Genetic constraint at single amino acid resolution in protein domains improves missense variant prioritisation and gene discovery.

BACKGROUND: One of the major hurdles in clinical genetics is interpreting the clinical consequences associated with germline missense variants in humans. Recent significant advances have leveraged natural variation observed in large-scale human populations to uncover genes or genomic regions that show a depletion of natural variation, indicative of selection pressure. We refer to this as "genetic constraint". Although existing genetic constraint metrics have been demonstrated to be successful in prioritising genes or genomic regions associated with diseases, their spatial resolution is limited in distinguishing pathogenic variants from benign variants within genes. METHODS: We aim to identify missense variants that are significantly depleted in the general human population. Given the size of currently available human populations with exome or genome sequencing data, it is not possible to directly detect depletion of individual missense variants, since the average expected number of observations of a variant at most positions is less than one. We instead focus on protein domains, grouping homologous variants with similar functional impacts to examine the depletion of natural variations within these comparable sets. To accomplish this, we develop the Homologous Missense Constraint (HMC) score. We utilise the Genome Aggregation Database (gnomAD) 125 K exome sequencing data and evaluate genetic constraint at quasi amino-acid resolution by combining signals across protein homologues. RESULTS: We identify one million possible missense variants under strong negative selection within protein domains. Though our approach annotates only protein domains, it nonetheless allows us to assess 22% of the exome confidently. It precisely distinguishes pathogenic variants from benign variants for both early-onset and adult-onset disorders. It outperforms existing constraint metrics and pathogenicity meta-predictors in prioritising de novo mutations from probands with developmental disorders (DD). It is also methodologically independent of these, adding power to predict variant pathogenicity when used in combination. We demonstrate utility for gene discovery by identifying seven genes newly significantly associated with DD that could act through an altered-function mechanism. CONCLUSIONS: Grouping variants of comparable functional impacts is effective in evaluating their genetic constraint. HMC is a novel and accurate predictor of missense consequence for improved variant interpretation.

Investigation of lactotransferrin messenger RNA expression levels as an anti-type 2 asthma biomarker.

BACKGROUND: Lactotransferrin (LTF) has an immunomodulatory function, and its expression levels are associated with asthma susceptibility. OBJECTIVES: We sought to investigate LTF messenger RNA (mRNA) expression levels in human bronchial epithelial cells (BECs) as an anti-type 2 (T2) asthma biomarker. METHODS: Association analyses between LTF mRNA expression levels in BECs and asthma-related phenotypes were performed in the Severe Asthma Research Program (SARP) cross-sectional (n = 155) and longitudinal (n = 156) cohorts using a generalized linear model. Correlation analyses of mRNA expression levels between LTF and all other genes were performed by Spearman correlation. RESULTS: Low LTF mRNA expression levels were associated with asthma susceptibility and severity (P < .025), retrospective and prospective asthma exacerbations, and low lung function (P < 8.3 × 10-3). Low LTF mRNA expression levels were associated with high airway T2 inflammation biomarkers (sputum eosinophils and fractional exhaled nitric oxide; P < 8.3 × 10-3) but were not associated with blood eosinophils or total serum IgE. LTF mRNA expression levels were negatively correlated with expression levels of TH2 or asthma-associated genes (POSTN, NOS2, and MUC5AC) and eosinophil-related genes (IL1RL1, CCL26, and IKZF2) and positively correlated with expression levels of TH1 and inflammation genes (IL12A, MUC5B, and CC16) and TH17-driven cytokines or chemokines for neutrophils (CXCL1, CXCL6, and CSF3) (P < 3.5 × 10-6). CONCLUSIONS: Low LTF mRNA expression levels in BECs are associated with asthma susceptibility, severity, and exacerbations through upregulation of airway T2 inflammation. LTF is a potential anti-T2 biomarker, and its expression levels may help determine the balance of eosinophilic and neutrophilic asthma.