A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Increased genetic contribution to wellbeing during the COVID-19 pandemic.

Physical and mental health are determined by an interplay between nature, for example genetics, and nurture, which encompasses experiences and exposures that can be short or long-lasting. The COVID-19 pandemic represents a unique situation in which whole communities were suddenly and simultaneously exposed to both the virus and the societal changes required to combat the virus. We studied 27,537 population-based biobank participants for whom we have genetic data and extensive longitudinal data collected via 19 questionnaires over 10 months, starting in March 2020. This allowed us to explore the interaction between genetics and the impact of the COVID-19 pandemic on individuals' wellbeing over time. We observe that genetics affected many aspects of wellbeing, but also that its impact on several phenotypes changed over time. Over the course of the pandemic, we observed that the genetic predisposition to life satisfaction had an increasing influence on perceived quality of life. We also estimated heritability and the proportion of variance explained by shared environment using variance components methods based on pedigree information and household composition. The results suggest that people's genetic constitution manifested more prominently over time, potentially due to social isolation driven by strict COVID-19 containment measures. Overall, our findings demonstrate that the relative contribution of genetic variation to complex phenotypes is dynamic rather than static.

The association of the Prime Diet Quality Score (PDQS) with depression and anxiety symptoms: the Isfahan functional disorders (ISFUN) cohort study.

Background: Due to scarce epidemiologic data regarding the Prime Diet Quality Score (PDQS) and mental health disorders, this study aimed to investigate the association of PDQS with depression and anxiety symptoms in Iranian adults.Methods: This cross-sectional analysis was performed using the baseline data collected for the Isfahan functional disorders (ISFUN) cohort study. ISFUN was established in 2017 and enrolled apparently healthy adults, aged 18-65 years in Isfahan, Iran. Information on usual dietary intakes was collected using a validated Dish-based, 106-item food frequency questionnaire. The severity of depression and anxiety was assessed using a validated Iranian version of the Hospital Anxiety and Depression Scale.Results: A total number of1892 participants were enrolled in the present study. In total, 54.50% of subjects were female. In the fully adjusted model, participants with PDQS lower than median compared with those with PDQS higher than median had higher risk of depression (OR = 1.62; 95% CI: 1.11, 2.37) and anxiety (OR: 1.57; 95% CI: 1.08, 2.27). In stratified analysis by sex, males with PDQS lower than median compared with those with PDQS higher than median had greater risk for depressive (OR: 1.57) and anxiety symptoms (OR = 1.52). However, in females, no significant association was found between PDQS and odds of depression, and anxiety in the fully adjusted model.Conclusion: We found evidence indicating a significant inverse association between PDQS and depression and anxiety symptoms among Iranian adults. Further studies, in particular with prospective design, are required to confirm these findings.

Familial co-aggregation and shared genetics of cardiometabolic disorders and traits: data from the multi-generational Lifelines Cohort Study.

BACKGROUND: It is unclear to what extent genetics explain the familial clustering and the co-occurrence of distinct cardiometabolic disorders in the general population. We therefore aimed to quantify the familial (co-)aggregation of various cardiometabolic disorders and to estimate the heritability of cardiometabolic traits and their genetic correlations using the large, multi-generational Lifelines Cohort Study. METHODS: We used baseline data of 162,416 participants from Lifelines. Cardiometabolic disorders including type 2 diabetes (T2D), cardiovascular diseases, hypertension, obesity, hypercholesterolemia, and metabolic syndrome (MetS), were defined in adult participants. Fifteen additional cardiometabolic traits indexing obesity, blood pressure, inflammation, glucose regulation, and lipid levels were measured in all included participants. Recurrence risk ratios (λR) for first-degree relatives (FDR) indexed familial (co-)aggregation of cardiometabolic disorders using modified conditional Cox proportional hazards models and were compared to those of spouses. Heritability (h2), shared environment, and genetic correlation (rg) were estimated using restricted maximum likelihood variance decomposition methods, adjusted for age, age2, and sex. RESULTS: Individuals with a first-degree relative with a cardiometabolic disorder had a higher risk of the same disorder, ranging from λFDR of 1.23 (95% CI 1.20-1.25) for hypertension to λFDR of 2.48 (95% CI 2.15-2.86) for T2D. Most of these were higher than in spouses (λSpouses < λFDR), except for obesity which was slightly higher in spouses. We found moderate heritability for cardiometabolic traits (from h2CRP: 0.26 to h2HDL: 0.50). Cardiometabolic disorders showed positive familial co-aggregation, particularly between T2D, MetS, and obesity (from λFDR obesity-MetS: 1.28 (95% CI 1.24-1.32) to λFDR MetS-T2D: 1.61 (95% CI 1.52-1.70)), consistent with the genetic correlations between continuous intermediate traits (ranging from rg HDL-Triglycerides: - 0.53 to rg LDL-Apolipoprotein B: 0.94). CONCLUSIONS: There is positive familial (co-)aggregation of cardiometabolic disorder, moderate heritability of intermediate traits, and moderate genetic correlations between traits. These results indicate that shared genetics and common genetic architecture contribute to cardiometabolic disease.

GWASinspector: comprehensive quality control of genome-wide association study results.

SUMMARY: Quality control (QC) of genome wide association study (GWAS) result files has become increasingly difficult due to advances in genomic technology. The main challenges include continuous increases in the number of polymorphic genetic variants contained in recent GWASs and reference panels, the rising number of cohorts participating in a GWAS consortium, and inclusion of new variant types. Here, we present GWASinspector, a flexible R package for comprehensive QC of GWAS results. This package is compatible with recent imputation reference panels, handles insertion/deletion and multi-allelic variants, provides extensive QC reports and efficiently processes big data files. Reference panels covering three human genome builds (NCBI36, GRCh37 and GRCh38) are available. GWASinspector has a user friendly design and allows easy set-up of the QC pipeline through a configuration file. In addition to checking and reporting on individual files, it can be used in preparation of a meta-analysis by testing for systemic differences between studies and generating cleaned, harmonized GWAS files. Comparison with existing GWAS QC tools shows that the main advantages of GWASinspector are its ability to more effectively deal with insertion/deletion and multi-allelic variants and its relatively low memory use. AVAILABILITY AND IMPLEMENTATION: Our package is available at The Comprehensive R Archive Network (CRAN): https://CRAN.R-project.org/package=GWASinspector. Reference datasets and a detailed tutorial can be found at the package website at http://gwasinspector.com/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pressure algometry in the general adult population: Age and sex differences.

Experimental pain studies have revealed inter-individual variations in pain perception that are influenced by age, sex, and country of origin. This study aimed to explore the age and sex differences in pressure pain thresholds within the Iranian general population. To assess the pressure pain thresholds, a handheld pressure algometer was applied bilaterally to the middle fingers of both hands. The participants also completed the short form of the McGill Pain Questionnaire to provide a clinical pain rating. This cross-sectional study included 1610 adult subjects (54.96% female, mean age 40.13 ±â€…10.18 years). The findings indicated that females generally exhibited lower pain thresholds than males when assessing pain detection and tolerance parameters (P < .001). Females also demonstrated a significant lower pressure thresholds and clinical pain ratings compared with men (P < .001). Additionally, significant differences were observed between age groups in terms of pain detection and tolerance thresholds (P = .02 and P = .03, respectively). However, the interaction between sex and age was not significant. No significant differences in pain detection thresholds were observed between the right and left hand (P = .11). This study underscores the potential utility of algometry as a valuable tool for objectifying pain in the Iranian population.

Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries.

We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.

A genome-wide association study of 24-hour urinary excretion of endocrine disrupting chemicals.

Ubiquitous exposure to environmental endocrine disrupting chemicals (EDCs) instigates a major public health problem, but much remains unknown on the inter-individual differences in metabolism and excretion of EDCs. To examine this we performed a two-stage genome-wide association study (GWAS) for 24-hour urinary excretions of four parabens, two bisphenols, and nine phthalate metabolites. Results showed five genome-wide significant (p-value < 5x10-8) and replicated single nucleotide polymorphisms (SNPs) representing four independent signals that associated with mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP) and mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP). Three of the four signals were located on chromosome 10 in a locus harboring the cytochrome P450 (CYP) genes CYP2C9, CYP2C58P, and CYP2C19 (rs117529685, pMECPP = 5.38x10-25; rs117033379, pMECPP = 1.96x10-19; rs4918798, pMECPP = 4.01x10-71; rs7895726, pMEHHP = 1.37x10-15, r2 with rs4918798 = 0.93). The other signal was on chromosome 6 close to the solute carrier (SLC) genes SLC17A1, SLC17A3, SLC17A4, and SCGN (rs1359232, pMECPP = 7.6x10-16). These four SNPs explained a substantial part (8.3 % - 9.2 %) of the variance in MECPP in the replication cohort. Bioinformatics analyses supported a likely causal role of CYP2C9 and SLC17A1 in metabolism and excretion of MECPP and MEHHP. Our results provide biological insights into mechanisms of phthalate metabolism and excretion with a likely causal role for CYP2C9 and SLC17A1.

Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits.

Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P < 5 × 10-8) from the largest single-stage blood pressure (BP) genome-wide association study to date (n = 1,028,980 European individuals). These associations explain more than 60% of single nucleotide polymorphism-based BP heritability. Comparing top versus bottom deciles of polygenic risk scores (PRSs) reveals clinically meaningful differences in BP (16.9 mmHg systolic BP, 95% CI, 15.5-18.2 mmHg, P = 2.22 × 10-126) and more than a sevenfold higher odds of hypertension risk (odds ratio, 7.33; 95% CI, 5.54-9.70; P = 4.13 × 10-44) in an independent dataset. Adding PRS into hypertension-prediction models increased the area under the receiver operating characteristic curve (AUROC) from 0.791 (95% CI, 0.781-0.801) to 0.826 (95% CI, 0.817-0.836, ∆AUROC, 0.035, P = 1.98 × 10-34). We compare the 2,103 loci results in non-European ancestries and show significant PRS associations in a large African-American sample. Secondary analyses implicate 500 genes previously unreported for BP. Our study highlights the role of increasingly large genomic studies for precision health research.

A multidimensional longitudinal dataset on functional somatic syndromes.

Functional disorders represent a prevalent health issue, significantly impacting both individuals and healthcare systems. This multidisciplinary dataset aims to enhance our comprehension of the complex interplay among various factors that contribute to functional somatic syndromes. The dataset comprises data from seemingly healthy adults (aged 18-65) in Isfahan, Iran, who were randomly selected and monitored for four consecutive years. The research data encompasses seven distinct datasets: (a) functional symptom evaluations across multiple body organs, (b) psychological assessments, (c) lifestyle factors, (d) demographic and socioeconomic variables, (e) laboratory measurements, (f) clinical examinations, and (g) historical information. A total of 1930 participants were enrolled at the study's outset in 2017. The first, second, and third annual follow-up rounds were completed with 1697 (2018), 1616 (2019), and 1176 (2020) participants, respectively. This dataset is made available for further analysis by a diverse range of researchers, healthcare policymakers, and clinicians.