A genomic mutational constraint map using variation in 76,156 human genomes.

The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders1-4, but attempts to assess constraint for non-protein-coding regions have proved more difficult. Here we aggregate, process and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD)-the largest public open-access human genome allele frequency reference dataset-and use it to build a genomic constraint map for the whole genome (genomic non-coding constraint of haploinsufficient variation (Gnocchi)). We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation. As expected, the average constraint for protein-coding sequences is stronger than that for non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants that are implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, which in turn suggests that non-coding constraint can aid the identification of constrained genes that are as yet unrecognized by current gene constraint metrics. We demonstrate that this genome-wide constraint map improves the identification and interpretation of functional human genetic variation.

The mutational constraint spectrum quantified from variation in 141,456 humans.

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.

Metabolomic pattern associated with physical sequelae in patients presenting with respiratory symptoms validates the aestivation concept in dehydrated patients.

Hypertonic dehydration is associated with muscle wasting and synthesis of organic osmolytes. We recently showed a metabolic shift to amino acid production and urea cycle activation in coronavirus-2019 (COVID-19), consistent with the aestivation response. The aim of the present investigation was to validate the metabolic shift and development of long-term physical outcomes in the non-COVID cohort of the Biobanque Québécoise de la COVID-19 (BQC19). We included 824 patients from BQC19, where 571 patients had data of dehydration in the form of estimated osmolality (eOSM = 2Na + 2K + glucose + urea), and 284 patients had metabolome data and long-term follow-up. We correlated the degree of dehydration to mortality, invasive mechanical ventilation, acute kidney injury, and long-term symptoms. As found in the COVID cohort, higher eOSM correlated with a higher proportion of urea and glucose of total eOSM, and an enrichment of amino acids compared with other metabolites. Sex-stratified analysis indicated that women may show a weaker aestivation response. More severe dehydration was associated with mortality, invasive mechanical ventilation, and acute kidney injury during the acute illness. Importantly, more severe dehydration was associated with physical long-term symptoms but not mental long-term symptoms after adjustment for age, sex, and disease severity. Patients with water deficit in the form of increased eOSM tend to have more severe disease and experience more physical symptoms after an acute episode of care. This is associated with amino acid and urea production, indicating dehydration-induced muscle wasting.NEW & NOTEWORTHY We have previously shown that humans exhibit an aestivation-like response where dehydration leads to a metabolic shift to urea synthesis, which is associated with long-term weakness indicating muscle wasting. In the present study, we validate this response in a new cohort and present a deeper metabolomic analysis and pathway analysis. Finally, we present a sex-stratified analysis suggesting weaker aestivation in women. However, women show less dehydration, so the association warrants further study.

Predicting ExWAS findings from GWAS data: a shorter path to causal genes.

GWAS has identified thousands of loci associated with disease, yet the causal genes within these loci remain largely unknown. Identifying these causal genes would enable deeper understanding of the disease and assist in genetics-based drug development. Exome-wide association studies (ExWAS) are more expensive but can pinpoint causal genes offering high-yield drug targets, yet suffer from a high false-negative rate. Several algorithms have been developed to prioritize genes at GWAS loci, such as the Effector Index (Ei), Locus-2-Gene (L2G), Polygenic Prioritization score (PoPs), and Activity-by-Contact score (ABC) and it is not known if these algorithms can predict ExWAS findings from GWAS data. However, if this were the case, thousands of associated GWAS loci could potentially be resolved to causal genes. Here, we quantified the performance of these algorithms by evaluating their ability to identify ExWAS significant genes for nine traits. We found that Ei, L2G, and PoPs can identify ExWAS significant genes with high areas under the precision recall curve (Ei: 0.52, L2G: 0.37, PoPs: 0.18, ABC: 0.14). Furthermore, we found that for every unit increase in the normalized scores, there was an associated 1.3-4.6-fold increase in the odds of a gene reaching exome-wide significance (Ei: 4.6, L2G: 2.5, PoPs: 2.1, ABC: 1.3). Overall, we found that Ei, L2G, and PoPs can anticipate ExWAS findings from widely available GWAS results. These techniques are therefore promising when well-powered ExWAS data are not readily available and can be used to anticipate ExWAS findings, allowing for prioritization of genes at GWAS loci.

Colocalization of expression transcripts with COVID-19 outcomes is rare across cell states, cell types and organs.

Identifying causal genes at GWAS loci can help pinpoint targets for therapeutic interventions. Expression studies can disentangle such loci but signals from expression quantitative trait loci (eQTLs) often fail to colocalize-which means that the genetic control of measured expression is not shared with the genetic control of disease risk. This may be because gene expression is measured in the wrong cell type, physiological state, or organ. We tested whether Mendelian randomization (MR) could identify genes at loci influencing COVID-19 outcomes and whether the colocalization of genetic control of expression and COVID-19 outcomes was influenced by cell type, cell stimulation, and organ. We conducted MR of cis-eQTLs from single cell (scRNA-seq) and bulk RNA sequencing. We then tested variables that could influence colocalization, including cell type, cell stimulation, RNA sequencing modality, organ, symptoms of COVID-19, and SARS-CoV-2 status among individuals with symptoms of COVID-19. The outcomes used to test colocalization were COVID-19 severity and susceptibility as assessed in the Host Genetics Initiative release 7. Most transcripts identified using MR did not colocalize when tested across cell types, cell state and in different organs. Most that did colocalize likely represented false positives due to linkage disequilibrium. In general, colocalization was highly variable and at times inconsistent for the same transcript across cell type, cell stimulation and organ. While we identified factors that influenced colocalization for select transcripts, identifying 33 that mediate COVID-19 outcomes, our study suggests that colocalization of expression with COVID-19 outcomes is partially due to noisy signals even after following quality control and sensitivity testing. These findings illustrate the present difficulty of linking expression transcripts to disease outcomes and the need for skepticism when observing eQTL MR results, even accounting for cell types, stimulation state and different organs.

The dynamic changes and sex differences of 147 immune-related proteins during acute COVID-19 in 580 individuals.

INTRODUCTION: Severe COVID-19 leads to important changes in circulating immune-related proteins. To date it has been difficult to understand their temporal relationship and identify cytokines that are drivers of severe COVID-19 outcomes and underlie differences in outcomes between sexes. Here, we measured 147 immune-related proteins during acute COVID-19 to investigate these questions. METHODS: We measured circulating protein abundances using the SOMAscan nucleic acid aptamer panel in two large independent hospital-based COVID-19 cohorts in Canada and the United States. We fit generalized additive models with cubic splines from the start of symptom onset to identify protein levels over the first 14 days of infection which were different between severe cases and controls, adjusting for age and sex. Severe cases were defined as individuals with COVID-19 requiring invasive or non-invasive mechanical respiratory support. RESULTS: 580 individuals were included in the analysis. Mean subject age was 64.3 (sd 18.1), and 47% were male. Of the 147 proteins, 69 showed a significant difference between cases and controls (p < 3.4 × 10-4). Three clusters were formed by 108 highly correlated proteins that replicated in both cohorts, making it difficult to determine which proteins have a true causal effect on severe COVID-19. Six proteins showed sex differences in levels over time, of which 3 were also associated with severe COVID-19: CCL26, IL1RL2, and IL3RA, providing insights to better understand the marked differences in outcomes by sex. CONCLUSIONS: Severe COVID-19 is associated with large changes in 69 immune-related proteins. Further, five proteins were associated with sex differences in outcomes. These results provide direct insights into immune-related proteins that are strongly influenced by severe COVID-19 infection.

Dehydration is associated with production of organic osmolytes and predicts physical long-term symptoms after COVID-19: a multicenter cohort study.

BACKGROUND: We have previously shown that iatrogenic dehydration is associated with a shift to organic osmolyte production in the general ICU population. The aim of the present investigation was to determine the validity of the physiological response to dehydration known as aestivation and its relevance for long-term disease outcome in COVID-19. METHODS: The study includes 374 COVID-19 patients from the Pronmed cohort admitted to the ICU at Uppsala University Hospital. Dehydration data was available for 165 of these patients and used for the primary analysis. Validation was performed in Biobanque Québécoise de la COVID-19 (BQC19) using 1052 patients with dehydration data. Dehydration was assessed through estimated osmolality (eOSM = 2Na + 2 K + glucose + urea), and correlated to important endpoints including death, invasive mechanical ventilation, acute kidney injury, and long COVID-19 symptom score grouped by physical or mental. RESULTS: Increasing eOSM was correlated with increasing role of organic osmolytes for eOSM, while the proportion of sodium and potassium of eOSM were inversely correlated to eOSM. Acute outcomes were associated with pronounced dehydration, and physical long-COVID was more strongly associated with dehydration than mental long-COVID after adjustment for age, sex, and disease severity. Metabolomic analysis showed enrichment of amino acids among metabolites that showed an aestivating pattern. CONCLUSIONS: Dehydration during acute COVID-19 infection causes an aestivation response that is associated with protein degradation and physical long-COVID. TRIAL REGISTRATION: The study was registered à priori (clinicaltrials.gov: NCT04316884 registered on 2020-03-13 and NCT04474249 registered on 2020-06-29).