Schizophrenia is a highly heritable disorder. Genome-wide association studies based largely on common alleles have identified over 100 schizophrenia risk loci, but it is also evident from studies of copy number variants (CNVs) and from exome-sequencing studies that rare alleles are also involved. Full characterization of the contribution of rare alleles to the disorder awaits the deployment of sequencing technology in very large sample sizes, meanwhile, as an interim measure, exome arrays allow rare non-synonymous variants to be sampled at a fraction of the cost. In an analysis of exome array data from 13 688 individuals (5585 cases and 8103 controls) from the UK, we found that rare (minor allele frequency < 0.1%) variant association signal was enriched among genes that map to autosomal loci that are genome-wide significant (GWS) in common variant studies of schizophrenia genome-wide association study (PGWAS = 0.01) as well as gene sets known to be enriched for rare variants in sequencing studies (PRARE = 0.026). We also identified the gene-wise equivalent of GWS support for WDR88 (WD repeat-containing protein 88), a gene of unknown function (P = 6.5 × 10(-7)). Rare alleles represented on exome chip arrays contribute to the genetic architecture of schizophrenia, but as is the case for GWAS, very large studies are required to reveal additional susceptibility alleles for the disorder.

Circulating cell-free DNA (cfDNA) released from cancerous tissues has been found to harbor tumor-associated alterations and to represent the molecular composition of the tumor. Recent advances in technologies, especially in next-generation sequencing, enable the analysis of low amounts of cfDNA from body fluids. We analyzed the exomes of tumor tissue and matched serum samples to investigate the molecular representation of the tumor exome in cfDNA. To this end, we implemented a workflow for sequencing of cfDNA from low serum volumes (200 μl) and performed whole-exome sequencing (WES) of serum and matched tumor tissue samples from six non-small cell lung cancer (NSCLC) patients and two control sera. Exomes, including untranslated regions (UTRs) of cfDNA were sequenced with an average coverage of 68.5x. Enrichment efficiency, target coverage, and sequencing depth of cfDNA reads were comparable to those from matched tissues. Discovered variants were compared between serum and tissue as well as to the COSMIC database of known mutations. Although not all tissue variants could be confirmed in the matched serum, up to 57% of the tumor variants were reflected in matched cfDNA with mutations in PIK3CA, ALK, and PTEN as well as variants at COSMIC annotated sites in all six patients analyzed. Moreover, cfDNA revealed a mutation in MTOR, which was not detected in the matched tissue, potentially from an untested region of the heterogeneous primary tumor or from a distant metastatic clone. WES of cfDNA may provide additional complementary molecular information about clinically relevant mutations and the clonal heterogeneity of the tumors.

Reproducibility is receiving increased attention across many domains of science and genomics is no exception. Efforts to identify copy number variations (CNVs) from exome sequence (ES) data have been increasing. Many algorithms have been published to discover CNVs from exomes and a major challenge is the reproducibility in other datasets. Here we test exome CNV calling reproducibility under three conditions: data generated by different sequencing centers; varying sample sizes; and varying capture methodology.

Whole exome sequencing (WES) has been proven to serve as a valuable basis for various applications such as variant calling and copy number variation (CNV) analyses. For those analyses the read coverage should be optimally balanced throughout protein coding regions at sufficient read depth. Unfortunately, WES is known for its uneven coverage within coding regions due to GC-rich regions or off-target enrichment.

Germline mutations in FASL and FAS impair Fas-dependent apoptosis and cause recessively or dominantly inherited autoimmune lymphoproliferative syndrome (ALPS). Patients with ALPS typically present with no other clinical phenotype. We investigated a large, consanguineous, multiplex kindred in which biological features of ALPS were found in the context of severe bacterial and viral disease, recurrent hepatopathy and encephalopathy, and cardiac malformations. By a combination of genome-wide linkage and whole-exome sequencing, we identified a homozygous missense mutation in FADD, encoding the Fas-associated death domain protein (FADD), in the patients. This FADD mutation decreases steady-state protein levels and impairs Fas-dependent apoptosis in vitro, accounting for biological ALPS phenotypes in vivo. It also impairs Fas-independent signaling pathways. The observed bacterial infections result partly from functional hyposplenism, and viral infections result from impaired interferon immunity. We describe here a complex clinical disorder, its genetic basis, and some of the key mechanisms underlying its pathogenesis. Our findings highlight the key role of FADD in Fas-dependent and Fas-independent signaling pathways in humans.

Ethnically specific data on genetic variation are crucial for understanding human biology and for clinical interpretation of variant pathogenicity. We analyzed data obtained by deep sequencing 1303 Korean whole exomes; the data were generated by three independent whole exome sequencing projects (named the KOEX study). The primary focus of this study was to comprehensively analyze the variant statistics, investigate secondary findings that may have clinical actionability, and identify loci that should be cautiously interpreted for pathogenicity. A total of 495 729 unique variants were identified at exonic regions, including 169 380 nonsynonymous variants and 4356 frameshift insertion/deletions. Among these, 76 607 were novel coding variants. On average, each individual had 7136 nonsynonymous single-nucleotide variants and 74 frameshift insertion/deletions. We classified 13 pathogenic and 13 likely pathogenic variants in 56 genes that may have clinical actionability according to the guidelines of the American College of Medical Genetics and Genomics, and the Association for Molecular Pathology. The carrier frequency of these 26 variants was 2.46% (95% confidence interval 1.73-3.46). To identify loci that require cautious interpretation in clinical sequencing, we identified 18 genes that are prone to sequencing errors, and 671 genes that are highly polymorphic and carry excess nonsynonymous variants. The catalog of identified variants, its annotation and frequency information are publicly available (http://koex.snu.ac.kr). These findings should be useful resources for investigating ethnically specific characteristics in human health and disease.

A novel approach is developed to address the challenge of annotating with phenotypic effects those exome variants for which relevant empirical data are lacking or minimal. The predictive annotation method is implemented as a stacked ensemble of supervised base-learners, including distributed random forest and gradient boosting machines. Ensemble models were trained and cross-validated on evidence-based categorical variant effect annotations from the ClinVar database, and were applied to 84 million non-synonymous single nucleotide variants (SNVs). The consensus model combined 39 functional mutation impacts, cross-species conservation score, and gene indispensability score. The indispensability score, accounting for differences in variant pathogenicities including in essential and mutation-tolerant genes, considerably improved the predictions. The consensus combination is consistent with as many input scores as possible while minimizing false predictions. The input scores are ranked based on their ability to predict effects. The score rankings and categorical phenotypic variant effect predictions are aimed for direct use in clinical and biological applications to prioritize human exome variants and mutations.

Variants with known or possible pathogenicity located in genes that are unrelated to primary disease conditions are defined as secondary findings. Secondary findings are not the primary targets of whole exome and genome sequencing (WES/WGS) assay but can be of great practical value in early disease prevention and intervention. The driving force for this study was to investigate the impact of racial difference and disease background on secondary findings. Here, we analyzed secondary findings frequencies in 421 whole exome-sequenced Chinese children who are phenotypically normal or bear congenital heart diseases/juvenile obesity. In total, 421 WES datasets were processed for potential deleterious variant screening. A reference gene list was defined according to the American College of Medical Genetics and Genomics (ACMG) recommendations for reporting secondary findings v2.0 (ACMG SF v2.0). The variant classification was performed according to the evidence-based guidelines recommended by the joint consensus of the ACMG and the Association for Molecular Pathology (AMP).

In the last decade, one of the biggest challenges in genomics research has been to distinguish definitive pathogenic variants from all likely pathogenic variants identified by next-generation sequencing. This task is particularly complex because of our lack of knowledge regarding overall genome variation and pathogenicity of the variants. Therefore, obtaining sufficient information about genome variants in the general population is necessary as such data could be used for the interpretation of de novo mutations (DNMs) in the context of patient's phenotype in cases of sporadic genetic disease. In this study, data from whole-exome sequencing of the general population in Lithuania were directly examined. In total, 84 (VarScan) and 95 (VarSeqTM) DNMs were identified and validated using different algorithms. Thirty-nine of these mutations were considered likely to be pathogenic based on gene function, evolutionary conservation, and mutation impact. The mutation rate estimated per position pair per generation was 2.74 × 10-8 [95% CI: 2.24 × 10-8-3.35 × 10-8] (VarScan) and 2.4 × 10-8 [95% CI: 1.96 × 10-8-2.99 × 10-8] (VarSeqTM), with 1.77 × 10-8 [95% CI: 6.03 × 10-9-5.2 × 10-8] de novo indels per position per generation. The rate of germline DNMs in the Lithuanian population and the effects of the genomic and epigenetic context on DNM formation were calculated for the first time in this study, providing a basis for further analysis of DNMs in individuals with genetic diseases. Considering these findings, additional studies in patient groups with genetic diseases with unclear etiology may facilitate our ability to distinguish certain pathogenic or adaptive DNMs from tolerated background DNMs and to reliably identify disease-causing DNMs by their properties through direct observation.

A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study2. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P ≤ 2.18 × 10-11. Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene-trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.

With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1-4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1-4 exons), combining computational prediction algorithms and a high-resolution custom CGH array.

Given the growing use of whole-exome sequencing (WES) for clinical diagnostics of complex human disorders, we evaluated coverage of clinically relevant cardiac genes on WES and factors influencing uniformity and depth of coverage of exonic regions.

In this study, we first performed whole exome sequencing of DNA from 10 untreated and clinically annotated fresh frozen nasopharyngeal carcinoma (NPC) biopsies and matched bloods to identify somatically mutated genes that may be amenable to targeted therapeutic strategies. We identified a total of 323 mutations which were either non-synonymous (n = 238) or synonymous (n = 85). Furthermore, our analysis revealed genes in key cancer pathways (DNA repair, cell cycle regulation, apoptosis, immune response, lipid signaling) were mutated, of which those in the lipid-signaling pathway were the most enriched. We next extended our analysis on a prioritized sub-set of 37 mutated genes plus top 5 mutated cancer genes listed in COSMIC using a custom designed HaloPlex target enrichment panel with an additional 88 NPC samples. Our analysis identified 160 additional non-synonymous mutations in 37/42 genes in 66/88 samples. Of these, 99/160 mutations within potentially druggable pathways were further selected for validation. Sanger sequencing revealed that 77/99 variants were true positives, giving an accuracy of 78%. Taken together, our study indicated that ~72% (n = 71/98) of NPC samples harbored mutations in one of the four cancer pathways (EGFR-PI3K-Akt-mTOR, NOTCH, NF-κB, DNA repair) which may be potentially useful as predictive biomarkers of response to matched targeted therapies.

Chronic central serous chorioretinopathy (cCSC) is a multifactorial eye disease characterized by subretinal fluid accumulation that leads to vision loss. Clinically, cCSC is associated with stress, hypercortisolism and corticosteroid use, and is more frequent in males (80%) than in females (20%). Current genetic studies on cCSC have thus far focussed on common variants, but familial occurrence of cCSC also suggests a role for rare variants in the disease susceptibility. Therefore, in this study, we performed exome sequencing of cCSC patients to elucidate the role of rare (protein-altering) variants in the disease. Exome sequencing was performed on 269 cCSC patients and 1,586 controls. Data were processed according to the Genome-Analysis-Toolkit (GATK) best practices. Principal component analysis was performed to check for genetic ancestry and only unrelated subjects of European descent were retained. Burden, SKAT and SKAT-O tests were performed using 2 different grouping criteria. One group included protein-altering variants only, while the other contained synonymous and splice site variants as well. The gene-based analyses were performed using the SKAT R-package correcting for two principal components using two approaches; (1) on the entire cohort correcting for sex and (2) on males and females separately. Additionally, the gene-based associations of genes at previously reported cCSC loci were investigated. After filtering, the dataset contained 263 cCSC patients (208 males [79%]) and 1352 controls (671 males [50%]) carrying 197,915 protein-altering variants in 16,370 genes and 330,689 exonic variants in 18,173 genes. Analysis stratified by sex identified significant associations with the PIGZ (PSKAT = 9.19 × 10-7 & PSKAT-O = 2.48 × 10-6), DUOX1 (PSKAT = 1.03 × 10-6), RSAD1 (PSKAT = 1.92 × 10-7 & PSKAT-O = 8.57 × 10-8) and LAMB3 (PBurden = 1.40 × 10-6 & PSKAT-O = 1.14 × 10-6) genes in female cCSC patients, after correction for multiple testing. The number of rare variant carriers in these genes was significantly higher in the female cCSC cohort compared to female controls (45,5% vs. 18.5%, P = 1.92 × 10-6, OR = 3.67 [95% CI = 2.09-6.46]). No significant associations were identified in the entire cohort nor in the male patients. In this exome study on cCSC patients, we have identified PIGZ, DUOX1, RSAD1 and LAMB3 as potential new candidate genes for cCSC in females. The sex-specific associations identified here suggest a possible interaction between rare genetic factors and sex for cCSC, but replication of these findings in additional cohorts of cCSC patients is necessary.

The genomic landscape of breast cancer (BC) is complex. The purpose of this study was to decipher the mutational profiles of Taiwanese patients with BC using next-generation sequencing. We performed whole-exome sequencing on DNA from 24 tumor tissue specimens from BC patients. Sanger sequencing was used to validate the identified variants. Sanger sequencing was also performed on paired adjacent nontumor tissues. After genotype calling and algorithmic annotations, we identified 49 deleterious variants in canonical cancer-related genes in our BC cohort. The most frequently mutated genes were PIK3CA (16.67%), FKBP9 (12.5%), TP53 (12.5%), ATM (8.33%), CHEK2 (8.33%), FOXO3 (8.33%), NTRK1 (8.33%), and NUTM2B (8.33%). Seven mutated variants (ATR p.V1581fs, CSF1R p.R579Q, GATA3 p.T356delinsTMKS, LRP5 p.W389*, MAP3K1 p.T918fs, MET p.K1161fs, and MTR p.P1178S) were novel variants that are not present in any gene mutation database. After grouping the samples according to molecular subtype, we found that the cell cycle, MAPK, and chemokine signaling pathways in the luminal A subtype of BC; the focal adhesion, axon guidance, and endocytosis pathways in the luminal B subtype; and amyotrophic lateral sclerosis in the basal-like subtype were exclusively altered. Survival curve analysis showed that the presence of the MAPK signaling pathway and endocytosis mutations were correlated with a poor prognosis. These survival data were consistent with cBioPortal analyses of 2,051 BC cases. We discovered novel mutations in patients with BC. These results have implications for developing strategic, adjuvant, and gene-targeted therapies.

To identify genomic mutations in lacrimal gland adenoid cystic carcinoma (LGACC) samples from patients.

Severe recessive diseases arise when both the maternal and the paternal copies of a gene carry, or are impacted by, a damaging genetic variant in the affected individual. When a patient carries two different potentially causal variants, accurate diagnosis requires determining that these two variants occur on different copies of the chromosome (i.e., are in trans ) rather than on the same copy (i.e., in cis ). However, current approaches for determining phase, beyond parental testing, are limited in clinical settings. We developed a strategy for inferring phase for rare variant pairs within genes, leveraging haplotype patterns observed in exome sequencing data from the Genome Aggregation Database (gnomAD v2, n=125,748). When applied to trio data where phase is known, our approach estimates phase with high accuracy, even for very rare variants (frequency <1x10 - 4 ), and also correctly phases 95.2% of variant pairs in a set of 293 patients carrying presumed causal compound heterozygous variants. We provide a public resource of phasing estimates from gnomAD, including phasing estimates for coding variants across the genome and counts per gene of rare variants in trans , that can aid interpretation of rare co-occurring variants in the context of recessive disease.

Although intellectual disability is one of the major indications for genetic counselling, there are no homogenous diagnostic algorithms for molecular testing. While whole exome sequencing is increasingly applied, we questioned whether analyzing a partial exome, enriched for genes associated with Mendelian disorders, might be a valid alternative approach that yields similar detection rates but requires less sequencing capacities. Within this context 106 patients with different intellectual disability forms were analyzed for mutations in 4.813 genes after pre-exclusion of copy number variations by array-CGH. Subsequent variant interpretation was performed in accordance with the ACMG guidelines. By this, a molecular diagnosis was established in 34% of cases and candidate mutations were identified in additional 24% of patients. Detection rates of causative mutations were above 30%, regardless of further symptoms, except for patients with seizures (23%). We did not detect an advantage from partial exome sequencing for patients with severe intellectual disability (36%) as compared to those with mild intellectual disability (44%). Specific clinical diagnoses pre-existed for 20 patients. Of these, 5 could be confirmed and an additional 6 cases could be solved, but showed mutations in other genes than initially suspected. In conclusion partial exome sequencing solved >30% of intellectual disability cases, which is similar to published rates obtained by whole exome sequencing. The approach therefore proved to be a valid alternative to whole exome sequencing for molecular diagnostics in this cohort. The method proved equally suitable for both syndromic and non-syndromic intellectual disability forms of all severity grades.

The highly challenging hexaploid wheat (Triticum aestivum) genome is becoming ever more accessible due to the continued development of multiple reference genomes, a factor which aids in the plight to better understand variation in important traits. Although the process of variant calling is relatively straightforward, selection of the best combination of the computational tools for read alignment and variant calling stages of the analysis and efficient filtering of the false variant calls are not always easy tasks. Previous studies have analyzed the impact of methods on the quality metrics in diploid organisms. Given that variant identification in wheat largely relies on accurate mining of exome data, there is a critical need to better understand how different methods affect the analysis of whole exome sequencing (WES) data in polyploid species. This study aims to address this by performing whole exome sequencing of 48 wheat cultivars and assessing the performance of various variant calling pipelines at their suggested settings. The results show that all the pipelines require filtering to eliminate false-positive calls. The high consensus among the reference SNPs called by the best-performing pipelines suggests that filtering provides accurate and reproducible results. This study also provides detailed comparisons for high sensitivity and precision at individual and population levels for the raw and filtered SNP calls.

Exome sequencing provides unprecedented insights into cancer biology and pharmacological response. Here we assess these two parameters for the NCI-60, which is among the richest genomic and pharmacological publicly available cancer cell line databases. Homozygous genetic variants that putatively affect protein function were identified in 1,199 genes (approximately 6% of all genes). Variants that are either enriched or depleted compared to non-cancerous genomes, and thus may be influential in cancer progression and differential drug response were identified for 2,546 genes. Potential gene knockouts are made available. Assessment of cell line response to 19,940 compounds, including 110 FDA-approved drugs, reveals ≈80-fold range in resistance versus sensitivity response across cell lines. 103,422 gene variants were significantly correlated with at least one compound (at p<0.0002). These include genes of known pharmacological importance such as IGF1R, BRAF, RAD52, MTOR, STAT2 and TSC2 as well as a large number of candidate genes such as NOM1, TLL2, and XDH. We introduce two new web-based CellMiner applications that enable exploration of variant-to-compound relationships for a broad range of researchers, especially those without bioinformatics support. The first tool, "Genetic variant versus drug visualization", provides a visualization of significant correlations between drug activity-gene variant combinations. Examples are given for the known vemurafenib-BRAF, and novel ifosfamide-RAD52 pairings. The second, "Genetic variant summation" allows an assessment of cumulative genetic variations for up to 150 combined genes together; and is designed to identify the variant burden for molecular pathways or functional grouping of genes. An example of its use is provided for the EGFR-ERBB2 pathway gene variant data and the identification of correlated EGFR, ERBB2, MTOR, BRAF, MEK and ERK inhibitors. The new tools are implemented as an updated web-based CellMiner version, for which the present publication serves as a compendium.