Exome sequencing and complex disease: practical aspects of rare variant association studies

Rani ZZ, Sulaiman SA, Chow YP, Abdullah N, Ahmad N, Ismail N, Abdul Jalal N, Kamaruddin MA, Saperi AA, Jamal R. Hypercholesterolemia in the Malaysian Cohort Participants: Genetic and Non-Genetic Risk Factors

Hypercholesterolemia was prevalent in 44.9% of The Malaysian Cohort participants, of which 51% were Malay. This study aimed to identify the variants involved in hypercholesterolemia among Malays and to determine the association between genetic and non-genetic risk factors. This nested case–control study included 25 Malay participants with the highest low-density lipoprotein cholesterol (LDL-C, >4.9 mmol/L) and total cholesterol (TC, >7.5 mmol/L) and 25 participants with the lowest LDL-C/TC. Genomic DNA was extracted, and whole-exome sequencing was performed using the Ion ProtonTM system. All variants were annotated, filtered, and cross-referenced against publicly available databases. Forty-five selected variants were genotyped in 677 TMC Malay participants using the MassARRAY® System. The association between genetic and non-genetic risk factors was determined using logistic regression analysis. Age, fasting blood glucose, tobacco use, and family history of hyperlipidemia were significantly associated with hypercholesterolemia. Participants with the novel OSBPL7 (oxysterol-binding protein-like 7) c.651_652del variant had 17 times higher odds for hypercholesterolemia. Type 2 diabetes patients on medication and those with PCSK9 (proprotein convertase subtilisin/kexin type 9) rs151193009 had low odds for hypercholesterolemia. Genetic predisposition can interact with non-genetic factors to increase hypercholesterolemia risk in Malaysian Malays.

Schizophrenia risk conferred by rare protein-truncating variants is conserved across diverse human populations

Schizophrenia (SCZ) is a chronic mental illness and among the most debilitating conditions encountered in medical practice. A recent landmark SCZ study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This recent study-and most other large-scale human genetics studies-was mainly composed of individuals of European (EUR) ancestry, and the generalizability of the findings in non-EUR populations remains unclear. To address this gap, we designed a custom sequencing panel of 161 genes selected based on the current knowledge of SCZ genetics and sequenced a new cohort of 11,580 SCZ cases and 10,555 controls of diverse ancestries. Replicating earlier work, we found that cases carried a significantly higher burden of rare protein-truncating variants (PTVs) among evolutionarily constrained genes (odds ratio = 1.48; P = 5.4 × 10-6). In meta-analyses with existing datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five ancestral populations. Two genes (SRRM2 and AKAP11) were newly implicated as SCZ risk genes, and one gene (PCLO) was identified as shared by individuals with SCZ and those with autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of SCZ being conserved across diverse human populations.

Contribution of Rare and Low-Frequency Variants to Multiple Sclerosis Susceptibility in the Italian Continental Population

Genome-wide association studies identified over 200 risk loci for multiple sclerosis (MS) focusing on common variants, which account for about 50% of disease heritability. The goal of this study was to investigate whether low-frequency and rare functional variants, located in MS-established associated loci, may contribute to disease risk in a relatively homogeneous population, testing their cumulative effect (burden) with gene-wise tests. We sequenced 98 genes in 588 Italian patients with MS and 408 matched healthy controls (HCs). Variants were selected using different filtering criteria based on allelic frequency and in silico functional impacts. Genes showing a significant burden (n = 17) were sequenced in an independent cohort of 504 MS and 504 HC. The highest signal in both cohorts was observed for the disruptive variants (stop-gain, stop-loss, or splicing variants) located in EFCAB13, a gene coding for a protein of an unknown function (p < 10^-4). Among these variants, the minor allele of a stop-gain variant showed a significantly higher frequency in MS versus HC in both sequenced cohorts (p = 0.0093 and p = 0.025), confirmed by a meta-analysis on a third independent cohort of 1298 MS and 1430 HC (p = 0.001) assayed with an SNP array. Real-time PCR on 14 heterozygous individuals for this variant did not evidence the presence of the stop-gain allele, suggesting a transcript degradation by non-sense mediated decay, supported by the evidence that the carriers of the stop-gain variant had a lower expression of this gene (p = 0.0184). In conclusion, we identified a novel low-frequency functional variant associated with MS susceptibility, suggesting the possible role of rare/low-frequency variants in MS as reported for other complex diseases.

Using Genomic Techniques in Sports and Exercise Science: Current Status and Future Opportunities

ABSTRACT

The past two decades have built on the successes of the Human Genome Project identifying the impact of genetics and genomics on human traits. Given the importance of exercise in the physical and psychological health of individuals across the lifespan, using genomics to understand the impact of genes in the sports medicine field is an emerging field. Given the complexity of the systems involved, high-throughput genomics is required to understand genetic variants, their functions, and ultimately their effect on the body. Consequently, genomic studies have been performed across several domains of sports medicine with varying degrees of success. While the breadth of these is great, they focus largely on the following three areas: 1) performance; 2) injury susceptibility; and 3) sports associated chronic conditions, such as osteoarthritis. Herein, we review literature on genetics and genomics in sports medicine, offer suggestions to bolster existing studies, and suggest ways to ideally impact clinical care.

Exome sequencing study revealed novel susceptibility loci in subarachnoid hemorrhage (SAH)

Aim

To expand our current understanding of the genetic basis of subarachnoid hemorrhage (SAH), and reveal the susceptibility genes in SAH risk.

Methods

We conducted whole-exome sequencing (WES) in a cohort of 196 individuals, including 94 SAH patients and 94 controls, as well as 8 samples that belong to two pedigrees. Systematically examination for rare variations (through direct genotyping) and common variations (through genotyping and imputation) for SAHs were performed in this study.

Results

A total of 16,029 single-nucleotide polymorphisms (SNPs) and 108,999 short indels were detected in all samples, and among them, 30 SNPs distributed on 17 genes presented a strong association signal with SAH. Two novel pathogenic gene variants were identified as associated risk loci, including mutation in TPO and PALD1. The statistical analysis for rare, damaging variations in SAHs identified several susceptibility genes which were involved in degradation of the extracellular matrix and transcription factor signal pathways. And 25 putative pathogenic genes for SAH were also identified basic on functional interaction network analysis with the published SAH-associated genes. Additionally, pedigree analysis revealed autosomal dominant inheritance of pathogenic genes.

Conclusion

Systematical analysis revealed a key role for rare variations in SAH risk and discovered SNPs in new complex loci. Our study expanded the list of candidate genes associated with SAH risk, and will facilitate the investigation of disease-related mechanisms and potential clinical therapies.

Methodological differences can affect sequencing depth with a possible impact on the accuracy of genetic diagnosis

For a better interpretation of variants, evidence-based databases, such as ClinVar, compile data on the presumed relationships between variants and phenotypes. In this study, we aimed to analyze the pattern of sequencing depth in variants from whole-exome sequencing data in the 1000 Genomes project phase 3, focusing on the variants present in the ClinVar database that were predicted to affect protein-coding regions. We demonstrate that the distribution of the sequencing depth varies across different sequencing centers (pair-wise comparison, p < 0.001). Most importantly, we found that the distribution pattern of sequencing depth is specific to each facility, making it possible to correctly assign 96.9% of the samples to their sequencing center. Thus, indicating the presence of a systematic bias, related to the methods used in the different facilities, which generates significant variations in breadth and depth in whole-exome sequencing data in clinically relevant regions. Our results show that methodological differences, leading to significant heterogeneity in sequencing depth, may potentially influence the accuracy of genetic diagnosis. Furthermore, our findings highlight how it is still challenging to integrate results from different sequencing centers, which may also have an impact on genomic research.

VCF/Plotein: visualization and prioritization of genomic variants from human exome sequencing projects

Motivation

Identifying disease-causing variants from exome sequencing projects remains a challenging task that often requires bioinformatics expertise. Here we describe a user-friendly graphical application that allows medical professionals and bench biologists to prioritize and visualize genetic variants from human exome sequencing data.

Results

We have implemented VCF/Plotein, a graphical, fully interactive web application able to display exome sequencing data in VCF format. Gene and variant information is extracted from Ensembl. Cross-referencing with external databases and application-based gene and variant filtering have also been implemented. All data processing is done locally by the user’s CPU to ensure the security of patient data.

Availability and implementation

Freely available on the web at https://vcfplotein.liigh.unam.mx. Website implemented in JavaScript using the Vue.js framework, with all major browsers supported. Source code freely available for download at https://github.com/raulossio/VCF-plotein.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification of rare variants of allergic rhinitis based on whole genome sequencing and gene expression profiling: A preliminary investigation in four families

Background

Despite the success of genome-wide association studies for allergic rhinitis (AR), no definitive causal variants have been identified, and a substantial portion of the heritability of the disease is yet to be discovered.

Methods

Four families, each with at least 1 parent and one child suffering from dust mite (DM) AR, were recruited, and whole-genome sequencing was performed on samples from 9 eligible individuals from these families. Conjoint analysis was performed for existing gene expression profiling data in the literature and the whole genome sequencing data obtained for these individuals; for presence of family-specific variants segregating with AR and the pathways involved. Similar analyses were also performed with data obtained for 96 sporadic house dust mite (HDM) AR patients and 96 healthy controls.

Results

Three rare variants in three genes (FLT1_c.603A ​> ​T; VEGFB_c.322A ​> ​C; and ITGA2_c.502+1G ​> ​A), which are involved in Focal Adhesion pathway, were identified in affected, but not unaffected, subjects in two families. VEGFB_c.322A ​> ​C and/or ITGA2_c.502+1G ​> ​A were further detected in all DM AR patients but not in any healthy individuals in 1 family; which was further investigated for members. The 3 identified variants were not found in any of the sporadic DM AR patients or healthy controls.

Conclusion

Despite the relatively small sample size, this study has identified several potentially functional rare variants in AR candidate genes, and it provides a platform for future work in larger numbers of families and sporadic individuals for a better understanding of the genetic basis of AR.

The Progress of CRISPR/Cas9-Mediated Gene Editing in Generating Mouse/Zebrafish Models of Human Skeletal Diseases

Genetic factors play a substantial role in the etiology of skeletal diseases, which involve 1) defects in skeletal development, including intramembranous ossification and endochondral ossification; 2) defects in skeletal metabolism, including late bone growth and bone remodeling; 3) defects in early developmental processes related to skeletal diseases, such as neural crest cell (NCC) and cilia functions; 4) disturbance of the cellular signaling pathways which potentially affect bone growth. Efficient and high-throughput genetic methods have enabled the exploration and verification of disease-causing genes and variants. Animal models including mouse and zebrafish have been extensively used in functional mechanism studies of causal genes and variants. The conventional approaches of generating mutant animal models include spontaneous mutagenesis, random integration, and targeted integration via mouse embryonic stem cells. These approaches are costly and time-consuming. Recent development and application of gene-editing tools, especially the CRISPR/Cas9 system, has significantly accelerated the process of gene-editing in diverse organisms. Here we review both mice and zebrafish models of human skeletal diseases generated by CRISPR/Cas9 system, and their contributions to deciphering the underpins of disease mechanisms.

A novel frameshift mutation in the PITX2 gene in a family with Axenfeld-Rieger syndrome using targeted exome sequencing

BACKGROUND

Axenfeld-Rieger syndrome (ARS) is an autosomal dominant genetic disorder that is characterized by specific abnormalities of the anterior segment of the eye. Heterozygous mutations in two developmental transcription factor genes PITX2 and FOXC1 have been identified within ARS patients, accounting for 40 to 70% of cases. Our purpose is to describe clinical and genetic findings in a Chinese family with ARS.

METHODS

An ARS family with three affected members was recruited. The patients underwent a series of complete ophthalmologic examinations, general physical examination and dental radiography. DNA samples of proband II-1 were used for targeted exome sequencing of the FOXC1 and PITX2 genes. Sanger sequencing was used to validate the variation in PITX2. Quantitative real-time PCR was carried out to detect the expression of PITX2 in patients and normal controls.

RESULTS

All affected members showed iris atrophy, corectopia, shallow anterior chamber, complete or partial angle closure, and advanced glaucoma. In addition, they revealed systemic anomalies, including microdontia, hypodontia, and redundant periumbilical skin. A novel heterozygous frameshift variation, c.515delA, in PITX2 was found in the proband, which might lead to a truncated PITX2 protein (p.Gln172ArgfsX36). Sanger sequencing validated that the variation completely cosegregated with the ARS phenotype among this family, but was absent in 100 unrelated controls. Quantitative real-time PCR analysis revealed that the mRNA expression of PITX2 was significantly decreased in patients compared with that in unrelated normal controls.

CONCLUSIONS

PITX2 c.515delA (p.Gln172ArgfsX36) was the genetic etiology of our pedigree. The mutation led to decreased PITX2 gene expression and a truncated mRNA transcript.

Family-based association tests for rare variants with censored traits

We propose a set of family-based burden and kernel tests for censored traits (FamBAC and FamKAC). Here, censored traits refer to time-to-event outcomes, for instance, age-at-onset of a disease. To model censored traits in family-based designs, we used the frailty model, which incorporated not only fixed genetic effects of rare variants in a region of interest but also random polygenic effects shared within families. We first partitioned genotype scores of rare variants into orthogonal between- and within-family components, and then derived their corresponding efficient score statistics from the frailty model. Finally, FamBAC and FamKAC were constructed by aggregating the weighted efficient scores of the within-family components across rare variants and subjects. FamBAC collapsed rare variants within subject first to form a burden test that followed a chi-squared distribution; whereas FamKAC was a variant component test following a mixture of chi-squared distributions. For FamKAC, p-values can be computed by permutation tests or for computational efficiency by approximation methods. Through simulation studies, we showed that type I error was correctly controlled by FamBAC for various variant weighting schemes (0.0371 to 0.0527). However, FamKAC type I error rates based on approximation methods were deflated (max 0.0376) but improved by permutation tests. Our simulations also demonstrated that burden test FamBAC had higher power than kernel test FamKAC when high proportion (e.g. ≥ 80%) of causal variants had effects in the same direction. In contrast, when the effects of causal variants on the censored trait were in mixed directions, FamKAC outperformed FamBAC and had comparable or higher power than an existing method, RVFam. Our proposed framework has the flexibility to accommodate general nuclear families, and can be used to analyze sequence data for censored traits such as age-at-onset of a complex disease of interest.

Rare variants in the neuronal ceroid lipofuscinosis gene MFSD8 are candidate risk factors for frontotemporal dementia

Pathogenic variation in MAPT, GRN, and C9ORF72 accounts for at most only half of frontotemporal lobar degeneration (FTLD) cases with a family history of neurological disease. This suggests additional variants and genes that remain to be identified as risk factors for FTLD. We conducted a case-control genetic association study comparing pathologically diagnosed FTLD patients (n = 94) to cognitively normal older adults (n = 3541), and found suggestive evidence that gene-wide aggregate rare variant burden in MFSD8 is associated with FTLD risk. Because homozygous mutations in MFSD8 cause neuronal ceroid lipofuscinosis (NCL), similar to homozygous mutations in GRN, we assessed rare variants in MFSD8 for relevance to FTLD through experimental follow-up studies. Using post-mortem tissue from middle frontal gyrus of patients with FTLD and controls, we identified increased MFSD8 protein levels in MFSD8 rare variant carriers relative to non-variant carrier patients with sporadic FTLD and healthy controls. We also observed an increase in lysosomal and autophagy-related proteins in MFSD8 rare variant carrier and sporadic FTLD patients relative to controls. Immunohistochemical analysis revealed that MFSD8 was expressed in neurons and astrocytes across subjects, without clear evidence of abnormal localization in patients. Finally, in vitro studies identified marked disruption of lysosomal function in cells from MFSD8 rare variant carriers, and identified one rare variant that significantly increased the cell surface levels of MFSD8. Considering the growing evidence for altered autophagy in the pathogenesis of neurodegenerative disorders, our findings support a role of NCL genes in FTLD risk and suggest that MFSD8-associated lysosomal dysfunction may contribute to FTLD pathology.

Whole exome sequencing for the identification of CYP3A7 variants associated with tacrolimus concentrations in kidney transplant patients

The purpose of this study was to identify genotypes associated with dose-adjusted tacrolimus trough concentrations (C₀/D) in kidney transplant recipients using whole-exome sequencing (WES). This study included 147 patients administered tacrolimus, including seventy-five patients in the discovery set and seventy-two patients in the replication set. The patient genomes in the discovery set were sequenced using WES. Also, known tacrolimus pharmacokinetics-related intron variants were genotyped. Tacrolimus C₀/D was log-transformed. Sixteen variants were identified including novel CYP3A7 rs12360 and rs10211 by ANOVA. CYP3A7 rs2257401 was found to be the most significant variant among the periods by ANOVA. Seven variants including CYP3A7 rs2257401, rs12360, and rs10211 were analyzed by SNaPshot in the replication set and the effects on tacrolimus C₀/D were verified. A linear mixed model (LMM) was further performed to account for the effects of the variants and clinical factors. The combined set LMM showed that only CYP3A7 rs2257401 was associated with tacrolimus C₀/D after adjusting for patient age, albumin, and creatinine. The CYP3A7 rs2257401 genotype variant showed a significant difference on the tacrolimus C₀/D in those expressing CYP3A5, showing its own effect. The results suggest that CYP3A7 rs2257401 may serve as a significant genetic marker for tacrolimus pharmacokinetics in kidney transplantation.

Novel Variants Identified in Multiple Sclerosis Patients From Southern China

Background: Multiple sclerosis (MS) is an autoimmune and demyelinating disease. Genome-wide association studies have shown that MS is associated with many genetic variants in some human leucocyte antigen genes and other immune-related genes, however, those studies were mostly specific to Caucasian populations. We attempt to address whether the same associations are also true for Asian populations by conducting whole-exome sequencing on MS patients from southern China.

Methods: Genomic DNA was extracted from the peripheral blood mononucleocytes of 8 MS patients and 26 healthy controls and followed by exome sequencing.

Results: In total, 41,227 variants were found to have moderate to high impact on their protein products. After filtering per allele frequencies according to known database, 17 variants with the allele frequency <1% or variants with undetermined frequency were identified to be unreported and have significantly different frequencies between the MS patients and healthy controls. After validation via Sanger sequencing, one rare variant located in exon 7 of TRIOBP (Chr22: 37723520G>T, Ala322Ser, rs201693690) was found to be a novel missense variant.

Conclusion: MS in southern China may have association with unique genetic variants, our data suggest TRIOBP as a potential novel risk gene.

Informatics and machine learning to define the phenotype

INTRODUCTION

For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

Benchmarking distributed data warehouse solutions for storing genomic variant information

Database URL

https://github.com/ZSI-Bio/variantsdwh.

Meta-Analysis of Common and Rare Variants

Meta-analysis is a statistical technique that is widely used for improving the power to detect associations, by synthesizing data from independent studies, and is extensively used in the genomic analyses of complex traits. Estimates from different studies are combined and the results effectively provide the power of a much larger study. Meta-analysis also has the potential of discovering heterogeneity in the effects among the different studies. This chapter provides an overview of the methods used for meta-analysis of common and rare single variants and also for gene/region-based analyses; common variants are mainly identified via genome-wide association studies (GWAS) and rare variants through various types of sequencing experiments.

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.

PhredEM: a phred-score-informed genotype-calling approach for next-generation sequencing studies

A fundamental challenge in analyzing next-generation sequencing (NGS) data is to determine an individual's genotype accurately, as the accuracy of the inferred genotype is essential to downstream analyses. Correctly estimating the base-calling error rate is critical to accurate genotype calls. Phred scores that accompany each call can be used to decide which calls are reliable. Some genotype callers, such as GATK and SAMtools, directly calculate the base-calling error rates from phred scores or recalibrated base quality scores. Others, such as SeqEM, estimate error rates from the read data without using any quality scores. It is also a common quality control procedure to filter out reads with low phred scores. However, choosing an appropriate phred score threshold is problematic as a too high threshold may lose data, while a too low threshold may introduce errors. We propose a new likelihood-based genotype-calling approach that exploits all reads and estimates the per-base error rates by incorporating phred scores through a logistic regression model. The approach, which we call PhredEM, uses the expectation-maximization (EM) algorithm to obtain consistent estimates of genotype frequencies and logistic regression parameters. It also includes a simple, computationally efficient screening algorithm to identify loci that are estimated to be monomorphic, so that only loci estimated to be nonmonomorphic require application of the EM algorithm. Like GATK, PhredEM can be used together with a linkage-disequilibrium-based method such as Beagle, which can further improve genotype calling as a refinement step. We evaluate the performance of PhredEM using both simulated data and real sequencing data from the UK10K project and the 1000 Genomes project. The results demonstrate that PhredEM performs better than either GATK or SeqEM, and that PhredEM is an improved, robust, and widely applicable genotype-calling approach for NGS studies. The relevant software is freely available.

A pipeline combining multiple strategies for prioritizing heterozygous variants for the identification of candidate genes in exome datasets

BACKGROUND

The identification of disease-causing variants in autosomal dominant diseases using exome-sequencing data remains a difficult task in small pedigrees. We combined several strategies to improve filtering and prioritizing of heterozygous variants using exome-sequencing datasets in familial Meniere disease: an in-house Pathogenic Variant (PAVAR) score, the Variant Annotation Analysis and Search Tool (VAAST-Phevor), Exomiser-v2, CADD, and FATHMM. We also validated the method by a benchmarking procedure including causal mutations in synthetic exome datasets.

RESULTS

PAVAR and VAAST were able to select the same sets of candidate variants independently of the studied disease. In contrast, Exomiser V2 and VAAST-Phevor had a variable correlation depending on the phenotypic information available for the disease on each family. Nevertheless, all the selected diseases ranked a limited number of concordant variants in the top 10 ranking, using the three systems or other combined algorithm such as CADD or FATHMM. Benchmarking analyses confirmed that the combination of systems with different approaches improves the prediction of candidate variants compared with the use of a single method. The overall efficiency of combined tools ranges between 68 and 71% in the top 10 ranked variants.

CONCLUSIONS

Our pipeline prioritizes a short list of heterozygous variants in exome datasets based on the top 10 concordant variants combining multiple systems.

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

Prediction of the impact of coding missense and nonsense single nucleotide polymorphisms on HD5 and HBD1 antibacterial activity against Escherichia coli

Defensins confer host defense against microorganisms and are important for human health. Single nucleotide polymorphisms (SNPs) in defensin gene-coding regions could lead to less active variants. Using SNP data available at the dbSNP database and frequency information from the 1000 Genomes Project, two DEFA5 (L26I and R13H) and eight DEFB1 (C35S, K31T, K33R, R29G, V06I, C12Y, Y28* and C05*) missense and nonsense SNPs that are located within mature regions of the coded defensins were retrieved. Such SNPs are rare and population restricted. In order to assess their antibacterial activity against Escherichia coli, two linear regression models were used from a previous work, which models the antibacterial activity as a function of solvation potential energy, using molecular dynamics data. Regarding only the antibacterial predictions, for HD5, no biological differences between wild-type and its variants were observed; while for HBD1, the results suggest that the R29G, K31T, Y28* and C05* variants could be less active than the wild-type one. The data here reported could lead to a substantial improvement in knowledge about the impact of missense SNPs in human defensins and their world distribution. © 2016 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 633-644, 2016.

Genetic study of congenital bile-duct dilatation identifies de novo and inherited variants in functionally related genes

Background

Congenital dilatation of the bile-duct (CDD) is a rare, mostly sporadic, disorder that results in bile retention with severe associated complications. CDD affects mainly Asians. To our knowledge, no genetic study has ever been conducted.

Methods

We aim to identify genetic risk factors by a “trio-based” exome-sequencing approach, whereby 31 CDD probands and their unaffected parents were exome-sequenced. Seven-hundred controls from the local population were used to detect gene-sets significantly enriched with rare variants in CDD patients.

Results

Twenty-one predicted damaging de novo variants (DNVs; 4 protein truncating and 17 missense) were identified in several evolutionarily constrained genes (p < 0.01). Six genes carrying DNVs were associated with human developmental disorders involving epithelial, connective or bone morphologies (PXDN, RTEL1, ANKRD11, MAP2K1, CYLD, ACAN) and four linked with cholangio- and hepatocellular carcinomas (PIK3CA, TLN1 CYLD, MAP2K1). Importantly, CDD patients have an excess of DNVs in cancer-related genes (p < 0.025). Thirteen genes were recurrently mutated at different sites, forming compound heterozygotes or functionally related complexes within patients.

Conclusions

Our data supports a strong genetic basis for CDD and show that CDD is not only genetically heterogeneous but also non-monogenic, requiring mutations in more than one genes for the disease to develop. The data is consistent with the rarity and sporadic presentation of CDD.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-016-0236-z) contains supplementary material, which is available to authorized users.

Var2GO: a web-based tool for gene variants selection

Background

One of the most challenging issue in the variant calling process is handling the resulting data, and filtering the genes retaining only the ones strictly related to the topic of interest. Several tools permit to gather annotations at different levels of complexity for the detected genes and to group them according to the pathways and/or processes they belong to. However, it might be a time consuming and frustrating task. This is partly due to the size of the file, that might contain many thousands of genes, and to the search of associated variants that requires a gene-by-gene investigation and annotation approach. As a consequence, the initial gene list is often reduced exploiting the knowledge of variants effect, novelty and genotype, with the potential risk of losing meaningful pieces of information.

Results

Here we present Var2GO, a new web-based tool to support the annotation and filtering of variants and genes coming from variant calling of high-throughput sequencing data. Var2GO permits to upload either the unprocessed Variant Calling Format file or a table containing the annotated variants. The raw data undergo a preliminary step of variants annotation, using the SnpEff tool, and are converted to a table format. The table is then uploaded into an on the fly generated database. Genes associated to the variants are automatically annotated with the corresponding Gene Ontology terms covering the three GO domains. Using the web interface it is then possible to filter and extract, from the whole list, genes having annotations in the domain of interest, by simply specifying filtering parameters and one or more keywords. The relevance of this tool is demonstrated on exome sequencing data.

Conclusions

Var2GO is a novel tool that implements a topic-based approach, expressly designed to help biologists in narrowing the search of relevant genes coming from variant calling analysis. Its main purpose is to support non-bioinformaticians in handling and processing raw variant calling data through an intuitive web interface. Furthermore, Var2GO offers a complete pipeline that, starting from the raw VCF file, allows to annotate both variants and associated genes and supports the extraction of relevant biological knowledge.

CRISPR/Cas9 in zebrafish: an efficient combination for human genetic diseases modeling

The next-generation sequencing identifies a growing number of candidate genes associated with human genetic diseases, which inevitably requires efficient methods to validate the causal links between genotype and phenotype. Recently, zebrafish, with sufficiently high-throughput capabilities, has become a favored option to study human pathogenesis. In addition, CRISPR/Cas9-based approaches have radically reduced the efforts to introduce targeted genome engineering in various organisms. Here, we systemically review the basic considerations in the design of gene editing in zebrafish with CRISPR/Cas9, and explore the potential of the combination of these two to support efficient functional analysis of human genetic variants.

The genetic architecture of resistance to virus infection in Drosophila

Variation in susceptibility to infection has a substantial genetic component in natural populations, and it has been argued that selection by pathogens may result in it having a simpler genetic architecture than many other quantitative traits. This is important as models of host-pathogen co-evolution typically assume resistance is controlled by a small number of genes. Using the Drosophila melanogaster multiparent advanced intercross, we investigated the genetic architecture of resistance to two naturally occurring viruses, the sigma virus and DCV (Drosophila C virus). We found extensive genetic variation in resistance to both viruses. For DCV resistance, this variation is largely caused by two major-effect loci. Sigma virus resistance involves more genes - we mapped five loci, and together these explained less than half the genetic variance. Nonetheless, several of these had a large effect on resistance. Models of co-evolution typically assume strong epistatic interactions between polymorphisms controlling resistance, but we were only able to detect one locus that altered the effect of the main effect loci we had mapped. Most of the loci we mapped were probably at an intermediate frequency in natural populations. Overall, our results are consistent with major-effect genes commonly affecting susceptibility to infectious diseases, with DCV resistance being a near-Mendelian trait.

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.

Inferring Crohn's disease association from exome sequences by integrating biological knowledge

Background

Exome sequencing has been emerged as a primary method to identify detailed sequence variants associated with complex diseases including Crohn’s disease in the protein-coding regions of human genome. However, constructing an interpretable model for exome sequencing data is challenging because of the huge diversity of genomic variation. In addition, it has been known that utilizing biologically relevant information in a rigorous manner is essential for effectively extracting disease-associated information.

Results

In this paper, we incorporate three different types of biological knowledge such as predicted pathogenicity, disease gene annotation, and functional interaction network of human genes, and integrate them with exome sequence data in non-negative matrix tri-factorization framework. Based on the proposed method, we successfully identified Crohn’s disease patients from exome sequencing data and achieved the area under the receiver operating characteristics curve (AUC) of 0.816, while other clustering methods not using biological information achieved the AUC of 0.786. Moreover, the disease association score derived from our method showed higher correlation with Crohn’s disease genes than other unrelated genes.

Conclusions

As a consequence, by integrating biological information across multiple levels such as variant, gene, and systems, our method could be useful for identifying disease susceptibility and its associated genes from exome sequencing data.

Testing Rare-Variant Association without Calling Genotypes Allows for Systematic Differences in Sequencing between Cases and Controls

Next-generation sequencing of DNA provides an unprecedented opportunity to discover rare genetic variants associated with complex diseases and traits. However, the common practice of first calling underlying genotypes and then treating the called values as known is prone to false positive findings, especially when genotyping errors are systematically different between cases and controls. This happens whenever cases and controls are sequenced at different depths, on different platforms, or in different batches. In this article, we provide a likelihood-based approach to testing rare variant associations that directly models sequencing reads without calling genotypes. We consider the (weighted) burden test statistic, which is the (weighted) sum of the score statistic for assessing effects of individual variants on the trait of interest. Because variant locations are unknown, we develop a simple, computationally efficient screening algorithm to estimate the loci that are variants. Because our burden statistic may not have mean zero after screening, we develop a novel bootstrap procedure for assessing the significance of the burden statistic. We demonstrate through extensive simulation studies that the proposed tests are robust to a wide range of differential sequencing qualities between cases and controls, and are at least as powerful as the standard genotype calling approach when the latter controls type I error. An application to the UK10K data reveals novel rare variants in gene BTBD18 associated with childhood onset obesity. The relevant software is freely available.

In next-generation sequencing studies, there are typically systematic differences in sequencing qualities (e.g., depth) between cases and controls, because the entire studies are rarely sequenced in exactly the same way. It has long been appreciated that, in the presence of such differences, the standard genotype calling approach to detecting rare variant associations generally leads to excessive false positive findings. To deal with this, the current “state of the art” is to impose stringent quality control procedures that much of the data is eliminated. We present a method that allows analyzing data with a wide range of differential sequencing qualities between cases and controls. Our method is more powerful than the current practice and can accelerate the search for disease-causing mutations.

What can we learn about lipoprotein metabolism and coronary heart disease from studying rare variants?

PURPOSE OF REVIEW

Rare variant association studies (RVAS) target the class of genetic variation with frequencies less than 1%. Recently, investigators have used exome sequencing in RVAS to identify rare alleles responsible for Mendelian diseases but have experienced greater difficulty discovering such alleles for complex diseases. In this review, we describe what we have learned about lipoprotein metabolism and coronary heart disease through the conduct of RVAS.

RECENT FINDINGS

Rare protein-altering genetic variation can provide important insights that are not as easily attainable from common variant association studies. First, RVAS can facilitate gene discovery by identifying novel rare protein-altering variants in specific genes that are associated with disease. Second, rare variant associations can provide supportive evidence for putative drug targets for novel therapies. Finally, rare variants can uncover new pathways and reveal new biologic mechanisms.

SUMMARY

The field of human genetics has already made tremendous progress in understanding lipoprotein metabolism and the causes of coronary heart disease in the context of rare variants. As next generation sequencing becomes more cost-effective, RVAS with larger sample sizes will be conducted. This will lead to more novel rare variant discoveries and the translation of genomic data into biological knowledge and clinical insights for cardiovascular disease.

Amplicon-based semiconductor sequencing of human exomes: performance evaluation and optimization strategies

The Ion Proton platform allows to perform whole exome sequencing (WES) at low cost, providing rapid turnaround time and great flexibility. Products for WES on Ion Proton system include the AmpliSeq Exome kit and the recently introduced HiQ sequencing chemistry. Here, we used gold standard variants from GIAB consortium to assess the performances in variants identification, characterize the erroneous calls and develop a filtering strategy to reduce false positives. The AmpliSeq Exome kit captures a large fraction of bases (>94 %) in human CDS, ClinVar genes and ACMG genes, but with 2,041 (7 %), 449 (13 %) and 11 (19 %) genes not fully represented, respectively. Overall, 515 protein coding genes contain hard-to-sequence regions, including 90 genes from ClinVar. Performance in variants detection was maximum at mean coverage >120×, while at 90× and 70× we measured a loss of variants of 3.2 and 4.5 %, respectively. WES using HiQ chemistry showed ~71/97.5 % sensitivity, ~37/2 % FDR and ~0.66/0.98 F1 score for indels and SNPs, respectively. The proposed low, medium or high-stringency filters reduced the amount of false positives by 10.2, 21.2 and 40.4 % for indels and 21.2, 41.9 and 68.2 % for SNP, respectively. Amplicon-based WES on Ion Proton platform using HiQ chemistry emerged as a competitive approach, with improved accuracy in variants identification. False-positive variants remain an issue for the Ion Torrent technology, but our filtering strategy can be applied to reduce erroneous variants.

A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants

Advanced age-related macular degeneration (AMD) is the leading cause of blindness in the elderly with limited therapeutic options. Here, we report on a study of >12 million variants including 163,714 directly genotyped, most rare, protein-altering variant. Analyzing 16,144 patients and 17,832 controls, we identify 52 independently associated common and rare variants (P < 5×10^–8) distributed across 34 loci. While wet and dry AMD subtypes exhibit predominantly shared genetics, we identify the first signal specific to wet AMD, near MMP9 (difference-P = 4.1×10^–10). Very rare coding variants (frequency < 0.1%) in CFH, CFI, and TIMP3 suggest causal roles for these genes, as does a splice variant in SLC16A8. Our results support the hypothesis that rare coding variants can pinpoint causal genes within known genetic loci and illustrate that applying the approach systematically to detect new loci requires extremely large sample sizes.

Systematic comparison of variant calling pipelines using gold standard personal exome variants

The success of clinical genomics using next generation sequencing (NGS) requires the accurate and consistent identification of personal genome variants. Assorted variant calling methods have been developed, which show low concordance between their calls. Hence, a systematic comparison of the variant callers could give important guidance to NGS-based clinical genomics. Recently, a set of high-confident variant calls for one individual (NA12878) has been published by the Genome in a Bottle (GIAB) consortium, enabling performance benchmarking of different variant calling pipelines. Based on the gold standard reference variant calls from GIAB, we compared the performance of thirteen variant calling pipelines, testing combinations of three read aligners--BWA-MEM, Bowtie2, and Novoalign--and four variant callers--Genome Analysis Tool Kit HaplotypeCaller (GATK-HC), Samtools mpileup, Freebayes and Ion Proton Variant Caller (TVC), for twelve data sets for the NA12878 genome sequenced by different platforms including Illumina2000, Illumina2500, and Ion Proton, with various exome capture systems and exome coverage. We observed different biases toward specific types of SNP genotyping errors by the different variant callers. The results of our study provide useful guidelines for reliable variant identification from deep sequencing of personal genomes.

Meta-Analysis of Rare Variant Association Tests in Multiethnic Populations

Several methods have been proposed to increase power in rare variant association testing by aggregating information from individual rare variants (MAF < 0.005). However, how to best combine rare variants across multiple ethnicities and the relative performance of designs using different ethnic sampling fractions remains unknown. In this study, we compare the performance of several statistical approaches for assessing rare variant associations across multiple ethnicities. We also explore how different ethnic sampling fractions perform, including single-ethnicity studies and studies that sample up to four ethnicities. We conducted simulations based on targeted sequencing data from 4,611 women in four ethnicities (African, European, Japanese American, and Latina). As with single-ethnicity studies, burden tests had greater power when all causal rare variants were deleterious, and variance component-based tests had greater power when some causal rare variants were deleterious and some were protective. Multiethnic studies had greater power than single-ethnicity studies at many loci, with inclusion of African Americans providing the largest impact. On average, studies including African Americans had as much as 20% greater power than equivalently sized studies without African Americans. This suggests that association studies between rare variants and complex disease should consider including subjects from multiple ethnicities, with preference given to genetically diverse groups.

Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers

We report ∼17.6 million genetic variants from whole-genome sequencing of 2,120 Sardinians; 22% are absent from previous sequencing-based compilations and are enriched for predicted functional consequences. Furthermore, ∼76,000 variants common in our sample (frequency >5%) are rare elsewhere (<0.5% in the 1000 Genomes Project). We assessed the impact of these variants on circulating lipid levels and five inflammatory biomarkers. We observe 14 signals, including 2 major new loci, for lipid levels and 19 signals, including 2 new loci, for inflammatory markers. The new associations would have been missed in analyses based on 1000 Genomes Project data, underlining the advantages of large-scale sequencing in this founder population.

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.

Low-Frequency IL23R Coding Variant Associated with Crohn's Disease Susceptibility in Japanese Subjects Identified by Personal Genomics Analysis

BACKGROUND

The common disease-common variant hypothesis is insufficient to explain the complexities of Crohn's disease (CD) genetics; therefore, rare variants are expected to be important in the disease. We explored rare variants associated with susceptibility to CD in Japanese individuals by personal genomic analysis.

METHODS

Two-step analyses were performed. The first step was a trio analysis with whole-exome sequence (WES) analysis and the second was a follow-up case-control association study. The WES analysis pipeline comprised Burrows-Wheeler Aligner, Picard, Genome Analysis Toolkit, and SAMTOOLS. Single nucleotide variants (SNVs)/indels were annotated and filtered by using programs implemented in ANNOVAR in combination with identity-by-descent (IBD), subsequently were subjected to the linkage based, and de novo based strategies. Finally, we conducted an association study that included 176 unrelated subjects with CD and 358 healthy control subjects.

RESULTS

In family members, 234,067-297,523 SNVs/indels were detected and they were educed to 106-146 by annotation based filtering. Fifty-four CD variants common to both individuals of the affected sib pair were identified. The linkage based strategy detected five candidate variants whereas the de novo based strategy identified no variants. Consequently, five candidates were analyzed in the case-control association study. CD showed a significant association with one variant in exon 4 of IL23R, G149R [rs76418789, P = 3.9E-5, odds ratio (OR) 0.21, 95% confidence interval (CI) 0.09-0.47 for the dominant model (AA + AG versus GG), and P = 7.3E-5, OR 0.21, 95% CI 0.10-0.48 for AG versus GG, and P = 7.2E-5, OR 0.23, 95% CI 0.10-0.50 for the allele model].

CONCLUSIONS

The present study, using personal genomics analysis of a small CD pedigree, is the first to show that the low-frequency non-synonymous variant of IL23R, rs76418789, protects against CD development in Japanese subjects.

Phenotypic extremes in rare variant study designs

Currently, next-generation sequencing studies aim to identify rare and low-frequency variation that may contribute to disease. For a given effect size, as the allele frequency decreases, the power to detect genes or variants of interest also decreases. Although many methods have been proposed for the analysis of such data, study design and analytic issues still persist in data interpretation. In this study we present sequencing data for ABCA1 that has known rare variants associated with high-density lipoprotein cholesterol (HDL-C). We contrast empirical findings from two study designs: a phenotypic extreme sample and a population-based random sample. We found differing strengths of association with HDL-C across the two study designs (P=0.0006 with n=701 phenotypic extremes vs P=0.03 with n=1600 randomly sampled individuals). To explore this apparent difference in evidence for association, we performed a simulation study focused on the impact of phenotypic selection on power. We demonstrate that the power gain for an extreme phenotypic selection study design is much greater in rare variant studies than for studies of common variants. Our study confirms that studying phenotypic extremes is critical in rare variant studies because it boosts power in two ways: the typical increases from extreme sampling and increasing the proportion of relevant functional variants ascertained and thereby tested for association. Furthermore, we show that when combining statistical evidence through meta-analysis from an extreme-selected sample and a second separate population-based random sample, power is lower when a traditional sample size weighting is used compared with weighting by the noncentrality parameter.

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.

Insights into blood lipids from rare variant discovery

Large-scale genome wide screens have discovered over 160 common variants associated with plasma lipids, which are risk factors often linked to heart disease. A large fraction of lipid heritability remains unexplained, and it is hypothesized that rare variants of functional consequence may account for some of the missing heritability. Finding lipid-associated variants that occur less frequently in the human population poses a challenge, primarily due to lack of power and difficulties to identify and test them. Interrogation of the protein-coding regions of the genome using array and sequencing techniques has led to important discoveries of rare variants that affect lipid levels and related disease risk. Here, we summarize the latest methods and findings that contribute to our current understanding of rare variant lipid genetics.

Detection of novel germline mutations for breast cancer in non-BRCA1/2 families

The identification of the breast cancer susceptibility genes BRCA1 and BRCA2 enhanced clinicians' ability to select high-risk individuals for aggressive surveillance and prevention, and led to the development of targeted therapies. However, BRCA1/2 mutations account for only 25% of familial breast cancer cases. To systematically identify rare, probably pathogenic variants in familial cases of breast cancer without BRCA1/2 mutations, we developed a list of 312 genes, and performed targeted DNA enrichment coupled to multiplex next-generation sequencing on 104 'BRCAx' patients and 101 geographically matched controls in Ireland. As expected, this strategy allowed us to identify mutations in several well-known high-susceptibility and moderate-susceptibility genes, including ATM (~ 5%), RAD50 (~ 3%), CHEK2 (~ 2%), TP53 (~ 1%), PALB2 (~ 1%), and MRE11A (~ 1%). However, we also identified novel pathogenic variants in 30 other genes, which, when taken together, potentially explain the etiology of the missing heritability in up to 35% of BRCAx patients. These included novel potential pathogenic mutations in MAP3K1, CASP8, RAD51B, ZNF217, CDKN2B-AS1, and ERBB2, including a splice site mutation, which we predict would generate a constitutively active HER2 protein. Taken together, this work extends our understanding of the genetics of familial breast cancer, and supports the need to implement hereditary multigene panel testing to more appropriately orientate clinical management.

Targeted next-generation sequencing effectively analyzed the cystic fibrosis transmembrane conductance regulator gene in pancreatitis

BACKGROUND

The cystic fibrosis transmembrane conductance regulator (CFTR) gene, responsible for the development of cystic fibrosis, is known as a pancreatitis susceptibility gene. Direct DNA sequencing of PCR-amplified CFTR gene segments is a first-line method to detect unknown mutations, but it is a tedious and labor-intensive endeavor given the large size of the gene (27 exons, 1,480 amino acids). Next-generation sequencing (NGS) is becoming standardized, reducing the cost of DNA sequencing, and enabling the generation of millions of reads per run. We here report a comprehensive analysis of CFTR variants in Japanese patients with chronic pancreatitis using NGS coupling with target capture.

METHODS

Exon sequences of the CFTR gene from 193 patients with chronic pancreatitis (121 idiopathic, 46 alcoholic, 17 hereditary, and nine familial) were captured by HaloPlex target enrichment technology, followed by NGS.

RESULTS

The sequencing data covered 91.6 % of the coding regions of the CFTR gene by ≥ 20 reads with a mean read depth of 449. We could identify 12 non-synonymous variants including three novel ones [c.A1231G (p.K411E), c.1753G>T (p.E585X) and c.2869delC (p.L957fs)] and seven synonymous variants including three novel ones in the exonic regions. The frequencies of the c.4056G>C (p.Q1352H) and the c.3468G>T (p.L1156F) variants were higher in patients with chronic pancreatitis than those in controls.

CONCLUSIONS

Target sequence capture combined with NGS is an effective method for the analysis of pancreatitis susceptibility genes.

Personalized biochemistry and biophysics

Whole human genome sequencing of individuals is becoming rapid and inexpensive, enabling new strategies for using personal genome information to help diagnose, treat, and even prevent human disorders for which genetic variations are causative or are known to be risk factors. Many of the exploding number of newly discovered genetic variations alter the structure, function, dynamics, stability, and/or interactions of specific proteins and RNA molecules. Accordingly, there are a host of opportunities for biochemists and biophysicists to participate in (1) developing tools to allow accurate and sometimes medically actionable assessment of the potential pathogenicity of individual variations and (2) establishing the mechanistic linkage between pathogenic variations and their physiological consequences, providing a rational basis for treatment or preventive care. In this review, we provide an overview of these opportunities and their associated challenges in light of the current status of genomic science and personalized medicine, the latter often termed precision medicine.