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As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1–3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

PurposeIn the United States, breast cancer clinical risk assessments are inconsistent and inequitable. The previous success ofBRCA1andBRCA2screening has demonstrated that genetics could be used to reduce these inequities, if they are available and accessible across the population. The aim of this study is to evaluate the performance of different genetic screening approaches to identify women at high-risk of breast cancer in the general population.MethodsWe did a retrospective study on 25,591 women participating in the Healthy Nevada Project. Electronic Health Records (EHR) data were used to identify women with a family history of, and women who were diagnosed with breast cancer. The genetic analysis assessed the role of rare predicted loss-of-function (pLOF) variants inBRCA1,BRCA2,PALB2,ATMandCHEK2, as well as the combined role of common variants via a polygenic risk score. Women were considered at high-risk of breast cancer if they had greater than 20% probability of being diagnosed with breast cancer by age 70.ResultsFamily history of breast cancer (FHx-BrCa) was ascertained on or after the record of breast cancer for 78% of women with both, indicating that this method for risk assessment is not being properly utilized for early screening. Genetics offered an alternative method for risk assessment. 11.4% of women in HNP were at high-risk of breast cancer based on their genetic risk: having a pLOF variant inBRCA1,BRCA2orPALB2(hazard ratio = 10.4, 95% confidence interval: 8.1-13.5), or a pLOF variant inATMorCHEK2(HR = 3.4, CI: 2.4-4.8), or being in the top 10% of the polygenic risk score distribution (HR = 2.4, CI: 2.0-2.8). We also showed that combining PRS with pLOF inATMandCHEK2allowed a better identification of participants with high risk while minimizing false positives. Women with a pLOF inATMorCHEK2and in the top 50% of the PRS are at high risk (39.2% probability of breast cancer at age 70), while those with a pLOF inATMorCHEK2and in the bottom 50% of the PRS are not at high risk (14.4% probability of breast cancer at age 70).ConclusionThese results suggest that a combined monogenic and polygenic approach may best capture the inherited risk for breast cancer across the population.

Purpose To identify conditions that are candidates for population genetic screening based on population prevalence, penetrance of rare variants, and actionability. Methods We analyzed exome and medical record data from >220,000 participants across two large population health cohorts with different demographics. We performed a gene-based collapsing analysis of rare variants to identify genes significantly associated with disease status. Results We identify 74 statistically significant gene-disease associations across 27 genes. Seven of these conditions have a positive predictive value (PPV) of at least 30% in both cohorts. Three are already used in population screening programs (BRCA1, BRCA2, LDLR), and we also identify four new candidates for population screening: GCK with diabetes mellitus, HBB with β-thalassemia minor and intermedia, PKD1 with cystic kidney disease, and MIP with cataracts. Importantly, the associations are actionable in that early genetic screening of each of these conditions is expected to improve outcomes. Conclusion We identify seven genetic conditions where rare variation appears appropriate to assess in population screening, four of which are not yet used in screening programs. The addition of GCK, HBB, PKD1, and MIP rare variants into genetic screening programs would reach an additional 0.21% of participants with actionable disease risk, depending on the population.

Systematic determination of rare and novel variant pathogenicity remains a major challenge, even when there is an established association between a gene and phenotype. Here we present Power Window (PW), a novel sliding window technique that identifies the clinically impactful regions of a gene using population-scale clinico-genomic datasets. By sizing windows based on the number of variant carriers, rather than the number of variants or nucleotides, statistical power is held constant during analysis, enabling the localization of clinical impact as well as the removal of unassociated gene regions. This method can be used to focus on: specific variant types such as loss of function (LoF) or other coding; parts of a gene, such as those expressed in different tissues; or isolating gene regions with opposite directions of effect. Using a training set of 300K exomes from the UKBiobank (UKB), we developed PW-based LoF and coding models for well-established gene-disease associations and tested their accuracy in two additional cohorts (128k exomes from the UKB and 30k exomes from the Healthy Nevada Project (HNP)). The significant PW models retained a mean of 64% of the rare variant carriers in each gene (range 16-98%), with quantitative traits showing a mean effect size improvement of 48% compared to aggregating rare variants across the entire gene, and the odds ratios for binary traits improving by a mean of 2.4-fold. PW showcases that EHR-based statistical analyses can accurately distinguish between novel coding variants that will have high phenotypic penetrance in a population and those that will not, unlocking new potential for population genetic screening.

BackgroundTruncating variants in TTN (TTNtvs) represent the largest known genetic cause of dilated cardiomyopathies (DCM). At the population level, even when limited to TTNtvs in cardiac-specific exons (hiPSI TTNtvs) penetrance estimates for DCM are low. Recent work shows that individuals harboring TTNtvs have a high prevalence of other cardiac conditions aside from heart failure, in particular, atrial fibrillation (Afib).ObjectivesPinpoint the genetic footprint TTN-related diagnoses aside from DCM, such as Afib, and determine if vetting additional significantly-associated phenotypes better stratifies cardiomyopathy risk across TTN carriers.MethodsWe leverage longitudinal EHR and exome sequencing data from two cohorts to determine the penetrance of TTNtvs using multiple gene expression models against Afib, CM, and other cardiac diagnoses.ResultsControlling for CM and Afib, related cardio phenotypes retain only nominal association with TTNtvs. An unbiased sliding window analysis of TTNtvs across the locus confirms the association is specific to hiPSI exons for both CM and Afib, with no meaningful associations in lowPSI exons nor improvements from LOFTEE designations. We find 34% of hiPSI TTNtv carriers with early Afib have a CM diagnosis – a 5-fold increase in risk over non-carriers with early Afib and 47-fold increase over population controls.ConclusionCM and Afib are often coincident in hiPSI TTNtv carriers, which represent varying and progressive manifestations of structurally-based heart failure. We provide statistical support for a hiPSI variant interpretation model for TTNtvs and evidence for the first population-level screening method with clinical utility for cardiomyopathies, especially in relation to an Afib finding.

Between November 2021 and February 2022, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta and Omicron variants co-circulated in the United States, allowing for co-infections and possible recombination events.We sequenced 29,719 positive samples during this period and analyzed the presence and fraction of reads supporting mutations specific to either the Delta or Omicron variant.We identified 18 co-infections, one of which displayed evidence of a low Delta-Omicron recombinant viral population. We also identified two independent cases of infection by a Delta-Omicron recombinant virus, where 100% of the viral RNA came from one clonal recombinant. In the three cases, the 5' end of the viral genome was from the Delta genome and the 3' end from Omicron, including the majority of the spike protein gene, though the breakpoints were different.Delta-Omicron recombinant viruses were rare, and there is currently no evidence that Delta-Omicron recombinant viruses are more transmissible between hosts compared with the circulating Omicron lineages.This research was supported by the NIH RADx initiative and by the Centers for Disease Control Contract 75D30121C12730 (Helix).

International audience; Genome-wide association studies have identified 240 independent loci associated with type 2 diabetes (T2D) risk, but this knowledge has not advanced precision medicine. In contrast, the genetic diagnosis of monogenic forms of diabetes (including maturity-onset diabetes of the young (MODY)) are textbook cases of genomic medicine. Recent studies trying to bridge the gap between monogenic diabetes and T2D have been inconclusive. Here, we show a significant burden of pathogenic variants in genes linked with monogenic diabetes among people with common T2D, particularly in actionable MODY genes, thus implying that there should be a substantial change in care for carriers with T2D. We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8. The carriers with T2D are leaner, which evidences a functional metabolic effect of these mutations. Pathogenic variants in actionable MODY genes are more frequent than was previously expected in common T2D. These results open avenues for future interventions assessing the clinical interest of these pathogenic mutations in precision medicine.

Significance To understand the value and clinical impact of surveying genome-wide disease-causing genes and variants, we used a prospective cohort study design that enrolled volunteers who agreed to have their whole genome sequenced and to participate in deep phenotyping using clinical laboratory tests, metabolomics technologies, and advanced noninvasive imaging. The genomic results are integrated with the phenotype results. Approximately 1 in 6 adult individuals (17.3%) had genetic findings and, when integrated with deep phenotyping data, including family/medical histories with genetic findings, 1 in 9 (11.5%) had genotype and phenotype associations. Genomics and metabolomics association analysis revealed 5.1% of heterozygotes with phenotype manifestations affecting serum metabolite levels. We report observations from our study in which health outcomes and benefits were not measured., Genome sequencing has established clinical utility for rare disease diagnosis. While increasing numbers of individuals have undergone elective genome sequencing, a comprehensive study surveying genome-wide disease-associated genes in adults with deep phenotyping has not been reported. Here we report the results of a 3-y precision medicine study with a goal to integrate whole-genome sequencing with deep phenotyping. A cohort of 1,190 adult participants (402 female [33.8%]; mean age, 54 y [range 20 to 89+]; 70.6% European) had whole-genome sequencing, and were deeply phenotyped using metabolomics, advanced imaging, and clinical laboratory tests in addition to family/medical history. Of 1,190 adults, 206 (17.3%) had at least 1 genetic variant with pathogenic (P) or likely pathogenic (LP) assessment that suggests a predisposition of genetic risk. A multidisciplinary clinical team reviewed all reportable findings for the assessment of genotype and phenotype associations, and 137 (11.5%) had genotype and phenotype associations. A high percentage of genotype and phenotype associations (>75%) was observed for dyslipidemia (n = 24), cardiomyopathy, arrhythmia, and other cardiac diseases (n = 42), and diabetes and endocrine diseases (n = 17). A lack of genotype and phenotype associations, a potential burden for patient care, was observed in 69 (5.8%) individuals with P/LP variants. Genomics and metabolomics associations identified 61 (5.1%) heterozygotes with phenotype manifestations affecting serum metabolite levels in amino acid, lipid and cofactor, and vitamin pathways. Our descriptive analysis provides results on the integration of whole-genome sequencing and deep phenotyping for clinical assessments in adults.

Understanding the impact of rare variants is essential to understanding human health. We analyze rare (MAF, Population-based association analyses of rare genetic variants with complex traits are limited by the availability of data from sufficiently large cohorts. Here, Cirulli et al. report gene-based collapsing analysis of exomes from 49,960 participants of the UK Biobank and 21,866 participants of the Healthy Nevada Project over a total of 4377 traits.

SummaryAs SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

COVID-19 vaccines are safe and highly effective, but some individuals experience unpleasant reactions to vaccination. As the majority of adults in the US have received a COVID-19 vaccine this year, there is an unprecedented opportunity to study the genetics of reactions to vaccination via surveys of individuals who are already part of genetic research studies. Here, we have queried 17,440 participants in the Helix DNA Discovery Project and Healthy Nevada Project about their reactions to COVID-19 vaccination. Our GWAS identifies an association between severe difficulties with daily routine after vaccination and HLA-A*03:01. This association was statistically significant only for those who received the Pfizer-BioNTech vaccine (BNT162b2; p=4.70E-11), but showed a trending association in those who received the Moderna vaccine (mRNA-1273; p=0.005) despite similar sample sizes for study. In Pfizer-BioNTech recipients, HLA-A*03:01 was associated with a two-fold increase in risk of severe vaccine reactions. The effect was consistent across ages, sexes, and whether the person had previously had a COVID-19 infection. The reactions experienced by HLA-A*03:01 carriers were driven by associations with chills, fever, fatigue, and in general feeling unwell.

This study reports on the displacement of Alpha (B.1.1.7) by Delta (B.1.617.2 and its substrains AY.1, AY.2, and AY.3) in the United States. By analyzing RT-qPCR testing results and viral sequencing results of samples collected across the United States, we show that the percentage of SARS-CoV-2 positive cases caused by Alpha dropped from 67% in May 2021 to less than 3.0% in just 10 weeks. We also show that the Delta variant has outcompeted the Iota (B.1.526) variant of interest and Gamma (P.1) variant of concern. An analysis of the mean quantification cycles (Cq) values in positive tests over time also reveal that Delta infections lead to a higher viral load on average compared to Alpha infections, but this increase is only 2 to 3x on average for our study design. Our results are consistent with the hypothesis that the Delta variant is more transmissible than the Alpha variant, and that this could be due to the Delta variant's ability to establish a higher viral load earlier in the infection compared to the Alpha variant.

Objectives The aim of this study was to provide an overview of the presenting features, etiologies, and outcomes of children enrolled in the Drug-induced Liver Injury Network (DILIN) prospective and retrospective studies. Methods Consecutive definite, highly likely, or probable cases in children enrolled into the ongoing DILIN prospective and retrospective studies between September 2004 and February 2017 were reviewed. Results Fifty-seven cases were adjudicated as definite (14), highly likely (30), or probable (13) DILI. Median age was 14.3 years (1.7-17.9), 67% female, and 82% Caucasian. At DILI onset, 82% had hepatocellular injury with a median alanine aminotransferase of 411 U/L (33-4185), alkaline phosphatase 203 U/L (62-1177), and total bilirubin 3.3 mg/dL (0.2-33.9). The median duration of suspect medication use was 55 days (1-2789) and the most frequently implicated individual agents were minocycline (n = 11) and valproate (n = 6). Sixty-three percent were hospitalized and 3 (5%) underwent liver transplant within 1 month of DILI onset. Among 46 children followed for at least 6 months, 8 (17%) met criteria for chronic DILI with 6 of them having persistent liver injury at 24 months of follow-up. A genome-wide association study in 39 Caucasian children focusing on regions associated with pediatric cholestatic liver disease failed to demonstrate any single nucleotide polymorphism associated with DILI susceptibility or outcome. Conclusions Antimicrobials (51%) and antiepileptic drugs (21%) are the most frequently implicated agents in pediatric DILI patients. Although the majority of cases are self-limited, there is potential for serious morbidity including acute liver failure, chronic liver injury, and death.

Large-scale sequencing efforts in amyotrophic lateral sclerosis (ALS) have implicated novel genes using gene-based collapsing methods. However, pathogenic mutations may be concentrated in specific genic regions. To address this, we developed two collapsing strategies: One focuses rare variation collapsing on homology-based protein domains as the unit for collapsing, and the other is a gene-level approach that, unlike standard methods, leverages existing evidence of purifying selection against missense variation on said domains. The application of these two collapsing methods to 3093 ALS cases and 8186 controls of European ancestry, and also 3239 cases and 11,808 controls of diversified populations, pinpoints risk regions of ALS genes, including SOD1, NEK1, TARDBP, and FUS. While not clearly implicating novel ALS genes, the new analyses not only pinpoint risk regions in known genes but also highlight candidate genes as well.

The highly transmissible B.1.1.7 variant of SARS-CoV-2, first identified in the United Kingdom, has gained a foothold across the world. Using S gene target failure (SGTF) and SARS-CoV-2 genomic sequencing, we investigated the prevalence and dynamics of this variant in the United States (U.S.), tracking it back to its early emergence. We found that while the fraction of B.1.1.7 varied by state, the variant increased at a logistic rate with a roughly weekly doubling rate and an increased transmission of 40-50%. We revealed several independent introductions of B.1.1.7 into the U.S. as early as late November 2020, with community transmission spreading it to most states within months. We show that the U.S. is on a similar trajectory as other countries where B.1.1.7 became dominant, requiring immediate and decisive action to minimize COVID-19 morbidity and mortality., Genomic epidemiology analyses explain the introduction and transmission of the B.1.1.7 variant of SARS-CoV-2 into the United States, with projections for it to soon be the dominant strain in the country.

Author(s): Washington, Nicole L; Gangavarapu, Karthik; Zeller, Mark; Bolze, Alexandre; Cirulli, Elizabeth T; Schiabor Barrett, Kelly M; Larsen, Brendan B; Anderson, Catelyn; White, Simon; Cassens, Tyler; Jacobs, Sharoni; Levan, Geraint; Nguyen, Jason; Ramirez, Jimmy M; Rivera-Garcia, Charlotte; Sandoval, Efren; Wang, Xueqing; Wong, David; Spencer, Emily; Robles-Sikisaka, Refugio; Kurzban, Ezra; Hughes, Laura D; Deng, Xianding; Wang, Candace; Servellita, Venice; Valentine, Holly; De Hoff, Peter; Seaver, Phoebe; Sathe, Shashank; Gietzen, Kimberly; Sickler, Brad; Antico, Jay; Hoon, Kelly; Liu, Jingtao; Harding, Aaron; Bakhtar, Omid; Basler, Tracy; Austin, Brett; Isaksson, Magnus; Febbo, Phil; Becker, David; Laurent, Marc; McDonald, Eric; Yeo, Gene W; Knight, Rob; Laurent, Louise C; de Feo, Eileen; Worobey, Michael; Chiu, Charles; Suchard, Marc A; Lu, James T; Lee, William; Andersen, Kristian G | Abstract: As of January of 2021, the highly transmissible B.1.1.7 variant of SARS-CoV-2, which was first identified in the United Kingdom (U.K.), has gained a strong foothold across the world. Because of the sudden and rapid rise of B.1.1.7, we investigated the prevalence and growth dynamics of this variant in the United States (U.S.), tracking it back to its early emergence and onward local transmission. We found that the RT-qPCR testing anomaly of S gene target failure (SGTF), first observed in the U.K., was a reliable proxy for B.1.1.7 detection. We sequenced 212 B.1.1.7 SARS-CoV-2 genomes collected from testing facilities in the U.S. from December 2020 to January 2021. We found that while the fraction of B.1.1.7 among SGTF samples varied by state, detection of the variant increased at a logistic rate similar to those observed elsewhere, with a doubling rate of a little over a week and an increased transmission rate of 35-45%. By performing time-aware Bayesian phylodynamic analyses, we revealed several independent introductions of B.1.1.7 into the U.S. as early as late November 2020, with onward community transmission enabling the variant to spread to at least 30 states as of January 2021. Our study shows that the U.S. is on a similar trajectory as other countries where B.1.1.7 rapidly became the dominant SARS-CoV-2 variant, requiring immediate and decisive action to minimize COVID-19 morbidity and mortality.

We report on the sequencing of 74,348 SARS-CoV-2 positive samples collected across the United States and show that the Delta variant, first detected in the United States in March 2021, made up the majority of SARS-CoV-2 infections by July 1, 2021 and accounted for99.9% of the infections by September 2021. Not only did Delta displace variant Alpha, which was the dominant variant at the time, it also displaced the Gamma, Iota, and Mu variants. Through an analysis of quantification cycle (Cq) values, we demonstrate that Delta infections tend to have a 1.7× higher viral load compared to Alpha infections (a decrease of 0.8 Cq) on average. Our results are consistent with the hypothesis that the increased transmissibility of the Delta variant could be due to the ability of the Delta variant to establish a higher viral load earlier in the infection as compared to the Alpha variant.

Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.

Recently, multiple novel strains of SARS-CoV-2 have been found to share the same deletion of amino acids H69 and V70 in the virus S gene. This includes strain B.1.1.7 / SARS-CoV-2 VUI 202012/01, which has been found to be more infectious than other strains of SARS-CoV-2, and its increasing presence has resulted in new lockdowns in and travel restrictions leaving the UK. Here, we analyze 2 million RT-PCR SARS-CoV-2 tests performed at Helix to identify the rate of S gene dropout, which has been recently shown to occur in tests from individuals infected with strains of SARS-CoV-2 that carry the H69del/V70del mutation. We observe a rise in S gene dropout in the US starting in early October, with 0.25% of our daily SARS-CoV-2-positive tests exhibiting this pattern during the first week. The rate of positive samples with S gene dropout has grown slowly over time, with last week exhibiting the highest level yet, at 0.5%. Focusing on the 14 states for which we have sufficient sample size to assess the frequency of this rare event (n>1000 SARS-CoV-2-positive samples), we see a recent expansion in the Eastern part of the US, concentrated in MA, OH, and FL. However, we cannot say from these data whether the S gene dropout samples we observe here represent the B.1.1.7. strain. Only with an expansion of genomic surveillance sequencing in the US will we know for certain the prevalence of the B.1.1.7 strain in the US.

It is increasingly recognized that SARS-CoV-2 can produce long-term complications after recovery from the acute effects of infection. Here, we report the analysis of 32 self-reported short and long-term symptoms in a general adult population cohort comprised of 357 COVID-19+ cases, 5,497 SARS-CoV-2-negative controls, and 19,095 non-tested individuals. The majority of our COVID-19+ cases are mild, with only 9 of the 357 COVID-19+ cases having been hospitalized. Our results show that 36.1% of COVID-19+ cases have symptoms lasting longer than 30 days, and 14.8% still have at least one symptom after 90 days. These numbers are higher for COVID-19+ cases who were initially more ill, 44.9% at 30 days and 20.8% at 90 days, but even for very mild and initially asymptomatic cases, 21.3% have complications persist for 30 days or longer. In contrast, only 8.4% of participants from the general untested population develop new symptoms lasting longer than 30 days due to any illness during the same study period. The long-term symptoms most enriched in those with COVID-19 are anosmia, ageusia, difficulty concentrating, dyspnea, memory loss, confusion, chest pain, and pain with deep breaths. In addition to individuals who are initially more sick having more long-term symptoms, we additionally observe that individuals who have an initial symptom of dyspnea are significantly more likely to develop long-term symptoms. Importantly, our study finds that the overall level of illness is an important variable to account for when assessing the statistical significance of symptoms that are associated with COVID-19. Our study provides a baseline from which to understand the frequency of COVID-19 long-term symptoms at the population level and demonstrates that, although those most likely to develop long-term COVID-19 complications are those who initially have more severe illness, even those with mild or asymptomatic courses of infection are at increased risk of long-term complications.

Background Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease signatures. Methods We collected 1385 data features from diverse modalities, including metabolome, microbiome, genetics, and advanced imaging, from 1253 individuals and from a longitudinal validation cohort of 1083 individuals. We utilized a combination of unsupervised machine learning methods to identify multimodal biomarker signatures of health and disease risk. Results Our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers. Stratification of individuals based on the signatures of these biomarkers identified distinct subsets of individuals with similar health statuses. Subset membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and body mass index. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We identified potential early signatures for hypertension and a poor metabolic health outcome. Additionally, we found novel associations between a uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family. Conclusions Our methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages—an essential step towards personalized, preventative health risk assessment.

Intelligence is highly heritable 1 and a major determinant of human health and well-being 2 . Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence 3-7 , but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

Pompe disease is a metabolic myopathy with a wide spectrum of clinical presentation. The gold-standard diagnostic test is acid alpha-glucosidase assay on skin fibroblasts, muscle or blood. Identification of two GAA pathogenic variants in-trans is confirmatory. Optimal effectiveness of enzyme replacement therapy hinges on early diagnosis, which is challenging in late-onset form of the disease due to non-specific presentation. Next-generation sequencing-based panels effectively facilitate diagnosis, but the sensitivity of whole-exome sequencing (WES) in detecting pathogenic GAA variants remains unknown. We analyzed WES data from 93 patients with confirmed Pompe disease and GAA genotypes based on PCR/Sanger sequencing. After ensuring that the common intronic variant c.-32-13T>G is not filtered out, whole-exome sequencing identified both GAA pathogenic variants in 77/93 (83%) patients. However, one variant was missed in 14/93 (15%), and both variants were missed in 2/93 (2%). One complex indel leading to a severe phenotype was incorrectly called a nonsynonymous substitution c.-32-13T>C due to misalignment. These results demonstrate that WES may fail to diagnose Pompe disease. Clinicians need to be aware of limitations of WES, and consider tests specific to Pompe disease when WES does not provide a diagnosis in patients with proximal myopathy, progressive respiratory failure or other subtle symptoms.

SummaryHigh quality population allele frequencies of DNA variants can be used to discover new biology, and study rare disorders. Here, we created a public catalog of mitochondrial DNA variants based on a population of 195,983 individuals. We focused on 3 criteria: (i) the population is not enriched for mitochondrial disorders, or other clinical phenotypes, (ii) all genomes are sequenced and analyzed in the same clinical laboratory, and (iii) both homoplasmic and heteroplasmic variants are reported. We found that 47% of the mitochondrial genome was invariant in this population, including large stretches in the 2 rRNA genes. This information could be used to annotate the mitochondrial genome in future studies. We also showed how to use this resource for the interpretation of pathogenic variants for rare mitochondrial disorders. For example, 42% of variants previously reported to be pathogenic for Leber Hereditary Optic Neuropathy (LHON) should be reclassified.

Liability to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published GWAS in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected (“Concordant”) direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants demonstrating the counterintuitive (“Discordant”) relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education and/or schizophrenia at p−8. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs, and many of these have been validated by larger, more recent single-phenotype GWAS. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms: early neurodevelopmental pathways that characterize concordant allelic variation, and adulthood synaptic pruning pathways that were linked to the paradoxical positive genetic association between education and schizophrenia. Further, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia, but also to the broader biological dimensions that are implicated in both general health outcomes and psychiatric illness.

Defining the effects that rare variants can have on human phenotypes is essential to advancing our understanding of human health and disease. Large-scale human genetic analyses have thus far focused on common variants, but the development of large cohorts of deeply phenotyped individuals with exome sequence data has now made comprehensive analyses of rare variants possible. We analyzed the effects of rare (MAFPCSK9 and APOB variation with LDL levels; we extend knowledge of variation in the TYRP1 gene, previously associated with blonde hair color only in Solomon Islanders to blonde hair color in individuals of European ancestry; we show that PAPPA, a gene in which common variants had previously associated with height via GWAS, contains rare variants that decrease height; and we make the novel discovery that STAB1 variation is associated with blood flow in the brain. Our results are available for download and interactive browsing in an app (https://ukb.research.helix.com). This comprehensive analysis of the effects of rare variants on human phenotypes marks one of the first steps in the next big phase of human genetics, where large, deeply phenotyped cohorts with next generation sequence data will elucidate the effects of rare variants.

Background Bodybuilding supplements can cause a profound cholestatic syndrome. Aim To describe the drug-Induced liver injury network's experience with liver injury due to bodybuilding supplements. Methods Liver injury pattern, severity and outcomes, potential genetic associations, and exposure to anabolic steroids by product analysis were analysed in prospectively enrolled subjects with bodybuilding supplement-induced liver injury with causality scores of probable or higher. Results Forty-four males (mean age 33 years) developed liver injury with a median latency of 73 days. Forty-one per cent presented with hepatocellular pattern of liver injury as defined by the R > 5 ([Fold elevation of ALT] ÷ [Fold elevation of Alk Phos] (mean, range = 6.4, 0.5-31.4, n = 42) despite all presenting with clinical features of cholestatic liver injury (100% with jaundice and 84% with pruritus). Liver biopsy (59% of subjects) demonstrated a mild hepatitis and profound cholestasis in most without bile duct injury, loss or fibrosis. Seventy-one per cent were hospitalised, and none died or required liver transplantation. In some, chemical analysis revealed anabolic steroid controlled substances not listed on the label. No enrichment of genetic variants associated with cholestatic syndromes was found, although mutations in ABCB11 (present in up to 20%) were significantly different than in ethnically matched controls. Conclusions Patients with bodybuilding supplements liver injury uniformly presented with cholestatic injury, which slowly resolved. The ingested products often contained anabolic steroids not identified on the label, and no enrichment in genetic variants was found, indicating a need for additional studies.

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article.

We report the results of a three-year precision medicine study that enrolled 1190 presumed healthy participants at a single research clinic. To enable a better assessment of disease risk and improve diagnosis, a precision health platform that integrates non-invasive functional measurements and clinical tests combined with whole genome sequencing (WGS) was developed. The platform included WGS, comprehensive quantitative non-contrast whole body (WB) and brain magnetic resonance imaging/angiography (MRI/MRA), computed tomography (CT) coronary artery calcium scoring, electrocardiogram, echocardiogram, continuous cardiac monitoring, clinical laboratory tests, and metabolomics. In our cohort, 24.3% had medically significant genetic findings (MSF) which may contribute to increased risk of disease. A total of 206 unique medically significant variants in 111 genes were identified, and forty individuals (3.4%) had more than one MSF. Phenotypic testing revealed: 34.2% of our cohort had a metabolomics profile suggestive of insulin resistance, 29.2% had elevated liver fat identified by MRI, 16.4% had clinically important cardiac structure or cardiac function abnormalities on cardiac MRI or ECHO, 8.8% had a high cardiovascular risk on CT coronary artery calcium scoring (Agatston calcium score > 400, Relative Risk of 7.2), 8.0% had arrhythmia found on continuous rhythm monitoring, 6.5% had cardiac conduction disorders found on EKG, 2% had previously undetected tumors detected by WB MRI, and 2.5% had previously undetected aneurysms detected by non-contrast MRI/MRA. Using family histories, personal histories, and test results, clinical and phenotypic findings were correlated with genomic findings in 130 study participants (63.1%) with high to moderate penetrance variants, suggesting the precision health platform improves the diagnostic process in asymptomatic individuals who were at risk. Cardiovascular and endocrine diseases achieved considerable clinical associations between MSFs and clinical phenotypes (89% and 72%, respectively). These findings demonstrate the value of integrating WGS and noninvasive clinical assessments for a rapid and integrated point-of-care clinical diagnosis of age-related diseases that contribute to premature mortality.

Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. We collected 1,385 data features from diverse modalities, including metabolome, microbiome, genetics and advanced imaging, from 1,253 individuals and from a longitudinal validation cohort of 1,083 individuals. We utilized an ensemble of unsupervised machine learning techniques to identify multimodal biomarker signatures of health and disease risk. In particular, our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers, which were used to cluster individuals into distinct health profiles. Cluster membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and BMI. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We also identified an early disease signature for hypertension, and individuals at-risk for a poor metabolic health outcome. We found novel associations between an uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family. Our methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages -- an essential step towards personalized, preventative health risk assessment.

Large-scale sequencing efforts in amyotrophic lateral sclerosis (ALS) have implicated novel genes using gene-based collapsing methods. However, pathogenic mutations may be concentrated in specific genic regions. To address this, we developed two collapsing strategies, one focuses rare variation collapsing on homology-based protein domains as the unit for collapsing and another gene-level approach that, unlike standard methods, leverages existing evidence of purifying selection against missense variation on said domains. The application of these two collapsing methods to 3,093 ALS cases and 8,186 controls of European ancestry, and also 3,239 cases and 11,808 controls of diversified populations, pinpoints risk regions of ALS genes including SOD1, NEK1, TARDBP and FUS. While not clearly implicating novel ALS genes, the new analyses not only pinpoint risk regions in known genes but also highlight candidate genes as well.

Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84–88) presented a critique of our recently published paper in Cell Reports entitled ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ (Lam et al., Cell Reports, Vol. 21, 2017, 2597–2613). Specifically, Hill offered several interrelated comments suggesting potential problems with our use of a new analytic method called Multi-Trait Analysis of GWAS (MTAG) (Turley et al., Nature Genetics, Vol. 50, 2018, 229–237). In this brief article, we respond to each of these concerns. Using empirical data, we conclude that our MTAG results do not suffer from ‘inflation in the FDR [false discovery rate]’, as suggested by Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84–88), and are not ‘more relevant to the genetic contributions to education than they are to the genetic contributions to intelligence’.

Background & Aims Terbinafine is an antifungal agent that has been associated with rare instances of hepatotoxicity. In this study we aimed to describe the presenting features and outcomes of patients with terbinafine hepatotoxicity and to investigate the role of human leukocyte antigen (HLA)-A*33:01. Methods Consecutive high causality cases of terbinafine hepatotoxicity enrolled into the Drug Induced Liver Injury Network were reviewed. DNA samples underwent high-resolution confirmatory HLA sequencing using the Ilumina MiSeq platform. Results All 15 patients with terbinafine hepatotoxicity were more than 40 years old (median = 57 years), 53% were female and the median latency to onset was 38 days (range 24 to 114 days). At the onset of drug-induced liver injury, 80% were jaundiced, median serum alanine aminotransferase was 448 U/L and alkaline phosphatase was 333 U/L. One individual required liver transplantation for acute liver failure during follow-up, and 7 of the 13 (54%) remaining individuals had ongoing liver injury at 6 months, with 4 demonstrating persistently abnormal liver biochemistries at month 24. High-resolution HLA genotyping confirmed that 10 of the 11 (91%) European ancestry participants were carriers of the HLA-A*33:01, B*14:02, C*08:02 haplotype, which has a carrier frequency of 1.6% in European Ancestry population controls. One African American patient was also an HLA-A*33:01 carrier while 2 East Asian patients were carriers of a similar HLA type: A*33:03. Molecular docking studies indicated that terbinafine may interact with HLA-A*33:01 and A*33:03. Conclusions Patients with terbinafine hepatotoxicity most commonly present with a mixed or cholestatic liver injury profile and frequently have residual evidence of chronic cholestatic injury. A strong genetic association of HLA-A*33:01 with terbinafine drug-induced liver injury was confirmed amongst Caucasians. Lay summary A locus in the human leukocyte antigen gene (HLA-A*33:01, B*14:02, C*08:02) was significantly overrepresented in Caucasian and African American patients with liver injury attributed to the antifungal medication, terbinafine. These data along with the molecular docking studies demonstrate that this genetic polymorphism is a plausible risk factor for developing terbinafine hepatotoxicity and could be used in the future to help doctors make a diagnosis more rapidly and confidently.

Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). More precise phenotyping and categorization of risk in large numbers of people with obesity is needed to advance clinical care and drug development. Here, we used non-targeted metabolome analysis and whole genome sequencing to identify metabolic and genetic signatures of obesity. We collected anthropomorphic and metabolic measurements at three timepoints over a median of 13 years in 1,969 adult twins of European ancestry and at a single timepoint in 427 unrelated volunteers. We observe that obesity results in a profound perturbation of the metabolome; nearly a third of the assayed metabolites are associated with changes in BMI. A metabolome signature identifies the healthy obese and also identifies lean individuals with abnormal metabolomes – these groups differ in health outcomes and underlying genetic risk. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.

Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). There is a need for a more precise yet portable method of phenotyping and categorizing risk in large numbers of people with obesity to advance clinical care and drug development. Here, we used non-targeted metabolomics and whole-genome sequencing to identify metabolic and genetic signatures of obesity. We find that obesity results in profound perturbation of the metabolome; nearly a third of the assayed metabolites associated with changes in BMI. A metabolome signature identifies the healthy obese and lean individuals with abnormal metabolomes - these groups differ in health outcomes and underlying genetic risk. Specifically, an abnormal metabolome associated with a 2- to 5-fold increase in cardiovascular events when comparing individuals who were matched for BMI but had opposing metabolome signatures. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.

© 2018 Elsevier Inc., To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.