Deciphering the digenic architecture of congenital heart disease using trio exome sequencing data.

Congenital heart disease (CHD) is the most common congenital anomaly and a leading cause of infant morbidity and mortality. Despite extensive exploration of the monogenic causes of CHD over the last decades, ∼55% of cases still lack a molecular diagnosis. Investigating digenic interactions, the simplest form of oligogenic interactions, using high-throughput sequencing data can elucidate additional genetic factors contributing to the disease.

Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework.

Background: Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms.

Methods: Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages.

Findings: The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder.

The landscape of rare genetic variation associated with inflammatory bowel disease and Parkinson's disease comorbidity.

Background: Inflammatory bowel disease (IBD) and Parkinson's disease (PD) are chronic disorders that have been suggested to share common pathophysiological processes. LRRK2 has been implicated as playing a role in both diseases. Exploring the genetic basis of the IBD-PD comorbidity through studying high-impact rare genetic variants can facilitate the identification of the novel shared genetic factors underlying this comorbidity.

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set.

Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, a machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants, trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics.

Identifying high-impact variants and genes in exomes of Ashkenazi Jewish inflammatory bowel disease patients.

Inflammatory bowel disease (IBD) is a group of chronic digestive tract inflammatory conditions whose genetic etiology is still poorly understood. The incidence of IBD is particularly high among Ashkenazi Jews. Here, we identify 8 novel and plausible IBD-causing genes from the exomes of 4453 genetically identified Ashkenazi Jewish IBD cases (1734) and controls (2719).

Identifying shared genetic factors underlying epilepsy and congenital heart disease in Europeans.

Epilepsy (EP) and congenital heart disease (CHD) are two apparently unrelated diseases that nevertheless display substantial mutual comorbidity. Thus, while congenital heart defects are associated with an elevated risk of developing epilepsy, the incidence of epilepsy in CHD patients correlates with CHD severity. Although genetic determinants have been postulated to underlie the comorbidity of EP and CHD, the precise genetic etiology is unknown.

Profiling human pathogenic repeat expansion regions by synergistic and multi-level impacts on molecular connections.

Whilst DNA repeat expansions cause numerous heritable human disorders, their origins and underlying pathological mechanisms are often unclear. We collated a dataset comprising 224 human repeat expansions encompassing 203 different genes, and performed a systematic analysis with respect to key topological features at the DNA, RNA and protein levels. Comparison with controls without known pathogenicity and genomic regions lacking repeats, allowed the construction of the first tool to discriminate repeat regions harboring pathogenic repeat expansions (DPREx).

Genome-wide detection of human variants that disrupt intronic branchpoints.

Pre-messenger RNA splicing is initiated with the recognition of a single-nucleotide intronic branchpoint (BP) within a BP motif by spliceosome elements. Forty-eight rare variants in 43 human genes have been reported to alter splicing and cause disease by disrupting BP. However, until now, no computational approach was available to efficiently detect such variants in massively parallel sequencing data.

X-CAP improves pathogenicity prediction of stopgain variants.

Stopgain substitutions are the third-largest class of monogenic human disease mutations and often examined first in patient exomes. Existing computational stopgain pathogenicity predictors, however, exhibit poor performance at the high sensitivity required for clinical use. Here, we introduce a new classifier, termed X-CAP, which uses a novel training methodology and unique feature set to improve the AUROC by 18% and decrease the false-positive rate 4-fold on large variant databases.

Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity.

We used a machine learning approach to analyze the within-gene distribution of missense variants observed in hereditary conditions and cancer. When applied to 840 genes from the ClinVar database, this approach detected a significant non-random distribution of pathogenic and benign variants in 387 (46%) and 172 (20%) genes, respectively, revealing that variant clustering is widespread across the human exome. This clustering likely occurs as a consequence of mechanisms shaping pathogenicity at the protein level, as illustrated by the overlap of some clusters with known functional domains.

Distinct sequence features underlie microdeletions and gross deletions in the human genome.

Microdeletions and gross deletions are important causes (~20%) of human inherited disease and their genomic locations are strongly influenced by the local DNA sequence environment. This notwithstanding, no study has systematically examined their underlying generative mechanisms. Here, we obtained 42,098 pathogenic microdeletions and gross deletions from the Human Gene Mutation Database (HGMD) that together form a continuum of germline deletions ranging in size from 1 to 28,394,429 bp.

Identification of discriminative gene-level and protein-level features associated with pathogenic gain-of-function and loss-of-function variants.

Identifying whether a given genetic mutation results in a gene product with increased (gain-of-function; GOF) or diminished (loss-of-function; LOF) activity is an important step toward understanding disease mechanisms because they may result in markedly different clinical phenotypes. Here, we generated an extensive database of documented germline GOF and LOF pathogenic variants by employing natural language processing (NLP) on the available abstracts in the Human Gene Mutation Database. We then investigated various gene- and protein-level features of GOF and LOF variants and applied machine learning and statistical analyses to identify discriminative features.

The genetic structure of the Turkish population reveals high levels of variation and admixture.

The construction of population-based variomes has contributed substantially to our understanding of the genetic basis of human inherited disease. Here, we investigated the genetic structure of Turkey from 3,362 unrelated subjects whose whole exomes ( = 2,589) or whole genomes ( = 773) were sequenced to generate a Turkish (TR) Variome that should serve to facilitate disease gene discovery in Turkey. Consistent with the history of present-day Turkey as a crossroads between Europe and Asia, we found extensive admixture between Balkan, Caucasus, Middle Eastern, and European populations with a closer genetic relationship of the TR population to Europeans than hitherto appreciated.

Compensatory epistasis explored by molecular dynamics simulations.

A non-negligible proportion of human pathogenic variants are known to be present as wild type in at least some non-human mammalian species. The standard explanation for this finding is that molecular mechanisms of compensatory epistasis can alleviate the mutations' otherwise pathogenic effects. Examples of compensated variants have been described in the literature but the interacting residue(s) postulated to play a compensatory role have rarely been ascertained.

The Human Gene Mutation Database (HGMD): optimizing its use in a clinical diagnostic or research setting.

The Human Gene Mutation Database (HGMD) constitutes a comprehensive collection of published germline mutations in nuclear genes that are thought to underlie, or are closely associated with human inherited disease. At the time of writing (June 2020), the database contains in excess of 289,000 different gene lesions identified in over 11,100 genes manually curated from 72,987 articles published in over 3100 peer-reviewed journals. There are primarily two main groups of users who utilise HGMD on a regular basis; research scientists and clinical diagnosticians.

Common homozygosity for predicted loss-of-function variants reveals both redundant and advantageous effects of dispensable human genes.

Humans homozygous or hemizygous for variants predicted to cause a loss of function (LoF) of the corresponding protein do not necessarily present with overt clinical phenotypes. We report here 190 autosomal genes with 207 predicted LoF variants, for which the frequency of homozygous individuals exceeds 1% in at least one human population from five major ancestry groups. No such genes were identified on the X and Y chromosomes.

AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature.

The diagnosis of Mendelian disorders requires labor-intensive literature research. Trained clinicians can spend hours looking for the right publication(s) supporting a single gene that best explains a patient's disease. AMELIE (Automatic Mendelian Literature Evaluation) greatly accelerates this process.

Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations.

Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations.

AVADA: toward automated pathogenic variant evidence retrieval directly from the full-text literature.

Purpose: Both monogenic pathogenic variant cataloging and clinical patient diagnosis start with variant-level evidence retrieval followed by expert evidence integration in search of diagnostic variants and genes. Here, we try to accelerate pathogenic variant evidence retrieval by an automatic approach.

Methods: Automatic VAriant evidence DAtabase (AVADA) is a novel machine learning tool that uses natural language processing to automatically identify pathogenic genetic variant evidence in full-text primary literature about monogenic disease and convert it to genomic coordinates.

SeqTailor: a user-friendly webserver for the extraction of DNA or protein sequences from next-generation sequencing data.

Human whole-genome-sequencing reveals about 4 000 000 genomic variants per individual. These data are mostly stored as VCF-format files. Although many variant analysis methods accept VCF as input, many other tools require DNA or protein sequences, particularly for splicing prediction, sequence alignment, phylogenetic analysis, and structure prediction.

S-CAP extends pathogenicity prediction to genetic variants that affect RNA splicing.

Exome analysis of patients with a likely monogenic disease does not identify a causal variant in over half of cases. Splice-disrupting mutations make up the second largest class of known disease-causing mutations. Each individual (singleton) exome harbors over 500 rare variants of unknown significance (VUS) in the splicing region.

Blacklisting variants common in private cohorts but not in public databases optimizes human exome analysis.

Computational analyses of human patient exomes aim to filter out as many nonpathogenic genetic variants (NPVs) as possible, without removing the true disease-causing mutations. This involves comparing the patient's exome with public databases to remove reported variants inconsistent with disease prevalence, mode of inheritance, or clinical penetrance. However, variants frequent in a given exome cohort, but absent or rare in public databases, have also been reported and treated as NPVs, without rigorous exploration.