Epigenetic-based age acceleration in a representative sample of older Americans: Associations with aging-related morbidity and mortality.

Biomarkers developed from DNA methylation (DNAm) data are of growing interest as predictors of health outcomes and mortality in older populations. However, it is unknown how epigenetic aging fits within the context of known socioeconomic and behavioral associations with aging-related health outcomes in a large, population-based, and diverse sample. This study uses data from a representative, panel study of US older adults to examine the relationship between DNAm-based age acceleration measures in the prediction of cross-sectional and longitudinal health outcomes and mortality. We examine whether recent improvements to these scores, using principal component (PC)-based measures designed to remove some of the technical noise and unreliability in measurement, improve the predictive capability of these measures. We also examine how well DNAm-based measures perform against well-known predictors of health outcomes such as demographics, SES, and health behaviors. In our sample, age acceleration calculated using "second and third generation clocks," PhenoAge, GrimAge, and DunedinPACE, is consistently a significant predictor of health outcomes including cross-sectional cognitive dysfunction, functional limitations and chronic conditions assessed 2 y after DNAm measurement, and 4-y mortality. PC-based epigenetic age acceleration measures do not significantly change the relationship of DNAm-based age acceleration measures to health outcomes or mortality compared to earlier versions of these measures. While the usefulness of DNAm-based age acceleration as a predictor of later life health outcomes is quite clear, other factors such as demographics, SES, mental health, and health behaviors remain equally, if not more robust, predictors of later life outcomes.

Dried blood spot based biomarkers in the Health and Retirement Study: 2006 to 2016.

INTRODUCTION: The Health and Retirement Study (HRS) has collected biomarker data over multiple waves. Such data can help improve our understanding of health changes in individuals and the causal pathways related to health. There are, however, technical challenges to using the HRS dried blood spots (DBS) biomarker data due to changes over time in assay protocols, platforms, and laboratories. We provide technical and summary information on biological indicators collected as part of the HRS from 2006 to 2016 that should be helpful to users of the data. METHODS: We describe the opportunities and challenges provided by the HRS DBS data as well as insights provided by the data. The HRS collected DBS from its nationally representative sample of respondents 51 years of age or older from 2006 to 2016. DBS-based biomarkers were collected from half the sample in 2006, 2010, and 2014, and from the other half of the sample in 2008, 2012, and 2016. These DBS specimens were used to assay total and HDL cholesterol, glycosylated hemoglobin, C-reactive protein, and cystatin C from 2006 to 2016, and Interleukin 6 was added in 2014/2016. Samples included approximately 6000 individuals at each wave, and completion rates ranged from 81% to 90%. HRS transformed DBS values into venous blood equivalents to make them more comparable to those of the whole blood-based assays collected in most other studies and to facilitate longitudinal analysis. RESULTS: Distribution of changes over time by age shows that total cholesterol levels decreased for each age, while HbA1c levels increased. Cystatin C shows a clear age gradient, but a number of other markers do not. Non-Hispanic Black persons and Hispanic respondents have a higher incidence of risk levels of each biomarker except for CRP among non-Hispanic Black older persons. CONCLUSION: These public-use DBS data provide analysis opportunities that can be used to improve our understanding of health change with age in both populations and among individuals.

Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning.

Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.

Explainable variational autoencoder (E-VAE) model using genome-wide SNPs to predict dementia.

OBJECTIVE: Alzheimer's disease (AD) and AD related dementias (ADRD) are complex multifactorial neurodegenerative diseases. The associations between genetic variants obtained from genome wide association studies (GWAS) are the most widely available and well documented variants associated with ADRD. Application of deep learning methods to analyze large scale GWAS data may be a powerful approach to elucidate the biological mechanisms in ADRD compared to penalized regression models that may lead to over-fitting. METHODS: We developed a deep learning frame work explainable variational autoencoder (E-VAE) classifier model using genotype (GWAS SNPs = 5474) data from 2714 study participants in the Health and Retirement Study (HRS) to classify ADRD. We validated the generalizability of this model among 234 participants in the Religious Orders Study and Memory and Aging Project (ROSMAP). Utilizing a linear decoder approach we have extracted the weights associated with latent features for biological interpretation. RESULTS: We obtained a predictive accuracy of 0.71 (95 % CI [0.59, 0.84]) with an AUC of 0.69 in the HRS test dataset and got an accuracy of 0.62 (95 % CI [0.56, 0.68]) with an AUC of 0.63 in the ROSMAP dataset. CONCLUSION: This is the first study showing the generalizability of a deep learning prediction model for dementia using genetic variants in an independent cohort. The latent features identified using E-VAE can help us understand the biology of AD/ ADRD and better characterize disease status.

Alzheimer's disease medication use and adherence patterns by race and ethnicity.

BACKGROUND: We examined racial and ethnic differences in medication use for a representative US population of patients with Alzheimer's disease and related dementias (ADRD). METHODS: We examined cholinesterase inhibitors and memantine initiation, non-adherence, and discontinuation by race and ethnicity, using data from the 2000-2016 Health and Retirement Study linked with Medicare and Medicaid claims. RESULTS: Among newly diagnosed ADRD patients (n = 1299), 26% filled an ADRD prescription ≤90 days and 36% ≤365 days after diagnosis. Among individuals initiating ADRD-targeted treatment (n = 1343), 44% were non-adherent and 24% discontinued the medication during the year after treatment initiation. Non-Hispanic Blacks were more likely than Whites to not adhere to ADRD medication therapy (odds ratio: 1.50 [95% confidence interval: 1.07-2.09]). DISCUSSION: Initiation of ADRD-targeted medications did not vary by ethnoracial group, but non-Hispanic Blacks had lower adherence than Whites. ADRD medication non-adherence and discontinuation were substantial and may relate to cost and access to care. HIGHLIGHTS: Initiation of anti-dementia medications among newly diagnosed Alzheimer's disease and related dementias (ADRD) patients was low in all ethnoracial groups. ADRD medication non-adherence and discontinuation were substantial and may relate to cost and access to care. Compared to Whites, Blacks and Hispanics had lower use, poorer treatment adherence, and more frequent discontinuation of ADRD medication, but when controlling for disease severity and socioeconomic factors, racial disparities diminish. Our findings demonstrate the importance of adjusting for socioeconomic characteristics and disease severity when studying medication use and adherence in ADRD patients.

Cross sectional association between cytomegalovirus seropositivity, inflammation and cognitive impairment in elderly cancer survivors.

PURPOSE: The higher prevalence of cognitive impairment/ dementia among cancer survivors is likely multifactorial. Since both exposures to cytomegalovirus (CMV) and inflammation are common among elderly cancer survivors, we evaluated their contribution towards dementia. METHODS: Data from 1387 cancer survivors and 7004 participants without cancer in the 2016 wave of the Health and Retirement Study (HRS) was used in this study. Two inflammatory biomarkers, C-reactive protein (CRP) and neutrophil-lymphocyte ratio (NLR), were used to create an inflammation score. We used survey logistic regression adjusted for survey design parameters. RESULTS: CMV seropositivity was not associated with cognitive impairment among cancer survivors (p = 0.2). In addition, inflammation was associated with elevated odds of cognitive impairment (OR = 2.2, 95% CI [1.2, 4.2]). Cancer survivors who were both CMV seropositive and had increased inflammation had the highest odds of cognitive impairment compared to those who were CMV seronegative and had low inflammation (OR = 3.8, 95% CI [1.5, 9.4]). The stratified analysis among cancer survivors showed this association was seen only among cancer survivors in whom the cancer was diagnosed within three years of measurement of inflammation score and CMV serostatus (OR = 18.5; 95% CI [6.1, 56.1]). CONCLUSION: The CMV seropositivity and high inflammation was associated with higher cognitive impairment among cancer survivors. The stronger associations seen among cancer survivors diagnosed within the last three years suggest that strategies to reduce CMV activation and inflammation during or immediately after cancer treatment may be important in reducing the prevalence of cognitive impairment/ dementia among cancer survivors.

Evaluation of T-cell aging-related immune phenotypes in the context of biological aging and multimorbidity in the Health and Retirement Study.

BACKGROUND: Cellular changes in adaptive immune system accompany the process of aging and contribute to an aging-related immune phenotype (ARIP) characterized by decrease in naïve T-cells (TN) and increase in memory T-cells (TM). A population-representative marker of ARIP and its associations with biological aging and age-related chronic conditions have not been studied previously. METHODS: We developed two ARIP indicators based on well understood age-related changes in T cell distribution: TN/(TCM (Central Memory) + TEM (Effector Memory) + TEFF (Effector)) (referred as TN/TM) in CD4 + and CD8 + T-cells. We compared them with existing ARIP measures including CD4/CD8 ratio and CD8 + TN cells by evaluating associations with chronological age and the Klemera Doubal measure of biological age (measured in years) using linear regression, multimorbidity using multinomial logistic regression and two-year mortality using logistic regression. RESULTS: CD8 + TN and CD8 + TN/TM had the strongest inverse association with chronological age (beta estimates: -3.41 and -3.61 respectively; p-value < 0.0001) after adjustment for sex, race/ethnicity and CMV status. CD4 + TN/TM and CD4 + TN had the strongest inverse association with biological age (ß = -0.23; p = 0.003 and ß = -0.24; p = 0.004 respectively) after adjustment for age, sex, race/ethnicity and CMV serostatus. CD4/CD8 ratio was not associated with chronological age or biological age. CD4 + TN/TM and CD4 + TN was inversely associated with multimorbidity. For CD4 + TN/TM, people with 2 chronic conditions had an odds ratio of for 0.74 (95%CI: 0.63-0.86 p = 0.0003) compared to those without any chronic conditions while those with 3 chronic conditions had an odds ratio of 0.75 (95% CI: 0.63-0.90; p = 0.003) after adjustment for age, sex, race/ethnicity, CMV serostatus, smoking, and BMI. The results for the CD4 + TN subset were very similar to the associations seen with the CD4 + TN/TM. CD4 + TN/TM and CD4 + TN were both associated with two-year mortality (OR = 0.80 (95% CI: 0.67-0.95; p = 0.01) and 0.81 (0.70-0.94; p = 0.01), respectively). CONCLUSION: CD4 + TN/TM and CD4 + TN had a stronger association with biological age, age-related morbidity and mortality compared to other ARIP measures. Future longitudinal studies are needed to evaluate the utility of the CD4 + subsets in predicting the risk of aging-related outcomes.

Dementia Diagnosis Disparities by Race and Ethnicity.

BACKGROUND: Dementia is often underdiagnosed and this problem is more common among some ethnoracial groups. OBJECTIVE: The objective of this study was to examine racial and ethnic disparities in the timeliness of receiving a clinical diagnosis of dementia. RESEARCH DESIGN: This was a prospective cohort study. SUBJECTS: A total of 3966 participants age 70 years and above with probable dementia in the Health and Retirement Study, linked with their Medicare and Medicaid claims. MEASURES: We performed logistic regression to compare the likelihood of having a missed or delayed dementia diagnosis in claims by race/ethnicity. We analyzed dementia severity, measured by cognition and daily function, at the time of a dementia diagnosis documented in claims, and estimated average dementia diagnosis delay, by race/ethnicity. RESULTS: A higher proportion of non-Hispanic Blacks and Hispanics had a missed/delayed clinical dementia diagnosis compared with non-Hispanic Whites (46% and 54% vs. 41%, P<0.001). Fully adjusted logistic regression results suggested more frequent missed/delayed dementia diagnoses among non-Hispanic Blacks (odds ratio=1.12; 95% confidence interval: 0.91-1.38) and Hispanics (odds ratio=1.58; 95% confidence interval: 1.20-2.07). Non-Hispanic Blacks and Hispanics had a poorer cognitive function and more functional limitations than non-Hispanic Whites around the time of receiving a claims-based dementia diagnosis. The estimated mean diagnosis delay was 34.6 months for non-Hispanic Blacks and 43.8 months for Hispanics, compared with 31.2 months for non-Hispanic Whites. CONCLUSIONS: Non-Hispanic Blacks and Hispanics may experience a missed or delayed diagnosis of dementia more often and have longer diagnosis delays. When diagnosed, non-Hispanic Blacks and Hispanics may have more advanced dementia. Public health efforts should prioritize racial and ethnic underrepresented communities when promoting early diagnosis of dementia.

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci.

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.

Development and validation of prediction model to estimate 10-year risk of all-cause mortality using modern statistical learning methods: a large population-based cohort study and external validation.

BACKGROUND: In increasingly ageing populations, there is an emergent need to develop a robust prediction model for estimating an individual absolute risk for all-cause mortality, so that relevant assessments and interventions can be targeted appropriately. The objective of the study was to derive, evaluate and validate (internally and externally) a risk prediction model allowing rapid estimations of an absolute risk of all-cause mortality in the following 10 years. METHODS: For the model development, data came from English Longitudinal Study of Ageing study, which comprised 9154 population-representative individuals aged 50-75 years, 1240 (13.5%) of whom died during the 10-year follow-up. Internal validation was carried out using Harrell's optimism-correction procedure; external validation was carried out using Health and Retirement Study (HRS), which is a nationally representative longitudinal survey of adults aged ≥50 years residing in the United States. Cox proportional hazards model with regularisation by the least absolute shrinkage and selection operator, where optimisation parameters were chosen based on repeated cross-validation, was employed for variable selection and model fitting. Measures of calibration, discrimination, sensitivity and specificity were determined in the development and validation cohorts. RESULTS: The model selected 13 prognostic factors of all-cause mortality encompassing information on demographic characteristics, health comorbidity, lifestyle and cognitive functioning. The internally validated model had good discriminatory ability (c-index=0.74), specificity (72.5%) and sensitivity (73.0%). Following external validation, the model's prediction accuracy remained within a clinically acceptable range (c-index=0.69, calibration slope ß=0.80, specificity=71.5% and sensitivity=70.6%). The main limitation of our model is twofold: 1) it may not be applicable to nursing home and other institutional populations, and 2) it was developed and validated in the cohorts with predominately white ethnicity. CONCLUSIONS: A new prediction model that quantifies absolute risk of all-cause mortality in the following 10-years in the general population has been developed and externally validated. It has good prediction accuracy and is based on variables that are available in a variety of care and research settings. This model can facilitate identification of high risk for all-cause mortality older adults for further assessment or interventions.

Directional dominance on stature and cognition in diverse human populations.

Homozygosity has long been associated with rare, often devastating, Mendelian disorders, and Darwin was one of the first to recognize that inbreeding reduces evolutionary fitness. However, the effect of the more distant parental relatedness that is common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power. Here we use runs of homozygosity to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts, and find statistically significant associations between summed runs of homozygosity and four complex traits: height, forced expiratory lung volume in one second, general cognitive ability and educational attainment (P < 1 × 10(-300), 2.1 × 10(-6), 2.5 × 10(-10) and 1.8 × 10(-10), respectively). In each case, increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months' less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing evidence that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been.

Genetic studies of body mass index yield new insights for obesity biology.

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

Correction: Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations.

[This corrects the article DOI: 10.1371/journal.pgen.1006728.].

Dried blood spots: Effects of less than optimal collection, shipping time, heat, and humidity.

OBJECTIVES: This study investigates how factors related to collection, storage, transport time, and environmental conditions affect the quality and accuracy of analyses of dried blood spot (DBS) samples. METHODS: Data come from the 2016 Health and Retirement Study (HRS) DBS laboratory reports and the HRS merged with the National Climatic Data Center (NCDC) Global Historical Climate Network Daily (NCDC GHCN-Daily) and the NCDC Local Climatological Data, by zip code. We ran regression models to examine the associations between assay values based on DBS for five analytes (total cholesterol, high-density lipoprotein (HDL) cholesterol, glycosylated hemoglobin (HbA1c), C-reactive protein (CRP), and cystatin C) and the characteristics of DBS cards and drops, shipping time, and temperature, and humidity at the time of collection. RESULTS: We found cholesterol measures to be sensitive to many factors including small spots, shipping time, high temperature and humidity. Small spots in DBS cards are related to lower values across all analytes. Longer DBS transit time before freezing is associated with lower values of total and HDL cholesterol and cystatin C. Results were similar whether or not venous blood sample values were included in equations. CONCLUSIONS: Small spots, long shipping time, and exposure to high temperature and humidity need to be avoided if possible. Quality of spots and cards and information on shipping time and conditions should be coded with the data to make adjustments in values when necessary. The different results across analytes indicate that results cannot be generalized to all DBS assays.

Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations.

Hypertension is a leading cause of global disease, mortality, and disability. While individuals of African descent suffer a disproportionate burden of hypertension and its complications, they have been underrepresented in genetic studies. To identify novel susceptibility loci for blood pressure and hypertension in people of African ancestry, we performed both single and multiple-trait genome-wide association analyses. We analyzed 21 genome-wide association studies comprised of 31,968 individuals of African ancestry, and validated our results with additional 54,395 individuals from multi-ethnic studies. These analyses identified nine loci with eleven independent variants which reached genome-wide significance (P < 1.25×10-8) for either systolic and diastolic blood pressure, hypertension, or for combined traits. Single-trait analyses identified two loci (TARID/TCF21 and LLPH/TMBIM4) and multiple-trait analyses identified one novel locus (FRMD3) for blood pressure. At these three loci, as well as at GRP20/CDH17, associated variants had alleles common only in African-ancestry populations. Functional annotation showed enrichment for genes expressed in immune and kidney cells, as well as in heart and vascular cells/tissues. Experiments driven by these findings and using angiotensin-II induced hypertension in mice showed altered kidney mRNA expression of six genes, suggesting their potential role in hypertension. Our study provides new evidence for genes related to hypertension susceptibility, and the need to study African-ancestry populations in order to identify biologic factors contributing to hypertension.

SOS2 and ACP1 Loci Identified through Large-Scale Exome Chip Analysis Regulate Kidney Development and Function.

Genome-wide association studies have identified >50 common variants associated with kidney function, but these variants do not fully explain the variation in eGFR. We performed a two-stage meta-analysis of associations between genotypes from the Illumina exome array and eGFR on the basis of serum creatinine (eGFRcrea) among participants of European ancestry from the CKDGen Consortium (nStage1: 111,666; nStage2: 48,343). In single-variant analyses, we identified single nucleotide polymorphisms at seven new loci associated with eGFRcrea (PPM1J, EDEM3, ACP1, SPEG, EYA4, CYP1A1, and ATXN2L; PStage1<3.7×10-7), of which most were common and annotated as nonsynonymous variants. Gene-based analysis identified associations of functional rare variants in three genes with eGFRcrea, including a novel association with the SOS Ras/Rho guanine nucleotide exchange factor 2 gene, SOS2 (P=5.4×10-8 by sequence kernel association test). Experimental follow-up in zebrafish embryos revealed changes in glomerular gene expression and renal tubule morphology in the embryonic kidney of acp1- and sos2-knockdowns. These developmental abnormalities associated with altered blood clearance rate and heightened prevalence of edema. This study expands the number of loci associated with kidney function and identifies novel genes with potential roles in kidney formation.

Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals.

Polymorphisms rs6232 and rs6234/rs6235 in PCSK1 have been associated with extreme obesity [e.g. body mass index (BMI) ≥ 40 kg/m(2)], but their contribution to common obesity (BMI ≥ 30 kg/m(2)) and BMI variation in a multi-ethnic context is unclear. To fill this gap, we collected phenotypic and genetic data in up to 331 175 individuals from diverse ethnic groups. This process involved a systematic review of the literature in PubMed, Web of Science, Embase and the NIH GWAS catalog complemented by data extraction from pre-existing GWAS or custom-arrays in consortia and single studies. We employed recently developed global meta-analytic random-effects methods to calculate summary odds ratios (OR) and 95% confidence intervals (CIs) or beta estimates and standard errors (SE) for the obesity status and BMI analyses, respectively. Significant associations were found with binary obesity status for rs6232 (OR = 1.15, 95% CI 1.06-1.24, P = 6.08 × 10(-6)) and rs6234/rs6235 (OR = 1.07, 95% CI 1.04-1.10, P = 3.00 × 10(-7)). Similarly, significant associations were found with continuous BMI for rs6232 (ß = 0.03, 95% CI 0.00-0.07; P = 0.047) and rs6234/rs6235 (ß = 0.02, 95% CI 0.00-0.03; P = 5.57 × 10(-4)). Ethnicity, age and study ascertainment significantly modulated the association of PCSK1 polymorphisms with obesity. In summary, we demonstrate evidence that common gene variation in PCSK1 contributes to BMI variation and susceptibility to common obesity in the largest known meta-analysis published to date in genetic epidemiology.

Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations.

High blood pressure (BP) is more prevalent and contributes to more severe manifestations of cardiovascular disease (CVD) in African Americans than in any other United States ethnic group. Several small African-ancestry (AA) BP genome-wide association studies (GWASs) have been published, but their findings have failed to replicate to date. We report on a large AA BP GWAS meta-analysis that includes 29,378 individuals from 19 discovery cohorts and subsequent replication in additional samples of AA (n = 10,386), European ancestry (EA) (n = 69,395), and East Asian ancestry (n = 19,601). Five loci (EVX1-HOXA, ULK4, RSPO3, PLEKHG1, and SOX6) reached genome-wide significance (p < 1.0 × 10(-8)) for either systolic or diastolic BP in a transethnic meta-analysis after correction for multiple testing. Three of these BP loci (EVX1-HOXA, RSPO3, and PLEKHG1) lack previous associations with BP. We also identified one independent signal in a known BP locus (SOX6) and provide evidence for fine mapping in four additional validated BP loci. We also demonstrate that validated EA BP GWAS loci, considered jointly, show significant effects in AA samples. Consequently, these findings suggest that BP loci might have universal effects across studied populations, demonstrating that multiethnic samples are an essential component in identifying, fine mapping, and understanding their trait variability.

GENOME-WIDE ASSOCIATION STUDY (GWAS) AND GENOME-WIDE BY ENVIRONMENT INTERACTION STUDY (GWEIS) OF DEPRESSIVE SYMPTOMS IN AFRICAN AMERICAN AND HISPANIC/LATINA WOMEN.

BACKGROUND: Genome-wide association studies (GWAS) have made little progress in identifying variants linked to depression. We hypothesized that examining depressive symptoms and considering gene-environment interaction (GxE) might improve efficiency for gene discovery. We therefore conducted a GWAS and genome-wide by environment interaction study (GWEIS) of depressive symptoms. METHODS: Using data from the SHARe cohort of the Women's Health Initiative, comprising African Americans (n = 7,179) and Hispanics/Latinas (n = 3,138), we examined genetic main effects and GxE with stressful life events and social support. We also conducted a heritability analysis using genome-wide complex trait analysis (GCTA). Replication was attempted in four independent cohorts. RESULTS: No SNPs achieved genome-wide significance for main effects in either discovery sample. The top signals in African Americans were rs73531535 (located 20 kb from GPR139, P = 5.75 × 10(-8) ) and rs75407252 (intronic to CACNA2D3, P = 6.99 × 10(-7) ). In Hispanics/Latinas, the top signals were rs2532087 (located 27 kb from CD38, P = 2.44 × 10(-7) ) and rs4542757 (intronic to DCC, P = 7.31 × 10(-7) ). In the GEWIS with stressful life events, one interaction signal was genome-wide significant in African Americans (rs4652467; P = 4.10 × 10(-10) ; located 14 kb from CEP350). This interaction was not observed in a smaller replication cohort. Although heritability estimates for depressive symptoms and stressful life events were each less than 10%, they were strongly genetically correlated (rG = 0.95), suggesting that common variation underlying self-reported depressive symptoms and stressful life event exposure, though modest on their own, were highly overlapping in this sample. CONCLUSIONS: Our results underscore the need for larger samples, more GEWIS, and greater investigation into genetic and environmental determinants of depressive symptoms in minorities.

Personality Polygenes, Positive Affect, and Life Satisfaction.

Approximately half of the variation in wellbeing measures overlaps with variation in personality traits. Studies of non-human primate pedigrees and human twins suggest that this is due to common genetic influences. We tested whether personality polygenic scores for the NEO Five-Factor Inventory (NEO-FFI) domains and for item response theory (IRT) derived extraversion and neuroticism scores predict variance in wellbeing measures. Polygenic scores were based on published genome-wide association (GWA) results in over 17,000 individuals for the NEO-FFI and in over 63,000 for the IRT extraversion and neuroticism traits. The NEO-FFI polygenic scores were used to predict life satisfaction in 7 cohorts, positive affect in 12 cohorts, and general wellbeing in 1 cohort (maximal N = 46,508). Meta-analysis of these results showed no significant association between NEO-FFI personality polygenic scores and the wellbeing measures. IRT extraversion and neuroticism polygenic scores were used to predict life satisfaction and positive affect in almost 37,000 individuals from UK Biobank. Significant positive associations (effect sizes <0.05%) were observed between the extraversion polygenic score and wellbeing measures, and a negative association was observed between the polygenic neuroticism score and life satisfaction. Furthermore, using GWA data, genetic correlations of -0.49 and -0.55 were estimated between neuroticism with life satisfaction and positive affect, respectively. The moderate genetic correlation between neuroticism and wellbeing is in line with twin research showing that genetic influences on wellbeing are also shared with other independent personality domains.