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The incidence of metabolic dysfunction-associated steatohepatitis (MASH) is on the rise, and with limited pharmacological therapy available, identification of new metabolic targets is urgently needed. Oxalate is a terminal metabolite produced from glyoxylate by hepatic lactate dehydrogenase (LDHA). The liver-specific alanine-glyoxylate aminotransferase (AGXT) detoxifies glyoxylate, preventing oxalate accumulation. Here we show that AGXT is suppressed and LDHA is activated in livers from patients and mice with MASH, leading to oxalate overproduction. In turn, oxalate promotes steatosis in hepatocytes by inhibiting peroxisome proliferator-activated receptor-α (PPARα) transcription and fatty acid β-oxidation and induces monocyte chemotaxis via C-C motif chemokine ligand 2. In male mice with diet-induced MASH, targeting oxalate overproduction through hepatocyte-specific AGXT overexpression or pharmacological inhibition of LDHA potently lowers steatohepatitis and fibrosis by inducing PPARα-driven fatty acid β-oxidation and suppressing monocyte chemotaxis, nuclear factor-κB and transforming growth factor-β targets. These findings highlight hepatic oxalate overproduction as a target for the treatment of MASH., (© 2024. The Author(s).)

The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10−5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10−4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research. [ABSTRACT FROM AUTHOR]

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood. Here, the authors identify six genetic variants associated with adult weight-change, by leveraging 24.5 million weight records from the UK, USA, and Estonia. These variants influence weight change independently of baseline weight. [ABSTRACT FROM AUTHOR]

Background: Several recent observational studies have associated obesity, lifestyle factors (smoking, sleep duration, and alcohol drinking), and glycemic traits with facial aging. However, whether this relationship is causal due to confounding and reverse causation is yet to be substantiated. Aims: We aimed to assess these relationships using Mendelian randomization (MR). Methods: For the instrumental variables, this paper selected independent single nucleotide polymorphisms (SNPs) linked to the exposures at a genome-wide state (P < 5 × 10−8) in equivalent genome-wide association studies (GWAS). Using the UK Biobank, we obtained summary-level data for facial aging on 423,999 individuals. The primary assessments were performed through the combination of complementing techniques (simple method approaches, weighted model, MR-Egger, and weighted median) and the inverse-variance-weighted method. Along with that, we examined the heterogeneity and horizontal pleiotropy through different types of sensitivity analyses. Results: The correlations were (a) facial aging for body mass index (BMI, OR = 1.054, 95% CI 1.044–1.64), (b) waist/hip ratio (OR = 1.056, 95% CI 1.023–1.091), and (c) smoking (OR = 1.023, 95% CI 1.007–1.039). Equally important, the correlations for waist/hip ratio remained robust after adjusting for the genetically predicted BMI (OR = 1.028, 95% CI 1.003–1.054). However, no causal effects of alcoholic drinking, glycemic traits, and sleep duration on facial aging were observed. Conclusions: The outcomes shed light on the potential correlation of obesity and cigarette smoking with facial aging while putting forward a more comprehensive and credible foundation for the optimization of facial aging strategies. No Level Assigned: This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266. [ABSTRACT FROM AUTHOR]

Background: Hypercholesterolemia and the related inflammatory response promote the development of osteoporosis, but whether targeted interventions are protective against this bone metabolic disease remains unknown. The aim of this study was to investigate the association between the use of statins (one well-recognized cholesterol-lowering drug with anti-inflammatory properties) and the risk of osteoporosis using a drug-targeted Mendelian randomization (MR) approach. Methods: Instrumental variables predicting three cholesterol-lowering target genes (including HGMCR) and the cholesterol effectors mediated by these genes (i.e., total cholesterol, LDL cholesterol, and non-HDL cholesterol) were extracted from expression quantitative trait loci and genome-wide association studies. Inverse variance-weighted (IVW), summary data-based MR (SMR), multivariate MR, and colocalization analysis were used to determine the association of the interventions represented by these instrumental variables with heel bone mineral density (one diagnostic indicator of osteoporosis). Results: The IVW reported that increased levels of HGMCR-mediated total cholesterol, LDL cholesterol, and non-HDL cholesterol were associated with the decreased level of heel bone mineral density (P = 4.086e−10, P = 1.487e−09, P = 1.967e−09). The colocalization analysis supported the relationship between HGMCR-mediated non-HDL cholesterol and heel bone mineral density. The SMR reported that higher expression of HGMCR was associated with the decreased level of this osteoporosis indicator (P = 0.036). The multivariate MR further confirmed the role of HGMCR in the correlation between cholesterol traits and heel bone mineral density, and also reported that estrogen played a mediating role in the above correlations. Conclusion: These evidence supported a protective effect of HMGCR-mediated non-HDL cholesterol reduction or statin use against osteoporosis. [ABSTRACT FROM AUTHOR]

Background: The objective of this document is to provide recommendations on the formal reliability of major clinical predictors often associated with intracerebral hemorrhage (ICH) neuroprognostication. Methods: A narrative systematic review was completed using the Grading of Recommendations Assessment, Development, and Evaluation methodology and the Population, Intervention, Comparator, Outcome, Timing, Setting questions. Predictors, which included both individual clinical variables and prediction models, were selected based on clinical relevance and attention in the literature. Following construction of the evidence profile and summary of findings, recommendations were based on Grading of Recommendations Assessment, Development, and Evaluation criteria. Good practice statements addressed essential principles of neuroprognostication that could not be framed in the Population, Intervention, Comparator, Outcome, Timing, Setting format. Results: Six candidate clinical variables and two clinical grading scales (the original ICH score and maximally treated ICH score) were selected for recommendation creation. A total of 347 articles out of 10,751 articles screened met our eligibility criteria. Consensus statements of good practice included deferring neuroprognostication—aside from the most clinically devastated patients—for at least the first 48–72 h of intensive care unit admission; understanding what outcomes would have been most valued by the patient; and counseling of patients and surrogates whose ultimate neurological recovery may occur over a variable period of time. Although many clinical variables and grading scales are associated with ICH poor outcome, no clinical variable alone or sole clinical grading scale was suggested by the panel as currently being reliable by itself for use in counseling patients with ICH and their surrogates, regarding functional outcome at 3 months and beyond or 30-day mortality. Conclusions: These guidelines provide recommendations on the formal reliability of predictors of poor outcome in the context of counseling patients with ICH and surrogates and suggest broad principles of neuroprognostication. Clinicians formulating their judgments of prognosis for patients with ICH should avoid anchoring bias based solely on any one clinical variable or published clinical grading scale. [ABSTRACT FROM AUTHOR]

This study tested interactions among puberty-related genetic risk, prenatal substance use, harsh discipline, and pubertal timing for the severity and directionality (i.e., differentiation) of externalizing and internalizing problems and adolescent substance use. This is a companion paper to Marceau et al. (2021) which examined the same influences in developmental cascade models. Data were from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n = 4504 White boys, n = 4287 White girls assessed from the prenatal period through 18.5 years). We hypothesized generally that later predictors would strengthen the influence of puberty-related genetic risk, prenatal substance use exposure, and pubertal risk on psychopathology and substance use (two-way interactions), and that later predictors would strengthen the interactions of earlier influences on psychopathology and substance use (three-way interactions). Interactions were sparse. Although all fourteen interactions showed that later influences can exacerbate or trigger the effects of earlier ones, they often were not in the expected direction. The most robust moderator was parental discipline, and differing and synergistic effects of biological and socially-relevant aspects of puberty were found. In all, the influences examined here operate more robustly in developmental cascades than in interaction with each other for the development of psychopathology and transitions to substance use. [ABSTRACT FROM AUTHOR]

Background: Previous studies have shown that the gut microbiota plays an important role in the maintenance of thyroid homeostasis. We aimed to evaluate the causal relationships between gut microbiota and hypothyroidism. Methods: Summary statistics for 211 gut microbiota taxa were obtained from the largest available genome-wide association study (GWAS) meta-analysis conducted by the MiBioGen consortium. Summary statistics for hypothyroidism were obtained from two distinct sources: the FinnGen consortium R9 release data (40,926 cases and 274,069 controls) and the UK Biobank data (22,687 cases and 440,246 controls), respectively. A two-sample Mendelian randomization (MR) design was employed, and thorough sensitivity analyses were carried out to ensure the reliability of the results. Results: Based on the FinnGen consortium, we found increased levels of Intestinimonas (OR = 1.09; 95%CI = 1.02–1.16; P = 0.01) and Ruminiclostridium5 (OR = 1.11; 95%CI = 1.02–1.22; P = 0.02) may be associated with a higher risk of hypothyroidism, while increased levels of Butyrivibrio (OR = 0.95; 95%CI = 0.92–0.99; P = 0.02), Eggerthella (OR = 0.93; 95%CI = 0.88–0.98; P = 0.01), Lachnospiraceae UCG008 (OR = 0.92; 95%CI = 0.85–0.99; P = 0.02), Ruminococcaceae UCG011 (OR = 0.95; 95%CI = 0.90–0.99; P = 0.02), and Actinobacteria (OR = 0.88; 95%CI = 0.80–0.97; P = 0.01) may be associated with a lower risk. According to the UK Biobank data, Eggerthella and Ruminiclostridium5 remain causally associated with hypothyroidism. The sensitivity analysis demonstrates consistent results without evidence of heterogeneity or pleiotropy. Conclusion: This study highlights the impact of specific gut microbiota on hypothyroidism. Strategies to change composition of gut microbiota may hold promise as potential interventions. [ABSTRACT FROM AUTHOR]

Imputation servers offer the exclusive possibility to harness the largest public reference panels which have been shown to deliver very high precision in the imputation of European genomes. Many studies have nonetheless stressed the importance of 'study specific panels' (SSPs) as an alternative and have shown the benefits of combining public reference panels with SSPs. But such combined approaches are not attainable when using external imputation servers. To investigate how to confront this challenge, we imputed 550 French individuals using either the University of Michigan imputation server with the Haplotype Reference Consortium (HRC) panel or an in-house SSP of 850 whole-genome sequenced French individuals. With approximate geo-localization of both our target and SSP individuals we are able to pinpoint different scenarios where SSP-based imputation would be preferred over server-based imputation or vice-versa. This is achieved by showing to a high degree of resolution the importance of the proximity of the reference panel to target individuals; with a focus on the clear added value of SSPs for estimating haplotype phase and for the imputation of rare variants (minor allele-frequency below 0.01). Such benefits were most evident for individuals from the same geographical regions in France as the SSP individuals. Overall, only 42.3% of all 125,442 variants evaluated were better imputed with an SSP from France compared to an external reference panel, however this rises to 58.1% for individuals from geographic regions well covered by the SSP. By investigating haplotype sharing and population fine-structure in France, we show the importance of including SSP haplotypes for imputation but also that they should ideally be combined with large public panels. In the absence of the unattainable results from a combined panel of the HRC and our French SSP, we put forward a pragmatic solution where server-based and SSP-based imputation outcomes can be combined based on comparing posterior genotype probabilities. We show that such an approach can give a level of imputation accuracy in excess of what could be achieved with either strategy alone. The results presented provide detailed insights into the accuracy of imputation that should be expected from different strategies for European populations. [ABSTRACT FROM AUTHOR]

Blood biochemical indicators play a crucial role in assessing an individual's overall health status and metabolic function. In this study, we measured five blood biochemical indicators, including total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL-CH), triglycerides (TG), high-density lipoprotein cholesterol (HDL-CH), and blood glucose (BG), as well as 19 growth traits of 206 male chickens. By integrating host whole-genome information and 16S rRNA sequencing of the duodenum, jejunum, ileum, cecum, and feces microbiota, we assessed the contributions of host genetics and gut microbiota to blood biochemical indicators and their interrelationships. Our results demonstrated significant negative phenotypic and genetic correlations (r = − 0.20 ~ − 0.67) between CHOL and LDL-CH with growth traits such as body weight, abdominal fat content, muscle content, and shin circumference. The results of heritability and microbiability indicated that blood biochemical indicators were jointly regulated by host genetics and gut microbiota. Notably, the heritability of HDL-CH was estimated to be 0.24, while the jejunal microbiability for BG and TG reached 0.45 and 0.23. Furthermore, by conducting genome-wide association study (GWAS) with the single-nucleotide polymorphism (SNPs), insertion/deletion (indels), and structural variation (SV), we identified RAP2C, member of the RAS oncogene family (RAP2C), dedicator of cytokinesis 11 (DOCK11), neurotensin (NTS) and BOP1 ribosomal biogenesis factor (BOP1) as regulators of HDL-CH, and glycerophosphodiester phosphodiesterase domain containing 5 (GDPD5), dihydrodiol dehydrogenase (DHDH), and potassium voltage-gated channel interacting protein 1 (KCNIP1) as candidate genes of BG. Moreover, our findings suggest that cecal RF39 and Clostridia_UCG_014 may be linked to the regulation of CHOL, and jejunal Streptococcaceae may be involved in the regulation of TG. Additionally, microbial GWAS results indicated that the presence of gut microbiota was under host genetic regulation. Our findings provide valuable insights into the complex interaction between host genetics and microbiota in shaping the blood biochemical profile of chickens. Key points: • Multiple candidate genes were identified for the regulation of CHOL, HDL-CH, and BG. • RF39, Clostridia_UCG_014, and Streptococcaceae were implicated in CHOL and TG modulation. • The composition of gut microbiota is influenced by host genetics. [ABSTRACT FROM AUTHOR]

Background: Both epidemiological and clinical studies have suggested the comorbidity between cutaneous melanoma (CM) and obesity-related physical traits. However, it remains unclear about their shared genetic architecture. Objective: To determine the shared genetic architecture between CM and obesity-related physical traits through conditional false discovery rate (cFDR) analysis. Method: Quantile–quantile plots were firstly built to assess the pleiotropic enrichment of shared single nucleotide polymorphisms between CM and each trait. Then, cFDR and conjunctional cFDR (ccFDR) were used to identify the shared risk loci between CM and each trait. Moreover, the functional evaluation of shared risk genes was carried out through analyses of expression quantitative trait loci (eQTL), Kyoto Encyclopedia of Genes and Genomes and gene ontology, respectively. Finally, single-cell sequence analysis was performed to locate the expression of eQTL-mapped genes in tissues. Results: Successive pleiotropic enrichment was found between CM and 5 obesity-related traits or height. 24 shared risk loci were identified between CM and 13 traits except appendicular lean mass using ccFDR analysis, with 17 novel and 4 validated loci. The functions of ccFDR-identified and eQTL-mapped genes were revealed to be mainly involved in cellular senescence, proliferation, meiotic nuclear division, cell cycle, and the metabolism of lipid, cholesterol and glucose. Single-cell sequence analysis showed that keratinocytes contribute to the occurrence and aggressiveness of CM through secreting paracrine cytokines. Conclusion: Our findings demonstrate the significant genetic correlation between CM and obesity-related physical traits, which may provide a novel genetical basis for the pathogenesis and treatment of CM. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have allowed the identification of disease-associated variants, which can be leveraged to build polygenic scores (PGSs). Even though PGSs can be a valuable tool in personalized medicine, their predictive power is limited in populations of non-European ancestry, particularly in admixed populations. Recent efforts have focused on increasing racial and ethnic diversity in GWAS, thus, addressing some of the limitations of genetic risk prediction in these populations. Even with these efforts, few studies focus exclusively on Hispanics/Latinos. Additionally, Hispanic/Latino populations are often considered a single population despite varying admixture proportions between and within ethnic groups, diverse genetic heterogeneity, and demographic history. Combined with highly heterogeneous environmental and socioeconomic exposures, this diversity can reduce the transferability of genetic risk prediction models. Given the recent increase of genomic studies that include Hispanics/Latinos, we review the milestones and efforts that focus on genetic risk prediction, summarize the potential for improving PGS transferability, and highlight the challenges yet to be addressed. Additionally, we summarize social-ethical considerations and provide ideas to promote genetic risk prediction models that can be implemented equitably. [ABSTRACT FROM AUTHOR]

Dyslipidemia is a major risk factor for the development of coronary artery disease (CAD). Understanding the genetic determinants of dyslipidemia can provide valuable information on the pathogenesis of CAD and aid in the development of early detection strategies. In this study, we used a Global Screening Array (GSA) to elucidate the genetic factors associated with dyslipidemia and their potential role in the prediction of CAD. We conducted a GSA-based association study in 265 subjects to identify the genetic loci associated with dyslipidemia traits using Multiple Linear Regression (MLR) and Logistic Regression (LR), Classification and Regression Tree (CART), and Manhattan plots. We identified an association between dyslipidemia and variants identified in genes such as JCAD, GLIS3, CD38, FN1, CELSR2, MTNR1B, GIPR, DYM, APOB, APOE, ADCY5. The MLR models explained 62%, 71%, and 81% of the variability in HDL, LDL, and triglycerides, respectively. The Area Under the Curve (AUC) values in the LR models of HDL, LDL, and triglycerides were 1.00, 0.94, and 0.95, respectively. CART models identified novel gene–gene interactions influencing the risk for dyslipidemia. To conclude, we have identified the association of 12 SNVs with dyslipidemia and demonstrated their clinical utility in four different models such as MLR, LR, CART, and Manhattan plots. The identified genetic variants and associated pathways shed light on the underlying biology of dyslipidemia and offer potential avenues for precision medicine strategies in the management of CAD. [ABSTRACT FROM AUTHOR]

With advances in genetic research, the understanding of the genetic structure of disease and the ability to predict disease risk have been enhanced. Polygenic risk scores (PRS) have been developed to assess a person's risk of developing any heritable disease. PRS has two primary utilities that make it particularly relevant for insurers: the ability to identify high-risk groups when using PRS independently or in combination with standard risk factors; and the ability to inform early interventions that may alter future morbidity and mortality. Using heart disease as a case study, a simulation-based model is designed that introduces polygenic risk scoring into the actuarial analysis framework and then quantifies the adverse selection due to information asymmetry introduced by PRS. Individual and parental disease liability as well as PRS were simulated under a liability threshold model. A series of validations were conducted to confirm the utility of our simulated data sets. We explored three scenarios describing how insurance applicants use their PRS results to guide their insurance purchasing decisions and calculated the increased premiums that insurers would need to change to counteract this. The accuracy of PRS has the most significant impact on premiums and the proportion of individuals who know their PRS also has a substantial impact. [ABSTRACT FROM AUTHOR]

Polygenic risk scores (PRS) have the potential to improve the accuracy of clinical risk assessments, yet questions about their clinical validity and readiness for clinical implementation persist. Understanding how individuals integrate and act on the information provided by PRS is critical for their effective integration into routine clinical care, yet few studies have examined how individuals respond to the receipt of polygenic risk information. We conducted an embedded Ethical, Legal, and Social Implications (ELSI) study to examine if and how unaffected participants in a US population breast cancer screening trial understood and utilized PRS, as part of a multifactorial risk score combining traditional risk factors with a genetic risk assessment, to make screening and risk-reduction decisions. Semi-structured qualitative interviews were conducted with 24 trial participants who were designated at elevated risk for breast cancer due to their combined risk score. Interviews were analyzed using a grounded theory approach. Participants understood PRS conceptually and accepted it as one of many risk factors to consider, yet the value and meaning they ascribed to this risk estimate varied. Most participants reported financial and insurance barriers to enhanced screening with MRI and were not interested in taking risk-reducing medications. These findings contribute to our understanding of how PRS may be best translated from research to clinical care. Furthermore, they illuminate ethical concerns about identifying risk and making recommendations based on polygenic risk in a population screening context where many may have trouble accessing appropriate care. [ABSTRACT FROM AUTHOR]

Inadequate representation of non-European ancestry populations in genome-wide association studies (GWAS) has limited opportunities to isolate functional variants. Fine-mapping in multi-ancestry populations should improve the efficiency of prioritizing variants for functional interrogation. To evaluate this hypothesis, we leveraged ancestry architecture to perform comparative GWAS and fine-mapping of obesity-related phenotypes in European ancestry populations from the UK Biobank (UKBB) and multi-ancestry samples from the Population Architecture for Genetic Epidemiology (PAGE) consortium with comparable sample sizes. In the investigated regions with genome-wide significant associations for obesity-related traits, fine-mapping in our ancestrally diverse sample led to 95% and 99% credible sets (CS) with fewer variants than in the European ancestry sample. Lead fine-mapped variants in PAGE regions had higher average coding scores, and higher average posterior probabilities for causality compared to UKBB. Importantly, 99% CS in PAGE loci contained strong expression quantitative trait loci (eQTLs) in adipose tissues or harbored more variants in tighter linkage disequilibrium (LD) with eQTLs. Leveraging ancestrally diverse populations with heterogeneous ancestry architectures, coupled with functional annotation, increased fine-mapping efficiency and performance, and reduced the set of candidate variants for consideration for future functional studies. Significant overlap in genetic causal variants across populations suggests generalizability of genetic mechanisms underpinning obesity-related traits across populations. [ABSTRACT FROM AUTHOR]

Physical activity (PA), aerobic fitness, and cardiometabolic diseases (CMD) are highly heritable multifactorial phenotypes. Shared genetic factors may underlie the associations between higher levels of PA and better aerobic fitness and a lower risk for CMDs. We aimed to study how PA genotype associates with self-reported PA, aerobic fitness, cardiometabolic risk factors and diseases. PA genotype, which combined variation in over one million of gene variants, was composed using the SBayesR polygenic scoring methodology. First, we constructed a polygenic risk score for PA in the Trøndelag Health Study (N = 47,148) using UK Biobank single nucleotide polymorphism-specific weights (N = 400,124). The associations of the PA PRS and continuous variables were analysed using linear regression models and with CMD incidences using Cox proportional hazard models. The results showed that genotypes predisposing to higher amount of PA were associated with greater self-reported PA (Beta [B] = 0.282 MET-h/wk per SD of PRS for PA, 95% confidence interval [CI] = 0.211, 0.354) but not with aerobic fitness. These genotypes were also associated with healthier cardiometabolic profile (waist circumference [B = -0.003 cm, 95% CI = -0.004, -0.002], body mass index [B = -0.002 kg/m2, 95% CI = -0.004, -0.001], high-density lipoprotein cholesterol [B = 0.004 mmol/L, 95% CI = 0.002, 0.006]) and lower incidence of hypertensive diseases (Hazard Ratio [HR] = 0.97, 95% CI = 0.951, 0.990), stroke (HR = 0.94, 95% CI = 0.903, 0.978) and type 2 diabetes (HR = 0.94, 95 % CI = 0.902, 0.970). Observed associations were independent of self-reported PA. These results support earlier findings suggesting small pleiotropic effects between PA and CMDs and provide new evidence about associations of polygenic inheritance of PA and intermediate cardiometabolic risk factors. [ABSTRACT FROM AUTHOR]

Shorter telomeres mark cellular aging and are linked to chronic stress exposure as well as negative physical and psychological outcomes. However, it is unclear whether telomere length mediates associations between early stress exposure and later externalizing problems, or whether boys and girls differ in pathways to these concerns. We therefore examined associations between telomere length, early stress via negative caregiving, and children's externalizing symptom development over time in 409 three-year-old children and their parents. Telomere length mediated the association between early parental intrusiveness and later rule-breaking behavior; however, this association was moderated by children's biological sex such that parent intrusiveness was related only to boys' rule-breaking. Findings support the notion that children's telomere length may mark individual differences in responses to negative early caregiving, and highlight a potential mechanism contributing to the development of rule-breaking problems in boys. [ABSTRACT FROM AUTHOR]

Mounting evidence has supported osteoporosis (OP) as a metabolic disorder. Recent metabolomics studies have discovered numerous metabolites related to bone mineral density (BMD). However, the causal effects of metabolites on BMD at distinct sites remained underexplored. Leveraging genome-wide association datasets, we conducted two-sample Mendelian randomization (MR) analyses to investigate the causal relationship between 486 blood metabolites and bone mineral density at five skeletal sites including heel (H), total body (TB), lumbar spine (LS), femoral neck (FN), and ultra-distal forearm (FA). Sensitivity analyses were performed to test the presence of the heterogeneity and the pleiotropy. To exclude the influences of reverse causation, genetic correlation, and linkage disequilibrium (LD), we further performed reverse MR, linkage disequilibrium regression score (LDSC), and colocalization analyses. In the primary MR analyses, 22, 10, 3, 7, and 2 metabolite associations were established respectively for H-BMD, TB-BMD, LS-BMD, FN-BMD, and FA-BMD at the nominal significance level (IVW, P < 0.05) and passing sensitivity analyses. Among these, one metabolite, androsterone sulfate showed a strong effect on four out of five BMD phenotypes (Odds ratio [OR] for H-BMD = 1.045 [1.020, 1.071]; Odds ratio [OR] for TB-BMD = 1.061 [1.017, 1.107]; Odds ratio [OR] for LS-BMD = 1.088 [1.023, 1.159]; Odds ratio [OR] for FN-BMD = 1.114 [1.054, 1.177]). Reverse MR analysis provided no evidence for the causal effects of BMD measurements on these metabolites. Colocalization analysis have found that several metabolite associations might be driven by shared genetic variants such as mannose for TB-BMD. This study identified some metabolites causally related to BMD at distinct sites and several key metabolic pathways, which shed light on predictive biomarkers and drug targets for OP. [ABSTRACT FROM AUTHOR]

Extensive evidence from genome-wide association studies (GWAS) has shown that jointly analyzing multiple phenotypes can improve the power of the association test compared to the traditional single variant versus single trait approach. Here we propose an adaptive test based on principal components (ATPC) that is powerful and efficient for discovering the association between a single variant and multiple traits. Our method only needs GWAS summary statistics that are often available. We first estimate the trait correlation matrix by LD score regression. Then, based on the correlation matrix, we construct a series of test statistics that contain different numbers of principal components. The ultimate test statistic combines the P values of these principal component-based statistics by using the aggregated Cauchy association test. The analytical P-value of the test statistic can be computed quickly without the permutation process, which is the notable feature of our proposed method. The extensive simulation studies demonstrate that ATPC can control the type I error rates and have powerful and robust performance compared to several existing tests in a wide range of simulation settings. The analysis of the lipids GWAS summary data from the Global Lipids Genetics Consortium shows that ATPC identifies 230 new SNPs that are missed by the original single trait association analysis. By searching the GWAS Catalog, some SNPs and mapped genes identified by ATPC are reported to be associated with lipid traits. Through further analysis for GWAS results, we also find some Gene Ontology terms and biological pathways related to lipids. [ABSTRACT FROM AUTHOR]

Although health disparities among same-sex attracted compared to heterosexual individuals are typically explained by minority stress, there is limited evidence for a causal effect. This study investigated whether same-sex attraction was causally associated with psychological distress and risky sexual behavior using sociosexual behavior as a proxy. The sample comprised monozygotic and dizygotic twins and their non-twin siblings (n = 2036, 3780 and 2356, respectively) genotyped and assessed for same-sex attraction, psychological distress (anxiety and depressive symptoms), and risky sexual behavior. Causal influences were investigated with same-sex attraction as the predictor and psychological distress and risky sexual behavior as the outcomes in two separate Mendelian Randomization-Direction of Causation (MRDoC) models using OpenMx in R. The MRDoC model improves on the Mendelian Randomization and Direction of Causation twin models by allowing analyses of variables with similar genetic architectures, incorporating polygenic scores as instrumental variables and specifying pleiotropy and residual covariance. There were significant causal influences flowing from same-sex attraction to psychological distress and risky sexual behavior (standardized coefficients = 0.13 and 0.16; 95% CIs 0.03–0.23 and 0.08–0.25, respectively). Further analyses also demonstrated causal influences flowing from psychological distress and risky sexual behavior toward same-sex attraction. Causal influences from same-sex attraction to psychological distress and risky sexual behavior may reflect minority stress, which reinforces ongoing measures to minimize social disparities. Causal influences flowing in the opposite direction may reflect rejection sensitivity, stigma-inducing outcomes of risky sexual behavior, and recall bias; however, further research is required to specifically investigate these processes. [ABSTRACT FROM AUTHOR]

Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and plasma lipidome (phenotype) in order to identify the genetic architecture of plasma lipidome profiled from 1,426 Finnish individuals aged 30–45 years. PGMRA involves biclustering genotype and lipidome data independently followed by their inter-domain integration based on hypergeometric tests of the number of shared individuals. Pathway enrichment analysis was performed on the SNP sets to identify their associated biological processes. We identified 93 statistically significant (hypergeometric p-value < 0.01) lipidome-genotype relations. Genotype biclusters in these 93 relations contained 5977 SNPs across 3164 genes. Twenty nine of the 93 relations contained genotype biclusters with more than 50% unique SNPs and participants, thus representing most distinct subgroups. We identified 30 significantly enriched biological processes among the SNPs involved in 21 of these 29 most distinct genotype-lipidome subgroups through which the identified genetic variants can influence and regulate plasma lipid related metabolism and profiles. This study identified 29 distinct genotype-lipidome subgroups in the studied Finnish population that may have distinct disease trajectories and therefore could be useful in precision medicine research. [ABSTRACT FROM AUTHOR]

Objective: Arterial aneurysms are life-threatening but usually asymptomatic before requiring hospitalization. Oculomics of retinal vascular features (RVFs) extracted from retinal fundus images can reflect systemic vascular properties and therefore were hypothesized to provide valuable information on detecting the risk of aneurysms. By integrating oculomics with genomics, this study aimed to (i) identify predictive RVFs as imaging biomarkers for aneurysms and (ii) evaluate the value of these RVFs in supporting early detection of aneurysms in the context of predictive, preventive and personalized medicine (PPPM). Methods: This study involved 51,597 UK Biobank participants who had retinal images available to extract oculomics of RVFs. Phenome-wide association analyses (PheWASs) were conducted to identify RVFs associated with the genetic risks of the main types of aneurysms, including abdominal aortic aneurysm (AAA), thoracic aneurysm (TAA), intracranial aneurysm (ICA) and Marfan syndrome (MFS). An aneurysm-RVF model was then developed to predict future aneurysms. The performance of the model was assessed in both derivation and validation cohorts and was compared with other models employing clinical risk factors. An RVF risk score was derived from our aneurysm-RVF model to identify patients with an increased risk of aneurysms. Results: PheWAS identified a total of 32 RVFs that were significantly associated with the genetic risks of aneurysms. Of these, the number of vessels in the optic disc ('ntreeA') was associated with both AAA (β = −0.36, P = 6.75e−10) and ICA (β = −0.11, P = 5.51e−06). In addition, the mean angles between each artery branch ('curveangle_mean_a') were commonly associated with 4 MFS genes (FBN1: β = −0.10, P = 1.63e−12; COL16A1: β = −0.07, P = 3.14e−09; LOC105373592: β = −0.06, P = 1.89e−05; C8orf81/LOC441376: β = 0.07, P = 1.02e−05). The developed aneurysm-RVF model showed good discrimination ability in predicting the risks of aneurysms. In the derivation cohort, the C-index of the aneurysm-RVF model was 0.809 [95% CI: 0.780–0.838], which was similar to the clinical risk model (0.806 [0.778–0.834]) but higher than the baseline model (0.739 [0.733–0.746]). Similar performance was observed in the validation cohort, with a C-index of 0.798 (0.727–0.869) for the aneurysm-RVF model, 0.795 (0.718–0.871) for the clinical risk model and 0.719 (0.620–0.816) for the baseline model. An aneurysm risk score was derived from the aneurysm-RVF model for each study participant. The individuals in the upper tertile of the aneurysm risk score had a significantly higher risk of aneurysm compared to those in the lower tertile (hazard ratio = 17.8 [6.5–48.8], P = 1.02e−05). Conclusion: We identified a significant association between certain RVFs and the risk of aneurysms and revealed the impressive capability of using RVFs to predict the future risk of aneurysms by a PPPM approach. Our finds have great potential to support not only the predictive diagnosis of aneurysms but also a preventive and more personalized screening plan which may benefit both patients and the healthcare system. [ABSTRACT FROM AUTHOR]

The Barker Hypothesis posits that adverse intrauterine environments result in fetal growth restriction and increased risk of cardiometabolic disease through developmental compensations. Here we introduce a new statistical model using the genomic SEM software that is capable of simultaneously partitioning the genetic covariation between birthweight and cardiometabolic traits into maternally mediated and offspring mediated contributions. We model the covariance between birthweight and later life outcomes, such as blood pressure, non-fasting glucose, blood lipids and body mass index in the Norwegian HUNT study, consisting of 15,261 mother-eldest offspring pairs with genetic and phenotypic data. Application of this model showed some evidence for maternally mediated effects of systolic blood pressure on offspring birthweight, and pleiotropy between birthweight and non-fasting glucose mediated through the offspring genome. This underscores the importance of genetic links between birthweight and cardiometabolic phenotypes and offer alternative explanations to environmentally based hypotheses for the phenotypic correlation between these variables. [ABSTRACT FROM AUTHOR]

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10–11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.Genome-wide association studies of individuals from an isolated population (data from the Finnish biobank study FinnGen) and consequent meta-analyses facilitate the identification of previously unknown coding variant associations for both rare and common diseases. [ABSTRACT FROM AUTHOR]

Otosclerosis is one of the most common causes of conductive hearing loss, affecting 0.3% of the population. It typically presents in adulthood and half of the patients have a positive family history. The pathophysiology of otosclerosis is poorly understood. A previous genome-wide association study (GWAS) identified a single association locus in an intronic region of RELN. Here, we report a meta-analysis of GWAS studies of otosclerosis in three population-based biobanks comprising 3504 cases and 861,198 controls. We identify 23 novel risk loci (p < 5 × 10−8) and report an association in RELN and three previously reported candidate gene or linkage regions (TGFB1, MEPE, and OTSC7). We demonstrate developmental stage-dependent immunostaining patterns of MEPE and RUNX2 in mouse otic capsules. In most association loci, the nearest protein-coding genes are implicated in bone remodelling, mineralization or severe skeletal disorders. We highlight multiple genes involved in transforming growth factor beta signalling for follow-up studies.Otosclerosis is a common form of hearing loss, with an unclear genetic etiology. Here, the authors perform a genome-wide association study meta-analysis of otosclerosis identifying 27 genetic loci, pointing to genes involved in bone remodeling, skeletal disorders and transforming growth factor β signaling. [ABSTRACT FROM AUTHOR]

Varicose veins is the most common manifestation of chronic venous disease that displays female-biased incidence. To identify protein-inactivating variants that could guide identification of drug target genes for varicose veins and genetic evidence for the disease prevalence difference between the sexes, we conducted a genome-wide association study of varicose veins in Finns using the FinnGen dataset with 17,027 cases and 190,028 controls. We identified 50 associated genetic loci (P < 5.0 × 10−8) of which 29 were novel including one near ERG with female-specificity (rs2836405-G, OR[95% CI] = 1.09[1.05–1.13], P = 3.1 × 10−8). These also include two X-chromosomal (ARHGAP6 and SRPX) and two autosomal novel loci (TGFB2 and GJD3) with protein-coding lead variants enriched above 56-fold in Finns over non-Finnish non-Estonian Europeans. A low-frequency missense variant in GJD3 (p.Pro59Thr) is exclusively associated with a lower risk for varicose veins (OR = 0.62 [0.55–0.70], P = 1.0 × 10−14) in a phenome-wide scan of the FinnGen data. The absence of observed pleiotropy and its membership of the connexin gene family underlines GJD3 as a potential connexin-modulating therapeutic strategy for varicose veins. Our results provide insights into varicose veins etiopathology and highlight the power of isolated populations, including Finns, to discover genetic variants that inform therapeutic development. A genome-wide association study in a Finnish cohort identifies several new loci associated with varicose veins, including the connexin gene family member, GJD3, as a future druggable target. [ABSTRACT FROM AUTHOR]

TyG (triglyceride and glucose) index using triglyceride and fasting blood glucose is recommended as a useful marker for insulin resistance. To clarify whether the TyG index is a marker for predicting metabolic syndrome (MetS) and to investigate the importance of single-nucleotide polymorphisms (SNPs) in MetS diagnosis. From 2001 to 2014, a longitudinal prospective cohort study of 3580 adults aged 40–70 years was conducted. The area under the receiver operating characteristic curves (AUROC) and Youden index (YI) was calculated to assess the diagnostic value. During the 14-year follow-up, 1270 subjects developed MetS. Five SNPs in four genes (BUD13 rs10790162, ZPR1 rs2075290, APOA5 rs2266788, APOA5 rs2075291, and MKL1 rs4507196) significantly correlated with susceptibility to MetS (p < 0.00005). The areas under the curve of TyG index and HOMA-IR were 0.854 (95% confidence interval [CI], 0.841–0.867) and 0.702 (95% CI, 0.684–0.721), respectively. Despite no statistical significance, AUROC and YI were increased when MetS was diagnosed using TyG index and the five SNPs. TyG index might be useful for identifying individuals at high risk of developing MetS. The combination of TyG index and SNPs showed better diagnostic accuracy than TyG index alone, indicating the potential value of novel SNPs for MetS diagnosis. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disorder characterized by chronic inflammation and airway remodeling. Cigarette smoke (CS) and respiratory viruses are major causes of COPD development and exacerbation, but the mechanisms of these compounding factors on inflammation and pathological changes in airway structure still need further investigation. Purpose: This work aimed to investigate the effects and mechanisms of Poly I:C on pathological changes in CS-induced COPD mice, such as airway inflammation and remodeling. Methods: From 1 to 8 weeks, the mice were exposed to CS, Poly I:C, or a combination of both. To compare the pathological changes among different groups over time, the mice were sacrificed at week 4, 8, 16, and 24, then the lungs were harvested to measure pulmonary pathology, inflammatory cytokines, and airway remodeling. Results: Our data revealed that the fundamental characteristics of COPD, such as pulmonary pathological damage, the release of inflammatory mediators, and the remodeling of airway walls, were observed at week 8 in CS-exposed mice and these pathological changes persisted to week 16. Compared with the CS group, the pathological changes, including decreased lung function, inflammatory cell infiltration, alveolar destruction, and airway wall thickening, were weaker in the Poly I:C group. These pathological changes were observed at week 8 and persisted to week 16 in Poly I:C-induced mice. Furthermore, Poly I:C exacerbated lung tissue damage in CS-induced COPD mice. The decreased lung function, airway inflammation and remodeling were observed in the combined group at week 4, and these pathological changes persisted to week 24. Our research indicated that Poly I:C enhanced the expression of p-P38, p-JNK and p-NF-κB in CS-exposed mice. Conclusion: Poly I:C could promote airway inflammation and remodeling in CS-induced COPD mice probably by NF-κB and MAPK signaling. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) is an acute infectious disease of the respiratory tract caused by a new SARS-CoV-2 coronavirus. A global vaccination program against SARS-CoV-2 continues, and the incidence of COVID-19 worldwide is significantly decreasing. However, among millions of those who survived COVID-19, numerous groups will need assistance due to increased clinical consequences after COVID-19. Currently, there is a need to search for molecular biomarkers for monitoring the onset and progression of post-COVID syndrome. For this purpose, the relative average length of chromosome regions was studied in the groups of women of reproductive age: in the group of patients (n = 64) recovered from COVID-19 and in the control group (n = 42) of women of the same age. The analysis was carried out using a method of multiplex monochrome quantitative real-time PCR on DNA samples isolated from the peripheral blood leukocytes. According to the results of the study, it was established that the relative average length of chromosomes in the peripheral blood leukocytes was statistically significantly lower in the group of patients with COVID-19 than in the control group (p < 0.05). The results obtained allow one to state that the observed shortening of the relative average length of telomeres in the group of patients that recovered from COVID-19 can indicate that SARS-CoV-2 infection can directly cause the erosion of telomeres in the blood cells, particularly, in leukocytes. Thus, the determination of the relative average length of telomeres can be an informative prognostic marker for estimating the risk of the severity of COVID-19 disease and the development of post-COVID syndrome. [ABSTRACT FROM AUTHOR]

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges. [ABSTRACT FROM AUTHOR]

Purpose: The study aimed to investigate the effect of metformin treatment on leukocyte telomere length (LTL) and the relationship of LTL with C-reactive protein (CRP), homocysteine, albumin, complete blood count, and HOMA-IR values in patients with polycystic ovary syndrome (PCOS). Material and method: A prospective case–control study consisting of 30 women with PCOS and 30 healthy women without PCOS was performed. The relationship between clinical and laboratory parameters and LTL was analyzed. PCOS patients were treated with metformin (850 mg/day) for three months. Before treatment (BT) and after treatment (AT), each patient's LTL was evaluated and compared with the control group. Results: In the comparison between PCOS and control groups, the difference was significant for LTL, age, body mass index (BMI), and CRP (p = 0.002; p < 0.001; p = 0.001; p = 0.01, respectively). In PCOS patients, the difference between BT and AT, LTL was not statistically significant (BT: 6.06 ± 2.12; AT: 6.30 ± 1.93; p = 0.623; 95% C.I: − 1.22–0.74); however, the difference for weight was significant (BT: 83.78 ± 15.31; AT: 80.62 ± 15.40; p = 0.02; 95% CI: 1.34–4.99). The logistic regression model established by BMI (group 1: 21–24, group 2: 24–29, group 3: 29–34, group 4: > 34), age, and RDW, which predicted the PCOS group by affecting the LTL level, was statistically significant (p < 0.001/PPV = 96.3%; NPV = 88.5%). Each unit reduction in telomere length increased women's probability of PCOS by 0.4 times (p = 0.013; OR = 0.419, 95% CI: 0.211–0.835). Conclusion: Although statistically insignificant, LTL increased after metformin use in PCOS patients, and the mean weight loss reduction was statistically significant. Telomere shortening increased the likelihood of PCOS 0.4 times. [ABSTRACT FROM AUTHOR]

Differentiation of smooth muscle cells (SMCs) depends on serum response factor (SRF) and its co-activator myocardin (MYOCD). The role of MYOCD for the SMC program of gene transcription is well established. In contrast, the role of MYOCD in control of SMC-specific alternative exon usage, including exon splicing, has not been explored. In the current work we identified four splicing factors (MBNL1, RBPMS, RBPMS2, and RBFOX2) that correlate with MYOCD across human SMC tissues. Forced expression of MYOCD family members in human coronary artery SMCs in vitro upregulated expression of these splicing factors. For global profiling of transcript diversity, we performed RNA-sequencing after MYOCD transduction. We analyzed alternative transcripts with three different methods. Exon-based analysis identified 1637 features with differential exon usage. For example, usage of 3´ exons in MYLK that encode telokin increased relative to 5´ exons, as did the 17 kDa telokin to 130 kDa MYLK protein ratio. Dedicated event-based analysis identified 239 MYOCD-driven splicing events. Events involving MBNL1, MCAM, and ACTN1 were among the most prominent, and this was confirmed using variant-specific PCR analyses. In support of a role for RBPMS and RBFOX2 in MYOCD-driven splicing we found enrichment of their binding motifs around differentially spliced exons. Moreover, knockdown of either RBPMS or RBFOX2 antagonized splicing events stimulated by MYOCD, including those involving ACTN1, VCL, and MBNL1. Supporting an in vivo role of MYOCD-SRF-driven splicing, we demonstrate altered Rbpms expression and splicing in inducible and SMC-specific Srf knockout mice. We conclude that MYOCD-SRF, in part via RBPMS and RBFOX2, induce a program of differential exon usage and alternative splicing as part of the broader program of SMC differentiation. [ABSTRACT FROM AUTHOR]

Pharmaceutical companies have increasingly utilized genomic data for the selection of drug targets and the development of precision medicine approaches. Most major pharmaceutical companies routinely collect DNA from clinical trial participants and conduct pharmacogenomic (PGx) studies. However, the implementation of PGx studies during clinical development presents a number of challenges. These challenges include adapting to a constantly changing global regulatory environment, challenges in study design and clinical implementation, and the increasing concerns over patient privacy. Advances in the field of genomics are also providing new opportunities for pharmaceutical companies, including the availability of large genomic databases linked to patient health information, the growing use of polygenic risk scores, and the direct sequencing of clinical trial participants. The Industry Pharmacogenomics Working Group (I-PWG) is an association of pharmaceutical companies actively working in the field of pharmacogenomics. This I-PWG perspective will provide an overview of the steps pharmaceutical companies are taking to address each of these challenges, and the approaches being taken to capitalize on emerging scientific opportunities. [ABSTRACT FROM AUTHOR]

Purpose of Review: Lipoprotein (a) [Lp(a)] is a highly atherogenic lipoprotein species. A unique feature of Lp(a) is the strong genetic determination of its concentration. The LPA gene is responsible for up to 90% of the variance in Lp(a), but other genes also have an impact. Recent Findings: Genome-wide associations studies indicate that the APOE gene, encoding apolipoprotein E (apoE), is the second most important locus modulating Lp(a) concentrations. Population studies clearly show that carriers of the apoE2 variant (ε2) display reduced Lp(a) levels, the lowest concentrations being observed in ε2/ε2 homozygotes. This genotype can lead predisposed adults to develop dysbetalipoproteinemia, a lipid disorder characterized by sharp elevations in cholesterol and triglycerides. However, dysbetalipoproteinemia does not significantly modulate circulating Lp(a). Mechanistically, apoE appears to impair the production but not the catabolism of Lp(a). Summary: These observations underline the complexity of Lp(a) metabolism and provide key insights into the pathways governing Lp(a) synthesis and secretion. [ABSTRACT FROM AUTHOR]

Background: Metabolic syndrome (MetS) is a group of very common human conditions promoting strong understand the impact of rare variants, beyond exome-wide association studies, to potentially discover causative variants, across different ethnic populations. Objective: We performed transethnic, exome-wide MetS association studies on MetS in men. Methods: We analyzed genotype data of 5302 European subjects (2658 cases and 2644 controls), in the discovery stage of the European METabolic Syndrome In Men study, generated from exome chips, and 2481 subjects (714 cases and 1767 controls), in the replication stage, across 6 independent cohorts of 5 ancestries (T2D-GENES consortium), using whole-exome sequencing. We therefore evaluated gene-level and variant-level associations, of rare variants for MetS, using logistic regression (LR) and multivariate analyses (MulA). Results: Gene-based association found the gene for the cholesteryl ester transfer protein (CETP) (from MulA, p value = 4.67 × 10–9; from LR, p value = 0.009) to well associate with MetS. At two missense variants, from 8 rare variants in CETP, Ala390Pro (rs5880) (from MulA, p value = 1.28 × 10–7; from LR, p value = 1.34 × 10–4) and Arg468Gln (rs1800777) (from MulA, p value = 2.40 × 10–5; from LR, p value = 1.49 × 10–3) significantly associated with MetS across five ancestries. Conclusions: Our findings highlight novel rare variants of genes that confer MetS susceptibility, in Europeans, that are shared with diverse populations, emphasizing an opportunity to further understand the biological target or genes that underlie MetS, across populations. [ABSTRACT FROM AUTHOR]

Identifying the genetic determinants of inter-individual variation in lipid species (lipidome) may provide deeper understanding and additional insight into the mechanistic effect of complex lipidomic pathways in CVD risk and progression beyond simple traditional lipids. Previous studies have been largely population based and thus only powered to discover associations with common genetic variants. Founder populations represent a powerful resource to accelerate discovery of previously unknown biology associated with rare population alleles that have risen to higher frequency due to genetic drift. We performed a genome-wide association scan of 355 lipid species in 650 individuals from the Amish founder population including 127 lipid species not previously tested. To the best of our knowledge, we report for the first time the lipid species associated with two rare-population but Amish-enriched lipid variants: APOB_rs5742904 and APOC3_rs76353203. We also identified novel associations for 3 rare-population Amish-enriched loci with several sphingolipids and with proposed potential functional/causal variant in each locus including GLTPD2_rs536055318, CERS5_rs771033566, and AKNA_rs531892793. We replicated 7 previously known common loci including novel associations with two sterols: androstenediol with UGT locus and estriol with SLC22A8/A24 locus. Our results show the double power of founder populations and detailed lipidome to discover novel trait-associated variants. A GWAS of 355 lipid species in the Old Order Amish founder population reveals associations between Amish-enriched loci and several sphingolipids. [ABSTRACT FROM AUTHOR]