Publications by authors named "Andrea Ganna"

74 Publications

Age-dependent impact of the major common genetic risk factor for COVID-19 on severity and mortality.

J Clin Invest 2021 Oct 1. Epub 2021 Oct 1.

Laboratory of Genetics and Molecular Cardiology, University of São Paulo Medical School, São Paulo, Brazil.

Background: There is considerable variability in COVID-19 outcomes amongst younger adults-and some of this variation may be due to genetic predisposition.

Methods: We combined individual level data from 13,888 COVID-19 patients (N=7,185 hospitalized) from 17 cohorts in nine countries to assess the association of the major common COVID-19 genetic risk factor (chromosome 3 locus tagged by rs10490770) with mortality, COVID-19-related complications and laboratory values. We next performed meta-analyses using FinnGen and the Columbia University COVID-19 Biobank.

Results: We found that rs10490770 risk allele carriers experienced an increased risk of all-cause mortality (HR 1.4, 95%CI 1.2-1.7). Risk allele carriers had increased odds of several COVID-19 complications: severe respiratory failure (OR 2.1, 95%CI 1.6-2.6), venous thromboembolism (OR 1.7, 95%CI 1.2-2.4), and hepatic injury (OR 1.5, 95%CI 1.2-2.0). Risk allele carriers ≤60 years had higher odds of death or severe respiratory failure (OR 2.7, 95%CI 1.8-3.9) compared to those >60 years (OR 1.5, 95%CI 1.2-1.8, interaction-p=0.038). Amongst individuals ≤60 years who died or experienced severe respiratory failure, 32.3% were risk variant carriers, compared to 13.9% of those not experiencing these outcomes. The genetic risk improved the prediction of death or severe respiratory failure similarly to, or better than, most established clinical risk factors.

Conclusions: The major common COVID-19 genetic risk factor is associated with increased risks of morbidity and mortality, which are more pronounced amongst individuals ≤60 years. The effect was similar in magnitude and more common than most established clinical risk factors, suggesting potential implications for future clinical risk management.
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http://dx.doi.org/10.1172/JCI152386DOI Listing
October 2021

Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility.

PLoS One 2021 11;16(8):e0255402. Epub 2021 Aug 11.

Helix OpCo LLC, San Mateo, California, United States of America.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255402PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357137PMC
August 2021

Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome.

Circ Genom Precis Med 2021 Aug 7;14(4):e003283. Epub 2021 Jul 7.

Broad Institute of MIT and Harvard, Cambridge (J.L., B.N., S.R., P.N., A.G.).

Background: Acute coronary syndrome (ACS) is a clinically significant presentation of coronary heart disease. Genetic information has been proposed to improve prediction beyond well-established clinical risk factors. While polygenic scores (PS) can capture an individual's genetic risk for ACS, its prediction performance may vary in the context of diverse correlated clinical conditions. Here, we aimed to test whether clinical conditions impact the association between PS and ACS.

Methods: We explored the association between 405 clinical conditions diagnosed before baseline and 9080 incident cases of ACS in 387 832 individuals from the UK Biobank. Results were replicated in 6430 incident cases of ACS in 177 876 individuals from FinnGen.

Results: We identified 80 conventional (eg, stable angina pectoris and type 2 diabetes) and unconventional (eg, diaphragmatic hernia and inguinal hernia) associations with ACS. The association between PS and ACS was consistent in individuals with and without most clinical conditions. However, a diagnosis of stable angina pectoris yielded a differential association between PS and ACS. PS was associated with a significantly reduced (interaction =2.87×10) risk for ACS in individuals with stable angina pectoris (hazard ratio, 1.163 [95% CI, 1.082-1.251]) compared with individuals without stable angina pectoris (hazard ratio, 1.531 [95% CI, 1.497-1.565]). These findings were replicated in FinnGen (interaction =1.38×10).

Conclusions: In summary, while most clinical conditions did not impact utility of PS for prediction of ACS, we found that PS was substantially less predictive of ACS in individuals with prevalent stable coronary heart disease. PS may be more appropriate for prediction of ACS in asymptomatic individuals than symptomatic individuals with clinical suspicion for coronary heart disease.
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http://dx.doi.org/10.1161/CIRCGEN.120.003283DOI Listing
August 2021

Hematopoietic mosaic chromosomal alterations increase the risk for diverse types of infection.

Nat Med 2021 06 7;27(6):1012-1024. Epub 2021 Jun 7.

Institute for Molecular Medicine Finland, Helsinki, Finland.

Age is the dominant risk factor for infectious diseases, but the mechanisms linking age to infectious disease risk are incompletely understood. Age-related mosaic chromosomal alterations (mCAs) detected from genotyping of blood-derived DNA, are structural somatic variants indicative of clonal hematopoiesis, and are associated with aberrant leukocyte cell counts, hematological malignancy, and mortality. Here, we show that mCAs predispose to diverse types of infections. We analyzed mCAs from 768,762 individuals without hematological cancer at the time of DNA acquisition across five biobanks. Expanded autosomal mCAs were associated with diverse incident infections (hazard ratio (HR) 1.25; 95% confidence interval (CI) = 1.15-1.36; P = 1.8 × 10), including sepsis (HR 2.68; 95% CI = 2.25-3.19; P = 3.1 × 10), pneumonia (HR 1.76; 95% CI = 1.53-2.03; P = 2.3 × 10), digestive system infections (HR 1.51; 95% CI = 1.32-1.73; P = 2.2 × 10) and genitourinary infections (HR 1.25; 95% CI = 1.11-1.41; P = 3.7 × 10). A genome-wide association study of expanded mCAs identified 63 loci, which were enriched at transcriptional regulatory sites for immune cells. These results suggest that mCAs are a marker of impaired immunity and confer increased predisposition to infections.
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http://dx.doi.org/10.1038/s41591-021-01371-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8245201PMC
June 2021

COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets.

Nature 2021 07 29;595(7865):107-113. Epub 2021 Apr 29.

Broad Institute of MIT and Harvard, Cambridge, MA, USA.

COVID-19, which is caused by SARS-CoV-2, can result in acute respiratory distress syndrome and multiple organ failure, but little is known about its pathophysiology. Here we generated single-cell atlases of 24 lung, 16 kidney, 16 liver and 19 heart autopsy tissue samples and spatial atlases of 14 lung samples from donors who died of COVID-19. Integrated computational analysis uncovered substantial remodelling in the lung epithelial, immune and stromal compartments, with evidence of multiple paths of failed tissue regeneration, including defective alveolar type 2 differentiation and expansion of fibroblasts and putative TP63 intrapulmonary basal-like progenitor cells. Viral RNAs were enriched in mononuclear phagocytic and endothelial lung cells, which induced specific host programs. Spatial analysis in lung distinguished inflammatory host responses in lung regions with and without viral RNA. Analysis of the other tissue atlases showed transcriptional alterations in multiple cell types in heart tissue from donors with COVID-19, and mapped cell types and genes implicated with disease severity based on COVID-19 genome-wide association studies. Our foundational dataset elucidates the biological effect of severe SARS-CoV-2 infection across the body, a key step towards new treatments.
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http://dx.doi.org/10.1038/s41586-021-03570-8DOI Listing
July 2021

Genetic analyses identify widespread sex-differential participation bias.

Nat Genet 2021 05 22;53(5):663-671. Epub 2021 Apr 22.

Faculty of Behavioural and Movement Sciences, Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands.

Genetic association results are often interpreted with the assumption that study participation does not affect downstream analyses. Understanding the genetic basis of participation bias is challenging since it requires the genotypes of unseen individuals. Here we demonstrate that it is possible to estimate comparative biases by performing a genome-wide association study contrasting one subgroup versus another. For example, we showed that sex exhibits artifactual autosomal heritability in the presence of sex-differential participation bias. By performing a genome-wide association study of sex in approximately 3.3 million males and females, we identified over 158 autosomal loci spuriously associated with sex and highlighted complex traits underpinning differences in study participation between the sexes. For example, the body mass index-increasing allele at FTO was observed at higher frequency in males compared to females (odds ratio = 1.02, P = 4.4 × 10). Finally, we demonstrated how these biases can potentially lead to incorrect inferences in downstream analyses and propose a conceptual framework for addressing such biases. Our findings highlight a new challenge that genetic studies may face as sample sizes continue to grow.
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http://dx.doi.org/10.1038/s41588-021-00846-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611642PMC
May 2021

Response to Comment on "Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior".

Science 2021 03;371(6536)

Centre for Psychology and Evolution, School of Psychology, University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia.

Hamer argue that the variable "ever versus never had a same-sex partner" does not capture the complexity of human sexuality. We agree and said so in our paper. But Hamer neglect to mention that we also reported follow-up analyses showing substantial overlap of the genetic influences on our main variable and on more nuanced measures of sexual behavior, attraction, and identity.
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http://dx.doi.org/10.1126/science.aba5693DOI Listing
March 2021

Age-dependent impact of the major common genetic risk factor for COVID-19 on severity and mortality.

medRxiv 2021 Mar 12. Epub 2021 Mar 12.

Department of Biomedical Sciences, Humanitas University, Milan, Italy.

Background: There is considerable variability in COVID-19 outcomes amongst younger adults-and some of this variation may be due to genetic predisposition. We characterized the clinical implications of the major genetic risk factor for COVID-19 severity, and its age-dependent effect, using individual-level data in a large international multi-centre consortium.

Method: The major common COVID-19 genetic risk factor is a chromosome 3 locus, tagged by the marker rs10490770. We combined individual level data for 13,424 COVID-19 positive patients (N=6,689 hospitalized) from 17 cohorts in nine countries to assess the association of this genetic marker with mortality, COVID-19-related complications and laboratory values. We next examined if the magnitude of these associations varied by age and were independent from known clinical COVID-19 risk factors.

Findings: We found that rs10490770 risk allele carriers experienced an increased risk of all-cause mortality (hazard ratio [HR] 1·4, 95% confidence interval [CI] 1·2-1·6) and COVID-19 related mortality (HR 1·5, 95%CI 1·3-1·8). Risk allele carriers had increased odds of several COVID-19 complications: severe respiratory failure (odds ratio [OR] 2·0, 95%CI 1·6-2·6), venous thromboembolism (OR 1·7, 95%CI 1·2-2·4), and hepatic injury (OR 1·6, 95%CI 1·2-2·0). Risk allele carriers ≤ 60 years had higher odds of death or severe respiratory failure (OR 2·6, 95%CI 1·8-3·9) compared to those > 60 years OR 1·5 (95%CI 1·3-1·9, interaction p-value=0·04). Amongst individuals ≤ 60 years who died or experienced severe respiratory COVID-19 outcome, we found that 31·8% (95%CI 27·6-36·2) were risk variant carriers, compared to 13·9% (95%CI 12·6-15·2%) of those not experiencing these outcomes. Prediction of death or severe respiratory failure among those ≤ 60 years improved when including the risk allele (AUC 0·82 vs 0·84, p=0·016) and the prediction ability of rs10490770 risk allele was similar to, or better than, most established clinical risk factors.

Interpretation: The major common COVID-19 risk locus on chromosome 3 is associated with increased risks of morbidity and mortality-and these are more pronounced amongst individuals ≤ 60 years. The effect on COVID-19 severity was similar to, or larger than most established risk factors, suggesting potential implications for clinical risk management.

Funding: Funding was obtained by each of the participating cohorts individually.
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http://dx.doi.org/10.1101/2021.03.07.21252875DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7987046PMC
March 2021

A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2.

bioRxiv 2021 Feb 25. Epub 2021 Feb 25.

The SARS-CoV-2 pandemic has caused over 1 million deaths globally, mostly due to acute lung injury and acute respiratory distress syndrome, or direct complications resulting in multiple-organ failures. Little is known about the host tissue immune and cellular responses associated with COVID-19 infection, symptoms, and lethality. To address this, we collected tissues from 11 organs during the clinical autopsy of 17 individuals who succumbed to COVID-19, resulting in a tissue bank of approximately 420 specimens. We generated comprehensive cellular maps capturing COVID-19 biology related to patients' demise through single-cell and single-nucleus RNA-Seq of lung, kidney, liver and heart tissues, and further contextualized our findings through spatial RNA profiling of distinct lung regions. We developed a computational framework that incorporates removal of ambient RNA and automated cell type annotation to facilitate comparison with other healthy and diseased tissue atlases. In the lung, we uncovered significantly altered transcriptional programs within the epithelial, immune, and stromal compartments and cell intrinsic changes in multiple cell types relative to lung tissue from healthy controls. We observed evidence of: alveolar type 2 (AT2) differentiation replacing depleted alveolar type 1 (AT1) lung epithelial cells, as previously seen in fibrosis; a concomitant increase in myofibroblasts reflective of defective tissue repair; and, putative TP63 intrapulmonary basal-like progenitor (IPBLP) cells, similar to cells identified in H1N1 influenza, that may serve as an emergency cellular reserve for severely damaged alveoli. Together, these findings suggest the activation and failure of multiple avenues for regeneration of the epithelium in these terminal lungs. SARS-CoV-2 RNA reads were enriched in lung mononuclear phagocytic cells and endothelial cells, and these cells expressed distinct host response transcriptional programs. We corroborated the compositional and transcriptional changes in lung tissue through spatial analysis of RNA profiles and distinguished unique tissue host responses between regions with and without viral RNA, and in COVID-19 donor tissues relative to healthy lung. Finally, we analyzed genetic regions implicated in COVID-19 GWAS with transcriptomic data to implicate specific cell types and genes associated with disease severity. Overall, our COVID-19 cell atlas is a foundational dataset to better understand the biological impact of SARS-CoV-2 infection across the human body and empowers the identification of new therapeutic interventions and prevention strategies.
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http://dx.doi.org/10.1101/2021.02.25.430130DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924267PMC
February 2021

Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability.

Nat Commun 2021 01 5;12(1):24. Epub 2021 Jan 5.

Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.
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http://dx.doi.org/10.1038/s41467-020-19366-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785747PMC
January 2021

Hematopoietic mosaic chromosomal alterations and risk for infection among 767,891 individuals without blood cancer.

medRxiv 2020 Nov 16. Epub 2020 Nov 16.

Age is the dominant risk factor for infectious diseases, but the mechanisms linking the two are incompletely understood . Age-related mosaic chromosomal alterations (mCAs) detected from blood-derived DNA genotyping, are structural somatic variants associated with aberrant leukocyte cell counts, hematological malignancy, and mortality . Whether mCAs represent independent risk factors for infection is unknown. Here we use genome-wide genotyping of blood DNA to show that mCAs predispose to diverse infectious diseases. We analyzed mCAs from 767,891 individuals without hematological cancer at DNA acquisition across four countries. Expanded mCA (cell fraction >10%) prevalence approached 4% by 60 years of age and was associated with diverse incident infections, including sepsis, pneumonia, and coronavirus disease 2019 (COVID-19) hospitalization. A genome-wide association study of expanded mCAs identified 63 significant loci. Germline genetic alleles associated with expanded mCAs were enriched at transcriptional regulatory sites for immune cells. Our results link mCAs with impaired immunity and predisposition to infections. Furthermore, these findings may also have important implications for the ongoing COVID-19 pandemic, particularly in prioritizing individual preventive strategies and evaluating immunization responses.
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http://dx.doi.org/10.1101/2020.11.12.20230821DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7685330PMC
November 2020

Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes.

Sci Rep 2020 10 5;10(1):16474. Epub 2020 Oct 5.

Department of Neurobiology, Care Sciences and Society (NVS), Family Medicine and Primary Care Unit, Karolinska Institutet, Huddinge, Sweden.

Better risk prediction and new molecular targets are key priorities in type 2 diabetes (T2D) research. Little is known about the role of the urine metabolome in predicting the risk of T2D. We aimed to use non-targeted urine metabolomics to discover biomarkers and improve risk prediction for T2D. Urine samples from two community cohorts of 1,424 adults were analyzed by ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). In a discovery/replication design, three out of 62 annotated metabolites were associated with prevalent T2D, notably lower urine levels of 3-hydroxyundecanoyl-carnitine. In participants without diabetes at baseline, LASSO regression in the training set selected six metabolites that improved prediction of T2D beyond established risk factors risk over up to 12 years' follow-up in the test sample, from C-statistic 0.866 to 0.892. Our results in one of the largest non-targeted urinary metabolomics study to date demonstrate the role of the urine metabolome in identifying at-risk persons for T2D and suggest urine 3-hydroxyundecanoyl-carnitine as a biomarker candidate.
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http://dx.doi.org/10.1038/s41598-020-72456-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536211PMC
October 2020

Association Between Episodic Memory and Genetic Risk Factors for Alzheimer's Disease in South Asians from the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (LASI-DAD).

J Am Geriatr Soc 2020 08;68 Suppl 3:S45-S53

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.

Background/objectives: Genetic factors play an important role in Alzheimer's disease (AD) and cognitive aging. However, it is unclear whether risk loci identified in European ancestry (EA) populations have similar effects in other groups, such as South Asians.

Design: We investigated the allelic distribution and cognitive associations of 56 known AD risk single-nucleotide polymorphisms (SNPs) identified from three EA genome-wide association studies (EA-GWASs) in a South Asian population. Single SNP and genetic risk score (GRS) associations with measures of episodic memory were assessed.

Setting: The Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD).

Participants: A total of 906 LASI-DAD participants from diverse states in India.

Measurements: Participants were genotyped using the Illumina Global Screening Array and imputed with 1000G Phase 3v5. Cognitive measures included total learning and delayed word recall.

Results: Although only a few SNPs were significantly associated with memory scores (P < .05), effect estimates from the EA-GWAS and the LASI-DAD showed moderate correlation (0.35-0.88) in the expected direction. GRSs were also associated with memory scores, although percentage variation explained was small (0.1%-0.6%).

Conclusions: Discrepancies in allele frequencies and cognitive association results suggest that genetic factors found predominantly through EA-GWASs may play a limited role in South Asians. However, the extent of differences in the genetic architecture of AD and cognition in EA and South Asians remains uncertain. There is also a critical need to perform a more comprehensive assessment of the mutational spectrum of South Asia to identify novel genetic variants associated with AD and cognition in this population. J Am Geriatr Soc 68:S45-S53, 2020.
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http://dx.doi.org/10.1111/jgs.16735DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507858PMC
August 2020

Development and validation of risk prediction models for multiple cardiovascular diseases and Type 2 diabetes.

PLoS One 2020 29;15(7):e0235758. Epub 2020 Jul 29.

Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States of America.

Accurate risk assessment of an individuals' propensity to develop cardiovascular diseases (CVDs) is crucial for the prevention of these conditions. Numerous published risk prediction models used for CVD risk assessment are based on conventional risk factors and include only a limited number of biomarkers. The addition of novel biomarkers can boost the discriminative ability of risk prediction models for CVDs with different pathogenesis. The present study reports the development of risk prediction models for a range of heterogeneous CVDs, including coronary artery disease (CAD), stroke, deep vein thrombosis (DVT), and abdominal aortic aneurysm (AAA), as well as for Type 2 diabetes mellitus (DM2), a major CVD risk factor. In addition to conventional risk factors, the models incorporate various blood biomarkers and comorbidities to improve both individual and population stratification. An automatic variable selection approach was developed to generate the best set of explanatory variables for each model from the initial panel of risk factors. In total, up to 254,220 UK Biobank participants (ranging from 215,269 to 254,220 for different CVDs and DM2) were included in the analyses. The derived prediction models utilizing Cox proportional hazards regression achieved consistent discrimination performance (C-index) for all diseases: CAD, 0.794 (95% CI, 0.787-0.801); DM2, 0.909 (95% CI, 0.903-0.916); stroke, 0.778 (95% CI, 0.756-0.801); DVT, 0.743 (95% CI, 0.737-0.749); and AAA, 0.893 (95% CI, 0.874-0.912). When validated on various subpopulations, they demonstrated higher discrimination in healthier and middle-age individuals. In general, calibration of a five-year risk of developing the CVDs and DM2 demonstrated incremental overestimation of disease-related conditions amongst the highest decile of risk probabilities. In summary, the risk prediction models described were validated with high discrimination and good calibration for several CVDs and DM2. These models incorporate multiple shared predictor variables and may be integrated into a single platform to enhance clinical stratification to impact health outcomes.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235758PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390268PMC
September 2020

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

Nature 2020 05 27;581(7809):434-443. Epub 2020 May 27.

Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

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 sizes. 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.
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http://dx.doi.org/10.1038/s41586-020-2308-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334197PMC
May 2020

Proteomic profiles before and during weight loss: Results from randomized trial of dietary intervention.

Sci Rep 2020 05 13;10(1):7913. Epub 2020 May 13.

Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.

Inflammatory and cardiovascular biomarkers have been associated with obesity, but little is known about how they change upon dietary intervention and concomitant weight loss. Further, protein biomarkers might be useful for predicting weight loss in overweight and obese individuals. We performed secondary analyses in the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) randomized intervention trial that included healthy 609 adults (18-50 years old) with BMI 28-40 kg/m, to evaluate associations between circulating protein biomarkers and BMI at baseline, during a weight loss diet intervention, and to assess predictive potential of baseline blood proteins on weight loss. We analyzed 263 plasma proteins at baseline and 6 months into the intervention using the Olink Proteomics CVD II, CVD III and Inflammation arrays. BMI was assessed at baseline, after 3 and 6 months of dietary intervention. At baseline, 102 of the examined inflammatory and cardiovascular biomarkers were associated with BMI (>90% with successful replication in 1,584 overweight/obese individuals from a community-based cohort study) and 130 tracked with weight loss shedding light into the pathophysiology of obesity. However, out of 263 proteins analyzed at baseline, only fibroblast growth factor 21 (FGF-21) predicted weight loss, and none helped individualize dietary assignment.
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http://dx.doi.org/10.1038/s41598-020-64636-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7220904PMC
May 2020

Discriminating bipolar depression from major depressive disorder with polygenic risk scores.

Psychol Med 2021 07 17;51(9):1451-1458. Epub 2020 Feb 17.

Department of Psychiatry and Behavioral Sciences, Johns Hopkins Institute of Medicine, Baltimore, MD21205, USA.

Background: Although accurate differentiation between bipolar disorder (BD) and unipolar major depressive disorder (MDD) has important prognostic and therapeutic implications, the distinction is often challenging based on clinical grounds alone. In this study, we tested whether psychiatric polygenic risk scores (PRSs) improve clinically based classification models of BD v. MDD diagnosis.

Methods: Our sample included 843 BD and 930 MDD subjects similarly genotyped and phenotyped using the same standardized interview. We performed multivariate modeling and receiver operating characteristic analysis, testing the incremental effect of PRSs on a baseline model with clinical symptoms and features known to associate with BD compared with MDD status.

Results: We found a strong association between a BD diagnosis and PRSs drawn from BD (R2 = 3.5%, p = 4.94 × 10-12) and schizophrenia (R2 = 3.2%, p = 5.71 × 10-11) genome-wide association meta-analyses. Individuals with top decile BD PRS had a significantly increased risk for BD v. MDD compared with those in the lowest decile (odds ratio 3.39, confidence interval 2.19-5.25). PRSs discriminated BD v. MDD to a degree comparable with many individual symptoms and clinical features previously shown to associate with BD. When compared with the full composite model with all symptoms and clinical features PRSs provided modestly improved discriminatory ability (ΔC = 0.011, p = 6.48 × 10-4).

Conclusions: Our study demonstrates that psychiatric PRSs provide modest independent discrimination between BD and MDD cases, suggesting that PRSs could ultimately have utility in subjects at the extremes of the distribution and/or subjects for whom clinical symptoms are poorly measured or yet to manifest.
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http://dx.doi.org/10.1017/S003329172000015XDOI Listing
July 2021

A Multi-Cohort Metabolomics Analysis Discloses Sphingomyelin (32:1) Levels to be Inversely Related to Incident Ischemic Stroke.

J Stroke Cerebrovasc Dis 2020 Feb 2;29(2):104476. Epub 2019 Dec 2.

Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Huddinge, Sweden; School of Health and Social Sciences, Dalarna University, Falun, Sweden. Electronic address:

Background And Purpose: To search for novel pathophysiological pathways related to ischemic stroke using a metabolomics approach.

Methods: We identified 204 metabolites in plasma by liquid chromatography mass spectrometry in 3 independent population-based samples (TwinGene, Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) and Uppsala Longitudinal Study of Adult Men). TwinGene was used for discovery and the other 2 samples were meta-analyzed as replication. In PIVUS, traditional cardiovascular (CV) risk factors, multiple markers of subclinical CV disease, markers of coagulation/fibrinolysis were measured and analyzed in relation to top metabolites.

Results: In TwinGene (177 incident cases, median follow-up 4.3 years), levels of 28 metabolites were associated with incident ischemic stroke at a false discover rate (FDR) of 5%. In the replication (together 194 incident cases, follow-up 10 and 12 years, respectively), only sphingomyelin (32:1) was significantly associated (HR .69 per SD change, 95% CI .57-0.83, P value = .00014; FDR <5%) when adjusted for systolic blood pressure, diabetes, smoking, low density lipoportein (LDL)- and high density lipoprotein (HDL), body mass index (BMI) and atrial fibrillation. In PIVUS, sphingomyelin (32:1) levels were significantly related to both LDL- and HDL-cholesterol in a positive fashion, and to serum triglycerides, BMI and diabetes in a negative fashion. Furthermore, sphingomyelin (32:1) levels were related to vasodilation in the forearm resistance vessels, and inversely to leukocyte count (P < .0069 and .0026, respectively).

Conclusions: An inverse relationship between sphingomyelin (32:1) and incident ischemic stroke was identified, replicated, and characterized. A possible protective role for sphingomyelins in stroke development has to be further investigated in additional experimental and clinical studies.
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http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2019.104476DOI Listing
February 2020

Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior.

Science 2019 08;365(6456)

Centre for Psychology and Evolution, School of Psychology, University of Queensland, St. Lucia, Brisbane QLD 4072, Australia.

Twin and family studies have shown that same-sex sexual behavior is partly genetically influenced, but previous searches for specific genes involved have been underpowered. We performed a genome-wide association study (GWAS) on 477,522 individuals, revealing five loci significantly associated with same-sex sexual behavior. In aggregate, all tested genetic variants accounted for 8 to 25% of variation in same-sex sexual behavior, only partially overlapped between males and females, and do not allow meaningful prediction of an individual's sexual behavior. Comparing these GWAS results with those for the proportion of same-sex to total number of sexual partners among nonheterosexuals suggests that there is no single continuum from opposite-sex to same-sex sexual behavior. Overall, our findings provide insights into the genetics underlying same-sex sexual behavior and underscore the complexity of sexuality.
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http://dx.doi.org/10.1126/science.aat7693DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082777PMC
August 2019

The metabolites urobilin and sphingomyelin (30:1) are associated with incident heart failure in the general population.

ESC Heart Fail 2019 08 30;6(4):764-773. Epub 2019 May 30.

Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, EpiHubben, MTC-huset, 75185, Uppsala, Sweden.

Aims: We aimed to investigate whether metabolomic profiling of blood can lead to novel insights into heart failure pathogenesis or improved risk prediction.

Methods And Results: Mass spectrometry-based metabolomic profiling was performed in plasma or serum samples from three community-based cohorts without heart failure at baseline (total n = 3924; 341 incident heart failure events; median follow-up ranging from 4.6 to 13.9 years). Cox proportional hazard models were applied to assess the association of each of the 206 identified metabolites with incident heart failure in the discovery cohorts Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) (n = 920) and Uppsala Longitudinal Study of Adult Men (ULSAM) (n = 1121). Replication was undertaken in the independent cohort TwinGene (n = 1797). We also assessed whether metabolites could improve the prediction of heart failure beyond established risk factors (age, sex, body mass index, low-density and high-density lipoprotein cholesterol, triglycerides, lipid medication, diabetes, systolic and diastolic blood pressure, blood pressure medication, glomerular filtration rate, smoking status, and myocardial infarction prior to or during follow-up). Higher circulating urobilin and lower sphingomyelin (30:1) were associated with incident heart failure in age-adjusted and sex-adjusted models in the discovery and replication sample. The hazard ratio for urobilin in the replication cohort was estimated to 1.29 per standard deviation unit, 95% confidence interval (CI 1.03-1.63), and for sphingomyelin (30:1) to 0.72 (95% CI 0.58-0.89). Results remained similar after further adjustment for established heart failure risk factors in meta-analyses of all three cohorts. Urobilin concentrations were inversely associated with left ventricular ejection fraction at baseline in the PIVUS cohort (β = -0.70, 95% CI -1.03 to -0.38). No major improvement in risk prediction was observed when adding the top 2 metabolites (C-index 0.787, 95% CI 0.752-0.823) or nine Lasso-selected metabolites (0.790, 95% CI 0.754-0.826) to a modified Atherosclerosis Risk in Communities heart failure risk score model (0.780, 95% CI 0.745-0.816).

Conclusions: Our metabolomic profiling of three community-based cohorts study identified associations of circulating levels of the haem breakdown product urobilin, and sphingomyelin (30:1), a cell membrane component involved in signal transduction and apoptosis, with incident heart failure.
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http://dx.doi.org/10.1002/ehf2.12453DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6676274PMC
August 2019

Variant Score Ranker-a web application for intuitive missense variant prioritization.

Bioinformatics 2019 11;35(21):4478-4479

Cologne Center for Genomics, University of Cologne, University Hospital Cologne, Cologne, Germany.

Motivation: The correct classification of missense variants as benign or pathogenic remains challenging. Pathogenic variants are expected to have higher deleterious prediction scores than benign variants in the same gene. However, most of the existing variant annotation tools do not reference the score range of benign population variants on gene level.

Results: We present a web-application, Variant Score Ranker, which enables users to rapidly annotate variants and perform gene-specific variant score ranking on the population level. We also provide an intuitive example of how gene- and population-calibrated variant ranking scores can improve epilepsy variant prioritization.

Availability And Implementation: http://vsranker.broadinstitute.org.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz252DOI Listing
November 2019

Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies.

Elife 2019 03 21;8. Epub 2019 Mar 21.

Department of Biomedical Informatics, Harvard Medical School, Boston, United States.

Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.

Editorial Note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
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http://dx.doi.org/10.7554/eLife.39702DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428571PMC
March 2019

Exploring Comorbidity Within Mental Disorders Among a Danish National Population.

JAMA Psychiatry 2019 03;76(3):259-270

Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.

Importance: Individuals with mental disorders often develop comorbidity over time. Past studies of comorbidity have often restricted analyses to a subset of disorders and few studies have provided absolute risks of later comorbidity.

Objectives: To undertake a comprehensive study of comorbidity within mental disorders, by providing temporally ordered age- and sex-specific pairwise estimates between the major groups of mental disorders, and to develop an interactive website to visualize all results and guide future research and clinical practice.

Design, Setting, And Participants: This population-based cohort study included all individuals born in Denmark between January 1, 1900, and December 31, 2015, and living in the country between January 1, 2000, and December 31, 2016. The analyses were conducted between June 2017 and May 2018.

Main Outcomes And Measures: Danish health registers were used to identify mental disorders, which were examined within the broad 10-level International Statistical Classification of Diseases and Related Health Problems, 10th Revision, subchapter groups (eg, codes F00-F09 and F10-F19). For each temporally ordered pair of disorders, overall and lagged hazard ratios and 95% CIs were calculated using Cox proportional hazards regression models. Absolute risks were estimated using competing risks survival analyses. Estimates for each sex were generated.

Results: A total of 5 940 778 persons were included in this study (2 958 293 men and 2 982 485 women; mean [SD] age at beginning of follow-up, 32.1 [25.4] years). They were followed up for 83.9 million person-years. All mental disorders were associated with an increased risk of all other mental disorders when adjusting for sex, age, and calendar time (hazard ratios ranging from 2.0 [95% CI, 1.7-2.4] for prior intellectual disabilities and later eating disorders to 48.6 [95% CI, 46.6-50.7] for prior developmental disorders and later intellectual disabilities). The hazard ratios were temporally patterned, with higher estimates during the first year after the onset of the first disorder, but with persistently elevated rates during the entire observation period. Some disorders were associated with substantial absolute risks of developing specific later disorders (eg, 30.6% [95% CI, 29.3%-32.0%] of men and 38.4% [95% CI, 37.5%-39.4%] of women with a diagnosis of mood disorders before age 20 years developed neurotic disorders within the following 5 years).

Conclusions And Relevance: Comorbidity within mental disorders is pervasive, and the risk persists over time. This study provides disorder-, sex-, and age-specific relative and absolute risks of the comorbidity of mental disorders. Web-based interactive data visualization tools are provided for clinical utility.
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http://dx.doi.org/10.1001/jamapsychiatry.2018.3658DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6439836PMC
March 2019

Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations.

Nat Genet 2018 11 8;50(11):1600-1607. Epub 2018 Oct 8.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5%≤ minor allele frequency <5%) and common (minor allele frequency ≥5%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 ± 1% of low-frequency variant heritability ([Formula: see text]) versus 2.1 ± 0.2% of common variant heritability ([Formula: see text]). Cell-type-specific non-coding annotations that were significantly enriched for [Formula: see text] of corresponding traits were similarly enriched for [Formula: see text] for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 ± 12% of [Formula: see text] versus 12 ± 2% of [Formula: see text] for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5%).
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http://dx.doi.org/10.1038/s41588-018-0231-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6236676PMC
November 2018
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