Publications by authors named "Lars G Fritsche"

89 Publications

Type 2 diabetes sex-specific effects associated with E167K coding variant in .

iScience 2021 Nov 2;24(11):103196. Epub 2021 Oct 2.

Cardiovascular Center, Department of Internal Medicine, University of Michigan Medical Center, NCRC Bldg 26, Rm 361S, 2800 Plymouth Road, Ann Arbor, MI 48109, USA.

The rs58542926C >T (E167K) variant of the transmembrane 6 superfamily member 2 gene () is associated with increased risks for nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2D). Nevertheless, the role of the rs58542926 variant in glucose metabolism is poorly understood. We performed a sex-stratified analysis of the association between the rs58542926C >T variant and T2D in multiple cohorts. The E167K variant was significantly associated with T2D, especially in males. Using an E167K knockin (KI) mouse model, we found that male but not the female KI mice exhibited impaired glucose tolerance. As an ER membrane protein, TM6SF2 was found to interact with inositol-requiring enzyme 1 α (IRE1α), a primary ER stress sensor. The male KI mice exhibited impaired IRE1α signaling in the liver. In conclusion, the E167K variant of TM6SF2 is associated with glucose intolerance primarily in males, both in humans and mice.
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http://dx.doi.org/10.1016/j.isci.2021.103196DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8554487PMC
November 2021

Understanding the Patterns of Serological Testing for COVID-19 Pre- and Post-Vaccination Rollout in Michigan.

J Clin Med 2021 Sep 24;10(19). Epub 2021 Sep 24.

Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.

Testing for SARS-CoV-2 antibodies is commonly used to determine prior COVID-19 infections and to gauge levels of infection- or vaccine-induced immunity. Michigan Medicine, a primary regional health center, provided an ideal setting to understand serologic testing patterns over time. Between 27 April 2020 and 3 May 2021, characteristics for 10,416 individuals presenting for SARS-CoV-2 antibody tests (10,932 tests in total) were collected. Relative to the COVID-19 vaccine roll-out date, 14 December 2020, the data were split into a pre- (8026 individuals) and post-vaccine launch (2587 individuals) period and contrasted with untested individuals to identify factors associated with tested individuals and seropositivity. Exploratory analysis of vaccine-mediated seropositivity was performed in 347 fully vaccinated individuals. Predictors of tested individuals included age, sex, smoking, neighborhood variables, and pre-existing conditions. Seropositivity in the pre-vaccine launch period was 9.2% and increased to 46.7% in the post-vaccine launch period. In the pre-vaccine launch period, seropositivity was significantly associated with age (10 year; OR = 0.80 (0.73, 0.89)), ever-smoker status (0.49 (0.35, 0.67)), respiratory disease (4.38 (3.13, 6.12)), circulatory disease (2.09 (1.48, 2.96)), liver disease (2.06 (1.11, 3.84)), non-Hispanic Black race/ethnicity (2.18 (1.33, 3.58)), and population density (1.10 (1.03, 1.18)). Except for the latter two, these associations remained statistically significant in the post-vaccine launch period. The positivity rate of fully vaccinated individual was 296/347(85.3% (81.0%, 88.8%)).
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http://dx.doi.org/10.3390/jcm10194341DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509702PMC
September 2021

On cross-ancestry cancer polygenic risk scores.

PLoS Genet 2021 09 16;17(9):e1009670. Epub 2021 Sep 16.

Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.

Polygenic risk scores (PRS) can provide useful information for personalized risk stratification and disease risk assessment, especially when combined with non-genetic risk factors. However, their construction depends on the availability of summary statistics from genome-wide association studies (GWAS) independent from the target sample. For best compatibility, it was reported that GWAS and the target sample should match in terms of ancestries. Yet, GWAS, especially in the field of cancer, often lack diversity and are predominated by European ancestry. This bias is a limiting factor in PRS research. By using electronic health records and genetic data from the UK Biobank, we contrast the utility of breast and prostate cancer PRS derived from external European-ancestry-based GWAS across African, East Asian, European, and South Asian ancestry groups. We highlight differences in the PRS distributions of these groups that are amplified when PRS methods condense hundreds of thousands of variants into a single score. While European-GWAS-derived PRS were not directly transferrable across ancestries on an absolute scale, we establish their predictive potential when considering them separately within each group. For example, the top 10% of the breast cancer PRS distributions within each ancestry group each revealed significant enrichments of breast cancer cases compared to the bottom 90% (odds ratio of 2.81 [95%CI: 2.69,2.93] in European, 2.88 [1.85, 4.48] in African, 2.60 [1.25, 5.40] in East Asian, and 2.33 [1.55, 3.51] in South Asian individuals). Our findings highlight a compromise solution for PRS research to compensate for the lack of diversity in well-powered European GWAS efforts while recruitment of diverse participants in the field catches up.
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http://dx.doi.org/10.1371/journal.pgen.1009670DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8445431PMC
September 2021

A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease.

Nat Genet 2021 09 7;53(9):1276-1282. Epub 2021 Sep 7.

23andMe Inc., Sunnyvale, CA, USA.

Late-onset Alzheimer's disease is a prevalent age-related polygenic disease that accounts for 50-70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer's disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer's disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer's disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer's disease to identify further genetic variants that contribute to Alzheimer's pathology.
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http://dx.doi.org/10.1038/s41588-021-00921-zDOI Listing
September 2021

Cluster Analysis and Genotype-Phenotype Assessment of Geographic Atrophy in Age-Related Macular Degeneration: Age-Related Eye Disease Study 2 Report 25.

Ophthalmol Retina 2021 11 26;5(11):1061-1073. Epub 2021 Jul 26.

Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.

Purpose: To explore whether phenotypes in geographic atrophy (GA) secondary to age-related macular degeneration can be separated into 2 or more partially distinct subtypes and if these have different genetic associations. This is important because distinct GA subtypes associated with different genetic factors might require customized therapeutic approaches.

Design: Cluster analysis of participants within a controlled clinical trial, followed by assessment of phenotype-genotype associations.

Participants: Age-Related Eye Disease Study 2 participants with incident GA during study follow-up: 598 eyes of 598 participants.

Methods: Phenotypic features from reading center grading of fundus photographs were subjected to cluster analysis, by k-means and hierarchical methods, in cross-sectional analyses (using 15 phenotypic features) and longitudinal analyses (using 14 phenotypic features). The identified clusters were compared by 4 pathway-based genetic risk scores (complement, extracellular matrix, lipid, and ARMS2). The analyses were repeated in reverse (clustering by genotype and comparison by phenotype).

Main Outcome Measures: Characteristics and quality of cluster solutions, assessed by Calinski-Harabasz scores, unexplained variance, and consistency; and genotype-phenotype associations, assessed by t test.

Results: In cross-sectional phenotypic analyses, k-means identified 2 clusters (labeled A and B), whereas hierarchical clustering identified 4 clusters (C-F); cluster membership differed principally by GA configuration but in few other ways. In longitudinal phenotypic analyses, k-means identified 2 clusters (G and H) that differed principally by smoking status but in few other ways. These 3 sets of cluster divisions were not similar to each other (r ≤ 0.20). Despite adequate power, pairwise cluster comparison by the 4 genetic risk scores demonstrated no significant differences (P > 0.05 for all). In clustering by genotype, k-means identified 2 clusters (I and J). These differed principally at ARMS2, but no significant genotype-phenotype associations were observed (P > 0.05 for all).

Conclusions: Phenotypic clustering resulted in GA subtypes defined principally by GA configuration in cross-sectional analyses, but these were not replicated in longitudinal analyses. These negative findings, together with the absence of significant phenotype-genotype associations, indicate that GA phenotypes may vary continuously across a spectrum, rather than consisting of distinct subtypes that arise from separate genetic causes.
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http://dx.doi.org/10.1016/j.oret.2021.07.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8578299PMC
November 2021

Genome-wide association study of cardiac troponin I in the general population.

Hum Mol Genet 2021 Oct;30(21):2027-2039

Division of Research and Innovation, Akershus University Hospital, 1478 Lørenskog, Norway.

Circulating cardiac troponin proteins are associated with structural heart disease and predict incident cardiovascular disease in the general population. However, the genetic contribution to cardiac troponin I (cTnI) concentrations and its causal effect on cardiovascular phenotypes are unclear. We combine data from two large population-based studies, the Trøndelag Health Study and the Generation Scotland Scottish Family Health Study, and perform a genome-wide association study of high-sensitivity cTnI concentrations with 48 115 individuals. We further use two-sample Mendelian randomization to investigate the causal effects of circulating cTnI on acute myocardial infarction (AMI) and heart failure (HF). We identified 12 genetic loci (8 novel) associated with cTnI concentrations. Associated protein-altering variants highlighted putative functional genes: CAND2, HABP2, ANO5, APOH, FHOD3, TNFAIP2, KLKB1 and LMAN1. Phenome-wide association tests in 1688 phecodes and 83 continuous traits in UK Biobank showed associations between a genetic risk score for cTnI and cardiac arrhythmias, metabolic and anthropometric measures. Using two-sample Mendelian randomization, we confirmed the non-causal role of cTnI in AMI (5948 cases, 355 246 controls). We found indications for a causal role of cTnI in HF (47 309 cases and 930 014 controls), but this was not supported by secondary analyses using left ventricular mass as outcome (18 257 individuals). Our findings clarify the biology underlying the heritable contribution to circulating cTnI and support cTnI as a non-causal biomarker for AMI in the general population. Using genetically informed methods for causal inference helps inform the role and value of measuring cTnI in the general population.
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http://dx.doi.org/10.1093/hmg/ddab124DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8522636PMC
October 2021

Genome-wide analysis of 944 133 individuals provides insights into the etiology of haemorrhoidal disease.

Gut 2021 Apr 22. Epub 2021 Apr 22.

Department of Medicine I, Institute of Cancer Research, Medical University Vienna, Vienna, Austria.

Objective: Haemorrhoidal disease (HEM) affects a large and silently suffering fraction of the population but its aetiology, including suspected genetic predisposition, is poorly understood. We report the first genome-wide association study (GWAS) meta-analysis to identify genetic risk factors for HEM to date.

Design: We conducted a GWAS meta-analysis of 218 920 patients with HEM and 725 213 controls of European ancestry. Using GWAS summary statistics, we performed multiple genetic correlation analyses between HEM and other traits as well as calculated HEM polygenic risk scores (PRS) and evaluated their translational potential in independent datasets. Using functional annotation of GWAS results, we identified HEM candidate genes, which differential expression and coexpression in HEM tissues were evaluated employing RNA-seq analyses. The localisation of expressed proteins at selected loci was investigated by immunohistochemistry.

Results: We demonstrate modest heritability and genetic correlation of HEM with several other diseases from the GI, neuroaffective and cardiovascular domains. HEM PRS validated in 180 435 individuals from independent datasets allowed the identification of those at risk and correlated with younger age of onset and recurrent surgery. We identified 102 independent HEM risk loci harbouring genes whose expression is enriched in blood vessels and GI tissues, and in pathways associated with smooth muscles, epithelial and endothelial development and morphogenesis. Network transcriptomic analyses highlighted HEM gene coexpression modules that are relevant to the development and integrity of the musculoskeletal and epidermal systems, and the organisation of the extracellular matrix.

Conclusion: HEM has a genetic component that predisposes to smooth muscle, epithelial and connective tissue dysfunction.
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http://dx.doi.org/10.1136/gutjnl-2020-323868DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292596PMC
April 2021

A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine.

J Clin Med 2021 Mar 25;10(7). Epub 2021 Mar 25.

Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.

Background: We performed a phenome-wide association study to identify pre-existing conditions related to Coronavirus disease 2019 (COVID-19) prognosis across the medical phenome and how they vary by race.

Methods: The study is comprised of 53,853 patients who were tested/diagnosed for COVID-19 between 10 March and 2 September 2020 at a large academic medical center.

Results: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with intensive care unit (ICU) admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks.

Conclusions: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.
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http://dx.doi.org/10.3390/jcm10071351DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8037108PMC
March 2021

A powerful subset-based method identifies gene set associations and improves interpretation in UK Biobank.

Am J Hum Genet 2021 04 16;108(4):669-681. Epub 2021 Mar 16.

Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Graduate School of Data Science, Seoul National University, Seoul 08826, Republic of Korea. Electronic address:

Tests of association between a phenotype and a set of genes in a biological pathway can provide insights into the genetic architecture of complex phenotypes beyond those obtained from single-variant or single-gene association analysis. However, most existing gene set tests have limited power to detect gene set-phenotype association when a small fraction of the genes are associated with the phenotype and cannot identify the potentially "active" genes that might drive a gene set-based association. To address these issues, we have developed Gene set analysis Association Using Sparse Signals (GAUSS), a method for gene set association analysis that requires only GWAS summary statistics. For each significantly associated gene set, GAUSS identifies the subset of genes that have the maximal evidence of association and can best account for the gene set association. Using pre-computed correlation structure among test statistics from a reference panel, our p value calculation is substantially faster than other permutation- or simulation-based approaches. In simulations with varying proportions of causal genes, we find that GAUSS effectively controls type 1 error rate and has greater power than several existing methods, particularly when a small proportion of genes account for the gene set signal. Using GAUSS, we analyzed UK Biobank GWAS summary statistics for 10,679 gene sets and 1,403 binary phenotypes. We found that GAUSS is scalable and identified 13,466 phenotype and gene set association pairs. Within these gene sets, we identify an average of 17.2 (max = 405) genes that underlie these gene set associations.
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http://dx.doi.org/10.1016/j.ajhg.2021.02.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8059336PMC
April 2021

Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease.

Nat Commun 2020 12 18;11(1):6417. Epub 2020 Dec 18.

The Institute for Translational Genomics and Population Sciences, Department of Pediatrics and Los Angeles Biomedical Research Institute, Harbor-UCLA, Torrance, CA, USA.

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD.
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http://dx.doi.org/10.1038/s41467-020-20086-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7749177PMC
December 2020

The Effect of Genetic Variants Associated With Age-Related Macular Degeneration Varies With Age.

Invest Ophthalmol Vis Sci 2020 12;61(14):17

Department of Ophthalmology, University Hospital of Cologne, Cologne, Germany.

Purpose: The prevalence of age-related macular degeneration (AMD) increases dramatically with age. This large collaborative study investigates the effects of 51 late-AMD-associated genetic variants in different ages, focusing on individuals above the age of 90 years.

Methods: The study included 27,996 individuals of the International AMD Genomics Consortium; 14,539 showed late AMD (51.9%) and 13,457 were controls (48.1%). Four age groups were compiled: 60 to 69 years, n = 6514, AMD = 2210 (33.9%); 70 to 79 years, n = 12228, AMD = 6217 (51.7%); 80 to 89 years, n = 8285, AMD = 5326 (64.3%); and ≥90 years, n = 969, AMD = 686 (70.8%). The effect sizes of 51 AMD-associated genetic variants were calculated for all age groups and were compared among the age groups.

Results: Six variants were associated with late AMD in individuals ≥ 90 years of age (P ≤ 0.0006). For rs10922109 and rs570618 (both in CFH), the minor allele (MA) was protective, and minor allele frequency (MAF) increased with age in cases and controls. For rs116503776 in C2/CFB/SKIV2L, the MA was protective, and MAF increased in cases. For rs3750846 in ARMS2/HTRA1, the MA increased risk, and MAF was lower in cases with increasing age. For rs6565597 in NPLOC4/TSPAN10, the MA increased risk. For rs5754227 in SYN3/TIMP3, the MA was protective, and there was no consistent variation in MAF with age. Variants in CFH and ARMS2 showed lower effect sizes at greater age. Interaction analysis showed strong age-related effects for rs570618 (P = 2.24 × 10-7) and rs3750846 (P = 0.001). Total genetic risk was lower in individuals ≥ 90 years old (area under the curve [AUC], 0.795) than in those 70 to 79 years old (AUC, 0.831; P = 0.03).

Conclusions: Effect sizes and MAF of genetic risk factors for late AMD differed among the age groups. These results could guide future work on AMD risk assessment in older individuals.
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http://dx.doi.org/10.1167/iovs.61.14.17DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745630PMC
December 2020

Common variants in SOX-2 and congenital cataract genes contribute to age-related nuclear cataract.

Commun Biol 2020 12 11;3(1):755. Epub 2020 Dec 11.

Institute of Molecular and Cell Biology, 138673, Singapore, Singapore.

Nuclear cataract is the most common type of age-related cataract and a leading cause of blindness worldwide. Age-related nuclear cataract is heritable (h = 0.48), but little is known about specific genetic factors underlying this condition. Here we report findings from the largest to date multi-ethnic meta-analysis of genome-wide association studies (discovery cohort N = 14,151 and replication N = 5299) of the International Cataract Genetics Consortium. We confirmed the known genetic association of CRYAA (rs7278468, P = 2.8 × 10) with nuclear cataract and identified five new loci associated with this disease: SOX2-OT (rs9842371, P = 1.7 × 10), TMPRSS5 (rs4936279, P = 2.5 × 10), LINC01412 (rs16823886, P = 1.3 × 10), GLTSCR1 (rs1005911, P = 9.8 × 10), and COMMD1 (rs62149908, P = 1.2 × 10). The results suggest a strong link of age-related nuclear cataract with congenital cataract and eye development genes, and the importance of common genetic variants in maintaining crystalline lens integrity in the aging eye.
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http://dx.doi.org/10.1038/s42003-020-01421-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733496PMC
December 2020

Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks.

J Biomed Inform 2021 01 3;113:103652. Epub 2020 Dec 3.

Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States. Electronic address:

Background: Traditional methods for disease risk prediction and assessment, such as diagnostic tests using serum, urine, blood, saliva or imaging biomarkers, have been important for identifying high-risk individuals for many diseases, leading to early detection and improved survival. For pancreatic cancer, traditional methods for screening have been largely unsuccessful in identifying high-risk individuals in advance of disease progression leading to high mortality and poor survival. Electronic health records (EHR) linked to genetic profiles provide an opportunity to integrate multiple sources of patient information for risk prediction and stratification. We leverage a constellation of temporally associated diagnoses available in the EHR to construct a summary risk score, called a phenotype risk score (PheRS), for identifying individuals at high-risk for having pancreatic cancer. The proposed PheRS approach incorporates the time with respect to disease onset into the prediction framework. We combine and contrast the PheRS with more well-known measures of inherited susceptibility, namely, the polygenic risk scores (PRS) for prediction of pancreatic cancer.

Methodology: We first calculated pairwise, unadjusted associations between pancreatic cancer diagnosis and all possible other diagnoses across the medical phenome. We call these pairwise associations co-occurrences. After accounting for cross-phenotype correlations, the multivariable association estimates from a subset of relatively independent diagnoses were used to create a weighted sum PheRS. We constructed time-restricted risk scores using data from 38,359 participants in the Michigan Genomics Initiative (MGI) based on the diagnoses contained in the EHR at 0, 1, 2, and 5 years prior to the target pancreatic cancer diagnosis. The PheRS was assessed for predictability in the UK Biobank (UKB). We tested the relative contribution of PheRS when added to a model containing a summary measure of inherited genetic susceptibility (PRS) plus other covariates like age, sex, smoking status, drinking status, and body mass index (BMI).

Results: Our exploration of co-occurrence patterns identified expected associations while also revealing unexpected relationships that may warrant closer attention. Solely using the pancreatic cancer PheRS at 5 years before the target diagnoses yielded an AUC of 0.60 (95% CI = [0.58, 0.62]) in UKB. A larger predictive model including PheRS, PRS, and the covariates at the 5-year threshold achieved an AUC of 0.74 (95% CI = [0.72, 0.76]) in UKB. We note that PheRS does contribute independently in the joint model. Finally, scores at the top percentiles of the PheRS distribution demonstrated promise in terms of risk stratification. Scores in the top 2% were 10.20 (95% CI = [9.34, 12.99]) times more likely to identify cases than those in the bottom 98% in UKB at the 5-year threshold prior to pancreatic cancer diagnosis.

Conclusions: We developed a framework for creating a time-restricted PheRS from EHR data for pancreatic cancer using the rich information content of a medical phenome. In addition to identifying hypothesis-generating associations for future research, this PheRS demonstrates a potentially important contribution in identifying high-risk individuals, even after adjusting for PRS for pancreatic cancer and other traditional epidemiologic covariates. The methods are generalizable to other phenotypic traits.
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http://dx.doi.org/10.1016/j.jbi.2020.103652DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7855433PMC
January 2021

LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks.

PLoS Genet 2020 11 11;16(11):e1009077. Epub 2020 Nov 11.

Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America.

Phenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative laboratory-derived phenotypes. We meta-analyzed 70 lab traits matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these traits, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 699 previous GWAS associations across 46 different traits. We discovered 31 novel associations at genome-wide significance for 22 distinct traits, including the first reported associations for two lab-based traits. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are freely available to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for EHR lab traits. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain circumstances. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative EHR lab traits.
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http://dx.doi.org/10.1371/journal.pgen.1009077DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682892PMC
November 2020

MEPE loss-of-function variant associates with decreased bone mineral density and increased fracture risk.

Nat Commun 2020 10 23;11(1):4093. Epub 2020 Oct 23.

Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, 1500 E. Medical Center Dr., Ann Arbor, MI, 48109, USA.

A major challenge in genetic association studies is that most associated variants fall in the non-coding part of the human genome. We searched for variants associated with bone mineral density (BMD) after enriching the discovery cohort for loss-of-function (LoF) mutations by sequencing a subset of the Nord-Trøndelag Health Study, followed by imputation in the remaining sample (N = 19,705), and identified ten known BMD loci. However, one previously unreported variant, LoF mutation in MEPE, p.(Lys70IlefsTer26, minor allele frequency [MAF] = 0.8%), was associated with decreased ultradistal forearm BMD (P-value = 2.1 × 10), and increased osteoporosis (P-value = 4.2 × 10) and fracture risk (P-value = 1.6 × 10). The MEPE LoF association with BMD and fractures was further evaluated in 279,435 UK (MAF = 0.05%, heel bone estimated BMD P-value = 1.2 × 10, any fracture P-value = 0.05) and 375,984 Icelandic samples (MAF = 0.03%, arm BMD P-value = 0.12, forearm fracture P-value = 0.005). Screening for the MEPE LoF mutations before adulthood could potentially prevent osteoporosis and fractures due to the lifelong effect on BMD observed in the study. A key implication for precision medicine is that high-impact functional variants missing from the publicly available cosmopolitan panels could be clinically more relevant than polygenic risk scores.
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http://dx.doi.org/10.1038/s41467-020-17315-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7585430PMC
October 2020

Characteristics Associated With Racial/Ethnic Disparities in COVID-19 Outcomes in an Academic Health Care System.

JAMA Netw Open 2020 10 1;3(10):e2025197. Epub 2020 Oct 1.

Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor.

Importance: Black patients are overrepresented in the number of COVID-19 infections, hospitalizations, and deaths in the US. Reasons for this disparity may be due to underlying comorbidities or sociodemographic factors that require further exploration.

Objective: To systematically determine patient characteristics associated with racial/ethnic disparities in COVID-19 outcomes.

Design, Setting, And Participants: This retrospective cohort study used comparative groups of patients tested or treated for COVID-19 at the University of Michigan from March 10, 2020, to April 22, 2020, with an outcome update through July 28, 2020. A group of randomly selected untested individuals were included for comparison. Examined factors included race/ethnicity, age, smoking, alcohol consumption, comorbidities, body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), and residential-level socioeconomic characteristics.

Exposure: In-house polymerase chain reaction (PCR) tests, commercial antibody tests, nasopharynx or oropharynx PCR deployed by the Michigan Department of Health and Human Services and reverse transcription-PCR tests performed in external labs.

Main Outcomes And Measures: The main outcomes were being tested for COVID-19, having test results positive for COVID-19 or being diagnosed with COVID-19, being hospitalized for COVID-19, requiring intensive care unit (ICU) admission for COVID-19, and COVID-19-related mortality (including inpatient and outpatient). Medical comorbidities were defined from the International Classification of Diseases, Ninth Revision, and International Classification of Diseases, Tenth Revision, codes and were aggregated into a comorbidity score. Associations with COVID-19 outcomes were examined using odds ratios (ORs).

Results: Of 5698 patients tested for COVID-19 (mean [SD] age, 47.4 [20.9] years; 2167 [38.0%] men; mean [SD] BMI, 30.0 [8.0]), most were non-Hispanic White (3740 patients [65.6%]) or non-Hispanic Black (1058 patients [18.6%]). The comparison group included 7168 individuals who were not tested (mean [SD] age, 43.1 [24.1] years; 3257 [45.4%] men; mean [SD] BMI, 28.5 [7.1]). Among 1139 patients diagnosed with COVID-19, 492 (43.2%) were White and 442 (38.8%) were Black; 523 (45.9%) were hospitalized, 283 (24.7%) were admitted to the ICU, and 88 (7.7%) died. Adjusting for age, sex, socioeconomic status, and comorbidity score, Black patients were more likely to be hospitalized compared with White patients (OR, 1.72 [95% CI, 1.15-2.58]; P = .009). In addition to older age, male sex, and obesity, living in densely populated areas was associated with increased risk of hospitalization (OR, 1.10 [95% CI, 1.01-1.19]; P = .02). In the overall population, higher risk of hospitalization was also observed in patients with preexisting type 2 diabetes (OR, 1.82 [95% CI, 1.25-2.64]; P = .02) and kidney disease (OR, 2.87 [95% CI, 1.87-4.42]; P < .001). Compared with White patients, obesity was associated with higher risk of having test results positive for COVID-19 among Black patients (White: OR, 1.37 [95% CI, 1.01-1.84]; P = .04. Black: OR, 3.11 [95% CI, 1.64-5.90]; P < .001; P for interaction = .02). Having any cancer was associated with higher risk of positive COVID-19 test results for Black patients (OR, 1.82 [95% CI, 1.19-2.78]; P = .005) but not White patients (OR, 1.08 [95% CI, 0.84-1.40]; P = .53; P for interaction = .04). Overall comorbidity burden was associated with higher risk of hospitalization in White patients (OR, 1.30 [95% CI, 1.11-1.53]; P = .001) but not in Black patients (OR, 0.99 [95% CI, 0.83-1.17]; P = .88; P for interaction = .02), as was type 2 diabetes (White: OR, 2.59 [95% CI, 1.49-4.48]; P < .001; Black: OR, 1.17 [95% CI, 0.66-2.06]; P = .59; P for interaction = .046). No statistically significant racial differences were found in ICU admission and mortality based on adjusted analysis.

Conclusions And Relevance: These findings suggest that preexisting type 2 diabetes or kidney diseases and living in high-population density areas were associated with higher risk for COVID-19 hospitalization. Associations of risk factors with COVID-19 outcomes differed by race.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.25197DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7578774PMC
October 2020

Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks.

Am J Hum Genet 2020 11 28;107(5):815-836. Epub 2020 Sep 28.

Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA; Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address:

To facilitate scientific collaboration on polygenic risk scores (PRSs) research, we created an extensive PRS online repository for 35 common cancer traits integrating freely available genome-wide association studies (GWASs) summary statistics from three sources: published GWASs, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWASs. Our framework condenses these summary statistics into PRSs using various approaches such as linkage disequilibrium pruning/p value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRSs in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRSs. We expect this integrated platform to accelerate PRS-related cancer research.
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http://dx.doi.org/10.1016/j.ajhg.2020.08.025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7675001PMC
November 2020

A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine.

medRxiv 2021 Feb 20. Epub 2021 Feb 20.

Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.

Background: We perform a phenome-wide scan to identify pre-existing conditions related to COVID-19 susceptibility and prognosis across the medical phenome and how they vary by race.

Methods: The study is comprised of 53,853 patients who were tested/positive for COVID-19 between March 10 and September 2, 2020 at a large academic medical center.

Results: Pre-existing conditions strongly associated with hospitalization were , and . Hematopoietic conditions were associated with ICU admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks.

Conclusions: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.
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http://dx.doi.org/10.1101/2020.06.29.20141564DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7418740PMC
February 2021

Understanding the patterns of repeated testing for COVID-19: Association with patient characteristics and outcomes.

medRxiv 2020 Jul 29. Epub 2020 Jul 29.

Importance The diagnostic tests for COVID-19 have a high false negative rate, but not everyone with an initial negative result is re-tested. Michigan Medicine, being one of the primary regional centers accepting COVID-19 cases, provided an ideal setting for studying COVID-19 repeated testing patterns during the first wave of the pandemic. Objective To identify the characteristics of patients who underwent repeated testing for COVID-19 and determine if repeated testing was associated with patient characteristics and with downstream outcomes among positive cases. Design This cross-sectional study described the pattern of testing for COVID-19 at Michigan Medicine. The main hypothesis under consideration is whether patient characteristics differed between those tested once and those who underwent multiple tests. We then restrict our attention to those that had at least one positive test and study repeated testing patterns in patients with severe COVID-19 related outcomes (testing positive, hospitalization and ICU care). Setting Demographic and clinical characteristics, test results, and health outcomes for 15,920 patients presenting to Michigan Medicine between March 10 and June 4, 2020 for a diagnostic test for COVID-19 were collected from their electronic medical records on June 24, 2020. Data on the number and types of tests administered to a given patient, as well as the sequences of patient-specific test results were derived from records of patient laboratory results. Participants Anyone tested between March 10 and June 4, 2020 at Michigan Medicine with a diagnostic test for COVID-19 in their Electronic Health Records were included in our analysis. Exposures Comparison of repeated testing across patient demographics, clinical characteristics, and patient outcomes Main Outcomes and Measures Whether patients underwent repeated diagnostic testing for SARS CoV-2 in Michigan Medicine Results Between March 10th and June 4th, 19,540 tests were ordered for 15,920 patients, with most patients only tested once (13596, 85.4%) and never testing positive (14753, 92.7%). There were 5 patients who got tested 10 or more times and there were substantial variations in test results within a patient. After fully adjusting for patient and neighborhood socioeconomic status (NSES) and demographic characteristics, patients with circulatory diseases (OR: 1.42; 95% CI: (1.18, 1.72)), any cancer (OR: 1.14; 95% CI: (1.01, 1.29)), Type 2 diabetes (OR: 1.22; 95% CI: (1.06, 1.39)), kidney diseases (OR: 1.95; 95% CI: (1.71, 2.23)), and liver diseases (OR: 1.30; 95% CI: (1.11, 1.50)) were found to have higher odds of undergoing repeated testing when compared to those without. Additionally, as compared to non-Hispanic whites, non-Hispanic blacks were found to have higher odds (OR: 1.21; 95% CI: (1.03, 1.43)) of receiving additional testing. Females were found to have lower odds (OR: 0.86; 95% CI: (0.76, 0.96)) of receiving additional testing than males. Neighborhood poverty level also affected whether to receive additional testing. For 1% increase in proportion of population with annual income below the federal poverty level, the odds ratio of receiving repeated testing is 1.01 (OR: 1.01; 95% CI: (1.00, 1.01)). Focusing on only those 1167 patients with at least one positive result in their full testing history, patient age in years (OR: 1.01; 95% CI: (1.00, 1.03)), prior history of kidney diseases (OR: 2.15; 95% CI: (1.36, 3.41)) remained significantly different between patients who underwent repeated testing and those who did not. After adjusting for both patient demographic factors and NSES, hospitalization (OR: 7.44; 95% CI: (4.92, 11.41)) and ICU-level care (OR: 6.97; 95% CI: (4.48, 10.98)) were significantly associated with repeated testing. Of these 1167 patients, 306 got repeated testing and 1118 tests were done on these 306 patients, of which 810 (72.5%) were done during inpatient stays, substantiating that most repeated tests for test positive patients were done during hospitalization or ICU care. Additionally, using repeated testing data we estimate the "real world" false negative rate of the RT-PCR diagnostic test was 23.8% (95% CI: (19.5%, 28.5%)). Conclusions and Relevance This study sought to quantify the pattern of repeated testing for COVID-19 at Michigan Medicine. While most patients were tested once and received a negative result, a meaningful subset of patients (2324, 14.6% of the population who got tested) underwent multiple rounds of testing (5,944 tests were done in total on these 2324 patients, with an average of 2.6 tests per person), with 10 or more tests for five patients. Both hospitalizations and ICU care differed significantly between patients who underwent repeated testing versus those only tested once as expected. These results shed light on testing patterns and have important implications for understanding the variation of repeated testing results within and between patients.
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http://dx.doi.org/10.1101/2020.07.26.20162453DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7418739PMC
July 2020

COVID-19 outcomes, risk factors and associations by race: a comprehensive analysis using electronic health records data in Michigan Medicine.

medRxiv 2020 Jun 18. Epub 2020 Jun 18.

Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI.

Importance: Blacks/African-Americans are overrepresented in the number of COVID-19 infections, hospitalizations and deaths. Reasons for this disparity have not been well-characterized but may be due to underlying comorbidities or sociodemographic factors.

Objective: To systematically determine patient characteristics associated with racial/ethnic disparities in COVID-19 outcomes.

Design: A retrospective cohort study with comparative control groups.

Setting: Patients tested for COVID-19 at University of Michigan Medicine from March 10, 2020 to April 22, 2020.

Participants: 5,698 tested patients and two sets of comparison groups who were not tested for COVID-19: randomly selected unmatched controls (n = 7,211) and frequency-matched controls by race, age, and sex (n = 13,351). Main Outcomes and Measures: We identified factors associated with testing and testing positive for COVID-19, being hospitalized, requiring intensive care unit (ICU) admission, and mortality (in/out-patient during the time frame). Factors included race/ethnicity, age, smoking, alcohol consumption, healthcare utilization, and residential-level socioeconomic characteristics (SES; i.e., education, unemployment, population density, and poverty rate). Medical comorbidities were defined from the International Classification of Diseases (ICD) codes, and were aggregated into a comorbidity score.

Results: Of 5,698 patients, (median age, 47 years; 38% male; mean BMI, 30.1), the majority were non-Hispanic Whites (NHW, 59.2%) and non-Hispanic Black/African-Americans (NHAA, 17.2%). Among 1,119 diagnosed, there were 41.2% NHW and 37.4% NHAA; 44.8% hospitalized, 20.6% admitted to ICU, and 3.8% died. Adjusting for age, sex, and SES, NHAA were 1.66 times more likely to be hospitalized (95% CI, 1.09-2.52; P=.02), 1.52 times more likely to enter ICU (95% CI, 0.92-2.52; P=.10). In addition to older age, male sex and obesity, high population density neighborhood (OR, 1.27 associated with one SD change [95% CI, 1.20-1.76]; P=.02) was associated with hospitalization. Pre-existing kidney disease led to 2.55 times higher risk of hospitalization (95% CI, 1.62-4.02; P<.001) in the overall population and 11.9 times higher mortality risk in NHAA (95% CI, 2.2-64.7, P=.004).

Conclusions And Relevance: Pre-existing type II diabetes/kidney diseases and living in high population density areas were associated with high risk for COVID-19 susceptibility and poor prognosis. Association of risk factors with COVID-19 outcomes differed by race. NHAA patients were disproportionately affected by obesity and kidney disease.
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http://dx.doi.org/10.1101/2020.06.16.20133140DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7418735PMC
June 2020

GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer.

Nat Commun 2020 08 7;11(1):3981. Epub 2020 Aug 7.

Center for Statistical Genetics and Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.

Thyroid stimulating hormone (TSH) is critical for normal development and metabolism. To better understand the genetic contribution to TSH levels, we conduct a GWAS meta-analysis at 22.4 million genetic markers in up to 119,715 individuals and identify 74 genome-wide significant loci for TSH, of which 28 are previously unreported. Functional experiments show that the thyroglobulin protein-altering variants P118L and G67S impact thyroglobulin secretion. Phenome-wide association analysis in the UK Biobank demonstrates the pleiotropic effects of TSH-associated variants and a polygenic score for higher TSH levels is associated with a reduced risk of thyroid cancer in the UK Biobank and three other independent studies. Two-sample Mendelian randomization using TSH index variants as instrumental variables suggests a protective effect of higher TSH levels (indicating lower thyroid function) on risk of thyroid cancer and goiter. Our findings highlight the pleiotropic effects of TSH-associated variants on thyroid function and growth of malignant and benign thyroid tumors.
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http://dx.doi.org/10.1038/s41467-020-17718-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414135PMC
August 2020

Age-of-onset information helps identify 76 genetic variants associated with allergic disease.

PLoS Genet 2020 06 30;16(6):e1008725. Epub 2020 Jun 30.

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Risk factors that contribute to inter-individual differences in the age-of-onset of allergic diseases are poorly understood. The aim of this study was to identify genetic risk variants associated with the age at which symptoms of allergic disease first develop, considering information from asthma, hay fever and eczema. Self-reported age-of-onset information was available for 117,130 genotyped individuals of European ancestry from the UK Biobank study. For each individual, we identified the earliest age at which asthma, hay fever and/or eczema was first diagnosed and performed a genome-wide association study (GWAS) of this combined age-of-onset phenotype. We identified 50 variants with a significant independent association (P<3x10-8) with age-of-onset. Forty-five variants had comparable effects on the onset of the three individual diseases and 38 were also associated with allergic disease case-control status in an independent study (n = 222,484). We observed a strong negative genetic correlation between age-of-onset and case-control status of allergic disease (rg = -0.63, P = 4.5x10-61), indicating that cases with early disease onset have a greater burden of allergy risk alleles than those with late disease onset. Subsequently, a multivariate GWAS of age-of-onset and case-control status identified a further 26 associations that were missed by the univariate analyses of age-of-onset or case-control status only. Collectively, of the 76 variants identified, 18 represent novel associations for allergic disease. We identified 81 likely target genes of the 76 associated variants based on information from expression quantitative trait loci (eQTL) and non-synonymous variants, of which we highlight ADAM15, FOSL2, TRIM8, BMPR2, CD200R1, PRKCQ, NOD2, SMAD4, ABCA7 and UBE2L3. Our results support the notion that early and late onset allergic disease have partly distinct genetic architectures, potentially explaining known differences in pathophysiology between individuals.
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http://dx.doi.org/10.1371/journal.pgen.1008725DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7367489PMC
June 2020

A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank.

Am J Hum Genet 2020 08 25;107(2):222-233. Epub 2020 Jun 25.

Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Graduate School of Data Science, Seoul National University, Seoul 08826, Republic of Korea. Electronic address:

With increasing biobanking efforts connecting electronic health records and national registries to germline genetics, the time-to-event data analysis has attracted increasing attention in the genetics studies of human diseases. In time-to-event data analysis, the Cox proportional hazards (PH) regression model is one of the most used approaches. However, existing methods and tools are not scalable when analyzing a large biobank with hundreds of thousands of samples and endpoints, and they are not accurate when testing low-frequency and rare variants. Here, we propose a scalable and accurate method, SPACox (a saddlepoint approximation implementation based on the Cox PH regression model), that is applicable for genome-wide scale time-to-event data analysis. SPACox requires fitting a Cox PH regression model only once across the genome-wide analysis and then uses a saddlepoint approximation (SPA) to calibrate the test statistics. Simulation studies show that SPACox is 76-252 times faster than other existing alternatives, such as gwasurvivr, 185-511 times faster than the standard Wald test, and more than 6,000 times faster than the Firth correction and can control type I error rates at the genome-wide significance level regardless of minor allele frequencies. Through the analysis of UK Biobank inpatient data of 282,871 white British European ancestry samples, we show that SPACox can efficiently analyze large sample sizes and accurately control type I error rates. We identified 611 loci associated with time-to-event phenotypes of 12 common diseases, of which 38 loci would be missed within a logistic regression framework with a binary phenotype defined as event occurrence status during the follow-up period.
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http://dx.doi.org/10.1016/j.ajhg.2020.06.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7413891PMC
August 2020

Assessment of a causal relationship between body mass index and atopic dermatitis.

J Allergy Clin Immunol 2021 01 17;147(1):400-403. Epub 2020 May 17.

Skin Research Group, School of Medicine, University of Dundee, Dundee, United Kingdom; Department of Dermatology, Ninewells Hospital and Medical School, Dundee, United Kingdom. Electronic address:

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http://dx.doi.org/10.1016/j.jaci.2020.04.050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794861PMC
January 2021

Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts.

Nat Genet 2020 06 18;52(6):634-639. Epub 2020 May 18.

Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.

With very large sample sizes, biobanks provide an exciting opportunity to identify genetic components of complex traits. To analyze rare variants, region-based multiple-variant aggregate tests are commonly used to increase power for association tests. However, because of the substantial computational cost, existing region-based tests cannot analyze hundreds of thousands of samples while accounting for confounders such as population stratification and sample relatedness. Here we propose a scalable generalized mixed-model region-based association test, SAIGE-GENE, that is applicable to exome-wide and genome-wide region-based analysis for hundreds of thousands of samples and can account for unbalanced case-control ratios for binary traits. Through extensive simulation studies and analysis of the HUNT study with 69,716 Norwegian samples and the UK Biobank data with 408,910 White British samples, we show that SAIGE-GENE can efficiently analyze large-sample data (N > 400,000) with type I error rates well controlled.
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http://dx.doi.org/10.1038/s41588-020-0621-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7871731PMC
June 2020

A Novel Variant in Gene Causes Extremely Low LDL-C Without Known Adverse Effects.

JACC Case Rep 2020 May 20;2(5):775-779. Epub 2020 May 20.

University of Michigan, Michigan Medicine, Ann Arbor, Michigan.

A novel frameshift variant was identified in that segregates in a dominant manner with low levels of low-density lipoprotein cholesterol. Affected family members show no apparent clinical complications. There is no consensus regarding clinical management, and the long-term consequences of low levels of low-density lipoprotein cholesterol remain unknown. ().
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http://dx.doi.org/10.1016/j.jaccas.2020.03.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8301695PMC
May 2020

Genome-wide association meta-analyses combining multiple risk phenotypes provide insights into the genetic architecture of cutaneous melanoma susceptibility.

Nat Genet 2020 05 27;52(5):494-504. Epub 2020 Apr 27.

Department of Dermatology, Instituto Valenciano de Oncología, Valencia, Spain.

Most genetic susceptibility to cutaneous melanoma remains to be discovered. Meta-analysis genome-wide association study (GWAS) of 36,760 cases of melanoma (67% newly genotyped) and 375,188 controls identified 54 significant (P < 5 × 10) loci with 68 independent single nucleotide polymorphisms. Analysis of risk estimates across geographical regions and host factors suggests the acral melanoma subtype is uniquely unrelated to pigmentation. Combining this meta-analysis with GWAS of nevus count and hair color, and transcriptome association approaches, uncovered 31 potential secondary loci for a total of 85 cutaneous melanoma susceptibility loci. These findings provide insights into cutaneous melanoma genetic architecture, reinforcing the importance of nevogenesis, pigmentation and telomere maintenance, together with identifying potential new pathways for cutaneous melanoma pathogenesis.
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http://dx.doi.org/10.1038/s41588-020-0611-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7255059PMC
May 2020

An analytic framework for exploring sampling and observation process biases in genome and phenome-wide association studies using electronic health records.

Stat Med 2020 06 20;39(14):1965-1979. Epub 2020 Mar 20.

Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.

Large-scale association analyses based on observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, challenges due to nonprobability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. The extent of the bias introduced by ignoring these factors is not well-characterized. In this paper, we develop an analytic framework for characterizing the bias expected in disease-gene association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis approach, this framework can be used to obtain plausible values for parameters of interest given summary results from standard analysis. We develop an online tool for performing this sensitivity analysis. Simulations demonstrate promising properties of the proposed method. We apply our approach to study bias in disease-gene association studies using electronic health record data from the Michigan Genomics Initiative, a longitudinal biorepository effort within The University Michigan health system.
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http://dx.doi.org/10.1002/sim.8524DOI Listing
June 2020
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