Computational and statistical approaches to analyzing variants identified by exome sequencing

The c.1243T>C mutation in the PROC gene is linked with inherited protein C deficiency and severe purpura fulminans

Purpura fulminans is a severe coagulation disorder that often leads to death in neonates. Mutations in the protein C (PROC) gene can cause protein C deficiency, leading to this disorder. This study aimed to investigate a family with a history of coagulopathies, particularly those related to protein C deficiency. The primary objective was to identify any genetic mutations in the PROC gene responsible for the coagulopathies. The study focused on a male neonate with purpura fulminans who ultimately died at 2 months of age. The patient had low protein C activity levels (6%). The entire PROC gene of the patient and his family was analyzed using next-generation sequencing to identify any genetic mutations. Segregation analysis was conducted to determine if the mutation followed an autosomal dominant inheritance pattern. In silico analysis was also conducted to evaluate the pathogenicity of the identified mutation. Analysis revealed a novel homozygous c.1243T>G variant PROC gene. The mutation resulted in a Phe415Val substitution. The mutation was found in at least three generations of the family. Carrier family members had lower protein C activity levels than wild-type homozygotes. Additionally, the mutation may account for the observed reduction in protein C enzyme activity.

Deficit of homozygosity among 1.52 million individuals and genetic causes of recessive lethality

Genotypes causing pregnancy loss and perinatal mortality are depleted among living individuals and are therefore difficult to find. To explore genetic causes of recessive lethality, we searched for sequence variants with deficit of homozygosity among 1.52 million individuals from six European populations. In this study, we identified 25 genes harboring protein-altering sequence variants with a strong deficit of homozygosity (10% or less of predicted homozygotes). Sequence variants in 12 of the genes cause Mendelian disease under a recessive mode of inheritance, two under a dominant mode, but variants in the remaining 11 have not been reported to cause disease. Sequence variants with a strong deficit of homozygosity are over-represented among genes essential for growth of human cell lines and genes orthologous to mouse genes known to affect viability. The function of these genes gives insight into the genetics of intrauterine lethality. We also identified 1077 genes with homozygous predicted loss-of-function genotypes not previously described, bringing the total set of genes completely knocked out in humans to 4785.

Ophthalmic genetic counselling: emerging trends in practice perspectives in Asia

Genetic counselling (GC) provides information to the patient and the family to make informed choices. Among the advanced Western countries and a few Asian countries, there are certified or trained professionals who perform GC. The Human Genome Project and next-generation sequencing diagnostics have provided an opportunity for increased genetic testing in the field of ophthalmology. The recent interventional therapeutic research strategies have also generated additional interest to seek GC globally, including in Asia. However, GC has several barriers to practise in the developing countries in Asia, namely, (a) shortage of qualified or trained genetic counsellors, (b) poor knowledge and reluctance in clinical adoption of genomics among the physicians in clinical practice, (c) overstretched public health services, and (d) negligible ophthalmic GC-related research and publications. The GC inadequacy in Asia is glaring in the most populous countries like China and India. Cultural differences, religious beliefs, misogyny, genetic discrimination, and a multitude of languages in Asia create unique challenges that counsellors in the West may only encounter with the immigrant minorities. Since there are currently 500 or more specific Mendelian genetic eye disorders, it is important for genetic counsellors to translate the genetic results at a level that the patient and family understand. There is therefore a need for governmental and healthcare organisations to train genetic counsellors in Asia and especially this practice must be included in the routine comprehensive ophthalmic care practice.

Frequencies of variants in genes associated with dyslipidemias identified in Costa Rican genomes

Dyslipidemias are risk factors in diseases of significant importance to public health, such as atherosclerosis, a condition that contributes to the development of cardiovascular disease. Unhealthy lifestyles, the pre-existence of diseases, and the accumulation of genetic variants in some loci contribute to the development of dyslipidemia. The genetic causality behind these diseases has been studied primarily on populations with extensive European ancestry. Only some studies have explored this topic in Costa Rica, and none have focused on identifying variants that can alter blood lipid levels and quantifying their frequency. To fill this gap, this study focused on identifying variants in 69 genes involved in lipid metabolism using genomes from two studies in Costa Rica. We contrasted the allelic frequencies with those of groups reported in the 1000 Genomes Project and gnomAD and identified potential variants that could influence the development of dyslipidemias. In total, we detected 2,600 variants in the evaluated regions. However, after various filtering steps, we obtained 18 variants that have the potential to alter the function of 16 genes, nine variants have pharmacogenomic or protective implications, eight have high risk in Variant Effect Predictor, and eight were found in other Latin American genetic studies of lipid alterations and the development of dyslipidemia. Some of these variants have been linked to changes in blood lipid levels in other global studies and databases. In future studies, we propose to confirm at least 40 variants of interest from 23 genes in a larger cohort from Costa Rica and Latin American populations to determine their relevance regarding the genetic burden for dyslipidemia. Additionally, more complex studies should arise that include diverse clinical, environmental, and genetic data from patients and controls and functional validation of the variants.

Whole exome sequencing in a juvenile idiopathic arthritis large family with SERPINA1 gene mutations

OBJECTIVES

Although the underlying mechanisms and mediators of arthritis in juvenile idiopathic arthritis are not well understood, accumulated evidence supports the mixt role of genetic and environmental factors. Few reports of multiplex families with JIA were published until now. The aim of this study was to describe the subjects affected by juvenile idiopathic arthritis and psoriatic features (JIAPs) in a large family.

METHODS

Here, we characterized an extended multiplex family of 5 patients with juvenile idiopathic arthritis and psoriatic features (PsA) at the clinical and genetic level, using whole exome sequencing.

RESULTS

We did not confirm in our family the linkage with the genetic factors already described that might be associated with increase susceptibility to JIA. We found a carrier status of siblings who inherited a pathogenic allele of the SERPINA1 gene from their mother who herself has two heterozygous pathogenic variants in the SERPINA1 gene.

CONCLUSIONS

This study didn't identify genetic contributive factors but highlights potentially environmental associations concerning the siblings of a family with juvenile idiopathic arthritis and psoriatic features (JIAPs). It is difficult to establish that SERPINA1 gene mutation has an etiological role as the levels of AAT are only slightly decreased and all the children harbor heterozygous variants.

NGS in Hereditary Ataxia: When Rare Becomes Frequent

The term hereditary ataxia (HA) refers to a heterogeneous group of neurological disorders with multiple genetic etiologies and a wide spectrum of ataxia-dominated phenotypes. Massive gene analysis in next-generation sequencing has entered the HA scenario, broadening our genetic and clinical knowledge of these conditions. In this study, we employed a targeted resequencing panel (TRP) in a large and highly heterogeneous cohort of 377 patients with a clinical diagnosis of HA, but no molecular diagnosis on routine genetic tests. We obtained a positive result (genetic diagnosis) in 33.2% of the patients, a rate significantly higher than those reported in similar studies employing TRP (average 19.4%), and in line with those performed using exome sequencing (ES, average 34.6%). Moreover, 15.6% of the patients had an uncertain molecular diagnosis. STUB1, PRKCG, and SPG7 were the most common causative genes. A comparison with published literature data showed that our panel would have identified 97% of the positive cases reported in previous TRP-based studies and 92% of those diagnosed by ES. Proper use of multigene panels, when combined with detailed phenotypic data, seems to be even more efficient than ES in clinical practice.

Rare Modifier Variants Alter the Severity of Cardiovascular Disease in Pseudoxanthoma Elasticum: Identification of Novel Candidate Modifier Genes and Disease Pathways Through Mixture of Effects Analysis

Introduction: Pseudoxanthoma elasticum (PXE), an ectopic mineralization disorder caused by pathogenic ABCC6 variants, is characterized by skin, ocular and cardiovascular (CV) symptoms. Due to striking phenotypic variability without genotype-phenotype correlations, modifier genes are thought to play a role in disease variability. In this study, we evaluated the collective modifying effect of rare variants on the cardiovascular phenotype of PXE.

Materials and Methods: Mixed effects of rare variants were assessed by Whole Exome Sequencing in 11 PXE patients with an extreme CV phenotype (mild/severe). Statistical analysis (SKAT-O and C-alpha testing) was performed to identify new modifier genes for the CV PXE phenotype and enrichment analysis for genes significantly associated with the severe cohort was used to evaluate pathway and gene ontology features.

Results Respectively 16 (SKAT-O) and 74 (C-alpha) genes were significantly associated to the severe cohort. Top significant genes could be stratified in 3 groups–calcium homeostasis, association with vascular disease and induction of apoptosis. Comparative analysis of both analyses led to prioritization of four genes (NLRP1, SELE, TRPV1, and CSF1R), all signaling through IL-1B.

Conclusion This study explored for the first time the cumulative effect of rare variants on the severity of cardiovascular disease in PXE, leading to a panel of novel candidate modifier genes and disease pathways. Though further validation is essential, this panel may aid in risk stratification and genetic counseling of PXE patients and will help to gain new insights in the PXE pathophysiology.

PolyBoost: An enhanced genomic variant classifier using extreme gradient boosting

PURPOSE

Human exome sequences contain 15,000-20,000 variants but many variants have unknown clinical impact. In silico predictive classifiers are recognized by the American College of Medical Genetics as a resource for interpreting these "variants of uncertain significance." Many in silico classifiers have been developed, of which PolyPhen-2 is highly successful and widely used. PolyPhen-2 uses a naïve Bayes model to synthesize sequence, structural and genomic information. I investigated whether predictive performance could be improved by replacing PolyPhen-2's naïve Bayes model with alternative machine learning methods.

EXPERIMENTAL DESIGN

Classifiers using the PolyPhen-2 feature set were retrained using extreme gradient boosting (XGBoost), random forests, artificial neural networks, and support vector machines. Classifiers were externally validated on "pathogenic" and "benign" ClinVar variants absent from the training datasets. Software is implemented in Python and is freely available at https://github.com/djparente/polyboost and the Python Package Index (PyPI) under the BSD license.

RESULTS

An XGBoost-based classifier-designated PolyBoost (PolyPhen-2 Booster)-improves discriminative performance and calibration relative to PolyPhen-2 in external validation on ClinVar.

CONCLUSIONS AND CLINICAL RELEVANCE

PolyBoost analyzes PolyPhen-2 output and can be incorporated into existing bioinformatics workflows as a post-analysis method to improve interpretation of clinical exome sequences obtained to identify monogenic disease.

Loss-of-Function Variants in the Tumor-Suppressor Gene PTPN14 Confer Increased Cancer Risk

The success of genome-wide association studies (GWAS) in identifying common, low-penetrance variant-cancer associations for the past decade is undisputed. However, discovering additional high-penetrance cancer mutations in unknown cancer predisposing genes requires detection of variant-cancer association of ultra-rare coding variants. Consequently, large-scale next-generation sequence data with associated phenotype information are needed. Here, we used genotype data on 166,281 Icelanders, of which, 49,708 were whole-genome sequenced and 408,595 individuals from the UK Biobank, of which, 41,147 were whole-exome sequenced, to test for association between loss-of-function burden in autosomal genes and basal cell carcinoma (BCC), the most common cancer in Caucasians. A total of 25,205 BCC cases and 683,058 controls were tested. Rare germline loss-of-function variants in PTPN14 conferred substantial risks of BCC (OR, 8.0; P = 1.9 × 10^-12), with a quarter of carriers getting BCC before age 70 and over half in their lifetime. Furthermore, common variants at the PTPN14 locus were associated with BCC, suggesting PTPN14 as a new, high-impact BCC predisposition gene. A follow-up investigation of 24 cancers and three benign tumor types showed that PTPN14 loss-of-function variants are associated with high risk of cervical cancer (OR, 12.7, P = 1.6 × 10^-4) and low age at diagnosis. Our findings, using power-increasing methods with high-quality rare variant genotypes, highlight future prospects for new discoveries on carcinogenesis. SIGNIFICANCE: This study identifies the tumor-suppressor gene PTPN14 as a high-impact BCC predisposition gene and indicates that inactivation of PTPN14 by germline sequence variants may also lead to increased risk of cervical cancer.

Genetic Variants Contributing to Early Recurrent Pregnancy Loss Etiology Identified by Sequencing Approaches

Recurrent pregnancy loss (RPL) affects up to 5% of couples. It is believed that genetic factors contribute to the disease's etiology and pathophysiology. Hundreds of genes represent coherent RPL candidates due to mammalian implantation's inherent complexity. Sanger sequencing (direct sequencing) of candidate genes has identified potential RPL causative genes (and variants), including those regulating embryo implantation and pregnancy maintenance. Although this approach is a reliable technique, the simultaneous analysis of large genomic regions is challenging. Next-generation sequencing (NGS) technology has thus emerged as a useful alternative for determining genetic variants and transcriptomic disturbances contributing to monogenic and polygenic diseases pathogenesis. However, interpreting results remains challenging as NGS experiments provide an enormous amount of complex data. The molecular aspects of specific diseases must be fully understood for accurate interpretation of NGS data. This review was thus aimed at describing (for the first time) the most relevant studies involving Sanger and NGS sequencing, leading to the description of variants related to RPL pathogenesis. Successful RPL-related NGS initiatives (including RNAseq-based studies) and future challenges are discussed. We consider that the information given here should be useful for clinicians, scientists, and students to enable a better understanding of RPL etiology. It may also provide a basis for the development of diagnostic/prognostic approaches contributing toward translational medicine.

Defining Extreme Phenotypes of OSA Across International Sleep Centers

BACKGROUND

Extreme phenotypes of OSA have not been systematically defined.

RESEARCH QUESTION

This study developed objective definitions of extreme phenotypes of OSA by using a multivariate approach. The utility of these definitions for identifying characteristics that confer predisposition toward or protection against OSA is shown in a new prospective sample.

STUDY DESIGN AND METHODS

In a large international sample, race-specific liability scores were calculated from a weighted logistic regression that included age, sex, and BMI. Extreme cases were defined as individuals with an apnea-hypopnea index (AHI) ≥ 30 events/hour but low likelihood of OSA based on age, sex, and BMI (liability scores > 90th percentile). Similarly, extreme controls were individuals with an AHI < 5 events/hour but high likelihood of OSA (liability scores < 10th percentile). Definitions were applied to a prospective sample from the Sleep Apnea Global Interdisciplinary Consortium, and differences in photography-based craniofacial and intraoral phenotypes were evaluated.

RESULTS

This study included retrospective data from 81,338 individuals. A total of 4,168 extreme cases and 1,432 extreme controls were identified by using liability scores. Extreme cases were younger (43.1 ± 14.7 years), overweight (28.6 ± 6.8 kg/m²), and predominantly female (71.1%). Extreme controls were older (53.8 ± 14.1 years), obese (34.0 ± 8.1 kg/m²), and predominantly male (65.8%). These objective definitions identified 29 extreme cases and 87 extreme controls among 1,424 Sleep Apnea Global Interdisciplinary Consortium participants with photography-based phenotyping. Comparisons suggest that a greater cervicomental angle increases risk for OSA in the absence of clinical risk factors, and smaller facial widths are protective in the presence of clinical risk factors.

INTERPRETATION

This objective definition can be applied in sleep centers throughout the world to consistently define OSA extreme phenotypes for future studies on genetic, anatomic, and physiologic pathways to OSA.

Laboratory Approach to Hemolytic Anemia

Hemolytic anemias are a group of disorders with varied clinical and molecular heterogeneity. They are characterized by decreased levels of circulating erythrocytes in blood. The pathognomic finding is a reduced red cell life span with severe anemia or, compensated hemolysis accompanied by reticulocytosis. The diagnostic workup or laboratory approach for hemolytic anemias is based on methodical step-wise testing which includes red blood cell morphology, hematological indices with increased reticulocyte count along with clinical features of hemolytic anemias. If conventional laboratory tests are unable to detect the underlying cause of hemolysis, genetic testing is recommended. Sanger sequencing along with conventional testing is the most efficient way to diagnose the underlying genetic causes, especially in thalassemias/hemoglobinopathies, if required. However, hemolytic anemias being highly heterogeneous disorders, next-generation sequencing-based screening is rapidly becoming an efficient way to decipher the etiologies where common causes have been excluded.

Genetic Variations and Precision Medicine

The time and costs associated with the sequencing of a human genome have decreased significantly in recent years. Many people have chosen to have their genomes sequenced to receive genomics-based personalized healthcare services. To reach the goal of genomics-based precision medicine, health information management (HIM) professionals need to manage and analyze patients' genomic data. Two important pieces of information from the genome sequence are the risk of genetic diseases and the specific medication or pharmacogenomic results for the individual patient, both of which are linked to a patient's genetic variations. In this review article, we introduce genetic variations, including their data types, relevant databases, and some currently available analysis methods and systems. HIM professionals can choose to use these databases, methods, and systems in the management and analysis of patients' genomic data.

Integrated Somatic and Germline Whole-Exome Sequencing Analysis in Women with Lung Cancer after a Previous Breast Cancer

Women treated for breast cancer (BC) are at risk of developing secondary tumors, such as lung cancer (LC). Since rare germline variants have been linked to multiple cancer development, we hypothesized that BC survivors might be prone to develop LC as a result of harboring rare variants. Sixty patients with LC with previous BC (the study population; SP) and 53 women with either BC or LC and no secondary cancer (control population; CP) were enrolled. Whole exome sequencing was performed in both tumors and unaffected tissues from 28/60 SP patients, and in germline DNA from 32/53 CP. Candidate genes were validated in the remaining individuals from both populations. We found two main mutational signature profiles: S1 (C>T) in all BCs and 16/28 LCs, and S2 (C>A) which is strongly associated with smoking, in 12/28 LCs. The burden test over rare germline variants in S1-LC vs CP identified 248 genes. Validation confirmed GSN as significantly associated with LC in never-smokers. In conclusion, our data suggest two signatures involved in LC onset in women with previous BC. One of these signatures is linked to smoking. Conversely, regardless of smoking habit, in a subgroup of BC survivors genetic susceptibility may contribute to LC risk.

Genetic Variants Contributing to Early Recurrent Pregnancy Loss Etiology Identified by Sequencing Approaches

Recurrent pregnancy loss (RPL) affects up to 5% of couples. It is believed that genetic factors contribute to the disease's etiology and pathophysiology. Hundreds of genes represent coherent RPL candidates due to mammalian implantation's inherent complexity. Sanger sequencing (direct sequencing) of candidate genes has identified potential RPL causative genes (and variants), including those regulating embryo implantation and pregnancy maintenance. Although this approach is a reliable technique, the simultaneous analysis of large genomic regions is challenging. Next-generation sequencing (NGS) technology has thus emerged as a useful alternative for determining genetic variants and transcriptomic disturbances contributing to monogenic and polygenic diseases pathogenesis. However, interpreting results remains challenging as NGS experiments provide an enormous amount of complex data. The molecular aspects of specific diseases must be fully understood for accurate interpretation of NGS data. This review was thus aimed at describing (for the first time) the most relevant studies involving Sanger and NGS sequencing, leading to the description of variants related to RPL pathogenesis. Successful RPL-related NGS initiatives (including RNAseq-based studies) and future challenges are discussed. We consider that the information given here should be useful for clinicians, scientists, and students to enable a better understanding of RPL etiology. It may also provide a basis for the development of diagnostic/prognostic approaches contributing toward translational medicine.

Modeling mutant/wild-type interactions to ascertain pathogenicity of PROKR2 missense variants in patients with isolated GnRH deficiency

A major challenge in human genetics is the validation of pathogenicity of heterozygous missense variants. This problem is well-illustrated by PROKR2 variants associated with Isolated GnRH Deficiency (IGD). Homozygous, loss of function variants in PROKR2 was initially implicated in autosomal recessive IGD; however, most IGD-associated PROKR2 variants are heterozygous. Moreover, while IGD patient cohorts are enriched for PROKR2 missense variants similar rare variants are also found in normal individuals. To elucidate the pathogenic mechanisms distinguishing IGD-associated PROKR2 variants from rare variants in controls, we assessed 59 variants using three approaches: (i) in silico prediction, (ii) traditional in vitro functional assays across three signaling pathways with mutant-alone transfections, and (iii) modified in vitro assays with mutant and wild-type expression constructs co-transfected to model in vivo heterozygosity. We found that neither in silico analyses nor traditional in vitro assessments of mutants transfected alone could distinguish IGD variants from control variants. However, in vitro co-transfections revealed that 15/34 IGD variants caused loss-of-function (LoF), including 3 novel dominant-negatives, while only 4/25 control variants caused LoF. Surprisingly, 19 IGD-associated variants were benign or exhibited LoF that could be rescued by WT co-transfection. Overall, variants that were LoF in ≥ 2 signaling assays under co-transfection conditions were more likely to be disease-associated than benign or 'rescuable' variants. Our findings suggest that in vitro modeling of WT/Mutant interactions increases the resolution for identifying causal variants, uncovers novel dominant negative mutations, and provides new insights into the pathogenic mechanisms underlying heterozygous PROKR2 variants.

Burden Testing of Rare Variants Identified through Exome Sequencing via Publicly Available Control Data

The genetic causes of many Mendelian disorders remain undefined. Factors such as lack of large multiplex families, locus heterogeneity, and incomplete penetrance hamper these efforts for many disorders. Previous work suggests that gene-based burden testing-where the aggregate burden of rare, protein-altering variants in each gene is compared between case and control subjects-might overcome some of these limitations. The increasing availability of large-scale public sequencing databases such as Genome Aggregation Database (gnomAD) can enable burden testing using these databases as controls, obviating the need for additional control sequencing for each study. However, there exist various challenges with using public databases as controls, including lack of individual-level data, differences in ancestry, and differences in sequencing platforms and data processing. To illustrate the approach of using public data as controls, we analyzed whole-exome sequencing data from 393 individuals with idiopathic hypogonadotropic hypogonadism (IHH), a rare disorder with significant locus heterogeneity and incomplete penetrance against control subjects from gnomAD (n = 123,136). We leveraged presumably benign synonymous variants to calibrate our approach. Through iterative analyses, we systematically addressed and overcame various sources of artifact that can arise when using public control data. In particular, we introduce an approach for highly adaptable variant quality filtering that leads to well-calibrated results. Our approach "re-discovered" genes previously implicated in IHH (FGFR1, TACR3, GNRHR). Furthermore, we identified a significant burden in TYRO3, a gene implicated in hypogonadotropic hypogonadism in mice. Finally, we developed a user-friendly software package TRAPD (Test Rare vAriants with Public Data) for performing gene-based burden testing against public databases.

Whole-Exome Sequencing in Age-Related Macular Degeneration Identifies Rare Variants in COL8A1, a Component of Bruch's Membrane

PURPOSE

Genome-wide association studies and targeted sequencing studies of candidate genes have identified common and rare variants that are associated with age-related macular degeneration (AMD). Whole-exome sequencing (WES) studies allow a more comprehensive analysis of rare coding variants across all genes of the genome and will contribute to a better understanding of the underlying disease mechanisms. To date, the number of WES studies in AMD case-control cohorts remains scarce and sample sizes are limited. To scrutinize the role of rare protein-altering variants in AMD cause, we performed the largest WES study in AMD to date in a large European cohort consisting of 1125 AMD patients and 1361 control participants.

DESIGN

Genome-wide case-control association study of WES data.

PARTICIPANTS

One thousand one hundred twenty-five AMD patients and 1361 control participants.

METHODS

A single variant association test of WES data was performed to detect variants that are associated individually with AMD. The cumulative effect of multiple rare variants with 1 gene was analyzed using a gene-based CMC burden test. Immunohistochemistry was performed to determine the localization of the Col8a1 protein in mouse eyes.

MAIN OUTCOME MEASURES

Genetic variants associated with AMD.

RESULTS

We detected significantly more rare protein-altering variants in the COL8A1 gene in patients (22/2250 alleles [1.0%]) than in control participants (11/2722 alleles [0.4%]; P = 7.07×10^-5). The association of rare variants in the COL8A1 gene is independent of the common intergenic variant (rs140647181) near the COL8A1 gene previously associated with AMD. We demonstrated that the Col8a1 protein localizes at Bruch's membrane.

CONCLUSIONS

This study supported a role for protein-altering variants in the COL8A1 gene in AMD pathogenesis. We demonstrated the presence of Col8a1 in Bruch's membrane, further supporting the role of COL8A1 variants in AMD pathogenesis. Protein-altering variants in COL8A1 may alter the integrity of Bruch's membrane, contributing to the accumulation of drusen and the development of AMD.

Human Genomic Loci Important in Common Infectious Diseases: Role of High-Throughput Sequencing and Genome-Wide Association Studies

HIV/AIDS, tuberculosis (TB), and malaria are 3 major global public health threats that undermine development in many resource-poor settings. Recently, the notion that positive selection during epidemics or longer periods of exposure to common infectious diseases may have had a major effect in modifying the constitution of the human genome is being interrogated at a large scale in many populations around the world. This positive selection from infectious diseases increases power to detect associations in genome-wide association studies (GWASs). High-throughput sequencing (HTS) has transformed both the management of infectious diseases and continues to enable large-scale functional characterization of host resistance/susceptibility alleles and loci; a paradigm shift from single candidate gene studies. Application of genome sequencing technologies and genomics has enabled us to interrogate the host-pathogen interface for improving human health. Human populations are constantly locked in evolutionary arms races with pathogens; therefore, identification of common infectious disease-associated genomic variants/markers is important in therapeutic, vaccine development, and screening susceptible individuals in a population. This review describes a range of host-pathogen genomic loci that have been associated with disease susceptibility and resistant patterns in the era of HTS. We further highlight potential opportunities for these genetic markers.

Molecular genetic testing for hereditary ataxia: What every neurologist should know

Purpose of review

Because of extensive clinical overlap among many forms of hereditary ataxia, molecular genetic testing is often required to establish a diagnosis. Interrogation of multiple genes has become a popular diagnostic approach as the cost of sequence analysis has decreased and the number of genes associated with overlapping phenotypes has increased. We describe the benefits and limitations of molecular genetic tests commonly used to determine the etiology of hereditary ataxia.

Recent findings

There are more than 300 hereditary disorders associated with ataxia. The most common causes of hereditary ataxia are expansion of nucleotide repeats within 7 genes: ATXN1, ATXN2, ATXN3, ATXN7, ATXN8, CACNA1A (spinocerebellar ataxia type 6), and FXN (Friedreich ataxia). Recent reports describing the use of clinical exome sequencing to identify causes of hereditary ataxia may lead neurologists to start their clinical investigation with a less sensitive molecular test providing a misleading "negative" result.

Summary

The majority of individuals with hereditary ataxias have nucleotide repeat expansions, pathogenic variants that are not detectable with clinical exome sequencing. Multigene panels that include specific assays to determine nucleotide repeat lengths should be considered first in individuals with hereditary ataxia.

Using variant databases for variant prioritization and to detect erroneous genotype-phenotype associations

Background

In the search for novel causal mutations, public and/or private variant databases are nearly always used to facilitate the search as they result in a massive reduction of putative variants in one step. Practically, variant filtering is often done by either using all variants from the variant database (called the absence-approach, i.e. it is assumed that disease-causing variants do not reside in variant databases) or by using the subset of variants with an allelic frequency > 1% (called the 1%-approach). We investigate the validity of these two approaches in terms of false negatives (the true disease-causing variant does not pass all filters) and false positives (a harmless mutation passes all filters and is erroneously retained in the list of putative disease-causing variants) and compare it with an novel approach which we named the quantile-based approach. This approach applies variable instead of static frequency thresholds and the calculation of these thresholds is based on prior knowledge of disease prevalence, inheritance models, database size and database characteristics.

Results

Based on real-life data, we demonstrate that the quantile-based approach outperforms the absence-approach in terms of false negatives. At the same time, this quantile-based approach deals more appropriately with the variable allele frequencies of disease-causing alleles in variant databases relative to the 1%-approach and as such allows a better control of the number of false positives.

We also introduce an alternative application for variant database usage and the quantile-based approach. If disease-causing variants in variant databases deviate substantially from theoretical expectancies calculated with the quantile-based approach, their association between genotype and phenotype had to be reconsidered in 12 out of 13 cases.

Conclusions

We developed a novel method and demonstrated that this so-called quantile-based approach is a highly suitable method for variant filtering. In addition, the quantile-based approach can also be used for variant flagging. For user friendliness, lookup tables and easy-to-use R calculators are provided.

Electronic supplementary material

The online version of this article (doi: 10.1186/s12859-017-1951-y) contains supplementary material, which is available to authorized users.

Lumbosacral stenosis in Labrador retriever military working dogs - an exomic exploratory study

Background

Canine lumbosacral stenosis is defined as narrowing of the caudal lumbar and/or sacral vertebral canal. A risk factor for neurologic problems in many large sized breeds, lumbosacral stenosis can also cause early retirement in Labrador retriever military working dogs. Though vital for conservative management of the condition, early detection is complicated by the ambiguous nature of clinical signs of lumbosacral stenosis in stoic and high-drive Labrador retriever military working dogs. Though clinical diagnoses of lumbosacral stenosis using CT imaging are standard, they are usually not performed unless dogs present with clinical symptoms. Understanding the underlying genomic mechanisms would be beneficial in developing early detection methods for lumbosacral stenosis, which could prevent premature retirement in working dogs. The exomes of 8 young Labrador retriever military working dogs (4 affected and 4 unaffected by lumbosacral stenosis, phenotypically selected by CT image analyses from 40 dogs with no reported clinical signs of the condition) were sequenced to identify and annotate exonic variants between dogs negative and positive for lumbosacral stenosis.

Results

Two-hundred and fifty-two variants were detected to be homozygous for the wild allele and either homozygous or heterozygous for the variant allele. Seventeen non-disruptive variants were detected that could affect protein effectiveness in 7 annotated (SCN1B, RGS9BP, ASXL3, TTR, LRRC16B, PTPRO, ZBBX) and 3 predicted genes (EEF1A1, DNAJA1, ZFX). No exonic variants were detected in any of the canine orthologues for human lumbar spinal stenosis candidate genes.

Conclusions

TTR (transthyretin) gene could be a possible candidate for lumbosacral stenosis in Labrador retrievers based on previous human studies that have reported an association between human lumbar spinal stenosis and transthyretin protein amyloidosis. Other genes identified with exonic variants in this study but with no known published association with lumbosacral stenosis and/or lumbar spinal stenosis could also be candidate genes for future canine lumbosacral stenosis studies but their roles remain currently unknown. Human lumbar spinal stenosis candidate genes also cannot be ruled out as lumbosacral stenosis candidate genes. More definitive genetic investigations of this condition are needed before any genetic test for lumbosacral stenosis in Labrador retriever can be developed.

Electronic supplementary material

The online version of this article (10.1186/s40575-017-0052-6) contains supplementary material, which is available to authorized users.

Rapid functional analysis of computationally complex rare human IRF6 gene variants using a novel zebrafish model

Large-scale sequencing efforts have captured a rapidly growing catalogue of genetic variations. However, the accurate establishment of gene variant pathogenicity remains a central challenge in translating personal genomics information to clinical decisions. Interferon Regulatory Factor 6 (IRF6) gene variants are significant genetic contributors to orofacial clefts. Although approximately three hundred IRF6 gene variants have been documented, their effects on protein functions remain difficult to interpret. Here, we demonstrate the protein functions of human IRF6 missense gene variants could be rapidly assessed in detail by their abilities to rescue the irf6^-/- phenotype in zebrafish through variant mRNA microinjections at the one-cell stage. The results revealed many missense variants previously predicted by traditional statistical and computational tools to be loss-of-function and pathogenic retained partial or full protein function and rescued the zebrafish irf6^-/- periderm rupture phenotype. Through mRNA dosage titration and analysis of the Exome Aggregation Consortium (ExAC) database, IRF6 missense variants were grouped by their abilities to rescue at various dosages into three functional categories: wild type function, reduced function, and complete loss-of-function. This sensitive and specific biological assay was able to address the nuanced functional significances of IRF6 missense gene variants and overcome many limitations faced by current statistical and computational tools in assigning variant protein function and pathogenicity. Furthermore, it unlocked the possibility for characterizing yet undiscovered human IRF6 missense gene variants from orofacial cleft patients, and illustrated a generalizable functional genomics paradigm in personalized medicine.

Advances in sequencing technologies have led to rapid increases in personalized genetics information. Millions of differences exist when comparing the genomes of two individuals, accounting for the diversity of humans and occasionally disease pathogenicity. Accurate determination of the functional consequences of these differences is critical for translating personal genetic information into clinical decision. Various methods have been devised to meet this challenge. However, they are imperfect, especially in their abilities to interpret rare variants. Rare variants must be evaluated by multi-pronged approaches to establish disease pathogenicity, one crucial approach being functional testing through experimental models. Here, we utilized a zebrafish model to rapidly evaluate the protein functions of rare gene variants of IRF6, a gene of established importance in orofacial cleft pathogenesis. IRF6 functions are well-conserved across species, and allowed us to test the functions of human IRF6 variants by their abilities to rescue zebrafish embryos depleted of irf6. Many variants previously labeled pathogenic and loss-of-function retained full wild type-level protein activity, suggesting they could potentially represent benign, rather than disease-causing genetic variations. This paradigm is applicable to other genes and will allow researchers to rapidly triage gene variants for further study and clinicians to provide more accurate genetic diagnoses.

Common sequence variants affect molecular function more than rare variants?

Any two unrelated individuals differ by about 10,000 single amino acid variants (SAVs). Do these impact molecular function? Experimental answers cannot answer comprehensively, while state-of-the-art prediction methods can. We predicted the functional impacts of SAVs within human and for variants between human and other species. Several surprising results stood out. Firstly, four methods (CADD, PolyPhen-2, SIFT, and SNAP2) agreed within 10 percentage points on the percentage of rare SAVs predicted with effect. However, they differed substantially for the common SAVs: SNAP2 predicted, on average, more effect for common than for rare SAVs. Given the large ExAC data sets sampling 60,706 individuals, the differences were extremely significant (p-value < 2.2e-16). We provided evidence that SNAP2 might be closer to reality for common SAVs than the other methods, due to its different focus in development. Secondly, we predicted significantly higher fractions of SAVs with effect between healthy individuals than between species; the difference increased for more distantly related species. The same trends were maintained for subsets of only housekeeping proteins and when moving from exomes of 1,000 to 60,000 individuals. SAVs frozen at speciation might maintain protein function, while many variants within a species might bring about crucial changes, for better or worse.

Teneurin transmembrane protein 4 is not a cause for essential tremor in a Canadian population

INTRODUCTION

Mutations in teneurin transmembrane protein 4 were reported to be a risk factor for essential tremor, but the relevance of this across different population remains to be examined. The aim of this study was to determine the frequency and spectrum of variations in teneurin transmembrane protein 4 in a cohort of Canadian essential tremor cases.

METHODS

The coding portion of teneurin transmembrane protein 4 was sequenced in 269 unrelated essential tremor cases and 288 matched control individuals using a targeted and high-throughput sequencing approach.

RESULTS

A total of 157 single nucleotide variations were identified, and from these 99 were a missense or nonsense mutation. A total of 68 cases were carriers of ≥1 rare missense or nonsense mutations, and 39 control individuals were carriers of the same types of variations. Gene-based association tests were used to jointly analyze the single nucleotide variations.

CONCLUSIONS

Our results do not support a positive association between teneurin transmembrane protein 4 and the Canadian population. © 2017 International Parkinson and Movement Disorder Society.

Determinants of Power in Gene-Based Burden Testing for Monogenic Disorders

Whole-exome sequencing has enabled new approaches for discovering genes associated with monogenic disorders. One such approach is gene-based burden testing, in which the aggregate frequency of "qualifying variants" is compared between case and control subjects for each gene. Despite substantial successes of this approach, the genetic causes for many monogenic disorders remain unknown or only partially known. It is possible that particular genetic architectures lower rates of discovery, but the influence of these factors on power has not been rigorously evaluated. Here, we leverage large-scale exome-sequencing data to create an empirically based simulation framework to evaluate the impact of key parameters (background variation rates, locus heterogeneity, mode of inheritance, penetrance) on power in gene-based burden tests in the context of monogenic disorders. Our results demonstrate that across genes, there is a wide range in sample sizes needed to achieve power due to differences in the background rate of rare variants in each gene. Increasing locus heterogeneity results in rapid increases in sample sizes needed to achieve adequate power, particularly when individual genes contribute to less than 5% of cases under a dominant model. Interestingly, incomplete penetrance as low as 10% had little effect on power due to the low prevalence of monogenic disorders. Our results suggest that moderate incomplete penetrance is not an obstacle in this gene-based burden testing approach but that dominant disorders with high locus heterogeneity will require large sample sizes. Our simulations also provide guidance on sample size needs and inform study design under various genetic architectures.

A biologically informed method for detecting rare variant associations

BACKGROUND

BioBin is a bioinformatics software package developed to automate the process of binning rare variants into groups for statistical association analysis using a biological knowledge-driven framework. BioBin collapses variants into biological features such as genes, pathways, evolutionary conserved regions (ECRs), protein families, regulatory regions, and others based on user-designated parameters. BioBin provides the infrastructure to create complex and interesting hypotheses in an automated fashion thereby circumventing the necessity for advanced and time consuming scripting.

PURPOSE OF THE STUDY

In this manuscript, we describe the software package for BioBin, along with type I error and power simulations to demonstrate the strengths and various customizable features and analysis options of this variant binning tool.

RESULTS

Simulation testing highlights the utility of BioBin as a fast, comprehensive and expandable tool for the biologically-inspired binning and analysis of low-frequency variants in sequence data.

CONCLUSIONS AND POTENTIAL IMPLICATIONS

The BioBin software package has the capability to transform and streamline the analysis pipelines for researchers analyzing rare variants. This automated bioinformatics tool minimizes the manual effort of creating genomic regions for binning such that time can be spent on the much more interesting task of statistical analyses. This software package is open source and freely available from http://ritchielab.com/software/biobin-download.

Whole exome sequencing of extreme age-related macular degeneration phenotypes

PURPOSE

Demographic, environmental, and genetic risk factors for age-related macular degeneration (AMD) have been identified; however, a substantial portion of the variance in AMD disease risk and heritability remains unexplained. To identify AMD risk variants and generate hypotheses for future studies, we performed whole exome sequencing for 75 individuals whose phenotype was not well predicted by their genotype at known risk loci. We hypothesized that these phenotypically extreme individuals were more likely to carry rare risk or protective variants with large effect sizes.

METHODS

A genetic risk score was calculated in a case-control set of 864 individuals (467 AMD cases, 397 controls) based on 19 common (≥1% minor allele frequency, MAF) single nucleotide variants previously associated with the risk of advanced AMD in a large meta-analysis of advanced cases and controls. We then selected for sequencing 39 cases with bilateral choroidal neovascularization with the lowest genetic risk scores to detect risk variants and 36 unaffected controls with the highest genetic risk score to detect protective variants. After minimizing the influence of 19 common genetic risk loci on case-control status, we targeted single variants of large effect and the aggregate effect of weaker variants within genes and pathways. Single variant tests were conducted on all variants, while gene-based and pathway analyses were conducted on three subsets of data: 1) rare (≤1% MAF in the European population) stop, splice, or damaging missense variants, 2) all rare variants, and 3) all variants. All analyses controlled for the effects of age and sex.

RESULTS

No variant, gene, or pathway outside regions known to be associated with risk for advanced AMD reached genome-wide significance. However, we identified several variants with substantial differences in allele frequency between cases and controls with strong additive effects on affection status after controlling for age and sex. Protective effects trending toward significance were detected at two loci identified in single-variant analyses: an intronic variant in FBLN7 (the gene encoding fibulin 7) and at three variants near pyridoxal (pyridoxine, vitamin B6) kinase (PDXK). Aggregate rare-variant analyses suggested evidence for association at ASRGL1, a gene previously linked to photoreceptor cell death, and at BSDC1. In known AMD loci we also identified 29 novel or rare damaging missense or stop/splice variants in our sample of cases and controls.

CONCLUSIONS

Identified variants and genes may highlight regions important in the pathogenesis of AMD and are key targets for replication.

VDAP-GUI: a user-friendly pipeline for variant discovery and annotation of raw next-generation sequencing data

Even though next-generation sequencing (NGS) has become an invaluable tool in molecular biology, several laboratories with NGS facilities lack trained Bioinformaticians for data analysis. Here, focusing on the variant detection application of NGS analysis, we have developed a fully automated pipeline, namely Variant Discovery and Annotation Tool-Graphical User Interface (VDAP-GUI), which detects and annotates single nucleotide polymorphisms and insertions/deletions from raw sequence reads. VDAP-GUI consolidates several proven methods in each step such as quality control, trimming, mapping, variant detection and annotation. It supports multiple NGS platforms and has four methodological choices for variant detection. Further, it can re-analyze existing data with alternate thresholds and generates easily interpretable reports in html and tab-delimited formats. Using VDAP-GUI, we have analyzed a publically available human whole-exome sequence dataset. VDAP-GUI is developed using Perl/Tk programming, and is available for free download and use at http://sourceforge.net/projects/vdapgui/.

Post-mortem whole-exome sequencing (WES) with a focus on cardiac disease-associated genes in five young sudden unexplained death (SUD) cases

Sudden death of healthy young adults in the absence of any medical reason is generally categorised as autopsy-negative sudden unexplained death (SUD). Approximately 30 % of all SUD cases can be explained by lethal sequence variants in cardiac genes causing disturbed ion channel functions (channelopathies) or minimal structural heart abnormalities (cardiomyopathies). The aim of this study was to perform whole-exome sequencing (WES) in five young SUD cases in order to identify potentially disease-causing mutations with a focus on 184 genes associated with cardiac diseases or sudden death. WES analysis enabled the identification of damaging-predicted cardiac sequence alterations in three out of five SUD cases. Two SUD victims carried disease-causing variants in long QT syndrome (LQTS)-associated genes (KCNH2, SCN5A). In a third case, WES identified variants in two genes involved in mitral valve prolapse and thoracic aortic aneurism (DCHS1, TGFβ2). The genome of a fourth case carried several minor variants involved in arrhythmia pointing to a multigene influence that might have contributed to sudden death. Our results confirm that post-mortem genetic testing in SUD cases in addition to the conventional autopsy can help to identify familial cardiac diseases and can contribute to the identification of genetic risk factors for sudden death.

Toward the most ideal case-control design with related and unrelated dogs in whole-exome sequencing studies

With the recent development of whole-exome sequencing enrichment designs for the dog, a novel tool for disease-association studies became available. The aim of disease-association studies is to identify one or a very limited number of putative causal variants or genes from the large pool of genetic variation. To maximize the efficiency of these studies and to provide some directions of what to expect, we evaluated the effect on variant reduction for various combinations of cases and controls for both dominant and recessive types of inheritance assuming variable degrees of penetrance and detectance. In this study, variant data of 14 dogs (13 Labrador Retrievers and one Dogue de Bordeaux), obtained by whole-exome sequencing, were analyzed. In the filtering process, we found that unrelated dogs from the same breed share up to 70% of their variants, which is likely a consequence of the breeding history of the dog. For the designs tested with unrelated dogs, combining two cases and two controls gave the best result. These results were improved further by adding closely related dogs. Reduced penetrance and/or detectance has a drastic effect on the efficiency and is likely to have a profound effect on the sample size needed to elucidate the causal variant. Overall, we demonstrated that sequencing a small number of dogs results in a marked reduction of variants that are likely sufficient to pinpoint causal variants or genes.

Exome sequencing explained: a practical guide to its clinical application

Next-generation sequencing has catapulted healthcare into a revolutionary genomics era. One such technology, whole-exome sequencing, which targets the protein-coding regions of the genome, has proven success in identifying new causal mutations for diseases of previously unknown etiology. With a successful diagnostic rate approaching 25% for rare disease in recent studies, its clinical utility is becoming increasingly popular. However, the interpretation of whole-exome sequencing data requires expertise in genomic informatics and clinical medicine to ensure the accurate and safe reporting of findings back to the bedside. This is challenged by vast amounts of sequencing data harbouring approximately 25 000 variants per sequenced individual. Computational strategies and fastidious filtering frameworks are thus required to extricate candidate variants in a sea of common polymorphisms. Once prioritized, identified variants require intensive scrutiny at a biological level, and require judicious assessment alongside the clinical phenotype. In the final step, all evidence is collated and documented alongside pathogenicity guidelines to produce an exome report that returns to the clinic. This review provides a practical guide for clinicians and genomic informaticians on the clinical application of whole-exome sequencing. We address sequencing capture and methodology, quality control parameters at different stages of sequencing analysis and propose an exome data filtering strategy that includes primary filtering (for the removal of probable benign variants) and secondary filtering for the prioritization of remaining candidates.

Histone Modifier Genes Alter Conotruncal Heart Phenotypes in 22q11.2 Deletion Syndrome

We performed whole exome sequence (WES) to identify genetic modifiers on 184 individuals with 22q11.2 deletion syndrome (22q11DS), of whom 89 case subjects had severe congenital heart disease (CHD) and 95 control subjects had normal hearts. Three genes including JMJD1C (jumonji domain containing 1C), RREB1 (Ras responsive element binding protein 1), and SEC24C (SEC24 family member C) had rare (MAF < 0.001) predicted deleterious single-nucleotide variations (rdSNVs) in seven case subjects and no control subjects (p = 0.005; Fisher exact and permutation tests). Because JMJD1C and RREB1 are involved in chromatin modification, we investigated other histone modification genes. Eighteen case subjects (20%) had rdSNVs in four genes (JMJD1C, RREB1, MINA, KDM7A) all involved in demethylation of histones (H3K9, H3K27). Overall, rdSNVs were enriched in histone modifier genes that activate transcription (Fisher exact p = 0.0004, permutations, p = 0.0003, OR = 5.16); however, rdSNVs in control subjects were not enriched. This implicates histone modification genes as influencing risk for CHD in presence of the deletion.

An heuristic filtering tool to identify phenotype-associated genetic variants applied to human intellectual disability and canine coat colors

Background

Identification of one or several disease causing variant(s) from the large collection of variants present in an individual is often achieved by the sequential use of heuristic filters. The recent development of whole exome sequencing enrichment designs for several non-model species created the need for a species-independent, fast and versatile analysis tool, capable of tackling a wide variety of standard and more complex inheritance models. With this aim, we developed “Mendelian”, an R-package that can be used for heuristic variant filtering.

Results

The R-package Mendelian offers fast and convenient filters to analyze putative variants for both recessive and dominant models of inheritance, with variable degrees of penetrance and detectance. Analysis of trios is supported. Filtering against variant databases and annotation of variants is also included. This package is not species specific and supports parallel computation. We validated this package by reanalyzing data from a whole exome sequencing experiment on intellectual disability in humans. In a second example, we identified the mutations responsible for coat color in the dog. This is the first example of whole exome sequencing without prior mapping in the dog.

Conclusion

We developed an R-package that enables the identification of disease-causing variants from the long list of variants called in sequencing experiments. The software and a detailed manual are available at https://github.com/BartBroeckx/Mendelian.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0822-7) contains supplementary material, which is available to authorized users.

Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment

Accurate assessment of genetic variation in human DNA sequencing studies remains a nontrivial challenge in clinical genomics and genome informatics. Ascribing functional roles and/or clinical significances to single nucleotide variants identified from a next-generation sequencing study is an important step in genome interpretation. Experimental characterization of all the observed functional variants is yet impractical; thus, the prediction of functional and/or regulatory impacts of the various mutations using in silico approaches is an important step toward the identification of functionally significant or clinically actionable variants. The relationships between genotypes and the expressed phenotypes are multilayered and biologically complex; such relationships present numerous challenges and at the same time offer various opportunities for the design of in silico variant assessment strategies. Over the past decade, many bioinformatics algorithms have been developed to predict functional consequences of single nucleotide variants in the protein coding regions. In this review, we provide an overview of the bioinformatics resources for the prediction, annotation and visualization of coding single nucleotide variants. We discuss the currently available approaches and major challenges from the perspective of protein sequence, structure, function and interactions that require consideration when interpreting the impact of putatively functional variants. We also discuss the relevance of incorporating integrated workflows for predicting the biomedical impact of the functionally important variations encoded in a genome, exome or transcriptome. Finally, we propose a framework to classify variant assessment approaches and strategies for incorporation of variant assessment within electronic health records.

Introduction to the analysis of next generation sequencing data and its application to venous thromboembolism

Despite knowledge of various inherited risk factors associated with venous thromboembolism (VTE), no definite cause can be found in about 50% of patients. The application of data-driven searches such as GWAS has not been able to identify genetic variants with implications for clinical care, and unexplained heritability remains. In the past years, the development of several so-called next generation sequencing (NGS) platforms is offering the possibility of generating fast, inexpensive and accurate genomic information. However, so far their application to VTE has been very limited. Here we review basic concepts of NGS data analysis and explore the application of NGS technology to VTE. We provide both computational and biological viewpoints to discuss potentials and challenges of NGS-based studies.

The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease

Genome and exome sequencing in large cohorts enables characterization of the role of rare variation in complex diseases. Success in this endeavor, however, requires investigators to test a diverse array of genetic hypotheses which differ in the number, frequency and effect sizes of underlying causal variants. In this study, we evaluated the power of gene-based association methods to interrogate such hypotheses, and examined the implications for study design. We developed a flexible simulation approach, using 1000 Genomes data, to (a) generate sequence variation at human genes in up to 10K case-control samples, and (b) quantify the statistical power of a panel of widely used gene-based association tests under a variety of allelic architectures, locus effect sizes, and significance thresholds. For loci explaining ~1% of phenotypic variance underlying a common dichotomous trait, we find that all methods have low absolute power to achieve exome-wide significance (~5-20% power at α=2.5×10^-6) in 3K individuals; even in 10K samples, power is modest (~60%). The combined application of multiple methods increases sensitivity, but does so at the expense of a higher false positive rate. MiST, SKAT-O, and KBAC have the highest individual mean power across simulated datasets, but we observe wide architecture-dependent variability in the individual loci detected by each test, suggesting that inferences about disease architecture from analysis of sequencing studies can differ depending on which methods are used. Our results imply that tens of thousands of individuals, extensive functional annotation, or highly targeted hypothesis testing will be required to confidently detect or exclude rare variant signals at complex disease loci.

Re-sequencing technologies allow for a more complete interrogation of the role of human variation in complex disease. The inadequate power of single variant methods to assess the role of less common variation has led to the development of numerous statistical methods for testing aggregate groups of variants for association with disease. Such endeavors pose substantial analytical challenges, however, due to the diverse array of genetic hypotheses that need to be considered. In this work, we systematically quantify and compare the performance of a panel of commonly used gene-based association methods under a range of allelic architectures, significance thresholds, locus effect sizes, sample sizes, and filters for neutral variation. We find that MiST, SKAT-O, and KBAC have the highest mean power across simulated datasets. Across all methods, however, the power to detect even loci of relatively large effect is very low at exome-wide significance thresholds for sample sizes comparable with those of ongoing sequencing studies; as such, the absence of signal in studies of a few thousand individuals does not exclude a role for rare variation in complex traits. Finally, we directly compare the results reported by different gene-based methods in order to identify their comparative advantages and disadvantages under distinct locus architectures. Our findings have implications for meaningful interpretation of both positive and negative findings in ongoing and future sequencing studies.

Identifying Highly Penetrant Disease Causal Mutations Using Next Generation Sequencing: Guide to Whole Process

Recent technological advances have created challenges for geneticists and a need to adapt to a wide range of new bioinformatics tools and an expanding wealth of publicly available data (e.g., mutation databases, and software). This wide range of methods and a diversity of file formats used in sequence analysis is a significant issue, with a considerable amount of time spent before anyone can even attempt to analyse the genetic basis of human disorders. Another point to consider that is although many possess “just enough” knowledge to analyse their data, they do not make full use of the tools and databases that are available and also do not fully understand how their data was created. The primary aim of this review is to document some of the key approaches and provide an analysis schema to make the analysis process more efficient and reliable in the context of discovering highly penetrant causal mutations/genes. This review will also compare the methods used to identify highly penetrant variants when data is obtained from consanguineous individuals as opposed to nonconsanguineous; and when Mendelian disorders are analysed as opposed to common-complex disorders.

Detecting associations of rare variants with common diseases: collapsing or haplotyping?

In recent years, a myriad of new statistical methods have been proposed for detecting associations of rare single-nucleotide variants (SNVs) with common diseases. These methods can be generally classified as 'collapsing' or 'haplotyping' based. The former is the predominant class, composed of most of the rare variant association methods proposed to date. However, recent works have suggested that haplotyping-based methods may offer advantages and can even be more powerful than collapsing methods in certain situations. In this article, we review and compare collapsing- versus haplotyping-based methods/software in terms of both power and type I error. For collapsing methods, we consider three approaches: Combined Multivariate and Collapsing, Sequence Kernel Association Test and Family-Based Association Test (FBAT): the first two are population based and are among the most popular; the last test is family based, a modification from the popular FBAT to accommodate rare SNVs. For haplotyping-based methods, we include Logistic Bayesian Lasso (LBL) for population data and family-based LBL (famLBL) for family (trio) data. These two methods are selected, as they can be used to test association for specific rare and common haplotypes. Our results show that haplotype methods can be more powerful than collapsing methods if there are interacting SNVs leading to larger haplotype effects. Even if only common SNVs are genotyped, haplotype methods can still detect specific rare haplotypes that tag rare causal SNVs. As expected, family-based methods are robust, whereas population-based methods are susceptible, to population substructure. However, the population-based haplotype approach appears to have smaller inflation of type I error than its collapsing counterparts.

Plant exomics: concepts, applications and methodologies in crop improvement

Molecular breeding has a crucial role in improvement of crops. Conventional breeding techniques have failed to ameliorate food production. Next generation sequencing has established new concepts of molecular breeding. Exome sequencing has proven to be a significant tool for assessing natural evolution in plants, studying host pathogen interactions and betterment of crop production as exons assist in interpretation of allelic variation with respect to their phenotype. This review covers the platforms for exome sequencing, next generation sequencing technologies that have revolutionized exome sequencing and led toward development of third generation sequencing. Also discussed in this review are the uses of these sequencing technologies to improve wheat, rice and cotton yield and how these technologies are used in exploring the biodiversity of crops, providing better understanding of plant-host pathogen interaction and assessing the process of natural evolution in crops and it also covers how exome sequencing identifies the gene pool involved in symbiotic and other co-existential systems. Furthermore, we conclude how integration of other methodologies including whole genome sequencing, proteomics, transcriptomics and metabolomics with plant exomics covers the areas which are left untouched with exomics alone and in the end how these integration will transform the future of crops.

Extended T(2) tests for longitudinal family data in whole genome sequencing studies

Family data and rare variants are two key features of whole genome sequencing analysis for hunting the missing heritability of common human diseases. Recently, Zhu and Xiong proposed the generalized T² tests that combine rare variant analysis and family data analysis. In similar fashion, we developed the extended T² tests for longitudinal whole genome sequencing data for family-based association studies. The new methods simultaneously incorporate three correlation sources: from linkage disequilibrium, from pedigree structure, and from the repeated measures of covariates. We assess and compare these methods using the simulated data from Genetic Analysis Workshop 18. We show that, in general, the extended T² tests incorporating longitudinal repeated measures have higher power than the single-time-point T² tests in detecting hypertension-associated genome segments.

MitImpact: an exhaustive collection of pre-computed pathogenicity predictions of human mitochondrial non-synonymous variants

Mitochondrial DNA carries a tiny, but fundamental portion of the eukaryotic genetic code. As its nuclear counterpart, it is susceptible to point mutations. Their level of pathogenicity has been assessed for the newly discovered mutations only, leaving some degree of uncertainty on the potential impact of the unknown mutations. Here we present Mitochondrial mutation Impact (MitImpact), a queryable lightweight web interface to a reasoned collection of structurally and evolutionary annotated pathogenicity predictions, obtained by assembling pre-computed with on-the-fly-computed sets of pathogenicity estimations, for all the possible mitochondrial missense variants. It presents itself as a resource for fast and reliable evaluation of gene-specific susceptibility of unknown and verified amino acid changes. MitImpact is freely available at http://bioinformatics.css-mendel.it/ (tools section). ©2014 Wiley Periodicals, Inc.

Missense variants in CFTR nucleotide-binding domains predict quantitative phenotypes associated with cystic fibrosis disease severity

Predicting the impact of genetic variation on human health remains an important and difficult challenge. Often, algorithmic classifiers are tasked with predicting binary traits (e.g. positive or negative for a disease) from missense variation. Though useful, this arrangement is limiting and contrived, because human diseases often comprise a spectrum of severities, rather than a discrete partitioning of patient populations. Furthermore, labeling variants as causal or benign can be error prone, which is problematic for training supervised learning algorithms (the so-called garbage in, garbage out phenomenon). We explore the potential value of training classifiers using continuous-valued quantitative measurements, rather than binary traits. Using 20 variants from cystic fibrosis transmembrane conductance regulator (CFTR) nucleotide-binding domains and six quantitative measures of cystic fibrosis (CF) severity, we trained classifiers to predict CF severity from CFTR variants. Employing cross validation, classifier prediction and measured clinical/functional values were significantly correlated for four of six quantitative traits (correlation P-values from 1.35 × 10(-4) to 4.15 × 10(-3)). Classifiers were also able to stratify variants by three clinically relevant risk categories with 85-100% accuracy, depending on which of the six quantitative traits was used for training. Finally, we characterized 11 additional CFTR variants using clinical sweat chloride testing, two functional assays, or all three diagnostics, and validated our classifier using blind prediction. Predictions were within the measured sweat chloride range for seven of eight variants, and captured the differential impact of specific variants on the two functional assays. This work demonstrates a promising and novel framework for assessing the impact of genetic variation.

A novel splice-site mutation in ALS2 establishes the diagnosis of juvenile amyotrophic lateral sclerosis in a family with early onset anarthria and generalized dystonias

The diagnosis of childhood neurological disorders remains challenging given the overlapping clinical presentation across subgroups and heterogeneous presentation within subgroups. To determine the underlying genetic cause of a severe neurological disorder in a large consanguineous Pakistani family presenting with severe scoliosis, anarthria and progressive neuromuscular degeneration, we performed genome-wide homozygosity mapping accompanied by whole-exome sequencing in two affected first cousins and their unaffected parents to find the causative mutation. We identified a novel homozygous splice-site mutation (c.3512+1G>A) in the ALS2 gene (NM_020919.3) encoding alsin that segregated with the disease in this family. Homozygous loss-of-function mutations in ALS2 are known to cause juvenile-onset amyotrophic lateral sclerosis (ALS), one of the many neurological conditions having overlapping symptoms with many neurological phenotypes. RT-PCR validation revealed that the mutation resulted in exon-skipping as well as the use of an alternative donor splice, both of which are predicted to cause loss-of-function of the resulting proteins. By examining 216 known neurological disease genes in our exome sequencing data, we also identified 9 other rare nonsynonymous mutations in these genes, some of which lie in highly conserved regions. Sequencing of a single proband might have led to mis-identification of some of these as the causative variant. Our findings established a firm diagnosis of juvenile ALS in this family, thus demonstrating the use of whole exome sequencing combined with linkage analysis in families as a powerful tool for establishing a quick and precise genetic diagnosis of complex neurological phenotypes.

Emerging hemodynamic signatures of the right heart (Third International Right Heart Failure Summit, part 2)

Despite the importance of preserved right ventricular structure and function with respect to outcome across the spectrum of lung, cardiac, and pulmonary vascular diseases, only recently have organized efforts developed to consider the pulmonary vascular-right ventricular apparatus as a specific unit within the larger context of cardiopulmonary pathophysiology. The Third International Right Heart Failure Summit (Boston, MA) was a multidisciplinary event dedicated to promoting a dialogue about the scientific and clinical basis of right heart disease. The current review provides a synopsis of key discussions presented during the section of the summit titled "Emerging Hemodynamic Signatures of the Right Heart." Specifically, topics emphasized in this element of the symposium included (1) the effects of pulmonary vascular dysfunction at rest or provoked by exercise on the right ventricular pressure-volume relationship, (2) the role of pressure-volume loop analysis as a method to characterize right ventricular inefficiency and predict right heart failure, and (3) the importance of a systems biology approach to identifying novel factors that contribute to pathophenotypes associated with pulmonary arterial hypertension and/or right ventricular dysfunction. Collectively, these concepts frame a forward-thinking paradigm shift in the approach to right heart disease by emphasizing factors that regulate the transition from adaptive to maladaptive right ventricular-pulmonary vascular (patho)physiology.

Case-only exome sequencing and complex disease susceptibility gene discovery: study design considerations

Whole exome sequencing (WES) provides an unprecedented opportunity to identify the potential aetiological role of rare functional variants in human complex diseases. Large-scale collaborations have generated germline WES data on patients with a number of diseases, especially cancer, but less often on healthy controls under the same sequencing procedures. These data can be a valuable resource for identifying new disease susceptibility loci if study designs are appropriately applied. This review describes suggested strategies and technical considerations when focusing on case-only study designs that use WES data in complex disease scenarios. These include variant filtering based on frequency and functionality, gene prioritisation, interrogation of different data types and targeted sequencing validation. We propose that if case-only WES designs were applied in an appropriate manner, new susceptibility genes containing rare variants for human complex diseases can be detected.

Exome-wide rare variant analysis identifies TUBA4A mutations associated with familial ALS

Exome sequencing is an effective strategy for identifying human disease genes. However, this methodology is difficult in late-onset diseases where limited availability of DNA from informative family members prohibits comprehensive segregation analysis. To overcome this limitation, we performed an exome-wide rare variant burden analysis of 363 index cases with familial ALS (FALS). The results revealed an excess of patient variants within TUBA4A, the gene encoding the Tubulin, Alpha 4A protein. Analysis of a further 272 FALS cases and 5,510 internal controls confirmed the overrepresentation as statistically significant and replicable. Functional analyses revealed that TUBA4A mutants destabilize the microtubule network, diminishing its repolymerization capability. These results further emphasize the role of cytoskeletal defects in ALS and demonstrate the power of gene-based rare variant analyses in situations where causal genes cannot be identified through traditional segregation analysis.

Genetic susceptibility to type 2 diabetes and obesity: from genome-wide association studies to rare variants and beyond

During the past 7 years, genome-wide association studies have shed light on the contribution of common genomic variants to the genetic architecture of type 2 diabetes, obesity and related intermediate phenotypes. The discoveries have firmly established more than 175 genomic loci associated with these phenotypes. Despite the tight correlation between type 2 diabetes and obesity, these conditions do not appear to share a common genetic background, since they have few genetic risk loci in common. The recent genetic discoveries do however highlight specific details of the interplay between the pathogenesis of type 2 diabetes, insulin resistance and obesity. The focus is currently shifting towards investigations of data from targeted array-based genotyping and exome and genome sequencing to study the individual and combined effect of low-frequency and rare variants in metabolic disease. Here we review recent progress as regards the concepts, methodologies and derived outcomes of studies of the genetics of type 2 diabetes and obesity, and discuss avenues to be investigated in the future within this research field.