Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study

CoPheScan: phenome-wide association studies accounting for linkage disequilibrium

Phenome-wide association studies (PheWAS) facilitate the discovery of associations between a single genetic variant with multiple phenotypes. For variants which impact a specific protein, this can help identify additional therapeutic indications or on-target side effects of intervening on that protein. However, PheWAS is restricted by an inability to distinguish confounding due to linkage disequilibrium (LD) from true pleiotropy. Here we describe CoPheScan (Coloc adapted Phenome-wide Scan), a Bayesian approach that enables an intuitive and systematic exploration of causal associations while simultaneously addressing LD confounding. We demonstrate its performance through simulation, showing considerably better control of false positive rates than a conventional approach not accounting for LD. We used CoPheScan to perform PheWAS of protein-truncating variants and fine-mapped variants from disease and pQTL studies, in 2275 disease phenotypes from the UK Biobank. Our results identify the complexity of known pleiotropic genes such as APOE, and suggest a new causal role for TGM3 in skin cancer.

Exome-wide evidence of compound heterozygous effects across common phenotypes in the UK Biobank

The phenotypic impact of compound heterozygous (CH) variation has not been investigated at the population scale. We phased rare variants (MAF ∼0.001%) in the UK Biobank (UKBB) exome-sequencing data to characterize recessive effects in 175,587 individuals across 311 common diseases. A total of 6.5% of individuals carry putatively damaging CH variants, 90% of which are only identifiable upon phasing rare variants (MAF < 0.38%). We identify six recessive gene-trait associations (p < 1.68 × 10-7) after accounting for relatedness, polygenicity, nearby common variants, and rare variant burden. Of these, just one is discovered when considering homozygosity alone. Using longitudinal health records, we additionally identify and replicate a novel association between bi-allelic variation in ATP2C2 and an earlier age at onset of chronic obstructive pulmonary disease (COPD) (p < 3.58 × 10-8). Genetic phase contributes to disease risk for gene-trait pairs: ATP2C2-COPD (p = 0.000238), FLG-asthma (p = 0.00205), and USH2A-visual impairment (p = 0.0084). We demonstrate the power of phasing large-scale genetic cohorts to discover phenome-wide consequences of compound heterozygosity.

Exploring penetrance of clinically relevant variants in over 800,000 humans from the Genome Aggregation Database

Incomplete penetrance, or absence of disease phenotype in an individual with a disease-associated variant, is a major challenge in variant interpretation. Studying individuals with apparent incomplete penetrance can shed light on underlying drivers of altered phenotype penetrance. Here, we investigate clinically relevant variants from ClinVar in 807,162 individuals from the Genome Aggregation Database (gnomAD), demonstrating improved representation in gnomAD version 4. We then conduct a comprehensive case-by-case assessment of 734 predicted loss of function variants (pLoF) in 77 genes associated with severe, early-onset, highly penetrant haploinsufficient disease. We identified explanations for the presumed lack of disease manifestation in 701 of the variants (95%). Individuals with unexplained lack of disease manifestation in this set of disorders rarely occur, underscoring the need and power of deep case-by-case assessment presented here to minimize false assignments of disease risk, particularly in unaffected individuals with higher rates of secondary properties that result in rescue.

Protein truncating variants in mitochondrial-related nuclear genes and the risk of chronic liver disease

BACKGROUND

Mitochondrial (MT) dysfunction is a hallmark of liver diseases. However, the effects of functional variants such as protein truncating variants (PTVs) in MT-related genes on the risk of liver diseases have not been extensively explored.

METHODS

We extracted 60,928 PTVs across 2466 MT-related nucleus genes using whole-exome sequencing data obtained from 442,603 participants in the UK Biobank. We examined their associations with liver dysfunction that represented by the liver-related biomarkers and the risks of chronic liver diseases and liver-related mortality.

RESULTS

96.10% of the total participants carried at least one PTV. We identified 866 PTVs that were positively associated with liver dysfunction at the threshold of P value < 8.21e - 07. The coding genes of these PTVs were mainly enriched in pathways related to lipid, fatty acid, amino acid, and carbohydrate metabolisms. The 866 PTVs were presented in 1.07% (4721) of participants. Compared with participants who did not carry any of the PTVs, the carriers had a 5.33-fold (95% CI 4.15-6.85), 2.82-fold (1.69-4.72), and 4.41-fold (3.04-6.41) increased risk for fibrosis and cirrhosis of liver, liver cancer, and liver disease-related mortality, respectively. These adverse effects were consistent across subgroups based on age, sex, body mass index, smoking status, and presence of hypertension, diabetes, dyslipidemia, and metabolic syndrome.

CONCLUSIONS

Our findings revealed a significant impact of PTVs in MT-related genes on liver disease risk, highlighting the importance of these variants in identifying populations at risk of liver diseases and facilitating early clinical interventions.

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank

The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk associations. We investigated associations of 1463 plasma proteins with incidence of 19 cancers and 9 cancer subsites in UK Biobank participants (average 12 years follow-up). Emerging protein-cancer associations were further explored using two genetic approaches, cis-pQTL and exome-wide protein genetic scores (exGS). We identify 618 protein-cancer associations, of which 107 persist for cases diagnosed more than seven years after blood draw, 29 of 618 were associated in genetic analyses, and four had support from long time-to-diagnosis ( > 7 years) and both cis-pQTL and exGS analyses: CD74 and TNFRSF1B with NHL, ADAM8 with leukemia, and SFTPA2 with lung cancer. We present multiple blood protein-cancer risk associations, including many detectable more than seven years before cancer diagnosis and that had concordant evidence from genetic analyses, suggesting a possible role in cancer development.

Prioritizing disease-related rare variants by integrating gene expression data

Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in diseased patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

Unveiling the structure-emulsifying function relationship of truncated recombinant forms of the SA01-OmpA protein opens up a new vista in bioemulsifiers

The emulsifying ability of SA01-OmpA (outer membrane protein A from Acinetobacter sp. SA01) was found to be constrained by challenges like low production efficiency and high costs associated with protein recovery from E. coli inclusion bodies, as described in our previous study. The present study sought to benefit from the advantages of the targeted truncating of SA01-OmpA protein, taking into account the reduced propensity of protein expression as inclusion bodies and cytotoxicity. Here, the structure and activity relationship of two truncated recombinant forms of SA01-OmpA protein was unraveled through a hybrid approach based on experimental data and computational methodologies, representing an innovative bioemulsifier with advantageous emulsifying activity. The recombinant truncated SA01-OmpA variants were cloned and heterologously expressed in E. coli host cells and subsequently purified. The results showed increased emulsifying activity of N-terminally truncated SA01-OmpA (NT-OmpA) compared to full-length SA01-OmpA. Molecular dynamics (MD) simulations analysis demonstrated a direct correlation between the C-terminally truncated SA01-OmpA (CT-OmpA) and its expression as inclusion bodies. Analysis of the structure-activity relationship of truncated variants of SA01-OmpA revealed that, compared to the full-length protein, deletion of the β-barrel portion from the N-terminal of SA01-OmpA increased the emulsifying activity of NT-OmpA while lowering its expression as inclusion bodies. Contrary to the full-length protein, the N-terminally truncated SA01-OmpA was not as cytotoxic, according to the MTT assay, FCM analysis, and AO/EB staining. The findings of this extensive study advance our knowledge of SA01-OmpA at the molecular level as well as the design and development of efficient bioemulsifiers.IMPORTANCEPrevious research (Shahryari et al. 2021, mSystems 6: e01175-20) introduced and characterized the SA01-OmpA protein as a multifaceted protein with a variety of functions, including maintaining cellular homeostasis under oxidative stress conditions, biofilm formation, outer membrane vesicles (OMV) biogenesis, and beneficial emulsifying capacity. By truncating the SA01-OmpA protein, the current study presents a unique method for developing protein-type bioemulsifiers. The findings indicate that the N-terminally truncated SA01-OmpA (NT-OmpA) has the potential to fully replace full-length SA01-OmpA as a novel bioemulsifier with significant emulsifying activity. This study opens up a new frontier in bioemulsifiers, shedding light on a possible relationship between the structure and activity of SA01-OmpA truncated forms.

Power of inclusion: Enhancing polygenic prediction with admixed individuals

Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals for developing more equitable PGS models.

Exome-wide evidence of compound heterozygous effects across common phenotypes in the UK Biobank

Exome-sequencing association studies have successfully linked rare protein-coding variation to risk of thousands of diseases. However, the relationship between rare deleterious compound heterozygous (CH) variation and their phenotypic impact has not been fully investigated. Here, we leverage advances in statistical phasing to accurately phase rare variants (MAF ~ 0.001%) in exome sequencing data from 175,587 UK Biobank (UKBB) participants, which we then systematically annotate to identify putatively deleterious CH coding variation. We show that 6.5% of individuals carry such damaging variants in the CH state, with 90% of variants occurring at MAF < 0.34%. Using a logistic mixed model framework, systematically accounting for relatedness, polygenic risk, nearby common variants, and rare variant burden, we investigate recessive effects in common complex diseases. We find six exome-wide significant (P < 1.68 × 10 - 7 ) and 17 nominally significant (P < 5.25 × 10 - 5 ) gene-trait associations. Among these, only four would have been identified without accounting for CH variation in the gene. We further incorporate age-at-diagnosis information from primary care electronic health records, to show that genetic phase influences lifetime risk of disease across 20 gene-trait combinations (FDR < 5%). Using a permutation approach, we find evidence for genetic phase contributing to disease susceptibility for a collection of gene-trait pairs, including FLG-asthma (P = 0.00205 ) and USH2A-visual impairment (P = 0.0084 ). Taken together, we demonstrate the utility of phasing large-scale genetic sequencing cohorts for robust identification of the phenome-wide consequences of compound heterozygosity.

Psychiatric polygenic risk as a predictor of COVID-19 risk and severity: insight into the genetic overlap between schizophrenia and COVID-19

Despite the high contagion and mortality rates that have accompanied the coronavirus disease-19 (COVID-19) pandemic, the clinical presentation of the syndrome varies greatly from one individual to another. Potential host factors that accompany greater risk from COVID-19 have been sought and schizophrenia (SCZ) patients seem to present more severe COVID-19 than control counterparts, with certain gene expression similarities between psychiatric and COVID-19 patients reported. We used summary statistics from the last SCZ, bipolar disorder (BD), and depression (DEP) meta-analyses available on the Psychiatric Genomics Consortium webpage to calculate polygenic risk scores (PRSs) for a target sample of 11,977 COVID-19 cases and 5943 subjects with unknown COVID-19 status. Linkage disequilibrium score (LDSC) regression analysis was performed when positive associations were obtained from the PRS analysis. The SCZ PRS was a significant predictor in the case/control, symptomatic/asymptomatic, and hospitalization/no hospitalization analyses in the total and female samples; and of symptomatic/asymptomatic status in men. No significant associations were found for the BD or DEP PRS or in the LDSC regression analysis. SNP-based genetic risk for SCZ, but not for BD or DEP, may be associated with higher risk of SARS-CoV-2 infection and COVID-19 severity, especially among women; however, predictive accuracy barely exceeded chance level. We believe that the inclusion of sexual loci and rare variations in the analysis of genomic overlap between SCZ and COVID-19 will help to elucidate the genetic commonalities between these conditions.

Whole-exome sequencing study of hypospadias

Hypospadias results from the impaired urethral development, which is influenced by androgens, but its genetic etiology is still unknown. Through whole exome sequencing analysis, we identified NR5A1, SRD5A2, and AR as mutational hotspots in the etiology of severe hypospadias, as these genes are related to androgen signaling. Additionally, rare damaging variants in cilia-related outer dynein arm heavy chain (ODNAH) genes (DNAH5, DNAH8, DNAH9, DNAH11, and DNAH17) (p = 8.5 × 10-47) were significantly enriched in hypospadias cases. The Dnah8 KO mice exhibited significantly decreased testosterone levels, which had an impact on urethral development and disrupted steroid biosynthesis. Combined with trios data, transcriptomic, and phenotypical and proteomic characterization of a mouse model, our work links ciliary genes with hypospadias. Overall, a panel of ODNAH genes with rare damaging variants was identified in 24% of hypospadias patients, providing significant insights into the underlying pathogenesis of hypospadias as well as genetic counseling.

Mendelian randomization as a tool to inform drug development using human genetics

Drug development is essential to the advancement of human health, however, the process is slow, costly, and at high risk of failure at all stages. A promising strategy for expediting and improving the probability of success in the drug development process is the use of naturally randomized human genetic variation for drug target identification and validation. These data can be harnessed using the Mendelian randomization (MR) analytic paradigm to proxy the lifelong consequences of genetic perturbations of drug targets. In this review, we discuss the myriad applications of the MR paradigm for human drug target identification and validation. We review the methodology and applications of MR, key limitations of MR, and potential future opportunities for research. Throughout the review, we refer to illustrative examples of MR analyses investigating the consequences of genetic inhibition of interleukin 6 signaling which, in some cases, have anticipated results from randomized controlled trials. As human genetic data become more widely available, we predict that MR will serve as a key pillar of support for drug development efforts.

Integrated top-down and bottom-up proteomics mass spectrometry for the characterization of endogenous ribosomal protein heterogeneity

Ribosomes are abundant, large RNA-protein complexes that are the sites of all protein synthesis in cells. Defects in ribosomal proteins (RPs), including proteoforms arising from genetic variations, alternative splicing of RNA transcripts, post-translational modifications and alterations of protein expression level, have been linked to a diverse range of diseases, including cancer and aging. Comprehensive characterization of ribosomal proteoforms is challenging but important for the discovery of potential disease biomarkers or protein targets. In the present work, using E. coli 70S RPs as an example, we first developed a top-down proteomics approach on a Waters Synapt G2 Si mass spectrometry (MS) system, and then applied it to the HeLa 80S ribosome. The results were complemented by a bottom-up approach. In total, 50 out of 55 RPs were identified using the top-down approach. Among these, more than 30 RPs were found to have their N-terminal methionine removed. Additional modifications such as methylation, acetylation, and hydroxylation were also observed, and the modification sites were identified by bottom-up MS. In a HeLa 80S ribosomal sample, we identified 98 ribosomal proteoforms, among which multiple truncated 80S ribosomal proteoforms were observed, the type of information which is often overlooked by bottom-up experiments. Although their relevance to diseases is not yet known, the integration of top-down and bottom-up proteomics approaches paves the way for the discovery of proteoform-specific disease biomarkers or targets.

Evaluation of AlphaFold structure-based protein stability prediction on missense variations in cancer

Identifying pathogenic missense variants in hereditary cancer is critical to the efforts of patient surveillance and risk-reduction strategies. For this purpose, many different gene panels consisting of different number and/or set of genes are available and we are particularly interested in a panel of 26 genes with a varying degree of hereditary cancer risk consisting of ABRAXAS1, ATM, BARD1, BLM, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, EPCAM, MEN1, MLH1, MRE11, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, STK11, TP53, and XRCC2. In this study, we have compiled a collection of the missense variations reported in any of these 26 genes. More than a thousand missense variants were collected from ClinVar and the targeted screen of a breast cancer cohort of 355 patients which contributed to this set with 160 novel missense variations. We analyzed the impact of the missense variations on protein stability by five different predictors including both sequence- (SAAF2EC and MUpro) and structure-based (Maestro, mCSM, CUPSAT) predictors. For the structure-based tools, we have utilized the AlphaFold (AF2) protein structures which comprise the first structural analysis of this hereditary cancer proteins. Our results agreed with the recent benchmarks that computed the power of stability predictors in discriminating the pathogenic variants. Overall, we reported a low-to-medium-level performance for the stability predictors in discriminating pathogenic variants, except MUpro which had an AUROC of 0.534 (95% CI [0.499-0.570]). The AUROC values ranged between 0.614-0.719 for the total set and 0.596-0.682 for the set with high AF2 confidence regions. Furthermore, our findings revealed that the confidence score for a given variant in the AF2 structure could alone predict pathogenicity more robustly than any of the tested stability predictors with an AUROC of 0.852. Altogether, this study represents the first structural analysis of the 26 hereditary cancer genes underscoring 1) the thermodynamic stability predicted from AF2 structures as a moderate and 2) the confidence score of AF2 as a strong descriptor for variant pathogenicity.

Gene therapy review: Duchenne muscular dystrophy case study

Gene therapy, i.e., any therapeutic approach involving the use of genetic material as a drug and more largely altering the transcription or translation of one or more genes, covers a wide range of innovative methods for treating diseases, including neurological disorders. Although they share common principles, the numerous gene therapy approaches differ greatly in their mechanisms of action. They also differ in their maturity for some are already used in clinical practice while others have never been used in humans. The aim of this review is to present the whole range of gene therapy techniques through the example of Duchenne muscular dystrophy (DMD). DMD is a severe myopathy caused by mutations in the dystrophin gene leading to the lack of functional dystrophin protein. It is a disease known to all neurologists and in which almost all gene therapy methods were applied. Here we discuss the mechanisms of gene transfer techniques with or without viral vectors, DNA editing with or without matrix repair and those acting at the RNA level (RNA editing, exon skipping and STOP-codon readthrough). For each method, we present the results obtained in DMD with a particular focus on clinical data. This review aims also to outline the advantages, limitations and risks of gene therapy related to the approach used.

Current Trends and Approaches to the Search for Genetic Determinants of Aging and Longevity

Aging is a natural process of extinction of the body and the main aspect that determines the life expectancy for individuals who have survived to the post-reproductive period. The process of aging is accompanied by certain physiological, immune, and metabolic changes in the body, as well as the development of age-related diseases. The contribution of genetic factors to human life expectancy is estimated at about 25-30%. Despite the success in identifying genes and metabolic pathways that may be involved in the life extension process in model organisms, the key question remains to what extent these data can be extrapolated to humans, for example, because of the complexity of its biological and sociocultural systems, as well as possible species differences in life expectancy and causes of mortality. New molecular genetic methods have significantly expanded the possibilities for searching for genetic factors of human life expectancy and identifying metabolic pathways of aging, the interaction of genes and transcription factors, the regulation of gene expression at the level of transcription, and epigenetic modifications. The review presents the latest research and current strategies for studying the genetic basis of human aging and longevity: the study of individual candidate genes in genetic population studies, variations identified by the GWAS method, immunogenetic differences in aging, and genomic studies to identify factors of "healthy aging." Understanding the mechanisms of the interaction between factors affecting the life expectancy and the possibility of their regulation can become the basis for developing comprehensive measures to achieve healthy longevity.

Supplementary Information

The online version contains supplementary material available at 10.1134/S1022795422120067.

The contribution of common and rare genetic variants to variation in metabolic traits in 288,137 East Asians

Metabolic traits are heritable phenotypes widely-used in assessing the risk of various diseases. We conduct a genome-wide association analysis (GWAS) of nine metabolic traits (including glycemic, lipid, liver enzyme levels) in 125,872 Korean subjects genotyped with the Korea Biobank Array. Following meta-analysis with GWAS from Biobank Japan identify 144 novel signals (MAF ≥ 1%), of which 57.0% are replicated in UK Biobank. Additionally, we discover 66 rare (MAF < 1%) variants, 94.4% of them co-incident to common loci, adding to allelic series. Although rare variants have limited contribution to overall trait variance, these lead, in carriers, substantial loss of predictive accuracy from polygenic predictions of disease risk from common variant alone. We capture groups with up to 16-fold variation in type 2 diabetes (T2D) prevalence by integration of genetic risk scores of fasting plasma glucose and T2D and the I349F rare protective variant. This study highlights the need to consider the joint contribution of both common and rare variants on inherited risk of metabolic traits and related diseases.

Whole-Genome Profile of Greek Patients with Teratozοοspermia: Identification of Candidate Variants and Genes

Male infertility is a global health problem that affects a large number of couples worldwide. It can be categorized into specific subtypes, including teratozoospermia. The present study aimed to identify new variants associated with teratozoospermia in the Greek population and to explore the role of genes on which these were identified. For this reason, whole-genome sequencing (WGS) was performed on normozoospermic and teratozoospermic individuals, and after selecting only variants found in teratozoospermic men, these were further prioritized using a wide range of tools, functional and predictive algorithms, etc. An average of 600,000 variants were identified, and of them, 61 were characterized as high impact and 153 as moderate impact. Many of these are mapped in genes previously associated with male infertility, yet others are related for the first time to teratozoospermia. Furthermore, pathway enrichment analysis and Gene ontology (GO) analyses revealed the important role of the extracellular matrix in teratozoospermia. Therefore, the present study confirms the contribution of genes studied in the past to male infertility and sheds light on new molecular mechanisms by providing a list of variants and candidate genes associated with teratozoospermia in the Greek population.

Nonsense Mutations in Eukaryotes

Nonsense mutations are a type of mutations which results in a premature termination codon occurrence. In general, these mutations have been considered to be among the most harmful ones which lead to premature protein translation termination and result in shortened nonfunctional polypeptide. However, there is evidence that not all nonsense mutations are harmful as well as some molecular mechanisms exist which allow to avoid pathogenic effects of these mutations. This review addresses relevant information on nonsense mutations in eukaryotic genomes, characteristics of these mutations, and different molecular mechanisms preventing or mitigating harmful effects thereof.

Human enteric viruses autonomously shape inflammatory bowel disease phenotype through divergent innate immunomodulation

Altered enteric microorganisms in concert with host genetics shape inflammatory bowel disease (IBD) phenotypes. However, insight is limited to bacteria and fungi. We found that eukaryotic viruses and bacteriophages (collectively, the virome), enriched from non-IBD, noninflamed human colon resections, actively elicited atypical anti-inflammatory innate immune programs. Conversely, ulcerative colitis or Crohn's disease colon resection viromes provoked inflammation, which was successfully dampened by non-IBD viromes. The IBD colon tissue virome was perturbed, including an increase in the enterovirus B species of eukaryotic picornaviruses, not previously detected in fecal virome studies. Mice humanized with non-IBD colon tissue viromes were protected from intestinal inflammation, whereas IBD virome mice exhibited exacerbated inflammation in a nucleic acid sensing-dependent fashion. Furthermore, there were detrimental consequences for IBD patient-derived intestinal epithelial cells bearing loss-of-function mutations within virus sensor MDA5 when exposed to viromes. Our results demonstrate that innate recognition of IBD or non-IBD human viromes autonomously influences intestinal homeostasis and disease phenotypes. Thus, perturbations in the intestinal virome, or an altered ability to sense the virome due to genetic variation, contribute to the induction of IBD. Harnessing the virome may offer therapeutic and biomarker potential.

Pathophysiological Heterogeneity of the BBSOA Neurodevelopmental Syndrome

The formation and maturation of the human brain is regulated by highly coordinated developmental events, such as neural cell proliferation, migration and differentiation. Any impairment of these interconnected multi-factorial processes can affect brain structure and function and lead to distinctive neurodevelopmental disorders. Here, we review the pathophysiology of the Bosch–Boonstra–Schaaf Optic Atrophy Syndrome (BBSOAS; OMIM 615722; ORPHA 401777), a recently described monogenic neurodevelopmental syndrome caused by the haploinsufficiency of NR2F1 gene, a key transcriptional regulator of brain development. Although intellectual disability, developmental delay and visual impairment are arguably the most common symptoms affecting BBSOAS patients, multiple additional features are often reported, including epilepsy, autistic traits and hypotonia. The presence of specific symptoms and their variable level of severity might depend on still poorly characterized genotype–phenotype correlations. We begin with an overview of the several mutations of NR2F1 identified to date, then further focuses on the main pathological features of BBSOAS patients, providing evidence—whenever possible—for the existing genotype–phenotype correlations. On the clinical side, we lay out an up-to-date list of clinical examinations and therapeutic interventions recommended for children with BBSOAS. On the experimental side, we describe state-of-the-art in vivo and in vitro studies aiming at deciphering the role of mouse Nr2f1, in physiological conditions and in pathological contexts, underlying the BBSOAS features. Furthermore, by modeling distinct NR2F1 genetic alterations in terms of dimer formation and nuclear receptor binding efficiencies, we attempt to estimate the total amounts of functional NR2F1 acting in developing brain cells in normal and pathological conditions. Finally, using the NR2F1 gene and BBSOAS as a paradigm of monogenic rare neurodevelopmental disorder, we aim to set the path for future explorations of causative links between impaired brain development and the appearance of symptoms in human neurological syndromes.

Recurrent de novo missense variants across multiple histone H4 genes underlie a neurodevelopmental syndrome

Chromatin is essentially an array of nucleosomes, each of which consists of the DNA double-stranded fiber wrapped around a histone octamer. This organization supports cellular processes such as DNA replication, DNA transcription, and DNA repair in all eukaryotes. Human histone H4 is encoded by fourteen canonical histone H4 genes, all differing at the nucleotide level but encoding an invariant protein. Here, we present a cohort of 29 subjects with de novo missense variants in six H4 genes (H4C3, H4C4, H4C5, H4C6, H4C9, and H4C11) identified by whole-exome sequencing and matchmaking. All individuals present with neurodevelopmental features of intellectual disability and motor and/or gross developmental delay, while non-neurological features are more variable. Ten amino acids are affected, six recurrently, and are all located within the H4 core or C-terminal tail. These variants cluster to specific regions of the core H4 globular domain, where protein-protein interactions occur with either other histone subunits or histone chaperones. Functional consequences of the identified variants were evaluated in zebrafish embryos, which displayed abnormal general development, defective head organs, and reduced body axis length, providing compelling evidence for the causality of the reported disorder(s). While multiple developmental syndromes have been linked to chromatin-associated factors, missense-bearing histone variants (e.g., H3 oncohistones) are only recently emerging as a major cause of pathogenicity. Our findings establish a broader involvement of H4 variants in developmental syndromes.

The burden of rare protein-truncating genetic variants on human lifespan

Genetic predisposition has been shown to contribute substantially to the age at which we die. Genome-wide association studies (GWASs) have linked more than 20 loci to phenotypes related to human lifespan¹. However, little is known about how lifespan is impacted by gene loss of function. Through whole-exome sequencing of 352,338 UK Biobank participants of European ancestry, we assessed the relevance of protein-truncating variant (PTV) gene burden on individual and parental survival. We identified four exome-wide significant (P < 4.2 × 10^-7) human lifespan genes, BRCA1, BRCA2, ATM and TET2. Gene and gene-set, PTV-burden, phenome-wide association studies support known roles of these genes in cancer to impact lifespan at the population level. The TET2 PTV burden was associated with a lifespan through somatic mutation events presumably due to clonal hematopoiesis. The overlap between PTV burden and common variant-based lifespan GWASs was modest, underscoring the value of exome sequencing in well-powered biobank cohorts to complement GWASs for identifying genes underlying complex traits.

Significant sparse polygenic risk scores across 813 traits in UK Biobank

We present a systematic assessment of polygenic risk score (PRS) prediction across more than 1,500 traits using genetic and phenotype data in the UK Biobank. We report 813 sparse PRS models with significant (p < 2.5 x 10-5) incremental predictive performance when compared against the covariate-only model that considers age, sex, types of genotyping arrays, and the principal component loadings of genotypes. We report a significant correlation between the number of genetic variants selected in the sparse PRS model and the incremental predictive performance (Spearman's ⍴ = 0.61, p = 2.2 x 10-59 for quantitative traits, ⍴ = 0.21, p = 9.6 x 10-4 for binary traits). The sparse PRS model trained on European individuals showed limited transferability when evaluated on non-European individuals in the UK Biobank. We provide the PRS model weights on the Global Biobank Engine (https://biobankengine.stanford.edu/prs).

Synergistic insights into human health from aptamer- and antibody-based proteomic profiling

Affinity-based proteomics has enabled scalable quantification of thousands of protein targets in blood enhancing biomarker discovery, understanding of disease mechanisms, and genetic evaluation of drug targets in humans through protein quantitative trait loci (pQTLs). Here, we integrate two partly complementary techniques-the aptamer-based SomaScan^® v4 assay and the antibody-based Olink assays-to systematically assess phenotypic consequences of hundreds of pQTLs discovered for 871 protein targets across both platforms. We create a genetically anchored cross-platform proteome-phenome network comprising 547 protein-phenotype connections, 36.3% of which were only seen with one of the two platforms suggesting that both techniques capture distinct aspects of protein biology. We further highlight discordance of genetically predicted effect directions between assays, such as for PILRA and Alzheimer's disease. Our results showcase the synergistic nature of these technologies to better understand and identify disease mechanisms and provide a benchmark for future cross-platform discoveries.

Functional screen of inflammatory bowel disease genes reveals key epithelial functions

BACKGROUND

Genetic studies have been tremendously successful in identifying genomic regions associated with a wide variety of phenotypes, although the success of these studies in identifying causal genes, their variants, and their functional impacts has been more limited.

METHODS

We identified 145 genes from IBD-associated genomic loci having endogenous expression within the intestinal epithelial cell compartment. We evaluated the impact of lentiviral transfer of the open reading frame (ORF) of these IBD genes into the HT-29 intestinal epithelial cell line via transcriptomic analyses. By comparing the genes in which expression was modulated by each ORF, as well as the functions enriched within these gene lists, we identified ORFs with shared impacts and their putative disease-relevant biological functions.

RESULTS

Analysis of the transcriptomic data for cell lines expressing the ORFs for known causal genes such as HNF4a, IFIH1, and SMAD3 identified functions consistent with what is already known for these genes. These analyses also identified two major clusters of genes: Cluster 1 contained the known IBD causal genes IFIH1, SBNO2, NFKB1, and NOD2, as well as genes from other IBD loci (ZFP36L1, IRF1, GIGYF1, OTUD3, AIRE and PITX1), whereas Cluster 2 contained the known causal gene KSR1 and implicated DUSP16 from another IBD locus. Our analyses highlight how multiple IBD gene candidates can impact on epithelial structure and function, including the protection of the mucosa from intestinal microbiota, and demonstrate that DUSP16 acts a regulator of MAPK activity and contributes to mucosal defense, in part via its regulation of the polymeric immunoglobulin receptor, involved in the protection of the intestinal mucosa from enteric microbiota.

CONCLUSIONS

This functional screen, based on expressing IBD genes within an appropriate cellular context, in this instance intestinal epithelial cells, resulted in changes to the cell's transcriptome that are relevant to their endogenous biological function(s). This not only helped in identifying likely causal genes within genetic loci but also provided insight into their biological functions. Furthermore, this work has highlighted the central role of intestinal epithelial cells in IBD pathophysiology, providing a scientific rationale for a drug development strategy that targets epithelial functions in addition to the current therapies targeting immune functions.

Targeted Sequencing of 242 Clinically Important Genes in the Russian Population From the Ivanovo Region

We performed a targeted sequencing of 242 clinically important genes mostly associated with cardiovascular diseases in a representative population sample of 1,658 individuals from the Ivanovo region northeast of Moscow. Approximately 11% of 11,876 detected variants were not found in the Single Nucleotide Polymorphism Database (dbSNP) or reported earlier in the Russian population. Most novel variants were singletons and doubletons in our sample, and virtually no novel alleles presumably specific for the Russian population were able to reach the frequencies above 0.1-0.2%. The overwhelming majority (99.3%) of variants detected in this study in three or more copies were shared with other populations. We found two dominant and seven recessive known pathogenic variants with allele frequencies significantly increased compared to those in the gnomAD non-Finnish Europeans. Of the 242 targeted genes, 28 were in the list of 59 genes for which the American College of Medical Genetics and Genomics (ACMG) recommended the reporting of incidental findings. Based on the number of variants detected in the sequenced subset of ACMG59 genes, we approximated the prevalence of known pathogenic and novel or rare protein-truncating variants in the complete set of ACMG59 genes in the Ivanovo population at 1.4 and 2.8%, respectively. We analyzed the available clinical data and observed the incomplete penetrance of known pathogenic variants in the 28 ACMG59 genes: only 1 individual out of 12 with such variants had the phenotype most likely related to the variant. When known pathogenic and novel or rare protein-truncating variants were considered together, the overall rate of confirmed phenotypes was about 19%, with maximum in the subset of novel protein-truncating variants. We report three novel protein truncating variants in APOB and one in MYH7 observed in individuals with hypobetalipoproteinemia and hypertrophic cardiomyopathy, respectively. Our results provide a valuable reference for the clinical interpretation of gene sequencing in Russian and other populations.

Whole genome sequencing identifies rare germline variants enriched in cancer related genes in first degree relatives of familial pancreatic cancer patients

First-degree relatives (FDRs) of familial pancreatic cancer (FPC) patients have increased risk of developing pancreatic ductal adenocarcinoma (PDAC). Investigating and understanding the genetic basis for PDAC susceptibility in FPC predisposed families may contribute toward future risk-assessment and management of high-risk individuals. Using a Danish cohort of 27 FPC families, we performed whole-genome sequencing of 61 FDRs of FPC patients focusing on rare genetic variants that may contribute to familial aggregation of PDAC. Statistical analysis was performed using the gnomAD database as external controls. Through analysis of heterozygous premature truncating variants (PTV), we identified cancer-related genes and cancer-driver genes harboring multiple germline mutations. Association analysis detected 20 significant genes with false discovery rate, q < 0.05 including: PALD1, LRP1B, COL4A2, CYLC2, ZFYVE9, BRD3, AHDC1, etc. Functional annotation showed that the significant genes were enriched by gene clusters encoding for extracellular matrix and associated proteins. PTV genes were over-represented by functions related to transport of small molecules, innate immune system, ion channel transport, and stimuli-sensing channels. In conclusion, FDRs of FPC patients carry rare germline variants related to cancer pathogenesis that may contribute to increased susceptibility to PDAC. The identified variants may potentially be useful for risk prediction of high-risk individuals in predisposed families.

Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses

Exome association studies to date have generally been underpowered to systematically evaluate the phenotypic impact of very rare coding variants. We leveraged extensive haplotype sharing between 49,960 exome-sequenced UK Biobank participants and the remainder of the cohort (total N~500K) to impute exome-wide variants with accuracy (R²>0.5) down to minor allele frequency (MAF) ~0.00005. Association and fine-mapping analyses of 54 quantitative traits identified 1,189 significant associations (P<5 x 10⁻⁸) involving 675 distinct rare protein-altering variants (MAF<0.01) that passed stringent filters for likely causality. Across all traits, 49% of associations (578/1,189) occurred in genes with two or more hits; follow-up analyses of these genes identified allelic series containing up to 45 distinct likely-causal variants. Our results demonstrate the utility of within-cohort imputation in population-scale GWAS cohorts, provide a catalog of likely-causal, large-effect coding variant associations, and foreshadow the insights that will be revealed as genetic biobank studies continue to grow.

Deep sequencing of 1320 genes reveals the landscape of protein-truncating variants and their contribution to psoriasis in 19,973 Chinese individuals

Protein-truncating variants (PTVs) have important impacts on phenotype diversity and disease. However, their population genetics characteristics in more globally diverse populations are not well defined. Here, we describe patterns of PTVs in 1320 genes sequenced in 10,539 healthy controls and 9434 patients with psoriasis, all of Han Chinese ancestry. We identify 8720 PTVs, of which 77% are novel, and estimate 88% of all PTVs are deleterious and subject to purifying selection. Furthermore, we show that individuals with psoriasis have a significantly higher burden of PTVs compared to controls (P = 0.02). Finally, we identified 18 PTVs in 14 genes with unusually high levels of population differentiation, consistent with the action of local adaptation. Our study provides insights into patterns and consequences of PTVs.

Fast Numerical Optimization for Genome Sequencing Data in Population Biobanks

MOTIVATION

Large-scale and high-dimensional genome sequencing data poses computational challenges. General purpose optimization tools are usually not optimal in terms of computational and memory performance for genetic data.

RESULTS

We develop two efficient solvers for optimization problems arising from large-scale regularized regressions on millions of genetic variants sequenced from hundreds of thousands of individuals. These genetic variants are encoded by the values in the set {0, 1, 2, NA}. We take advantage of this fact and use two bits to represent each entry in a genetic matrix, which reduces memory requirement by a factor of 32 compared to a double precision floating point representation. Using this representation, we implemented an iteratively reweighted least square algorithm to solve Lasso regressions on genetic matrices, which we name snpnet-2.0. When the dataset contains many rare variants, the predictors can be encoded in a sparse matrix. We utilize the sparsity in the predictor matrix to further reduce memory requirement and computational speed. Our sparse genetic matrix implementation uses both the compact 2-bit representation and a simplified version of compressed sparse block format so that matrix-vector multiplications can be effectively parallelized on multiple CPU cores. To demonstrate the effectiveness of this representation, we implement an accelerated proximal gradient method to solve group Lasso on these sparse genetic matrices. This solver is named sparse-snpnet, and will also be included as part of snpnet R package. Our implementation is able to solve Lasso and group Lasso, linear, logistic and Cox regression problems on sparse genetic matrices that contain 1,000,000 variants and almost 100,000 individuals within 10 minutes and using less than 32GB of memory.

AVAILABILITY

https://github.com/rivas-lab/snpnet/tree/compact.

Mapping the 17q12-21.1 Locus for Variants Associated with Early-Onset Asthma in African Americans

Rationale: The 17q12-21.1 locus is one of the most highly replicated genetic associations with asthma. Individuals of African descent have lower linkage disequilibrium in this region, which could facilitate identifying causal variants.Objectives: To identify functional variants at 17q12-21.1 associated with early-onset asthma among African American individuals.Methods: We evaluated African American participants from SAPPHIRE (Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity) (n = 1,940), SAGE II (Study of African Americans, Asthma, Genes and Environment) (n = 885), and GCPD-A (Study of the Genetic Causes of Complex Pediatric Disorders-Asthma) (n = 2,805). Associations with asthma onset at ages under 5 years were meta-analyzed across cohorts. The lead signal was reevaluated considering haplotypes informed by genetic ancestry (i.e., African vs. European). Both an expression-quantitative trait locus analysis and a phenome-wide association study were performed on the lead variant.Measurements and Main Results: The meta-analyzed results from SAPPHIRE, SAGE II, and the GCPD-A identified rs11078928 as the top association for early-onset asthma. A haplotype analysis suggested that the asthma association partitioned most closely with the rs11078928 genotype. Genetic ancestry did not appear to influence the effect of this variant. In the expression-quantitative trait locus analysis, rs11078928 was related to alternative splicing of GSDMB (gasdermin-B) transcripts. The phenome-wide association study of rs11078928 suggested that this variant was predominantly associated with asthma and asthma-associated symptoms.Conclusions: A splice-acceptor polymorphism appears to be a causal variant for asthma at the 17q12-21.1 locus. This variant appears to have the same magnitude of effect in individuals of African and European descent.

Survival Analysis on Rare Events Using Group-Regularized Multi-Response Cox Regression

MOTIVATION

The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data.

RESULTS

We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank (Sudlow et al., 2015) dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. (2020).

AVAILABILITY

https://github.com/rivas-lab/multisnpnet-Cox.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Polygenic risk modeling with latent trait-related genetic components

Polygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual's genetic risk for a trait across DeGAs components, with examples for body mass index (BMI) and myocardial infarction (heart attack) in 337,151 white British individuals in the UK Biobank, with replication in a further set of 25,486 non-British white individuals. We find that BMI polygenic risk factorizes into components related to fat-free mass, fat mass, and overall health indicators like physical activity. Most individuals with high dPRS for BMI have strong contributions from both a fat-mass component and a fat-free mass component, whereas a few "outlier" individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.

Genetics of 35 blood and urine biomarkers in the UK Biobank

Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.

Dissemination of Mycobacterium tuberculosis is associated to a SIGLEC1 null variant that limits antigen exchange via trafficking extracellular vesicles

The identification of individuals with null alleles enables studying how the loss of gene function affects infection. We previously described a non-functional variant in SIGLEC1, which encodes the myeloid-cell receptor Siglec-1/CD169 implicated in HIV-1 cell-to-cell transmission. Here we report a significant association between the SIGLEC1 null variant and extrapulmonary dissemination of Mycobacterium tuberculosis (Mtb) in two clinical cohorts comprising 6,256 individuals. Local spread of bacteria within the lung is apparent in Mtb-infected Siglec-1 knockout mice which, despite having similar bacterial load, developed more extensive lesions compared to wild type mice. We find that Siglec-1 is necessary to induce antigen presentation through extracellular vesicle uptake. We postulate that lack of Siglec-1 delays the onset of protective immunity against Mtb by limiting antigen exchange via extracellular vesicles, allowing for an early local spread of mycobacteria that increases the risk for extrapulmonary dissemination.

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks

Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.

Rare variant contribution to human disease in 281,104 UK Biobank exomes

Genome-wide association studies have uncovered thousands of common variants associated with human disease, but the contribution of rare variants to common disease remains relatively unexplored. The UK Biobank contains detailed phenotypic data linked to medical records for approximately 500,000 participants, offering an unprecedented opportunity to evaluate the effect of rare variation on a broad collection of traits^1,2. Here we study the relationships between rare protein-coding variants and 17,361 binary and 1,419 quantitative phenotypes using exome sequencing data from 269,171 UK Biobank participants of European ancestry. Gene-based collapsing analyses revealed 1,703 statistically significant gene-phenotype associations for binary traits, with a median odds ratio of 12.4. Furthermore, 83% of these associations were undetectable via single-variant association tests, emphasizing the power of gene-based collapsing analysis in the setting of high allelic heterogeneity. Gene-phenotype associations were also significantly enriched for loss-of-function-mediated traits and approved drug targets. Finally, we performed ancestry-specific and pan-ancestry collapsing analyses using exome sequencing data from 11,933 UK Biobank participants of African, East Asian or South Asian ancestry. Our results highlight a significant contribution of rare variants to common disease. Summary statistics are publicly available through an interactive portal ( http://azphewas.com/ ).

Unique roles of rare variants in the genetics of complex diseases in humans

Genome-wide association studies have identified >10,000 genetic variants associated with various phenotypes and diseases. Although the majority are common variants, rare variants with >0.1% of minor allele frequency have been investigated by imputation and using disease-specific custom SNP arrays. Rare variants sequencing analysis mainly revealed have played unique roles in the genetics of complex diseases in humans due to their distinctive features, in contrast to common variants. Unique roles are hypothesis-free evidence for gene causality, a precise target of functional analysis for understanding disease mechanisms, a new favorable target for drug development, and a genetic marker with high disease risk for personalized medicine. As whole-genome sequencing continues to identify more rare variants, the roles associated with rare variants will also increase. However, a better estimation of the functional impact of rare variants across whole genome is needed to enhance their contribution to improvements in human health.

A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population

The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a single phenotype of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore the phenotypic expressivity of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population.

Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, NOTCH2 FBN1, PTPN1 and RAS-opathy genes, and genes in the 22q11.2 locus) and performed a gene burden test.

Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome.

Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population.

Standard medical evaluation of genetic syndromes relies upon recognizing a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. This may lead to missing diagnoses in patients with silent or a low expressed form of the syndrome. Here we take advantage of a rich electronic health record, various phenotypic measurements, and genetic information in 337,198 unrelated white British from the UKBB, to study the relation between single syndromic disease phenotypes and genes related to syndromic disease. We show multiple phenotype-genotype associations in concordance with phenotypes variations found in syndromic diseases. For example, we show that a commonly found variant in FBN1 was associated with high standing/sitting height ratio and reduced body fat percentage as observed in individuals with Marfan syndrome. Our findings suggest that common and rare alleles in syndromic disease genes are causative of individual component phenotypes present in a general population; further research is needed to characterize the pleiotropic effect of alleles in syndromic genes in persons without the syndromic disease.

A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank

The UK Biobank is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with genome-wide association studies (GWAS), have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso, since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large-scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet and optimizes for single nucleotide polymorphism (SNP) datasets. It currently supports ℓ1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with ℓ1/ℓ2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve competitive predictive performance for all four phenotypes considered (height, body mass index, asthma, high cholesterol) using only a small fraction of the variants compared with other established polygenic risk score methods.

The support of genetic evidence for cardiovascular risk induced by antineoplastic drugs

Cardiovascular dysfunction is one of the most common complications of long-term cancer treatment. Growing evidence has shown that antineoplastic drugs can increase cardiovascular risk during cancer therapy, seriously affecting patient survival. However, little is known about the genetic factors associated with the cardiovascular risk of antineoplastic drugs. We established a compendium of genetic evidence that supports cardiovascular risk induced by antineoplastic drugs. Most of this genetic evidence is attributed to causal alleles altering the expression of cardiovascular disease genes. We found that antineoplastic drugs predicted to induce cardiovascular risk are significantly enriched in drugs associated with cardiovascular adverse reactions, including many first-line cancer treatments. Functional experiments validated that retinoid X receptor agonists can reduce triglyceride lipolysis, thus modulating cardiovascular risk. Our results establish a link between the causal allele of cardiovascular disease genes and the direction of pharmacological modulation, which could facilitate cancer drug discovery and clinical trial design.

Statistical approaches for meta-analysis of genetic mutation prevalence

Estimating the prevalence of rare germline genetic mutations in the general population is of interest as it can inform genetic counseling and risk management. Most studies that estimate the prevalence of mutations are performed in high-risk populations, and each study is designed with differing inclusion criteria, resulting in ascertained populations. Quantifying the effects of ascertainment is necessary to estimate the prevalence in the general population. This quantification is difficult as the inclusion criteria is often based on disease status and/or family history. Combining estimates from multiple studies through a meta-analysis is challenging due to the variety of study designs and ascertainment mechanisms as well as the complexity of quantifying the effect of these mechanisms. We provide guidelines on how to quantify the ascertainment mechanism for a wide range of settings and propose a general approach for conducting a meta-analysis in these complex settings by incorporating study-specific ascertainment mechanisms into a joint likelihood function. We implement the proposed likelihood-based approach using both frequentist and Bayesian methodologies. We evaluate these approaches in simulations and show that the methods are robust and produce unbiased estimates of the prevalence. An advantage of the Bayesian approach is that it can easily incorporate uncertainty in ascertainment probability values. We apply our methods to estimate the prevalence of PALB2 mutations in the United States by combining data from multiple studies and obtain a prevalence estimate of around 0.02%.

Sex-specific genetic effects across biomarkers

Sex differences have been shown in laboratory biomarkers; however, the extent to which this is due to genetics is unknown. In this study, we infer sex-specific genetic parameters (heritability and genetic correlation) across 33 quantitative biomarker traits in 181,064 females and 156,135 males from the UK Biobank study. We apply a Bayesian Mixture Model, Sex Effects Mixture Model (SEMM), to Genome-wide Association Study summary statistics in order to (1) estimate the contributions of sex to the genetic variance of these biomarkers and (2) identify variants whose statistical association with these traits is sex-specific. We find that the genetics of most biomarker traits are shared between males and females, with the notable exception of testosterone, where we identify 119 female and 445 male-specific variants. These include protein-altering variants in steroid hormone production genes (POR, UGT2B7). Using the sex-specific variants as genetic instruments for Mendelian randomization, we find evidence for causal links between testosterone levels and height, body mass index, waist and hip circumference, and type 2 diabetes. We also show that sex-specific polygenic risk score models for testosterone outperform a combined model. Overall, these results demonstrate that while sex has a limited role in the genetics of most biomarker traits, sex plays an important role in testosterone genetics.

Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks

Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named ‘core genes’, while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer’s Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets—consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer–where we identify 109 candidate core genes.

A recent theory suggests that only a small number of genes underpin the biology of a disease, these genes are called ‘core genes’, and for most diseases, these core genes remain unknown. The suggested methods for finding them requires complex and expensive experiments. We reasoned that if we merge currently available datasets in smart ways, we may be able to uncover these ‘core genes’. Our method finds “hub” proteins by merging lists of genes previously linked with disease to information on how proteins interact with each other. We found that many of these hub proteins have central roles in disease, such as insulin for both A1C measurement and Type 2 Diabetes, BRCA1 in Breast cancer, and Amyloid Precursor Protein in Alzheimer’s Disease. We think these ‘hub’ proteins are candidate ‘core genes’, and offer our method as a way to find ‘core genes’ by utilizing publicly available reference datasets.

Sex differences in the genetic architecture of depression

The prevalence and clinical characteristics of depressive disorders differ between women and men; however, the genetic contribution to sex differences in depressive disorders has not been elucidated. To evaluate sex-specific differences in the genetic architecture of depression, whole exome sequencing of samples from 1000 patients (70.7% female) with depressive disorder was conducted. Control data from healthy individuals with no psychiatric disorder (n = 72, 26.4% female) and East-Asian subpopulation 1000 Genome Project data (n = 207, 50.7% female) were included. The genetic variation between men and women was directly compared using both qualitative and quantitative research designs. Qualitative analysis identified five genetic markers potentially associated with increased risk of depressive disorder in females, including three variants (rs201432982 within PDE4A, and rs62640397 and rs79442975 within FDX1L) mapping to chromosome 19p13.2 and two novel variants (rs820182 and rs820148) within MYO15B at the chromosome 17p25.1 locus. Depressed patients homozygous for these variants showed more severe depressive symptoms and higher suicidality than those who were not homozygotes (i.e., heterozygotes and homozygotes for the non-associated allele). Quantitative analysis demonstrated that the genetic burden of protein-truncating and deleterious variants was higher in males than females, even after permutation testing. Our study provides novel genetic evidence that the higher prevalence of depressive disorders in women may be attributable to inherited variants.

Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases

Population-scale biobanks that combine genetic data and high-dimensional phenotyping for a large number of participants provide an exciting opportunity to perform genome-wide association studies (GWAS) to identify genetic variants associated with diverse quantitative traits and diseases. A major challenge for GWAS in population biobanks is ascertaining disease cases from heterogeneous data sources such as hospital records, digital questionnaire responses, or interviews. In this study, we use genetic parameters, including genetic correlation, to evaluate whether GWAS performed using cases in the UK Biobank ascertained from hospital records, questionnaire responses, and family history of disease implicate similar disease genetics across a range of effect sizes. We find that hospital record and questionnaire GWAS largely identify similar genetic effects for many complex phenotypes and that combining together both phenotyping methods improves power to detect genetic associations. We also show that family history GWAS using cases ascertained on family history of disease agrees with combined hospital record and questionnaire GWAS and that family history GWAS has better power to detect genetic associations for some phenotypes. Overall, this work demonstrates that digital phenotyping and unstructured phenotype data can be combined with structured data such as hospital records to identify cases for GWAS in biobanks and improve the ability of such studies to identify genetic associations.

Evaluating drug targets through human loss-of-function genetic variation

Naturally occurring human genetic variants that are predicted to inactivate protein-coding genes provide an in vivo model of human gene inactivation that complements knockout studies in cells and model organisms. Here we report three key findings regarding the assessment of candidate drug targets using human loss-of-function variants. First, even essential genes, in which loss-of-function variants are not tolerated, can be highly successful as targets of inhibitory drugs. Second, in most genes, loss-of-function variants are sufficiently rare that genotype-based ascertainment of homozygous or compound heterozygous 'knockout' humans will await sample sizes that are approximately 1,000 times those presently available, unless recruitment focuses on consanguineous individuals. Third, automated variant annotation and filtering are powerful, but manual curation remains crucial for removing artefacts, and is a prerequisite for recall-by-genotype efforts. Our results provide a roadmap for human knockout studies and should guide the interpretation of loss-of-function variants in drug development.

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.