Common and rare variant analyses combined with single-cell multiomics reveal cell-type-specific molecular mechanisms of COVID-19 severity.

The determinants of severe COVID-19 in non-elderly adults are poorly understood, which limits opportunities for early intervention and treatment. Here we present novel machine learning frameworks for identifying common and rare disease-associated genetic variation, which outperform conventional approaches. By integrating single-cell multiomics profiling of human lungs to link genetic signals to cell-type-specific functions, we have discovered and validated over 1,000 risk genes underlying severe COVID-19 across 19 cell types. Identified risk genes are overexpressed in healthy lungs but relatively downregulated in severely diseased lungs. Genetic risk for severe COVID-19, within both common and rare variants, is particularly enriched in natural killer (NK) cells, which places these immune cells upstream in the pathogenesis of severe disease. Mendelian randomization indicates that failed NKG2D-mediated activation of NK cells leads to critical illness. Network analysis further links multiple pathways associated with NK cell activation, including type-I-interferon-mediated signalling, to severe COVID-19. Our rare variant model, PULSE, enables sensitive prediction of severe disease in non-elderly patients based on whole-exome sequencing; individualized predictions are accurate independent of age and sex, and are consistent across multiple populations and cohorts. Risk stratification based on exome sequencing has the potential to facilitate post-exposure prophylaxis in at-risk individuals, potentially based around augmentation of NK cell function. Overall, our study characterizes a comprehensive genetic landscape of COVID-19 severity and provides novel insights into the molecular mechanisms of severe disease, leading to new therapeutic targets and sensitive detection of at-risk individuals.

Association of mitochondrial DNA variants with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) symptoms.

Earlier this year, we described an analysis of mitochondrial DNA (mtDNA) variants in myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) patients and healthy controls. We reported that there was no significant association of haplogroups or singe nucleotide polymorphisms (SNPs) with disease status. Nevertheless, a commentary about our paper appeared (Finsterer and Zarrouk-Mahjoub. J Transl Med14:182, 2016) that criticized the association of mtDNA haplogroups with ME/CFS, a conclusion that was absent from our paper. The aforementioned commentary also demanded experiments that were outside of the scope of our study, ones that we had suggested as follow-up studies. Because they failed to consult a published and cited report describing the cohorts we studied, the authors also cast aspersions on the method of selection of cases for inclusion. We reiterate that we observed statistically significant association of mtDNA variants with particular symptoms and their severity, though we observed no association with disease status.

The Genetics of Bene Israel from India Reveals Both Substantial Jewish and Indian Ancestry.

The Bene Israel Jewish community from West India is a unique population whose history before the 18th century remains largely unknown. Bene Israel members consider themselves as descendants of Jews, yet the identity of Jewish ancestors and their arrival time to India are unknown, with speculations on arrival time varying between the 8th century BCE and the 6th century CE. Here, we characterize the genetic history of Bene Israel by collecting and genotyping 18 Bene Israel individuals. Combining with 486 individuals from 41 other Jewish, Indian and Pakistani populations, and additional individuals from worldwide populations, we conducted comprehensive genome-wide analyses based on FST, principal component analysis, ADMIXTURE, identity-by-descent sharing, admixture linkage disequilibrium decay, haplotype sharing and allele sharing autocorrelation decay, as well as contrasted patterns between the X chromosome and the autosomes. The genetics of Bene Israel individuals resemble local Indian populations, while at the same time constituting a clearly separated and unique population in India. They are unique among Indian and Pakistani populations we analyzed in sharing considerable genetic ancestry with other Jewish populations. Putting together the results from all analyses point to Bene Israel being an admixed population with both Jewish and Indian ancestry, with the genetic contribution of each of these ancestral populations being substantial. The admixture took place in the last millennium, about 19-33 generations ago. It involved Middle-Eastern Jews and was sex-biased, with more male Jewish and local female contribution. It was followed by a population bottleneck and high endogamy, which can lead to increased prevalence of recessive diseases in this population. This study provides an example of how genetic analysis advances our knowledge of human history in cases where other disciplines lack the relevant data to do so.

Mitochondrial DNA variants correlate with symptoms in myalgic encephalomyelitis/chronic fatigue syndrome.

BACKGROUND: Mitochondrial dysfunction has been hypothesized to occur in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a disease characterized by fatigue, cognitive difficulties, pain, malaise, and exercise intolerance. We investigated whether haplogroup, single nucleotide polymorphisms (SNPs), or heteroplasmy of mitochondrial DNA (mtDNA) were associated with health status and/or symptoms. METHODS: Illumina sequencing of PCR-amplified mtDNA was performed to analyze sequence and extent of heteroplasmy of mtDNAs of 193 cases and 196 age- and gender-matched controls from DNA samples collected by the Chronic Fatigue Initiative. Association testing was carried out to examine possible correlations of mitochondrial sequences with case/control status and symptom constellation and severity as reported by subjects on Short Form-36 and DePaul Symptom Questionnaires. RESULTS: No ME/CFS subject exhibited known disease-causing mtDNA mutations. Extent of heteroplasmy was low in all subjects. Although no association between mtDNA SNPs and ME/CFS vs. healthy status was observed, haplogroups J, U and H as well as eight SNPs in ME/CFS cases were significantly associated with individual symptoms, symptom clusters, or symptom severity. CONCLUSIONS: Analysis of mitochondrial genomes in ME/CFS cases indicates that individuals of a certain haplogroup or carrying specific SNPs are more likely to exhibit certain neurological, inflammatory, and/or gastrointestinal symptoms. No increase in susceptibility to ME/CFS of individuals carrying particular mitochondrial genomes or SNPs was observed.

Phylogenetic placement of metagenomic reads using the minimum evolution principle.

BACKGROUND: A central problem of computational metagenomics is determining the correct placement into an existing phylogenetic tree of individual reads (nucleotide sequences of varying lengths, ranging from hundreds to thousands of bases) obtained using next-generation sequencing of DNA samples from a mixture of known and unknown species. Correct placement allows us to easily identify or classify the sequences in the sample as to taxonomic position or function. RESULTS: Here we propose a novel method (PhyClass), based on the Minimum Evolution (ME) phylogenetic inference criterion, for determining the appropriate phylogenetic position of each read. Without using heuristics, the new approach efficiently finds the optimal placement of the unknown read in a reference phylogenetic tree given a sequence alignment for the taxa in the tree. In short, the total resulting branch length for the tree is computed for every possible placement of the unknown read and the placement that gives the smallest value for this total is the best (optimal) choice. By taking advantage of computational efficiencies and mathematical formulations, we are able to find the true optimal ME placement for each read in the phylogenetic tree. Using computer simulations, we assessed the accuracy of the new approach for different read lengths over a variety of data sets and phylogenetic trees. We found the accuracy of the new method to be good and comparable to existing Maximum Likelihood (ML) approaches. CONCLUSIONS: In particular, we found that the consensus assignments based on ME and ML approaches are more correct than either method individually. This is true even when the statistical support for read assignments was low, which is inevitable given that individual reads are often short and come from only one gene.

A Protocol for Diagnosing the Effect of Calibration Priors on Posterior Time Estimates: A Case Study for the Cambrian Explosion of Animal Phyla.

We present a procedure to test the effect of calibration priors on estimated times, which applies a recently developed calibration-free approach (RelTime) method that produces relative divergence times for all nodes in the tree. We illustrate this protocol by applying it to a timetree of metazoan diversification (Erwin DH, Laflamme M, Tweedt SM, Sperling EA, Pisani D, Peterson KJ. 2011. The Cambrian conundrum: early divergence and later ecological success in the early history of animals. Science 334:1091-1097.), which placed the divergence of animal phyla close to the time of the Cambrian explosion inferred from the fossil record. These analyses revealed that the two maximum-only calibration priors in the pre-Cambrian are the primary determinants of the young divergence times among animal phyla in this study. In fact, these two maximum-only calibrations produce divergence times that severely violate minimum boundaries of almost all of the other 22 calibration constraints. The use of these 22 calibrations produces dates for metazoan divergences that are hundreds of millions of years earlier in the Proterozoic. Our results encourage the use of calibration-free approaches to identify most influential calibration constraints and to evaluate their impact in order to achieve biologically robust interpretations.

Accounting for eXentricities: analysis of the X chromosome in GWAS reveals X-linked genes implicated in autoimmune diseases.

Many complex human diseases are highly sexually dimorphic, suggesting a potential contribution of the X chromosome to disease risk. However, the X chromosome has been neglected or incorrectly analyzed in most genome-wide association studies (GWAS). We present tailored analytical methods and software that facilitate X-wide association studies (XWAS), which we further applied to reanalyze data from 16 GWAS of different autoimmune and related diseases (AID). We associated several X-linked genes with disease risk, among which (1) ARHGEF6 is associated with Crohn's disease and replicated in a study of ulcerative colitis, another inflammatory bowel disease (IBD). Indeed, ARHGEF6 interacts with a gastric bacterium that has been implicated in IBD. (2) CENPI is associated with three different AID, which is compelling in light of known associations with AID of autosomal genes encoding centromere proteins, as well as established autosomal evidence of pleiotropy between autoimmune diseases. (3) We replicated a previous association of FOXP3, a transcription factor that regulates T-cell development and function, with vitiligo; and (4) we discovered that C1GALT1C1 exhibits sex-specific effect on disease risk in both IBDs. These and other X-linked genes that we associated with AID tend to be highly expressed in tissues related to immune response, participate in major immune pathways, and display differential gene expression between males and females. Combined, the results demonstrate the importance of the X chromosome in autoimmunity, reveal the potential of extensive XWAS, even based on existing data, and provide the tools and incentive to properly include the X chromosome in future studies.

Estimating divergence times in large molecular phylogenies.

Molecular dating of species divergences has become an important means to add a temporal dimension to the Tree of Life. Increasingly larger datasets encompassing greater taxonomic diversity are becoming available to generate molecular timetrees by using sophisticated methods that model rate variation among lineages. However, the practical application of these methods is challenging because of the exorbitant calculation times required by current methods for contemporary data sizes, the difficulty in correctly modeling the rate heterogeneity in highly diverse taxonomic groups, and the lack of reliable clock calibrations and their uncertainty distributions for most groups of species. Here, we present a method that estimates relative times of divergences for all branching points (nodes) in very large phylogenetic trees without assuming a specific model for lineage rate variation or specifying any clock calibrations. The method (RelTime) performed better than existing methods when applied to very large computer simulated datasets where evolutionary rates were varied extensively among lineages by following autocorrelated and uncorrelated models. On average, RelTime completed calculations 1,000 times faster than the fastest Bayesian method, with even greater speed difference for larger number of sequences. This speed and accuracy will enable molecular dating analysis of very large datasets. Relative time estimates will be useful for determining the relative ordering and spacing of speciation events, identifying lineages with significantly slower or faster evolutionary rates, diagnosing the effect of selected calibrations on absolute divergence times, and estimating absolute times of divergence when highly reliable calibration points are available.

Fast and slow implementations of relaxed-clock methods show similar patterns of accuracy in estimating divergence times.

Phylogenetic analyses are using increasingly larger data sets for estimating divergence times. With this increase in data sizes, the computation time required is becoming a bottleneck in evolutionary investigations. Our recent study of two relaxed-clock programs (BEAST and MultiDivTime [MDT]) showed their usefulness in time estimation; however, they place a significant computational time burden on biologists even for moderately small data sets. Here, we report speed and accuracy of another relaxed-clock program (MCMCTree, MC2T). We find it to be much faster than both MDT and BEAST while producing comparable time estimates. These results will encourage the analysis of larger data sets as well as the evaluation of the robustness of estimated times to changes in the model of evolutionary rates and clock calibrations.