occurring events that can essentially be defined as harmful or unpleasant symptoms secondary to the use of a medicinal product. ADRs involve a wide spectrum of clinical manifestations ranging from minor itching and rash to life-threatening reactions. Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare ADRs. SJS-TEN may be considered a polygenic pathology due to additive/epistatic effects caused by sequence variants in numerous genes. Next-generation sequencing (NGS) represents a potentially interesting exploration tool in such scenario as it facilitates the simultaneous analysis of large genomic regions and genes at affordable cost.

Methods

The present study has involved using whole-exome sequencing (WES) for the first time on SJS-TEN patients. It involved robust and innovative multistep bioinformatics analysis focusing on 313 candidate genes potentially participating in the disease’s aetiology, specific drugs’ metabolism and gene regulation.

Results

We identified combinations of frequently occurring and rare variants that may contribute to the disease’s pathogenesis. Depending on the specific drug being taken, different variants (and alleles) in NAT2, CYP2D8, CYP2B6, ABCC2, UGT2B7 and TCF3 were identified as coherent candidates representing potential future markers for SJS-TEN.

Conclusion

The present study proposed and has described (for the first time) a large-scale genomic analysis of patients affected by SJS-TEN. The genes and variants identified represent relevant candidates potentially participating in the disease’s pathogenesis. Corroborating that proposed by others, we found that complex combinations of frequently occurring and rare variants participating in particular drug metabolism molecular cascades could be associated with the phenotype. TCF3 TF may be considered a coherent candidate for SJS-TEN that should be analysed in new cohorts of patients having ADRs.

KDM5A mutations identified in autism spectrum disorder using forward genetics

Autism spectrum disorder (ASD) is a constellation of neurodevelopmental disorders with high phenotypic and genetic heterogeneity, complicating the discovery of causative genes. Through a forward genetics approach selecting for defective vocalization in mice, we identified Kdm5a as a candidate ASD gene. To validate our discovery, we generated a Kdm5a knockout mouse model (Kdm5a^-/-) and confirmed that inactivating Kdm5a disrupts vocalization. In addition, Kdm5a^-/- mice displayed repetitive behaviors, sociability deficits, cognitive dysfunction, and abnormal dendritic morphogenesis. Loss of KDM5A also resulted in dysregulation of the hippocampal transcriptome. To determine if KDM5A mutations cause ASD in humans, we screened whole exome sequencing and microarray data from a clinical cohort. We identified pathogenic KDM5A variants in nine patients with ASD and lack of speech. Our findings illustrate the power and efficacy of forward genetics in identifying ASD genes and highlight the importance of KDM5A in normal brain development and function.

Mathematical Properties of Linkage Disequilibrium Statistics Defined by Normalization of the Coefficient D = pAB - pApB

BACKGROUND

Many statistics for measuring linkage disequilibrium (LD) take the form of a normalization of the LD coefficient D. Different normalizations produce statistics with different ranges, interpretations, and arguments favoring their use.

METHODS

Here, to compare the mathematical properties of these normalizations, we consider 5 of these normalized statistics, describing their upper bounds, the mean values of their maxima over the set of possible allele frequency pairs, and the size of the allele frequency regions accessible given specified values of the statistics.

RESULTS

We produce detailed characterizations of these properties for the statistics d and ρ, analogous to computations previously performed for r2. We examine the relationships among the statistics, uncovering conditions under which some of them have close connections.

CONCLUSION

The results contribute insight into LD measurement, particularly the understanding of differences in the features of different LD measures when computed on the same data.

Novel idiopathic pulmonary fibrosis susceptibility variants revealed by deep sequencing

Background

Specific common and rare single nucleotide variants (SNVs) increase the likelihood of developing sporadic idiopathic pulmonary fibrosis (IPF). We performed target-enriched sequencing on three loci previously identified by a genome-wide association study to gain a deeper understanding of the full spectrum of IPF genetic risk and performed a two-stage case–control association study.

Methods

A total of 1.7 Mb of DNA from 181 IPF patients was deep sequenced (>100×) across 11p15.5, 14q21.3 and 17q21.31 loci. Comparisons were performed against 501 unrelated controls and replication studies were assessed in 3968 subjects.

Results

36 SNVs were associated with IPF susceptibility in the discovery stage (p<5.0×10⁻⁸). After meta-analysis, the strongest association corresponded to rs35705950 (p=9.27×10⁻⁵⁷) located upstream from the mucin 5B gene (MUC5B). Additionally, a novel association was found for two co-inherited low-frequency SNVs (<5%) in MUC5AC, predicting a missense amino acid change in mucin 5AC (lowest p=2.27×10⁻²²). Conditional and haplotype analyses in 11p15.5 supported the existence of an additional contribution of MUC5AC variants to IPF risk.

Conclusions

This study reinforces the significant IPF associations of these loci and implicates MUC5AC as another key player in IPF susceptibility.

Deep sequencing of genome-wide association study hits identified novel low-frequency variants associated with IPF susceptibility.http://bit.ly/2IF4AT8

VMAT2 gene (SLC18A2) variants associated with a greater risk for developing opioid dependence

Aim: To determine if selected serotonergic and noradrenergic gene variants are associated with heroin addiction. Subjects & methods: A total of 126 variants in 19 genes in subjects with Dutch European ancestry from The Netherlands. Subjects included 281 opioid-dependent volunteers in methadone maintenance or heroin-assisted treatment, 163 opioid-exposed but not opioid-dependent volunteers who have been using illicit opioids but never became opioid-dependent and 153 healthy controls. Results: Nominal associations were indicated for 20 variants in six genes including an experiment-wise significant association from the combined effect of three SLC18A2 SNPs (rs363332, rs363334 and rs363338) with heroin dependence (p_final = 0.047). Conclusion: Further studies are warranted to confirm and elucidate the role of these variants in the vulnerability to opioid addiction.

Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits

With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. © 2019 by John Wiley & Sons, Inc.

Association of FMO3 Variants with Blood Pressure in the Atherosclerosis Risk in Communities Study

Flavin containing monooxygenase 3 [FMO3] encodes dimethylaniline monooxygenase [N-oxide-forming] 3, which breaks down nitrogen-containing compounds, and has been implicated in blood pressure regulation. Studies have reported conflicting results of the association of a common nonsynonymous variant, E158K (rs2266782), with hypertension. We examined the associations of E158K, along with rare and low frequency exonic variants (minor allele frequency [MAF]<5%) in FMO3 with hypertension, systolic blood pressure (SBP), and diastolic blood pressure (DBP). We included 7,350 European Americans and 2,814 African Americans in the Atherosclerosis Risk in Communities (ARIC) study with exome sequencing of FMO3. The association of FMO3 variants with SBP and DBP was tested using single variant and gene-based tests followed by the replication or interrogation of significant variants in ancestry-specific cohorts based on Bonferroni corrected thresholds. E158K had significant association with higher SBP in African Americans in ARIC (p=0.03), and two low frequency variants had significant association with higher SBP in African Americans (rs200985584, MAF 0.1%, p=0.0003) and European Americans (rs75904274, MAF 1.7%, p=0.006). These associations were not significant with additional samples: E158K in a meta-analysis of SBP of African ancestry (N=30,841, p=0.43) that included ARIC participants and the two low frequency variants in an independent ancestry-specific exome sequencing study of blood pressure (rs200985584, p=0.94; rs75904274, p=0.81). Our study does not support the association of E158K and low frequency variants in FMO3 with blood pressure and demonstrates the importance of replication in genetic studies.

Exome-wide rare variant analyses of two bone mineral density phenotypes: the challenges of analyzing rare genetic variation

Performance of a recently developed test for association between multivariate phenotypes and sets of genetic variants (MURAT) is demonstrated using measures of bone mineral density (BMD). By combining individual-level whole genome sequenced data from the UK10K study, and imputed genome-wide genetic data on individuals from the Study of Osteoporotic Fractures (SOF) and the Osteoporotic Fractures in Men Study (MrOS), a data set of 8810 individuals was assembled; tests of association were performed between autosomal gene-sets of genetic variants and BMD measured at lumbar spine and femoral neck. Distributions of p-values obtained from analyses of a single BMD phenotype are compared to those from the multivariate tests, across several region definitions and variant weightings. There is evidence of increased power with the multivariate test, although no new loci for BMD were identified. Among 17 genes highlighted either because there were significant p-values in region-based association tests or because they were in well-known BMD genes, 4 windows in 2 genes as well as 6 single SNPs in one of these genes showed association at genome-wide significant thresholds with the multivariate phenotype test but not with the single-phenotype test, Sequence Kernel Association Test (SKAT).

Family studies to find rare high risk variants in migraine

INTRODUCTION

Migraine has long been known as a common complex disease caused by genetic and environmental factors. The pathophysiology and the specific genetic susceptibility are poorly understood. Common variants only explain a small part of the heritability of migraine. It is thought that rare genetic variants with bigger effect size may be involved in the disease. Since migraine has a tendency to cluster in families, a family approach might be the way to find these variants. This is also indicated by identification of migraine-associated loci in classical linkage-analyses in migraine families. A single migraine study using a candidate-gene approach was performed in 2010 identifying a rare mutation in the TRESK potassium channel segregating in a large family with migraine with aura, but this finding has later become questioned. The technologies of next-generation sequencing (NGS) now provides an affordable tool to investigate the genetic variation in the entire exome or genome. The family-based study design using NGS is described in this paper. We also review family studies using NGS that have been successful in finding rare variants in other common complex diseases in order to argue the promising application of a family approach to migraine.

METHOD

PubMed was searched to find studies that looked for rare genetic variants in common complex diseases through a family-based design using NGS, excluding studies looking for de-novo mutations, or using a candidate-gene approach and studies on cancer. All issues from Nature Genetics and PLOS genetics 2014, 2015 and 2016 (UTAI June) were screened for relevant papers. Reference lists from included and other relevant papers were also searched. For the description of the family-based study design using NGS an in-house protocol was used.

RESULTS

Thirty-two successful studies, which covered 16 different common complex diseases, were included in this paper. We also found a single migraine study. Twenty-three studies found one or a few family specific variants (less than five), while other studies found several possible variants. Not all of them were genome wide significant. Four studies performed follow-up analyses in unrelated cases and controls and calculated odds ratios that supported an association between detected variants and risk of disease. Studies of 11 diseases identified rare variants that segregated fully or to a large degree with the disease in the pedigrees.

CONCLUSION

It is possible to find rare high risk variants for common complex diseases through a family-based approach. One study using a family approach and NGS to find rare variants in migraine has already been published but with strong limitations. More studies are under way.

DoEstRare: A statistical test to identify local enrichments in rare genomic variants associated with disease

Next-generation sequencing technologies made it possible to assay the effect of rare variants on complex diseases. As an extension of the "common disease-common variant" paradigm, rare variant studies are necessary to get a more complete insight into the genetic architecture of human traits. Association studies of these rare variations show new challenges in terms of statistical analysis. Due to their low frequency, rare variants must be tested by groups. This approach is then hindered by the fact that an unknown proportion of the variants could be neutral. The risk level of a rare variation may be determined by its impact but also by its position in the protein sequence. More generally, the molecular mechanisms underlying the disease architecture may involve specific protein domains or inter-genic regulatory regions. While a large variety of methods are optimizing functionality weights for each single marker, few evaluate variant position differences between cases and controls. Here, we propose a test called DoEstRare, which aims to simultaneously detect clusters of disease risk variants and global allele frequency differences in genomic regions. This test estimates, for cases and controls, variant position densities in the genetic region by a kernel method, weighted by a function of allele frequencies. We compared DoEstRare with previously published strategies through simulation studies as well as re-analysis of real datasets. Based on simulation under various scenarios, DoEstRare was the sole to consistently show highest performance, in terms of type I error and power both when variants were clustered or not. DoEstRare was also applied to Brugada syndrome and early-onset Alzheimer's disease data and provided complementary results to other existing tests. DoEstRare, by integrating variant position information, gives new opportunities to explain disease susceptibility. DoEstRare is implemented in a user-friendly R package.

Genetic variation in IBD: progress, clues to pathogenesis and possible clinical utility

Epidemiological and clinical studies have suggested that the pathogenesis of inflammatory bowel disease (IBD) is strongly influenced by genetic predisposition. Beyond the limitations of linkage analysis, multiple genome-wide association studies, their meta-analyses, and targeted genotyping array techniques have broadened our understanding of the genetic architecture of IBD. Currently, over 200 single nucleotide polymorphisms are known to be associated with susceptibility to IBD and through functional analysis of genes and loci, a substantial proportion of pathophysiologic mechanisms have been revealed. However, because only a modest fraction of predicted heritability can be explained by known genes/loci, additional strategies are needed including the identification of rare variants with large effect sizes to help explain the missing heritability. Considerable progress is also being made on applying outcomes of genetic research in diagnostics, classification, prognostics, and the development of new therapeutics of IBD.

Population-specific single-nucleotide polymorphism confers increased risk of venous thromboembolism in African Americans

INTRODUCTION

African Americans have a higher incidence of venous thromboembolism (VTE) than European descent individuals. However, the typical genetic risk factors in populations of European descent are nearly absent in African Americans, and population-specific genetic factors influencing the higher VTE rate are not well characterized.

METHODS

We performed a candidate gene analysis on an exome-sequenced African American family with recurrent VTE and identified a variant in Protein S (PROS1) V510M (rs138925964). We assessed the population impact of PROS1 V510M using a multicenter African American cohort of 306 cases with VTE compared to 370 controls. Additionally, we compared our case cohort to a background population cohort of 2203 African Americans in the NHLBI GO Exome Sequencing Project (ESP).

RESULTS

In the African American family with recurrent VTE, we found prior laboratories for our cases indicating low free Protein S levels, providing functional support for PROS1 V510M as the causative mutation. Additionally, this variant was significantly enriched in the VTE cases of our multicenter case-control study (Fisher's Exact Test, P = 0.0041, OR = 4.62, 95% CI: 1.51-15.20; allele frequencies - cases: 2.45%, controls: 0.54%). Similarly, PROS1 V510M was also enriched in our VTE case cohort compared to African Americans in the ESP cohort (Fisher's Exact Test, P = 0.010, OR = 2.28, 95% CI: 1.26-4.10).

CONCLUSIONS

We found a variant, PROS1 V510M, in an African American family with VTE and clinical laboratory abnormalities in Protein S. Additionally, we found that this variant conferred increased risk of VTE in a case-control study of African Americans. In the ESP cohort, the variant is nearly absent in ESP European descent subjects (n = 3, allele frequency: 0.03%). Additionally, in 1000 Genomes Phase 3 data, the variant only appears in African descent populations. Thus, PROS1 V510M is a population-specific genetic risk factor for VTE in African Americans.

Use of Genomic Tools to Improve Cattle Health in the Context of Infectious Diseases

Although infectious diseases impose a heavy economic burden on the cattle industry, the etiology of many disorders that affect livestock is not fully elucidated, and effective countermeasures are often lacking. The main tools available until now have been vaccines, antibiotics and antiparasitic drugs. Although these have been very successful in some cases, the appearance of parasite and microbial resistance to these treatments is a cause of concern. Next-generation sequencing provides important opportunities to tackle problems associated with pathogenic illnesses. This review describes the rapid gains achieved to track disease progression, identify the pathogens involved, and map pathogen interactions with the host. Use of novel genomic tools subsequently aids in treatment development, as well as successful creation of breeding programs aimed toward less susceptible livestock. These may be important tools for mitigating the long term effects of combating infection and helping reduce the reliance on antibiotic treatment.

Common and Rare Variant Association Study for Plasma Lipids and Coronary Artery Disease

Blood lipid levels are highly heritable and modifiable risk factors for coronary artery disease (CAD), and are the leading cause of death worldwide. These facts have motivated human genetic association studies that have the substantial potential to define the risk factors that are causal and to identify pathways and therapeutic targets for lipids and CAD.The success of the HapMap project that provided an extensive catalog of human genetic variations and the development of microarray based genotyping chips (typically containing variations with allele frequencies ＞ 5%) facilitated common variant association study (CVAS; formerly termed genome-wide association study, GWAS) identifying disease-associated variants in a genome-wide manner. To date, 157 loci associated with blood lipids and 46 loci with CAD have been successfully identified, accounting for approximately 12%-14% of heritability for lipids and 10% of heritability for CAD. However, there is yet a major challenge termed "missing heritability problem," namely the observation that loci detected by CVAS explain only a small fraction of the inferred genetic variations. To explain such missing portions, focuses in genetic association studies have shifted from common to rare variants. However, it is challenging to apply rare variant association study (RVAS) in an unbiased manner because such variants typically lack the sufficient number to be identified statistically.In this review, we provide a current understanding of the genetic architecture mostly derived from CVAS, and several updates on the progress and limitations of RVAS for lipids and CAD.

Choosing Subsamples for Sequencing Studies by Minimizing the Average Distance to the Closest Leaf

Imputation of genotypes in a study sample can make use of sequenced or densely genotyped external reference panels consisting of individuals that are not from the study sample. It also can employ internal reference panels, incorporating a subset of individuals from the study sample itself. Internal panels offer an advantage over external panels because they can reduce imputation errors arising from genetic dissimilarity between a population of interest and a second, distinct population from which the external reference panel has been constructed. As the cost of next-generation sequencing decreases, internal reference panel selection is becoming increasingly feasible. However, it is not clear how best to select individuals to include in such panels. We introduce a new method for selecting an internal reference panel—minimizing the average distance to the closest leaf (ADCL)—and compare its performance relative to an earlier algorithm: maximizing phylogenetic diversity (PD). Employing both simulated data and sequences from the 1000 Genomes Project, we show that ADCL provides a significant improvement in imputation accuracy, especially for imputation of sites with low-frequency alleles. This improvement in imputation accuracy is robust to changes in reference panel size, marker density, and length of the imputation target region.

A haplotype-based framework for group-wise transmission/disequilibrium tests for rare variant association analysis

MOTIVATION

A major focus of current sequencing studies for human genetics is to identify rare variants associated with complex diseases. Aside from reduced power of detecting associated rare variants, controlling for population stratification is particularly challenging for rare variants. Transmission/disequilibrium tests (TDT) based on family designs are robust to population stratification and admixture, and therefore provide an effective approach to rare variant association studies to eliminate spurious associations. To increase power of rare variant association analysis, gene-based collapsing methods become standard approaches for analyzing rare variants. Existing methods that extend this strategy to rare variants in families usually combine TDT statistics at individual variants and therefore lack the flexibility of incorporating other genetic models.

RESULTS

In this study, we describe a haplotype-based framework for group-wise TDT (gTDT) that is flexible to encompass a variety of genetic models such as additive, dominant and compound heterozygous (CH) (i.e. recessive) models as well as other complex interactions. Unlike existing methods, gTDT constructs haplotypes by transmission when possible and inherently takes into account the linkage disequilibrium among variants. Through extensive simulations we showed that type I error was correctly controlled for rare variants under all models investigated, and this remained true in the presence of population stratification. Under a variety of genetic models, gTDT showed increased power compared with the single marker TDT. Application of gTDT to an autism exome sequencing data of 118 trios identified potentially interesting candidate genes with CH rare variants.

AVAILABILITY AND IMPLEMENTATION

We implemented gTDT in C++ and the source code and the detailed usage are available on the authors' website (https://medschool.vanderbilt.edu/cgg).

CONTACT

bingshan.li@vanderbilt.edu or wei.chen@chp.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetic analysis of schizophrenia and bipolar disorder reveals polygenicity but also suggests new directions for molecular interrogation

Over the last few years, genetics research has made significant strides in identifying many risk factors for schizophrenia and bipolar disorder. These risk factors include inherited common single nucleotide polymorphisms, copy number variants, and rare single nucleotide variants, as well as rare de novo variants. For all variants, the common theme has been that of polygenicity, meaning that many small genetic risk factors influence risk in the population and that no gene or variant on its own has been shown to be fully deterministic of schizophrenia or bipolar. When taken together, biological themes that have emerged including the importance of synaptic function and calcium signaling. This has implications for our understanding of the biological underpinnings of these diseases.

Pooled sequencing and rare variant association tests for identifying the determinants of emerging drug resistance in malaria parasites

We explored the potential of pooled sequencing to swiftly and economically identify selective sweeps due to emerging artemisinin (ART) resistance in a South-East Asian malaria parasite population. ART resistance is defined by slow parasite clearance from the blood of ART-treated patients and mutations in the kelch gene (chr. 13) have been strongly implicated to play a role. We constructed triplicate pools of 70 slow-clearing (resistant) and 70 fast-clearing (sensitive) infections collected from the Thai–Myanmar border and sequenced these to high (∼150-fold) read depth. Allele frequency estimates from pools showed almost perfect correlation (Lin’s concordance = 0.98) with allele frequencies at 93 single nucleotide polymorphisms measured directly from individual infections, giving us confidence in the accuracy of this approach. By mapping genome-wide divergence (F_ST) between pools of drug-resistant and drug-sensitive parasites, we identified two large (>150 kb) regions (on chrs. 13 and 14) and 17 smaller candidate genome regions. To identify individual genes within these genome regions, we resequenced an additional 38 parasite genomes (16 slow and 22 fast-clearing) and performed rare variant association tests. These confirmed kelch as a major molecular marker for ART resistance (P = 6.03 × 10⁻⁶). This two-tier approach is powerful because pooled sequencing rapidly narrows down genome regions of interest, while targeted rare variant association testing within these regions can pinpoint the genetic basis of resistance. We show that our approach is robust to recurrent mutation and the generation of soft selective sweeps, which are predicted to be common in pathogen populations with large effective population sizes, and may confound more traditional gene mapping approaches.

Effective filtering strategies to improve data quality from population-based whole exome sequencing studies

Background

Genotypes generated in next generation sequencing studies contain errors which can significantly impact the power to detect signals in common and rare variant association tests. These genotyping errors are not explicitly filtered by the standard GATK Variant Quality Score Recalibration (VQSR) tool and thus remain a source of errors in whole exome sequencing (WES) projects that follow GATK’s recommended best practices. Therefore, additional data filtering methods are required to effectively remove these errors before performing association analyses with complex phenotypes. Here we empirically derive thresholds for genotype and variant filters that, when used in conjunction with the VQSR tool, achieve higher data quality than when using VQSR alone.

Results

The detailed filtering strategies improve the concordance of sequenced genotypes with array genotypes from 99.33% to 99.77%; improve the percent of discordant genotypes removed from 10.5% to 69.5%; and improve the Ti/Tv ratio from 2.63 to 2.75. We also demonstrate that managing batch effects by separating samples based on different target capture and sequencing chemistry protocols results in a final data set containing 40.9% more high-quality variants. In addition, imputation is an important component of WES studies and is used to estimate common variant genotypes to generate additional markers for association analyses. As such, we demonstrate filtering methods for imputed data that improve genotype concordance from 79.3% to 99.8% while removing 99.5% of discordant genotypes.

Conclusions

The described filtering methods are advantageous for large population-based WES studies designed to identify common and rare variation associated with complex diseases. Compared to data processed through standard practices, these strategies result in substantially higher quality data for common and rare association analyses.

Impact of exome sequencing in inflammatory bowel disease

Approaches to understanding the genetic contribution to inflammatory bowel disease (IBD) have continuously evolved from family- and population-based epidemiology, to linkage analysis, and most recently, to genome-wide association studies (GWAS). The next stage in this evolution seems to be the sequencing of the exome, that is, the regions of the human genome which encode proteins. The GWAS approach has been very fruitful in identifying at least 163 loci as being associated with IBD, and now, exome sequencing promises to take our genetic understanding to the next level. In this review we will discuss the possible contributions that can be made by an exome sequencing approach both at the individual patient level to aid with disease diagnosis and future therapies, as well as in advancing knowledge of the pathogenesis of IBD.