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Maternal microbial molecules affect offspring health.
Ferguson J
(2020) Science 367: 978-979
MeSH Terms: Animals, Child, Child Health, Diet, High-Fat, Female, Gastrointestinal Microbiome, Mice, Obesity, Phenotype, Pregnancy
Added March 3, 2020
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10 MeSH Terms
Predictive Accuracy of a Polygenic Risk Score Compared With a Clinical Risk Score for Incident Coronary Heart Disease.
Mosley JD, Gupta DK, Tan J, Yao J, Wells QS, Shaffer CM, Kundu S, Robinson-Cohen C, Psaty BM, Rich SS, Post WS, Guo X, Rotter JI, Roden DM, Gerszten RE, Wang TJ
(2020) JAMA 323: 627-635
MeSH Terms: Aged, Cohort Studies, Coronary Disease, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Incidence, Male, Middle Aged, Multifactorial Inheritance, Myocardial Infarction, Odds Ratio, Phenotype, Polymorphism, Single Nucleotide, Predictive Value of Tests, Proportional Hazards Models, Retrospective Studies, Risk, Risk Assessment
Show Abstract · Added March 24, 2020
Importance - Polygenic risk scores comprising millions of single-nucleotide polymorphisms (SNPs) could be useful for population-wide coronary heart disease (CHD) screening.
Objective - To determine whether a polygenic risk score improves prediction of CHD compared with a guideline-recommended clinical risk equation.
Design, Setting, and Participants - A retrospective cohort study of the predictive accuracy of a previously validated polygenic risk score was assessed among 4847 adults of white European ancestry, aged 45 through 79 years, participating in the Atherosclerosis Risk in Communities (ARIC) study and 2390 participating in the Multi-Ethnic Study of Atherosclerosis (MESA) from 1996 through December 31, 2015, the final day of follow-up. The performance of the polygenic risk score was compared with that of the 2013 American College of Cardiology and American Heart Association pooled cohort equations.
Exposures - Genetic risk was computed for each participant by summing the product of the weights and allele dosage across 6 630 149 SNPs. Weights were based on an international genome-wide association study.
Main Outcomes and Measures - Prediction of 10-year first CHD events (including myocardial infarctions, fatal coronary events, silent infarctions, revascularization procedures, or resuscitated cardiac arrest) assessed using measures of model discrimination, calibration, and net reclassification improvement (NRI).
Results - The study population included 4847 adults from the ARIC study (mean [SD] age, 62.9 [5.6] years; 56.4% women) and 2390 adults from the MESA cohort (mean [SD] age, 61.8 [9.6] years; 52.2% women). Incident CHD events occurred in 696 participants (14.4%) and 227 participants (9.5%), respectively, over median follow-up of 15.5 years (interquartile range [IQR], 6.3 years) and 14.2 (IQR, 2.5 years) years. The polygenic risk score was significantly associated with 10-year CHD incidence in ARIC with hazard ratios per SD increment of 1.24 (95% CI, 1.15 to 1.34) and in MESA, 1.38 (95% CI, 1.21 to 1.58). Addition of the polygenic risk score to the pooled cohort equations did not significantly increase the C statistic in either cohort (ARIC, change in C statistic, -0.001; 95% CI, -0.009 to 0.006; MESA, 0.021; 95% CI, -0.0004 to 0.043). At the 10-year risk threshold of 7.5%, the addition of the polygenic risk score to the pooled cohort equations did not provide significant improvement in reclassification in either ARIC (NRI, 0.018, 95% CI, -0.012 to 0.036) or MESA (NRI, 0.001, 95% CI, -0.038 to 0.076). The polygenic risk score did not significantly improve calibration in either cohort.
Conclusions and Relevance - In this analysis of 2 cohorts of US adults, the polygenic risk score was associated with incident coronary heart disease events but did not significantly improve discrimination, calibration, or risk reclassification compared with conventional predictors. These findings suggest that a polygenic risk score may not enhance risk prediction in a general, white middle-aged population.
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Phenome-based approach identifies RIC1-linked Mendelian syndrome through zebrafish models, biobank associations and clinical studies.
Unlu G, Qi X, Gamazon ER, Melville DB, Patel N, Rushing AR, Hashem M, Al-Faifi A, Chen R, Li B, Cox NJ, Alkuraya FS, Knapik EW
(2020) Nat Med 26: 98-109
MeSH Terms: Abnormalities, Multiple, Animals, Behavior, Animal, Biological Specimen Banks, Chondrocytes, Disease Models, Animal, Extracellular Matrix, Fibroblasts, Guanine Nucleotide Exchange Factors, Humans, Models, Biological, Musculoskeletal System, Osteogenesis, Phenomics, Phenotype, Procollagen, Protein Transport, Secretory Pathway, Syndrome, Zebrafish, Zebrafish Proteins
Show Abstract · Added January 15, 2020
Discovery of genotype-phenotype relationships remains a major challenge in clinical medicine. Here, we combined three sources of phenotypic data to uncover a new mechanism for rare and common diseases resulting from collagen secretion deficits. Using a zebrafish genetic screen, we identified the ric1 gene as being essential for skeletal biology. Using a gene-based phenome-wide association study (PheWAS) in the EHR-linked BioVU biobank, we show that reduced genetically determined expression of RIC1 is associated with musculoskeletal and dental conditions. Whole-exome sequencing identified individuals homozygous-by-descent for a rare variant in RIC1 and, through a guided clinical re-evaluation, it was discovered that they share signs with the BioVU-associated phenome. We named this new Mendelian syndrome CATIFA (cleft lip, cataract, tooth abnormality, intellectual disability, facial dysmorphism, attention-deficit hyperactivity disorder) and revealed further disease mechanisms. This gene-based, PheWAS-guided approach can accelerate the discovery of clinically relevant disease phenome and associated biological mechanisms.
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21 MeSH Terms
Post-GWAS analysis of six substance use traits improves the identification and functional interpretation of genetic risk loci.
Marees AT, Gamazon ER, Gerring Z, Vorspan F, Fingal J, van den Brink W, Smit DJA, Verweij KJH, Kranzler HR, Sherva R, Farrer L, International Cannabis Consortium, Gelernter J, Derks EM
(2020) Drug Alcohol Depend 206: 107703
MeSH Terms: Blood, Brain, Drug Users, Gene Expression Profiling, Gene Expression Regulation, Genetic Predisposition to Disease, Humans, Meta-Analysis as Topic, Phenotype, Quantitative Trait Loci, Substance-Related Disorders, Transcriptome
Show Abstract · Added December 5, 2019
BACKGROUND - Little is known about the functional mechanisms through which genetic loci associated with substance use traits ascertain their effect. This study aims to identify and functionally annotate loci associated with substance use traits based on their role in genetic regulation of gene expression.
METHODS - We evaluated expression Quantitative Trait Loci (eQTLs) from 13 brain regions and whole blood of the Genotype-Tissue Expression (GTEx) database, and from whole blood of the Depression Genes and Networks (DGN) database. The role of single eQTLs was examined for six substance use traits: alcohol consumption (N = 537,349), cigarettes per day (CPD; N = 263,954), former vs. current smoker (N = 312,821), age of smoking initiation (N = 262,990), ever smoker (N = 632,802), and cocaine dependence (N = 4,769). Subsequently, we conducted a gene level analysis of gene expression on these substance use traits using S-PrediXcan.
RESULTS - Using an FDR-adjusted p-value <0.05 we found 2,976 novel candidate genetic loci for substance use traits, and identified genes and tissues through which these loci potentially exert their effects. Using S-PrediXcan, we identified significantly associated genes for all substance traits.
DISCUSSION - Annotating genes based on transcriptomic regulation improves the identification and functional characterization of candidate loci and genes for substance use traits.
Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.
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12 MeSH Terms
CYP2C19 Phenotype and Risk of Proton Pump Inhibitor-Associated Infections.
Bernal CJ, Aka I, Carroll RJ, Coco JR, Lima JJ, Acra SA, Roden DM, Van Driest SL
(2019) Pediatrics 144:
MeSH Terms: Cohort Studies, Cytochrome P-450 CYP2C19, Female, Humans, Infant, Infections, Male, Phenotype, Proton Pump Inhibitors, Retrospective Studies, Risk Factors
Show Abstract · Added March 24, 2020
OBJECTIVES - Proton pump inhibitors (PPIs) are often used in pediatrics to treat common gastrointestinal disorders, and there are growing concerns for infectious adverse events. Because CYP2C19 inactivates PPIs, genetic variants that increase CYP2C19 function may decrease PPI exposure and infections. We tested the hypothesis that CYP2C19 metabolizer phenotypes are associated with infection event rates in children exposed to PPIs.
METHODS - This retrospective biorepository cohort study included individuals aged 0 to 36 months at the time of PPI exposure. Respiratory tract and gastrointestinal tract infection events were identified by using codes in the year after the first PPI mention. Variants defining , , , , , and were genotyped, and all individuals were classified as CYP2C19 poor or intermediate, normal metabolizers (NMs), or rapid or ultrarapid metabolizers (RM/UMs). Infection rates were compared by using univariate and multivariate analyses.
RESULTS - In all, 670 individuals were included (median age 7 months; 44% girls). CYP2C19 NMs ( = 267; 40%) had a higher infection rate than RM/UMs ( = 220; 33%; median 2 vs 1 infections per person per year; = .03). There was no difference between poor or intermediate ( = 183; 27%) and NMs. In multivariable analysis of NMs and RM/UMs adjusting for age, sex, PPI dose, and comorbidities, CYP2C19 metabolizer status remained a significant risk factor for infection events (odds ratio 0.70 [95% confidence interval 0.50-0.97] for RM/UMs versus NMs).
CONCLUSIONS - PPI therapy is associated with higher infection rates in children with normal CYP2C19 function than in those with increased CYP2C19 function, highlighting this adverse effect of PPI therapy and the relevance of genotypes to PPI therapeutic decision-making.
Copyright © 2019 by the American Academy of Pediatrics.
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Stoichiometric optimization of Gata4, Hand2, Mef2c, and Tbx5 expression for contractile cardiomyocyte reprogramming.
Zhang Z, Zhang W, Nam YJ
(2019) Sci Rep 9: 14970
MeSH Terms: Animals, Basic Helix-Loop-Helix Transcription Factors, Cells, Cultured, Cellular Reprogramming, Fibroblasts, GATA4 Transcription Factor, MEF2 Transcription Factors, Mice, Mice, Transgenic, Myocardial Contraction, Myocytes, Cardiac, Phenotype, Plasmids, T-Box Domain Proteins, Transcriptome, Transduction, Genetic
Show Abstract · Added March 24, 2020
Reprogramming of fibroblasts to induced cardiomyocyte-like cells (iCMs) offers potential strategies for new cardiomyocyte generation. However, a major challenge of this approach remains its low efficiency for contractile iCMs. Here, we showed that controlled stoichiometric expression of Gata4 (G), Hand2 (H), Mef2c (M), and Tbx5 (T) significantly enhanced contractile cardiomyocyte reprogramming over previously defined stoichiometric expression of GMT or uncontrolled expression of GHMT. We generated quad-cistronic vectors expressing distinct relative protein levels of GHMT within the context of a previously defined splicing order of M-G-T with high Mef2c level. Transduction of the quad-cistronic vector with a splicing order of M-G-T-H (referred to as M-G-T-H) inducing relatively low Hand2 and high Mef2c protein levels not only increased sarcomeric protein induction, but also markedly promoted the development of contractile structures and functions in fibroblasts. The expressed Gata4 and Tbx5 protein levels by M-G-T-H transduction were relatively higher than those by transductions of other quad-cistronic vectors, but lower than those by previously defined M-G-T tri-cistronic vector transduction. Taken together, our results demonstrate the stoichiometric requirement of GHMT expression for structural and functional progresses of cardiomyocyte reprogramming and provide a new basic tool-set for future studies.
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16 MeSH Terms
Inferred divergent gene regulation in archaic hominins reveals potential phenotypic differences.
Colbran LL, Gamazon ER, Zhou D, Evans P, Cox NJ, Capra JA
(2019) Nat Ecol Evol 3: 1598-1606
MeSH Terms: Animals, Female, Genome, Human, Haplotypes, Hominidae, Humans, Neanderthals, Phenotype
Show Abstract · Added October 12, 2019
Sequencing DNA derived from archaic bones has enabled genetic comparison of Neanderthals and anatomically modern humans (AMHs), and revealed that they interbred. However, interpreting what genetic differences imply about their phenotypic differences remains challenging. Here, we introduce an approach for identifying divergent gene regulation between archaic hominins, such as Neanderthals, and AMH sequences, and find 766 genes that are likely to have been divergently regulated (DR) by Neanderthal haplotypes that do not remain in AMHs. DR genes include many involved in phenotypes known to differ between Neanderthals and AMHs, such as the structure of the rib cage and supraorbital ridge development. They are also enriched for genes associated with spontaneous abortion, polycystic ovary syndrome, myocardial infarction and melanoma. Phenotypes associated with modern human variation in these genes' regulation in ~23,000 biobank patients further support their involvement in immune and cardiovascular phenotypes. Comparing DR genes between two Neanderthals and a Denisovan revealed divergence in the immune system and in genes associated with skeletal and dental morphology that are consistent with the archaeological record. These results establish differences in gene regulatory architecture between AMHs and archaic hominins, and provide an avenue for exploring phenotypic differences between archaic groups from genomic information alone.
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8 MeSH Terms
CNV Association of Diverse Clinical Phenotypes from eMERGE reveals novel disease biology underlying cardiovascular disease.
Glessner JT, Li J, Desai A, Palmer M, Kim D, Lucas AM, Chang X, Connolly JJ, Almoguera B, Harley JB, Jarvik GP, Ritchie MD, Sleiman PMA, Roden DM, Crosslin D, Hakonarson H
(2020) Int J Cardiol 298: 107-113
MeSH Terms: Aged, Aged, 80 and over, Body Mass Index, Cardiovascular Diseases, DNA Copy Number Variations, Electrocardiography, Electronic Health Records, Female, Genome-Wide Association Study, Genomics, Humans, Male, Middle Aged, Phenotype, Polymorphism, Single Nucleotide
Show Abstract · Added March 24, 2020
BACKGROUND - Cardiovascular disease is the leading cause of death in the United States. Consequently, individuals who are genetically predisposed for high risk of cardiovascular disease would benefit most from prevention and early intervention approaches. Among common health risk factors affecting adult populations, we evaluated 23 cardiovascular disease-related traits, including BMI, glucose levels and lipid profiling to determine their associations with low-frequency recurrent copy number variations (CNV) (population frequency < 5%).
RESULTS - We examined 10,619 unrelated subjects of European ancestry from the Electronic Medical Records and Genomics (eMERGE) Network who were genotyped with 657,366 markers genome-wide on the Illumina Infinium Quad 660 array. We performed CNV calling based on array marker intensity and evaluated data quality, ancestry stratification, and relatedness to ensure unbiased association discovery. Using a segment-based scoring approach, we assessed the association of all CNVs with each trait. In this large genome-wide analysis of low-frequency CNVs, we observed 11 novel genome-wide significant associations of low-frequency CNVs with major cardiovascular disease traits.
CONCLUSION - In one of the largest genome-wide studies for low-frequency recurrent CNVs, we identified 11 loci associated with cardiovascular disease and related traits at the genome-wide significance level that may serve as biomarkers for prevention and early intervention studies in subjects who are at elevated risk. Our study further supports the role of low-frequency recurrent CNVs in the pathogenesis of common complex disease traits.
Copyright © 2019. Published by Elsevier B.V.
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15 MeSH Terms
Prevention and Reversion of Pancreatic Tumorigenesis through a Differentiation-Based Mechanism.
Krah NM, Narayanan SM, Yugawa DE, Straley JA, Wright CVE, MacDonald RJ, Murtaugh LC
(2019) Dev Cell 50: 744-754.e4
MeSH Terms: Acinar Cells, Animals, Carcinogenesis, Cell Differentiation, Cell Line, Tumor, Cell Proliferation, Clone Cells, Disease Models, Animal, Gene Expression Regulation, Neoplastic, Humans, Inflammation, Mice, Pancreatic Neoplasms, Pancreatitis, Phenotype, Proto-Oncogene Proteins p21(ras), Signal Transduction, Transcription Factors
Show Abstract · Added September 3, 2019
Activating mutations in Kras are nearly ubiquitous in human pancreatic cancer and initiate precancerous pancreatic intraepithelial neoplasia (PanINs) when induced in mouse acinar cells. PanINs normally take months to form but are accelerated by deletion of acinar cell differentiation factors such as Ptf1a, suggesting that loss of cell identity is rate limiting for pancreatic tumor initiation. Using a genetic mouse model that allows for independent control of oncogenic Kras and Ptf1a expression, we demonstrate that sustained Ptf1a is sufficient to prevent Kras-driven tumorigenesis, even in the presence of tumor-promoting inflammation. Furthermore, reintroducing Ptf1a into established PanINs reverts them to quiescent acinar cells in vivo. Similarly, Ptf1a re-expression in human pancreatic cancer cells inhibits their growth and colony-forming ability. Our results suggest that reactivation of an endogenous differentiation program can prevent and reverse oncogene-driven transformation in cells harboring tumor-driving mutations, introducing a potential paradigm for solid tumor prevention and treatment.
Copyright © 2019 Elsevier Inc. All rights reserved.
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18 MeSH Terms
Genomic medicine for undiagnosed diseases.
Wise AL, Manolio TA, Mensah GA, Peterson JF, Roden DM, Tamburro C, Williams MS, Green ED
(2019) Lancet 394: 533-540
MeSH Terms: Adult, Child, Early Diagnosis, Genomics, Humans, Phenotype, Prenatal Diagnosis, Rare Diseases, Sequence Analysis, DNA, Whole Exome Sequencing, Whole Genome Sequencing
Show Abstract · Added March 24, 2020
One of the primary goals of genomic medicine is to improve diagnosis through identification of genomic conditions, which could improve clinical management, prevent complications, and promote health. We explore how genomic medicine is being used to obtain molecular diagnoses for patients with previously undiagnosed diseases in prenatal, paediatric, and adult clinical settings. We focus on the role of clinical genomic sequencing (exome and genome) in aiding patients with conditions that are undiagnosed even after extensive clinical evaluation and testing. In particular, we explore the impact of combining genomic and phenotypic data and integrating multiple data types to improve diagnoses for patients with undiagnosed diseases, and we discuss how these genomic sequencing diagnoses could change clinical management.
Copyright © 2019 Elsevier Ltd. All rights reserved.
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