Functional dissection of inherited non-coding variation influencing multiple myeloma risk

Thousands of non-coding variants have been associated with increased risk of human diseases, yet the causal variants and their mechanisms-of-action remain obscure. In an integrative study combining massively parallel reporter assays (MPRA), expression analyses (eQTL, meQTL, PCHiC) and chromatin accessibility analyses in primary cells (caQTL), we investigate 1,039 variants associated with multiple myeloma (MM). We demonstrate that MM susceptibility is mediated by gene-regulatory changes in plasma cells and B-cells, and identify putative causal variants at six risk loci (SMARCD3, WAC, ELL2, CDCA7L, CEP120, and PREX1). Notably, three of these variants co-localize with significant plasma cell caQTLs, signaling the presence of causal activity at these precise genomic positions in an endogenous chromosomal context in vivo. Our results provide a systematic functional dissection of risk loci for a hematologic malignancy.

CFTR variants are associated with chronic bronchitis in smokers

IntroductionLoss of function variants in both copies of the cystic fibrosis transmembrane conductance regulator (CFTR) gene cause cystic fibrosis (CF); however, there is evidence that reduction in CFTR function due to the presence of one deleterious variant can have clinical consequences. Here, we hypothesize that CFTR variants in individuals with a history of smoking are associated with COPD and related phenotypes.MethodsWhole genome sequencing was performed through the NHLBI TOPMed program in 8597 subjects from the COPDGene study, an observational study of current and former smokers. We extracted clinically annotated CFTR variants and performed single variant and variant-set testing for COPD and related phenotypes. Replication was performed in 2,118 subjects from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study.ResultsWe identified 301 coding variants within the CFTR gene boundary: 147 of these have been reported in individuals with CF, including 36 CF-causing variants. We found that CF causing variants were associated with chronic bronchitis in variant-set testing in COPDGene (one sided p-value=0.0025, OR=1.53) and in meta-analysis of COPDGene and ECLIPSE (one sided p-value=0.0060, OR=1.52). Single variant testing revealed that the F508del variant was associated with chronic bronchitis in COPDGene (one sided p-value=0.015, OR=1.47). In addition, we identified 32 subjects with two or more CFTR variants on separate alleles, and these subjects were enriched for COPD cases (p=0.010).ConclusionsCigarette smokers who carry one deleterious CFTR variant have higher rates of chronic bronchitis, while presence of two CFTR variants may be associated with COPD. These results indicate that genetically-mediated reduction in CFTR function contributes to COPD related phenotypes, in particular chronic bronchitis.

Analysis of coding variants in the human FTO gene from the gnomAD database

Single nucleotide polymorphisms (SNPs) in the first intron of the FTO gene reported in 2007 continue to be the known variants with the greatest effect on adiposity in different human populations. Coding variants in the FTO gene, on the other hand, have been little explored, although data from complete sequencing of the exomes of various populations are available in public databases and provide an excellent opportunity to investigate potential functional variants in FTO. In this context, this study aimed to track nonsynonymous variants in the exons of the FTO gene in different population groups employing the gnomAD database and analyze the potential functional impact of these variants on the FTO protein using five publicly available pathogenicity prediction programs. The findings revealed 345 rare mutations, of which 321 are missense (93%), 19 are stop gained (5.6%) and five mutations are located in the splice region (1.4%). Of these, 134 (38.8%) were classified as pathogenic, 144 (41.7%) as benign and 67 (19.5%) as unknown. The available data, however, suggest that these variants are probably not associated with BMI and obesity, but instead, with other diseases. Functional studies are, therefore, required to identify the role of these variants in disease genesis.

Genetic analysis of the postsynaptic transmembrane X-linked neuroligin 3 gene in autism

Autism is a complex neurodevelopmental disorder, the prevalence of which has increased drastically in India in recent years. Neuroligin is a type I transmembrane protein that plays a crucial role in synaptogenesis. Alterations in synaptic genes are most commonly implicated in autism and other cognitive disorders. The present study investigated the neuroligin 3 gene in the Indian autistic population by sequencing and in silico pathogenicity prediction of molecular changes. In total, 108 clinically described individuals with autism were included from the North Karnataka region of India, along with 150 age-, sex-, and ethnicity-matched healthy controls. Genomic DNA was extracted from peripheral blood, and exonic regions were sequenced. The functional and structural effects of variants of the neuroligin 3 protein were predicted. One coding sequence variant (a missense variant) and four non-coding variants (two 5'-untranslated region [UTR] variants and two 3'-UTR variants) were recorded. The novel missense variant was found in 25% of the autistic population. The C/C genotype of c.551T>C was significantly more common in autistic children than in controls (p = 0.001), and a significantly increased risk of autism (24.7-fold) was associated with this genotype (p = 0.001). The missense variant showed pathogenic effects and high evolutionary conservation over the functions of the neuroligin 3 protein. In the present study, we reported a novel missense variant, V184A, which causes abnormal neuroligin 3 and was found with high frequency in the Indian autistic population. Therefore, neuroligin is a candidate gene for future molecular investigations and functional analysis in the Indian autistic population.

Identification of rare coding variants associated with Kawasaki disease by whole exome sequencing

Kawasaki disease (KD) is an acute pediatric vasculitis that affects genetically susceptible infants and children. To identify coding variants that influence susceptibility to KD, we conducted whole exome sequencing of 159 patients with KD and 902 controls, and performed a replication study in an independent 586 cases and 732 controls. We identified five rare coding variants in five genes (FCRLA, PTGER4, IL17F, CARD11, and SIGLEC10) associated with KD (odds ratio [OR], 1.18–4.41; p = 0.0027–0.031). We also performed association analysis in 26 KD patients with coronary artery aneurysms (CAAs; diameter > 5 mm) and 124 patients without CAAs (diameter < 3 mm), and identified another five rare coding variants in five genes (FGFR4, IL31RA, FNDC1, MMP8, and FOXN1), which may be associated with CAA (OR, 3.89–37.3; p = 0.0058–0.0261). These results provide insights into new candidate genes and genetic variants potentially involved in the development of KD and CAA.

Assessing the contribution of rare-to-common protein-coding variants to circulating metabolic biomarker levels via 412,394 UK Biobank exome sequences

Genome-wide association studies have established the contribution of common and low frequency variants to metabolic biomarkers in the UK Biobank (UKB); however, the role of rare variants remains to be assessed systematically. We evaluated rare coding variants for 198 metabolic biomarkers, including metabolites assayed by Nightingale Health, using exome sequencing in participants from four genetically diverse ancestries in the UKB (N=412,394). Gene-level collapsing analysis, that evaluated a range of genetic architectures, identified a total of 1,303 significant relationships between genes and metabolic biomarkers (p<1x10-8), encompassing 207 distinct genes. These include associations between rare non-synonymous variants in GIGYF1 and glucose and lipid biomarkers, SYT7 and creatinine, and others, which may provide insights into novel disease biology. Comparing to a previous microarray-based genotyping study in the same cohort, we observed that 40% of gene-biomarker relationships identified in the collapsing analysis were novel. Finally, we applied Gene-SCOUT, a novel tool that utilises the gene-biomarker association statistics from the collapsing analysis to identify genes having similar biomarker fingerprints and thus expand our understanding of gene networks.

De novo coding variants in the AGO1 gene cause a neurodevelopmental disorder with intellectual disability

BackgroundHigh-impact pathogenic variants in more than a thousand genes are involved in Mendelian forms of neurodevelopmental disorders (NDD).MethodsThis study describes the molecular and clinical characterisation of 28 probands with NDD harbouring heterozygous AGO1 coding variants, occurring de novo for all those whose transmission could have been verified (26/28).ResultsA total of 15 unique variants leading to amino acid changes or deletions were identified: 12 missense variants, two in-frame deletions of one codon, and one canonical splice variant leading to a deletion of two amino acid residues. Recurrently identified variants were present in several unrelated individuals: p.(Phe180del), p.(Leu190Pro), p.(Leu190Arg), p.(Gly199Ser), p.(Val254Ile) and p.(Glu376del). AGO1 encodes the Argonaute 1 protein, which functions in gene-silencing pathways mediated by small non-coding RNAs. Three-dimensional protein structure predictions suggest that these variants might alter the flexibility of the AGO1 linker domains, which likely would impair its function in mRNA processing. Affected individuals present with intellectual disability of varying severity, as well as speech and motor delay, autistic behaviour and additional behavioural manifestations.ConclusionOur study establishes that de novo coding variants in AGO1 are involved in a novel monogenic form of NDD, highly similar to the recently reported AGO2-related NDD.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.