Common Disease-common Variant Research Articles

Behavioral and Neuropsychiatric Differences Across Two Atypical Alzheimer's Disease Variants: Logopenic Progressive Aphasia and Posterior Cortical Atrophy.

Posterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA) are two common atypical Alzheimer's disease (AD) variants. Little is known about behavioral and neuropsychiatric symptoms or activities of daily living (ADLs) in PCA and LPA, and whether they differ across syndromes. To characterize the behavioral and neuropsychiatric profiles and ADLs of PCA and LPA and compare presence/absence and severity of symptoms between syndromes. Seventy-eight atypical AD patients, 46 with PCA and 32 with LPA, completed the Neuropsychiatric Inventory Questionnaire (NPI-Q) and Cambridge Behavioral Inventory-Revised (CBI-R) at baseline and longitudinally over-time. Mann-Whitney U and Fisher's Exact Tests assessed for differences in symptoms between the two syndromes with significance set at p≤0.01. To eliminate demographic differences as confounders the groups were matched, and differences reanalyzed. PCA were younger at onset (p = 0.006), at time of baseline assessment (p = 0.02) and had longer disease duration (p = 0.01). Neuropsychiatric symptoms were common in PCA and LPA, although more common and severe in PCA. At baseline, PCA had a higher NPI-Q total score (p = 0.01) and depression subscore (p = 0.01) than LPA. Baseline total CBI-R scores were also higher in PCA than LPA (p = 0.001) with PCA having worse scores in all 10 CBI-R categories. Longitudinally, there was no difference between groups on the NPI-Q. However, on the CBI-R, PCA had faster rates of worsening on self-grooming (p = 0.01) and self-dressing (p = 0.01) compared to LPA. Behavioral and neuropsychiatric symptoms are common in PCA and LPA although these symptoms are more common and severe in PCA.

Strategies in Aggregation Tests for Rare Variants.

Genome-wide association studies (GWAS) successfully identified numerous common variants involved in complex diseases, but only limited heritability was explained by these findings. Advances in high-throughput sequencing technology made it possible to assess the contribution of rare variants in common diseases. However, study of rare variants introduces challenges due to low frequency of rare variants. Well-established common variant methods were underpowered to identify the rare variants in GWAS. To address this challenge, several new methods have been developed to examine the role of rare variants in complex diseases. These approaches are based on testing the aggregate effect of multiple rare variants in a predefined genetic region. Provided here is an overview of statistical approaches and the protocols explaining step-by-step analysis of aggregations tests with the hands-on experience using R scripts in four categories: burden tests, adaptive burden tests, variance-component tests, and combined tests. Also explained are the concepts of rare variants, permutation tests, kernel methods, and genetic variant annotation. At the end we discuss relevant topics of bioinformatics tools for annotation, family-based design of rare-variant analysis, population stratification adjustment, and meta-analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Leveraging primate-specific genomic information for genetic studies of complex diseases.

Genomic changes specific to higher primates are regarded as primate-specific genomic information (PSI). Using PSI to inform genetic studies is highly desirable but hampered by three factors: heterogeneity among PSI studies, lack of integrated profiles of the identified PSI elements and dearth of relevant functional information. We report a database of 19,767 PSI elements collated from nine types of brain-related studies, which form 19,473 non-overlapping PSI regions that distribute unevenly but jointly cover only 0.81% of the genome. About 2.5% of the PSI regions colocalized with variants identified in genome-wide association studies, with disease loci more likely colocalized than quantitative trait loci (p = 1.6 × 10-5), particularly in regions without obvious regulatory roles. We further showed an LRP4 exemplar region with PSI elements orchestrated with common and rare disease variants and other functional elements. Our results render PSI elements as a valuable source to inform genetic studies of complex diseases.

The contribution of Neanderthal introgression and natural selection to neurodegenerative diseases

Humans are thought to be more susceptible to neurodegeneration than equivalently-aged primates. It is not known whether this vulnerability is specific to anatomically-modern humans or shared with other hominids. The contribution of introgressed Neanderthal DNA to neurodegenerative disorders remains uncertain. It is also unclear how common variants associated with neurodegenerative disease risk are maintained by natural selection in the population despite their deleterious effects. In this study, we aimed to quantify the genome-wide contribution of Neanderthal introgression and positive selection to the heritability of complex neurodegenerative disorders to address these questions.We used stratified-linkage disequilibrium score regression to investigate the relationship between five SNP-based signatures of natural selection, reflecting different timepoints of evolution, and genome-wide associated variants of the three most prevalent neurodegenerative disorders: Alzheimer's disease, amyotrophic lateral sclerosis and Parkinson's disease.We found no evidence for enrichment of positively-selected SNPs in the heritability of Alzheimer's disease, amyotrophic lateral sclerosis and Parkinson's disease, suggesting that common deleterious disease variants are unlikely to be maintained by positive selection. There was no enrichment of Neanderthal introgression in the SNP-heritability of these disorders, suggesting that Neanderthal admixture is unlikely to have contributed to disease risk.These findings provide insight into the origins of neurodegenerative disorders within the evolution of Homo sapiens and addresses a long-standing debate, showing that Neanderthal admixture is unlikely to have contributed to common genetic risk of neurodegeneration in anatomically-modern humans.

Systematic characterization of SNPs associated with congenital heart disease.

Genome wide association studies (GWAS) produced a wealth of information on associations between distinct genetic loci and human diseases. Over 90% of predicted variants for common diseases are located in the non-coding genome mainly affecting cis-regulatory elements (CREs) like enhancers. Single nucleotide polymorphisms (SNPs) in enhancers can affect gene expression and contribute to disease mechanisms, but for the majority of SNPs their functional contribution to disease is not understood. Current high-throughput methods to study enhancers use CRISPRi to perturb them entirely.

Glucocorticoids unmask silent non-coding genetic risk variants for common diseases.

Understanding the function of non-coding genomic sequence variants represents a challenge for biomedicine. Many diseases are products of gene-by-environment interactions with complex mechanisms. This study addresses these themes by mechanistic characterization of non-coding variants that influence gene expression only after drug or hormone exposure. Using glucocorticoid signaling as a model system, we integrated genomic, transcriptomic, and epigenomic approaches to unravel mechanisms by which variant function could be revealed by hormones or drugs. Specifically, we identified cis-regulatory elements and 3D interactions underlying ligand-dependent associations between variants and gene expression. One-quarter of the glucocorticoid-modulated variants that we identified had already been associated with clinical phenotypes. However, their affected genes were 'unmasked' only after glucocorticoid exposure and often with function relevant to the disease phenotypes. These diseases involved glucocorticoids as risk factors or therapeutic agents and included autoimmunity, metabolic and mood disorders, osteoporosis and cancer. For example, we identified a novel breast cancer risk gene, MAST4, with expression that was repressed by glucocorticoids in cells carrying the risk genotype, repression that correlated with MAST4 expression in breast cancer and treatment outcomes. These observations provide a mechanistic framework for understanding non-coding genetic variant-chemical environment interactions and their role in disease risk and drug response.

58. Improving the molecular diagnosis of SBDS-related disorders by identifying rare gene conversion events.

Biallelic disease-causing variants in the SBDS gene account for over 90 percent of Shwachman-Diamond Syndrome, a congenital autosomal recessive disorder characterized by pancreatic insufficiency, hematological malfunction, and skeletal abnormalities. Molecular genetic tests of SBDS include single-gene Sanger sequencing or next generation sequencing (NGS) panels. The detection of SBDS variants by either method, however, is complicated by the pseudogene, SBDSP1. In fact, common disease variants in SBDS are created by gene conversion events between SBDS and SBDSP1, making it difficult to determine whether a variant affects the functional gene or the pseudogene. We evaluated the performance of SBDS sequencing tests offered at our institution and determined if additional test strategies are needed for accurate variant detection. Sanger sequencing of SBDS identified the 2 known, common disease variants (c.183_184delinsCT and c.258+2T>C), as well as several less common disease variants. NGS testing of SBDS detected the common c.183_184delinsCT as 2 separate variants, such that those 2 variants had to be flagged and manually inspected when they were present. NGS also identified the second common disease variant, c.258+2T>C, and could determine the phase of the 2 common disease variants in individuals carrying them both. It remains possible to miss a rare gene conversion event by both sequencing methods. Extra attention should be given to patients negative by sequencing that are a strong phenotypic match for Shwachman-Diamond Syndrome. A reflex Sanger test can be considered for such patients, as well as analysis of low allele fraction NGS variants that might be constitutively heterozygous.

Genome-Wide Association Study for Idiopathic Ventricular Tachyarrhythmias Identifies Key Role of CCR7 and PKN2 in Calcium Homeostasis and Cardiac Rhythm Maintenance.

Idiopathic ventricular tachycardia (VT) occurs in structurally normal hearts and accounts for a significant number of all types of VT. The genome-wide association study is the most effective strategy for identifying novel genetic variants for common diseases. However, no genome-wide association study has been reported for idiopathic VT. We conducted the first genome-wide association study for idiopathic VT in the Chinese Han population using a discovery population with 246 cases and 648 controls and a replication population with 222 cases and >4072 controls. Candidate VT genes were functionally characterized in zebrafish. Real-time RT-PCR analysis was used to determine the effects of candidate genes on expression of ion channels and regulators. Patch-clamping was used to record L-type calcium current from neonatal rat cardiomyocytes with overexpression of candidate genes. We identified 4 significant loci represented by rs78960694 (minor allele frequency [MAF]=5.02% in cases and 1.84% in controls; P=4.30×10-12, odds ratio [OR]=3.91) and rs2229095 (MAF=3.25% in cases and 1.63% in controls; P=1.02×10-7, OR=3.44) near and in CCR7, respectively, rs68126098 in NELL1 (MAF=40.98% in cases and 32.07% in controls; P=2.40×10-8, OR=1.53), rs2390325 between PKN2 and LMO4 (MAF=21.19% in cases and 15.12% in controls; P=1.92×10-7, OR=1.62), and rs270065 in CSMD1 (MAF=33.63% in cases and 40.25% in controls; P=9.51×10-7, OR=0.69). Note that the associations of idiopathic VT for CCR7 variant rs78960694 and NELL1 variant rs68126098 reach genome-wide significance (P<5.00×10-8). Overexpression of either PKN2 or CCR7 increased the heart rate in zebrafish, and enhanced expression of CACNA1C, RYR2, or NOS1AP in zebrafish embryos, HEK293, and AC16 cardiomyocytes. Overexpression of either PKN2 or CCR7 significantly increased L-type Ca2+ current density. The first genome-wide association study identifies 4 novel loci and 2 risk genes (PKN2 and CCR7) for idiopathic VT. These findings identify new molecular determinants for cardiac calcium homeostasis and rhythm maintenance and provide novel targets for diagnosis and treatment for idiopathic VT.

An atlas of mitochondrial DNA genotype-phenotype associations in the UK Biobank.

Mitochondrial genome (mtDNA) variation in common diseases has been under-explored, partly due to a lack of genotype calling and quality control procedures. Developing an at-scale workflow for mtDNA variant analyses, we show correlations between nuclear and mitochondrial genomic structures within sub-populations of Great Britain and establish a UK Biobank reference atlas of mtDNA-phenotype associations. A total of 260 mtDNA-phenotype associations were novel (P<1x10-5) including, rs2853822/m.8655C>T (MT-ATP6) with type 2 diabetes, rs878966690/m.13117A>G (MT-ND5) with multiple sclerosis, six mtDNA associations with adult height, 24 with two liver biomarkers and 16 with parameters of renal function. Rare variant gene-based tests implicated Complex I genes modulating mean corpuscular volume and mean corpuscular hemoglobin. Seven traits had both rare and common mtDNA associations where rare variants tended to have larger effects than common variants. Our work illustrates the value of studying mtDNA variants in common complex diseases and lays foundations for future large-scale mtDNA association studies.

Impact of variant-level batch effects on identification of genetic risk factors in large sequencing studies.

Genetic studies have shifted to sequencing-based rare variants discovery after decades of success in identifying common disease variants by Genome-Wide Association Studies using Single Nucleotide Polymorphism chips. Sequencing-based studies require large sample sizes for statistical power and therefore often inadvertently introduce batch effects because samples are typically collected, processed, and sequenced at multiple centers. Conventionally, batch effects are first detected and visualized using Principal Components Analysis and then controlled by including batch covariates in the disease association models. For sequencing-based genetic studies, because all variants included in the association analyses have passed sequencing-related quality control measures, this conventional approach treats every variant as equal and ignores the substantial differences still remaining in variant qualities and characteristics such as genotype quality scores, alternative allele fractions (fraction of reads supporting alternative allele at a variant position) and sequencing depths. In the Alzheimer’s Disease Sequencing Project (ADSP) exome dataset of 9,904 cases and controls, we discovered hidden variant-level differences between sample batches of three sequencing centers and two exome capture kits. Although sequencing centers were included as a covariate in our association models, we observed differences at the variant level in genotype quality and alternative allele fraction between samples processed by different exome capture kits that significantly impacted both the confidence of variant detection and the identification of disease-associated variants. Furthermore, we found that a subset of top disease-risk variants came exclusively from samples processed by one exome capture kit that was more effective at capturing the alternative alleles compared to the other kit. Our findings highlight the importance of additional variant-level quality control for large sequencing-based genetic studies. More importantly, we demonstrate that automatically filtering out variants with batch differences may lead to false negatives if the batch discordances come largely from quality differences and if the batch-specific variants have better quality.

Integrative analysis of liver-specific non-coding regulatory SNPs associated with the risk of coronary artery disease

Genetic factors underlying coronary artery disease (CAD) have been widely studied using genome-wide association studies (GWASs). However, the functional understanding of the CAD loci has been limited by the fact that a majority of GWAS variants are located within non-coding regions with no functional role. High cholesterol and dysregulation of the liver metabolism such as non-alcoholic fatty liver disease confer an increased risk of CAD. Here, we studied the function of non-coding single-nucleotide polymorphisms in CAD GWAS loci located within liver-specific enhancer elements by identifying their potential target genes using liver cis-eQTL analysis and promoter Capture Hi-C in HepG2 cells. Altogether, 734 target genes were identified of which 121 exhibited correlations to liver-related traits. To identify potentially causal regulatory SNPs, the allele-specific enhancer activity was analyzed by (1) sequence-based computational predictions, (2) quantification of allele-specific transcription factor binding, and (3) STARR-seq massively parallel reporter assay. Altogether, our analysis identified 1,277 unique SNPs that display allele-specific regulatory activity. Among these, susceptibility enhancers near important cholesterol homeostasis genes (APOB, APOC1, APOE, and LIPA) were identified, suggesting that altered gene regulatory activity could represent another way by which genetic variation regulates serum lipoprotein levels. Using CRISPR-based perturbation, we demonstrate how the deletion/activation of a single enhancer leads to changes in the expression of many target genes located in a shared chromatin interaction domain. Our integrative genomics approach represents a comprehensive effort in identifying putative causal regulatory regions and target genes that could predispose to clinical manifestation of CAD by affecting liver function.

Improving the informativeness of Mendelian disease-derived pathogenicity scores for common disease

Despite considerable progress on pathogenicity scores prioritizing variants for Mendelian disease, little is known about the utility of these scores for common disease. Here, we assess the informativeness of Mendelian disease-derived pathogenicity scores for common disease and improve upon existing scores. We first apply stratified linkage disequilibrium (LD) score regression to evaluate published pathogenicity scores across 41 common diseases and complex traits (average N = 320K). Several of the resulting annotations are informative for common disease, even after conditioning on a broad set of functional annotations. We then improve upon published pathogenicity scores by developing AnnotBoost, a machine learning framework to impute and denoise pathogenicity scores using a broad set of functional annotations. AnnotBoost substantially increases the informativeness for common disease of both previously uninformative and previously informative pathogenicity scores, implying that Mendelian and common disease variants share similar properties. The boosted scores also produce improvements in heritability model fit and in classifying disease-associated, fine-mapped SNPs. Our boosted scores may improve fine-mapping and candidate gene discovery for common disease.

Ultrafast and scalable variant annotation and prioritization with big functional genomics data.

The advances of large-scale genomics studies have enabled compilation of cell type–specific, genome-wide DNA functional elements at high resolution. With the growing volume of functional annotation data and sequencing variants, existing variant annotation algorithms lack the efficiency and scalability to process big genomic data, particularly when annotating whole-genome sequencing variants against a huge database with billions of genomic features. Here, we develop VarNote to rapidly annotate genome-scale variants in large and complex functional annotation resources. Equipped with a novel index system and a parallel random-sweep searching algorithm, VarNote shows substantial performance improvements (two to three orders of magnitude) over existing algorithms at different scales. It supports both region-based and allele-specific annotations and introduces advanced functions for the flexible extraction of annotations. By integrating massive base-wise and context-dependent annotations in the VarNote framework, we introduce three efficient and accurate pipelines to prioritize the causal regulatory variants for common diseases, Mendelian disorders, and cancers.

Polygenic risk score as clinical utility in psychiatry: a clinical viewpoint.

Genome-wide association studies (GWASs) have detected many susceptible variants for common diseases, including psychiatric disorders. However, because of the small effect size of each variant, clinical utility that aims for risk prediction and/or diagnostic assistance based on the individual "variants" is difficult to use. Therefore, to improve the statistical power, polygenic risk score (PRS) has been established and applied in the GWAS as a robust analytic tool. Although PRS has potential predictive ability, because of its current "insufficient" discriminative power at the individual level for clinical use, it remains limited solely in the research area, specifically in the psychiatric field. For a better understanding of the PRS, in this review, we (1) introduce the clinical features of psychiatric disorders, (2) summarize the recent GWAS/PRS findings in the psychiatric disorders, (3) evaluate the problems of PRS, and (4) propose its possible utility to apply PRS into the psychiatric clinical setting.

Epigenetics and the exposomes: Obesity and beyond

ABSTRACT: The specific genetic alterations that result in diseases and complex syndromes have been and continue to be identified. Search for the origins of disease have led to investigations into the roles of dietary and environmental factors as potential triggers or modifiers of risk. Genome-wide association studies have identified concepts such as the rare variant-common disease hypothesis and the common variant-common disease hypothesis.1 Through association studies, unique gene-environment interactions, which may occur with or without specific periods of permissiveness or vulnerabilities, have also been identified. Major conditions where the role of exposomes and epigenetics are rapidly evolving are obesity, neurological disorders, immune disorders and cancers. These concepts are particularly intriguing in the context of obesity.

Methodology in cardiologists’ postgraduate education

Aim. To develop the methodological ware for distance cardiologists’ postgraduate education by virtual patients’ simulations. The article describes the first stage of the project to create a database of virtual patients. It includes the integration of text and multimedia clinical and diagnostic information about patients and software for its presentation. A virtual patient is considered as an informational model of the diagnostic and treatment processes.Material and methods. To create a database of virtual patients, a description of completed clinical cases in specially designed patterns was used. To provide distance access to information about virtual patients, the JavaScript was used. Results. When forming the database of virtual patients, two categories of case histories were used — the most common cases and rare disease variants. From archival case histories and examination data, information has been selected that plays a significant role in the diagnosis and treatment. It was decided to present the information in the form of separate portions-visits. Each visit includes the results of tests and specialty consultations necessary to make a decision on further treatment. In the template developed by doctors and analysts, patient information is broken down into blocks that included complaints; anamnesis; physical examination; provisional diagnosis; diagnostic and treatment plan; tests’ results; administrations; final diagnosis; clinical report. These blocks served as the basis for the development of relational database tables. To visualize information about the virtual patient, prototypes of screen forms were implemented. Information about different visits was placed on different marks, and the user is able to see previously available information.Conclusion.After testing, this software will be used in education to demonstrate complete clinical cases to students. In addition, by each virtual case, tasks that require students to make medical decisions can be formed. The technology of virtual patients can be used both for postgraduates (doctors) and medical students.

Population-specific long-range linkage disequilibrium in the human genome and its influence on identifying common disease variants

Despite the availability of large-scale sequencing data, long-range linkage disequilibrium (LRLD) has not been extensively studied. The theoretical aspects of LRLD estimates were studied to determine the best estimation method for the sequencing data of three different populations of African (AFR), European (EUR), and East-Asian (EAS) descent from the 1000 Genomes Project. Genome-wide LRLDs excluding centromeric regions revealed clear population specificity, presenting substantially more population-specific LRLDs than coincident LRLDs. Clear relationships between the functionalities of the regions in LRLDs denoted long-range interactions in the genome. The proportions of gene regions were increased in LRLD variants, and the coding sequence (CDS)-CDS LRLDs showed obvious functional similarities between genes in LRLDs. Application to theoretical case-control associations confirmed that the LRLDs in genome-wide association studies (GWASs) could contribute to false signals, although the impacts might not be severe in most cases. LRLDs with variants with functional similarity exist in the human genome indicating possible gene-gene interactions, and they differ depending on populations. Based on the current study, LRLDs should be examined in GWASs to identify true signals. More importantly, population specificity in LRLDs should be examined in relevant studies.

Comprehensive Analysis of Rare Variants of 101 Autism-Linked Genes in a Hungarian Cohort of Autism Spectrum Disorder Patients.

BackgroundAutism spectrum disorder (ASD) is genetically and phenotypically heterogeneous. Former genetic studies suggested that both common and rare genetic variants play a role in the etiology. In this study, we aimed to analyze rare variants detected by next generation sequencing (NGS) in an autism cohort from Hungary.MethodsWe investigated the yield of NGS panel sequencing of an unselected ASD cohort (N = 174 ) for the detection of ASD associated syndromes. Besides, we analyzed rare variants in a common disease-rare variant framework and performed rare variant burden analysis and gene enrichment analysis in phenotype based clusters.ResultsWe have diagnosed 13 molecularly proven syndromic autism cases. Strongest indicators of syndromic autism were intellectual disability, epilepsy or other neurological plus symptoms. Rare variant analysis on a cohort level confirmed the association of five genes with autism (AUTS2, NHS, NSD1, SLC9A9, and VPS13). We found no correlation between rare variant burden and number of minor malformation or autism severity. We identified four phenotypic clusters, but no specific gene was enriched in a given cluster.ConclusionOur study indicates that NGS panel gene sequencing can be useful, where the clinical picture suggests a clinically defined syndromic autism. In this group, targeted panel sequencing may provide reasonable diagnostic yield. Unselected NGS panel screening in the clinic remains controversial, because of uncertain utility, and difficulties of the variant interpretation. However, the detected rare variants may still significantly influence autism risk and subphenotypes in a polygenic model, but to detect the effects of these variants larger cohorts are needed.

Machine Learning SNP Based Prediction for Precision Medicine.

In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalized medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. Despite recent improvements, the results of polygenic risk scoring remain limited due to the approaches that are currently used. By contrast, machine learning algorithms have increased predictive abilities for complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenic risk scoring and machine learning in complex disease risk prediction. We highlight recent machine learning application developments and describe how machine learning approaches can lead to improved complex disease prediction, which will help to incorporate genetic features into future personalized healthcare. Finally, we discuss how the future application of machine learning prediction models might help manage complex disease by providing tissue-specific targets for customized, preventive interventions.

High burden of birthweight-lowering genetic variants in Africans and Asians

BackgroundBirthweight is an important predictor of infant morbidity and mortality, and is associated with cardiovascular diseases, obesity, and diabetes in childhood and adulthood. Birthweight and fetal growth show regional and population variations even under similar maternal conditions, and a large proportion of these differences are not explained by environmental factors. Whether and to what extent population genetic variations at key birthweight-associated loci account for the residual birthweight disparities not explained by environmental determinants is unknown. We hypothesized that the cumulative burden of genetic variants with a birthweight-lowering effect (GRB) is different among ancestrally diverse populations.MethodsGenotype data were extracted from phase 3 of the 1000 Genomes Project for 2504 participants from 26 global populations grouped into five super-populations. GRB was calculated in offspring as the weighted sum of the number of birthweight-lowering genetic variants of 59 autosomal single-nucleotide polymorphisms associated with birthweight, and comparisons were made between Europeans and non-Europeans.ResultsGRB was significantly higher in Africans (mean difference 3.15; 95% confidence interval 2.64, 3.66), admixed Americans (3.02; 2.34, 3.70), East Asians (2.85; 2.29, 3.41), and South Asians (1.07; 0.49, 1.65) compared to Europeans. Birthweight-lowering genetic variants in Africans and East Asians were enriched for rare and frequency-fixed alleles (P < 0.001). African and Asian populations had the greatest deviation from the expectation of the common disease-common variant hyothesis. Compared to Europeans, the GRB of ancestral alleles was significantly higher and that of derived alleles was significantly lower in non-Europeans (P < 0.001).ConclusionsThe burden of birthweight-lowering genetic variants is higher in Africans and East Asians. This finding is consistent with the high incidence of low birthweight in the two populations. The genetic variants we studied may not be causal and the extent to which they tag the causal variants in non-Europeans is unknown; however, our findings highlight that genetic variations contribute to population differences in birthweight.