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Background: Inflammation is thought to be a risk factor for kidney disease. However, whether inflammatory status is either a cause or an outcome of chronic kidney disease remains controversial. We aimed to investigate the causal relationship between high-sensitivity C-reactive protein (hs-CRP) and estimated glomerular filtration rate (eGFR) using Mendelian randomization (MR) approaches. Methods: A total of 10,521 participants of the Japan Multi-institutional Collaborative Cohort Study was analyzed in this study. We used two-sample MR approaches (the inverse-variance weighted (IVW), the weighted median (WM), and the MR-Egger method) to estimate the effect of genetically determined hs-CRP on kidney function. We selected four and three hs-CRP associated single nucleotide polymorphisms (SNPs) as two instrumental variables (IV): IVCRP and IVAsian, based on SNPs previously identified in European and Asian populations. IVCRP and IVAsian explained 3.4% and 3.9% of the variation in hs-CRP, respectively. Results: Using the IVCRP, genetically determined hs-CRP was not significantly associated with eGFR in the IVW and the WM methods (estimate per 1 unit increase in ln(hs-CRP), 0.000; 95% confidence interval [CI], −0.019 to 0.020 and −0.003; 95% CI, −0.019 to 0.014, respectively). For IVAsian, we found similar results using the IVW and the WM methods (estimate, 0.005; 95% CI, −0.020 to 0.010 and −0.004; 95% CI, −0.020 to 0.012, respectively). The MR-Egger method also showed no causal relationships between hs-CRP and eGFR (IVCRP: −0.008; 95% CI, −0.058 to 0.042; IVAsian: 0.001; 95% CI, −0.036 to 0.036). Conclusion: Our two-sample MR analyses with different IVs did not support a causal effect of hs-CRP on eGFR.

Molecular surveillance for malaria has great potential to support national malaria control programs (NMCPs). To bridge the gap between research and implementation, several applications (use cases) have been identified to align research, technology development, and public health efforts. For implementation at NMCPs, there is an urgent need for feasible and cost-effective tools. We designed a new highly multiplexed deep sequencing assay (Pf AmpliSeq), which is compatible with benchtop sequencers, that allows high-accuracy sequencing with higher coverage and lower cost than whole-genome sequencing (WGS), targeting genomic regions of interest. The novelty of the assay is its high number of targets multiplexed into one easy workflow, combining population genetic markers with 13 nearly full-length resistance genes, which is applicable for many different use cases. We provide the first proof of principle for hrp2 and hrp3 deletion detection using amplicon sequencing. Initial sequence data processing can be performed automatically, and subsequent variant analysis requires minimal bioinformatic skills using any tabulated data analysis program. The assay was validated using a retrospective sample collection (n = 254) from the Peruvian Amazon between 2003 and 2018. By combining phenotypic markers and a within-country 28-single-nucleotide-polymorphism (SNP) barcode, we were able to distinguish different lineages with multiple resistance haplotypes (in dhfr, dhps, crt and mdr1) and hrp2 and hrp3 deletions, which have been increasing in recent years. We found no evidence to suggest the emergence of artemisinin (ART) resistance in Peru. These findings indicate a parasite population that is under drug pressure but is susceptible to current antimalarials and demonstrate the added value of a highly multiplexed molecular tool to inform malaria strategies and surveillance systems.IMPORTANCE While the power of next-generation sequencing technologies to inform and guide malaria control programs has become broadly recognized, the integration of genomic data for operational incorporation into malaria surveillance remains a challenge in most countries where malaria is endemic. The main obstacles include limited infrastructure, limited access to high-throughput sequencing facilities, and the need for local capacity to run an in-country analysis of genomes at a large-enough scale to be informative for surveillance. In addition, there is a lack of standardized laboratory protocols and automated analysis pipelines to generate reproducible and timely results useful for relevant stakeholders. With our standardized laboratory and bioinformatic workflow, malaria genetic surveillance data can be readily generated by surveillance researchers and malaria control programs in countries of endemicity, increasing ownership and ensuring timely results for informed decision- and policy-making. While the power of next-generation sequencing technologies to inform and guide malaria control programs has become broadly recognized, the integration of genomic data for operational incorporation into malaria surveillance remains a challenge in most countries where malaria is endemic. The main obstacles include limited infrastructure, limited access to high-throughput sequencing facilities, and the need for local capacity to run an in-country analysis of genomes at a large-enough scale to be informative for surveillance.

Measurement of circulating immunoglobulin E (IgE) concentration is helpful for diagnosing and treating asthma and allergic diseases. Identifying gene expression signatures associated with IgE might elucidate novel pathways for IgE regulation. To this end, we performed a discovery transcriptome-wide association study to identify differentially expressed genes associated with circulating IgE levels in whole-blood derived RNA from 5,345 participants in the Framingham Heart Study across 17,873 mRNA gene-level transcripts. We identified 216 significant transcripts at a false discovery rate CLC, CCDC21, S100A13, and GCNT1) as putatively causal (pGCNT1 (beta=1.5, p=0.01)—which is a top result in the MR analysis of expression in relation to asthma and allergic diseases—plays a role in regulating T helper type 1 cell homing, lymphocyte trafficking, and B cell differentiation. Our findings build upon prior knowledge of IgE regulation and provide a deeper understanding of underlying molecular mechanisms. The IgE-associated genes that we identified—particularly those implicated in MR analysis—can be explored as promising therapeutic targets for asthma and IgE-related diseases.

The annual epidemics of seasonal influenza is partly attributed to the continued virus evolution. It is challenging to evaluate the effect of influenza virus mutations on evading population immunity. In this study, we introduce a novel statistical and computational approach to measure the dynamic molecular determinants underlying epidemics using effective mutations (EMs), and account for the time of waning mutation advantage against herd immunity by measuring the effective mutation periods (EMPs). Extensive analysis is performed on the sequencing and epidemiology data of H3N2 epidemics in ten regions from season to season. We systematically identified 46 EMs in the hemagglutinin (HA) gene, in which the majority were antigenic sites. Eight EMs were located in immunosubdominant stalk domain, an important target for developing broadly reactive antibodies. The EMs might provide timely information on key substitutions for influenza vaccines antigen design. The EMP suggested that major genetic variants of H3N2 circulated in South-east Asia for an average duration of 4.5 years (SD 2.4) compared to a significantly shorter 2.0 years (SD 1.0) in temperate regions. The proposed method bridges population epidemics and molecular characteristics of infectious diseases, and would find broad applications in various pathogens mutation estimations.

On the basis of the data from the archive of the Laboratory of Genetic Epidemiology of the Research Centre for Medical Genetics, marriage records are selected for three large cities of the North Caucasus with a significant proportion of Russians: Vladikavkaz (25% Russians), Mozdok (59%), and Cherkessk (54%). In the selected marriage records, one or both spouses are Russian. The places of birth of migrants are analyzed at the level of federal districts. It is shown that three quarters of the Russian population of North Caucasian cities are natives of the North Caucasian Federal District. The majority of Russian migrants are natives of Ukraine and Kazakhstan.

Here, we report the first population genetic study to examine the impact of indoor residual spraying (IRS) on Plasmodium falciparum in humans. This study was conducted in an area of high seasonal malaria transmission in Bongo District, Ghana. IRS was implemented during the dry season (November–May) in three consecutive years between 2013 and 2015 to reduce transmission and attempt to bottleneck the parasite population in humans towards lower diversity with greater linkage disequilibrium. The study was done against a background of widespread use of long‐lasting insecticidal nets, typical for contemporary malaria control in West Africa. Microsatellite genotyping with 10 loci was used to construct 392 P. falciparum multilocus infection haplotypes collected from two age‐stratified cross‐sectional surveys at the end of the wet seasons pre‐ and post‐IRS. Three‐rounds of IRS, under operational conditions, led to a >90% reduction in transmission intensity and a 35.7% reduction in the P. falciparum prevalence (p

Objectives Up to 0.3% of Japanese have hypouricaemia. Most cases appear to result from a hereditary disease, renal hypouricaemia (RHUC), which causes exercise-induced acute kidney injury and urolithiasis. However, to what extent RHUC accounts for hypouricaemia is not known. We therefore investigated its frequency and evaluated its risks by genotyping a general Japanese population. Methods A cohort of 4993 Japanese was examined by genotyping the non-functional variants R90H (rs121907896) and W258X (rs121907892) of URAT1/SLC22A12, the two most common causative variants of RHUC in Japanese. Results Participants’ fractional excretion of uric acid and risk allele frequencies markedly increased at lower serum uric acid (SUA) levels. Ten participants (0.200%) had an SUA level ≤2.0 mg/dl and nine had R90H or W258X and were likely to have RHUC. Logistic regression analysis revealed these URAT1 variants to be significantly and independently associated with the risk of hypouricaemia and mild hypouricaemia (SUA ≤3.0 mg/dl) as well as sex, age and BMI, but these URAT1 variants were the only risks in the hypouricaemia population (SUA ≤2.0 mg/dl). W258X was only a risk in males with SUA ≤3.0 mg/dl. Conclusion Our study accurately reveals the prevalence of RHUC and provides genetic evidence for its definition (SUA ≤2.0 mg/dl). We also show that individuals with SUA ≤3.0 mg/dl, especially males, are prone to RHUC. Our findings will help to promote a better epidemiological understanding of RHUC as well as more accurate diagnosis, especially in males with mild hypouricaemia.

Immigration impact on genetic epidemiology of thalassemia worldwide is well-recognized. Over the past decade, the Duhok Province of Northern Iraq attracted a large number of immigrants. To assess whether immigration had contributed to changes in the mutation spectrum of β-thalassemia (β-thal) in the region, we recruited 218 registered patients with symptomatic β-thal. The recruited patients included 50 (22.9%) from resettled migrant families. A total of 431 β-thal alleles were fully characterized, with 20 different thalassemia mutations, the most frequent being IVS-II-1 (G>A) (HBB: c.315 + 1G>A), IVS-I-6 (T>C) (HBB: c.92 + 6T>C), codon 5 (-CT) (HBB: c.17_18delCT), IVS-I-110 (G>A) (HBB: c.93-21G>A), codon 44 (-C) (HBB: c.135delC), codon 8 (-AA) (HBB: c.25_26delAA) and IVS-I-1 (G>A) (HBB: c.92 + 1G>A) constituting 72.8% of the total. Some differences in mutation spectrum were observed compared to earlier studies from this same province, the most notable of which were the higher frequencies of IVS-I-110 and codon 8. Interestingly, the highest proportions of alleles related to immigrants were encountered in these two allele groups. Ethnic variation was also documented, so that while Muslim Kurds had IVS-II-1, IVS-I-6, IVS-I-110, codon 5 and codon 44 as their most frequent mutations, the most frequent among Kurdish Yazidis, were codon 5, codon 44, codon 8 and IVS-I-6. These ethnic variations and changes in mutation spectrums are important and should be taken in consideration to ensure effective implementation of the thalassemia preventive program.

BackgroundObsessive-compulsive disorder (OCD) is a psychiatric disorder with multiple symptom dimensions (e.g. contamination, symmetry). OCD clusters in families and decades of twin studies clearly demonstrate an important role for genetics in the etiology of the disorder.MethodsIn this review, we summarize the genetic epidemiology and molecular genetic studies of OCD and obsessive-compulsive symptoms.ResultsOCD is a heritable, polygenic disorder with contributions from both common and rare variants, includingde novodeleterious variations. Multiple studies have provided reliable support for a large additive genetic contribution to liability to OCD, with discrete OCD symptom dimensions having both shared and unique genetic risks. Genome-wide association studies have not produced significant results yet, likely because of small sample sizes, but larger meta-analyses are forthcoming. Both twin and genome-wide studies show that OCD shares genetic risk with its comorbid conditions (e.g. Tourette syndrome and anorexia nervosa).ConclusionsDespite significant efforts to uncover the genetic basis of OCD, the mechanistic understanding of how genetic and environmental risk factors interact and converge at the molecular level to result in OCD's heterogeneous phenotype is still mostly unknown. Future investigations should increase ancestral genetic diversity, explore age and/or sex differences in genetic risk for OCD and expand the study of pharmacogenetics, gene expression, gene × environment interactions and epigenetic mechanisms for OCD.

The publication of genetic epidemiology meta-analyses has increased rapidly, but it has been suggested that many of the statistically significant results are false positive. In addition, most such meta-analyses have been redundant, duplicate, and erroneous, leading to research waste. In addition, since most claimed candidate gene associations were false-positives, correctly interpreting the published results is important. In this review, we emphasize the importance of interpreting the results of genetic epidemiology meta-analyses using Bayesian statistics and gene network analysis, which could be applied in other diseases.

Substance use disorders (SUDs) are prevalent and result in an array of negative consequences. They are influenced by genetic factors (h2 = ~50%). Recent years have brought substantial progress in our understanding of the genetic etiology of SUDs and related traits. The present review covers the current state of the field for SUD genetics, including the epidemiology and genetic epidemiology of SUDs, findings from the first-generation of SUD genome-wide association studies (GWAS), cautions about translating GWAS findings to clinical settings, and suggested prioritizations for the next wave of SUD genetics efforts. Recent advances in SUD genetics have been facilitated by the assembly of large GWAS samples, and the development of state-of-the-art methods modeling the aggregate effect of genome-wide variation. These advances have confirmed that SUDs are highly polygenic with many variants across the genome conferring risk, the vast majority of which are of small effect. Downstream analyses have enabled finer resolution of the genetic architecture of SUDs and revealed insights into their genetic relationship with other psychiatric disorders. Recent efforts have also prioritized a closer examination of GWAS findings that have suggested non-uniform genetic influences across measures of substance use (e.g. consumption) and problematic use (e.g. SUD). Additional highlights from recent SUD GWAS include the robust confirmation of loci in alcohol metabolizing genes (e.g. ADH1B and ALDH2) affecting alcohol-related traits, and loci within the CHRNA5-CHRNA3-CHRNB4 gene cluster influencing nicotine-related traits. Similar successes are expected for cannabis, opioid, and cocaine use disorders as sample sizes approach those assembled for alcohol and nicotine.

Major depressive disorder (MDD) is a common, debilitating, phenotypically heterogeneous disorder with heritability ranges from 30% to 50%. Compared to other psychiatric disorders, its high prevalence, moderate heritability, and strong polygenicity have posed major challenges for gene-mapping in MDD. Studies of common genetic variation in MDD, driven by large international collaborations such as the Psychiatric Genomics Consortium, have confirmed the highly polygenic nature of the disorder and implicated over 100 genetic risk loci to date. Rare copy number variants associated with MDD risk were also recently identified. The goal of this review is to present a broad picture of our current understanding of the epidemiology, genetic epidemiology, molecular genetics, and gene–environment interplay in MDD. Insights into the impact of genetic factors on the aetiology of this complex disorder hold great promise for improving clinical care.

Human blood cells (HBCs) play essential roles in multiple biological processes but their roles in development of uterine polyps are unknown. Here we implemented a Mendelian randomization (MR) analysis to investigate the effects of 36 HBC traits on endometrial polyps (EPs) and cervical polyps (CPs). The random-effect inverse-variance weighted method was adopted as standard MR analysis and three additional MR methods (MR-Egger, weighted median, and MR-PRESSO) were used for sensitivity analyses. Genetic instruments of HBC traits was extracted from a large genome-wide association study of 173,480 individuals, while data for EPs and CPs were obtained from the UK Biobank. All samples were Europeans. Using genetic variants as instrumental variables, our study found that both eosinophil count (OR 0.85, 95% CI 0.79–0.93, P = 1.06 × 10−4) and eosinophil percentage of white cells (OR 0.84, 95% CI 0.77–0.91, P = 2.43 × 10−5) were associated with decreased risk of EPs. The results were robust in sensitivity analyses and no evidences of horizontal pleiotropy were observed. While we found no significant associations between HBC traits and CPs. Our findings suggested eosinophils might play important roles in the pathogenesis of EPs. Besides, out study provided novel insight into detecting uterine polyps biomarkers using genetic epidemiology approaches.

Over 892 million people in 48 countries are at risk of infection by nematodes that cause lymphatic filariasis. As part of the Global Programme to Eliminate Lymphatic Filariasis, mass drug administration is distributed to communities until surveillance indicates infection rates are below target prevalence thresholds. In some countries, including American Samoa, lymphatic filariasis transmission persists despite years of mass drug administration and/or has resurged after cessation. Nothing is known about the population genetics of Wuchereria bancrofti worms in Polynesia, or whether local transmission is persisting and/or increasing due to inadequate mass drug administration coverage, expansion from residual hotspots, reintroduction from elsewhere, or a combination. We extracted DNA from microfilariae on blood slides collected during prevalence surveys in 2014 and 2016, comprising 31 pools of five microfilariae from 22 persons living in eight villages. We sequenced 1104 bp across three mitochondrial markers (ND4, COI, CYTB). We quantified parasite genetic differentiation using variant calls and estimated haplotypes using principal components analysis, F-statistics, and haplotype networks. Of the variants called, all but eight were shared across the main island of Tutuila, and three of those were from a previously described hotspot village, Fagali’i. Genotypic data did not support population genetic structure among regions or villages in 2016, although differences were observed between worms collected in Fagali’i in 2014 and those from 2016. Because estimated haplotype frequency varied between villages, these statistics suggested genetic differentiation, but were not consistent among villages. Finally, haplotype networks demonstrated American Samoan sequence clusters were related to previously published sequences from Papua New Guinea. These are, to our knowledge, the first reports of W. bancrofti genetic variation in Polynesia. The resurgent parasites circulating on the main island of American Samoa represent a single population. This study is the first step towards investigating how parasite population structure might inform strategies to manage resurgence and elimination of lymphatic filariasis.

The identification of genetic variation that directly impacts infection susceptibility to SARS-CoV-2 and disease severity of COVID-19 is an important step towards risk stratification, personalized treatment plans, therapeutic, and vaccine development and deployment. Given the importance of study design in infectious disease genetic epidemiology, we use simulation and draw on current estimates of exposure, infectivity, and test accuracy of COVID-19 to demonstrate the feasibility of detecting host genetic factors associated with susceptibility and severity in published COVID-19 study designs. We demonstrate that limited phenotypic data and exposure/infection information in the early stages of the pandemic significantly impact the ability to detect most genetic variants with moderate effect sizes, especially when studying susceptibility to SARS-CoV-2 infection. Our insights can aid in the interpretation of genetic findings emerging in the literature and guide the design of future host genetic studies. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-020-00818-2.

Background: Much of the heritable risk of renal cell carcinoma (RCC) associated with common genetic variation is unexplained. New analytic approaches have been developed to increase the discovery of risk variants in genome-wide association studies (GWAS), including multi-locus testing through pathway analysis. Methods: We conducted a pathway analysis using GWAS summary data from six previous scans (10,784 cases and 20,406 controls) and evaluated 3,678 pathways and gene sets drawn from the Molecular Signatures Database. To replicate findings, we analyzed GWAS summary data from the UK Biobank (903 cases and 451,361 controls) and the Genetic Epidemiology Research on Adult Health and Aging cohort (317 cases and 50,511 controls). Results: We identified 14 pathways/gene sets associated with RCC in both the discovery (P < 1.36 × 10−5, the Bonferroni correction threshold) and replication (P < 0.05) sets, 10 of which include components of the PI3K/AKT pathway. In tests across 2,035 genes in these pathways, associations (Bonferroni corrected P < 2.46 × 10−5 in discovery and replication sets combined) were observed for CASP9, TIPIN, and CDKN2C. The strongest SNP signal was for rs12124078 (PDiscovery = 2.6 × 10−5; PReplication = 1.5 × 10−4; PCombined = 6.9 × 10−8), a CASP9 expression quantitative trait locus. Conclusions: Our pathway analysis implicates genetic variation within the PI3K/AKT pathway as a source of RCC heritability and identifies several promising novel susceptibility genes, including CASP9, which warrant further investigation. Impact: Our findings illustrate the value of pathway analysis as a complementary approach to analyzing GWAS data.

Increasing evidence indicates that specific genetic variants influence the severity of outcomes after infection with COVID-19. However, it is not clear whether the effect of these genetic factors is independent of the risk due to more established non-genetic demographic and metabolic risk factors such as male sex, poor cardiometabolic health, and low socioeconomic status. We sought to identify interactions between genetic variants and non-genetic risk factors influencing COVID-19 severity via a genome-wide interaction study in the UK Biobank. Of 378,051 unrelated individuals of European ancestry, 2,402 were classified as having experienced severe COVID-19, defined as hospitalization or death due to COVID-19. Exposures included sex, cardiometabolic risk factors (obesity and type 2 diabetes [T2D], tested jointly), and multiple deprivation index. Multiplicative interaction was tested using a logistic regression model, conducting both an interaction test and a joint test of genetic main and interaction effects. Five independent variants reached genome-wide significance in the joint test, one of which also reached significance in the interaction test. One of these, rs2268616 in the PGF gene, showed stronger effects in males and in individuals with T2D. None of the five variants showed effects on a similarly-defined phenotype in a lookup in the COVID-19 Host Genetics Initiative. These results reveal potential additional genetic loci contributing to COVID-19 severity and demonstrate the value of including non-genetic risk factors in an interaction testing approach for genetic discovery.

Lipoprotein(a) is an atherogenic low-density lipoprotein-like particle and circulating levels are largely determined by genetics. Patients with familial hypercholesterolemia (FH) have elevated lipoprotein(a); however, it remains unclear why.This study compared the levels of lipoprotein(a) and associated genetic factors between individuals that were ascertained for FH clinically versus genetically.We investigated causes of elevated lipoprotein(a) in individuals with clinically diagnosed FH (FH cohort, n = 391) and in individuals with genetically diagnosed FH from the general population (UK Biobank; n = 37,486).Patients in the FH cohort had significantly greater lipoprotein(a) levels than either the general population or non-FH dyslipidemic patients. This was accounted for by increased frequency of the rs10455872-G LPA risk allele (15.1% vs. 8.8%; p 0.05). However, within the FH cohort, lipoprotein(a) levels did not differ based on the presence or absence of an FH-causing variant (means = 1.43 log mg/dl vs. 1.42 log mg/dl; p = 0.97). Lipoprotein(a) levels were also not statistically different between individuals with and without an FH-causing variant in the UK Biobank cohort, which represents a population sample not biased to cardiovascular ascertainment (n = 221 vs. 37,486). We performed a phenome-wide association study between LPA genotypes and 19,202 phenotypes to demonstrate that elevated lipoprotein(a) is associated with increased low-density lipoprotein cholesterol, a family history of cardiovascular disease, premature coronary artery disease, and a diagnosis of FH.These results suggest that FH does not cause elevated lipoprotein(a), but that elevated lipoprotein(a) increases the likelihood that an individual with genetic FH will be clinically recognized.

Congruent, low-friction relative movement between the articulating elements of a synovial joint is an essential pre-requisite for sustained, efficient, function. Where disorders of joint formation or maintenance exist, mechanical overloading and osteoarthritis (OA) follow. The heritable component of OA accounts for ~ 50% of susceptible risk. Although almost 100 genetic risk loci for OA have now been identified, and the epidemiological relationship between joint development, joint shape and osteoarthritis is well established, we still have only a limited understanding of the contribution that genetic variation makes to joint shape and how this modulates OA risk. In this article, a brief overview of synovial joint development and its genetic regulation is followed by a review of current knowledge on the genetic epidemiology of established joint shape disorders and common shape variation. A summary of current genetic epidemiology of OA is also given, together with current evidence on the genetic overlap between shape variation and OA. Finally, the established genetic risk loci for both joint shape and osteoarthritis are discussed.

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness among the elderly population. To accelerate the understanding of the genetics of AMD, we conducted a meta-analysis of genome-wide association studies (GWAS) combining data from the International AMD Genomics Consortium AMD-2016 GWAS (16,144 advanced AMD cases and 17,832 controls), AMD-2013 GWAS (17,181 cases and 60,074 controls), and new data on 4017 AMD cases and 14,984 controls from Genetic Epidemiology Research on Aging study. We identified 12 novel AMD loci near or within C4BPA-CD55, ZNF385B, ZBTB38, NFKB1, LINC00461, ADAM19, CPN1, ACSL5, CSK, RLBP1, CLUL1, and LBP. We then replicated the associations of the novel loci in independent cohorts, UK Biobank (5860 cases and 126,726 controls) and FinnGen (1266 cases and 47,560 control). In general, the concordance in effect sizes was very high (correlation in effect size estimates 0.89), 11 of 12 novel loci were in the expected direction, 5 were associated with AMD at a nominal significance level, and rs3825991 (near gene RLBP1) after Bonferroni correction. We identified an additional 21 novel genes using a gene-based test. Most of the novel genes are expressed in retinal tissue and could be involved in the pathogenesis of AMD (i.e., complement, inflammation, and lipid pathways). These findings enhance our understanding of the genetic architecture of AMD and shed light on the biological process underlying AMD pathogenesis.

Graphical abstract, ATP7A is a critical copper transporter involved in Menkes Disease, Occipital horn Syndrome and X-linked distal spinal muscular atrophy type 3 which are X linked genetic disorders. These are rare diseases and their genetic epidemiology of the diseases is unknown. A number of genetic variants in the genes have been reported in published literature as well as databases, however, understanding the pathogenicity of variants and genetic epidemiology requires the data to be compiled in a unified format. To this end, we systematically compiled genetic variants from published literature and datasets. Each of the variants were systematically evaluated for evidences with respect to their pathogenicity and classified as per the American College of Medical Genetics and the Association of Molecular Pathologists (ACMG-AMP) guidelines into Pathogenic, Likely Pathogenic, Benign, Likely Benign and Variants of Uncertain Significance. Additional integrative analysis of population genomic datasets provides insights into the genetic epidemiology of the disease through estimation of carrier frequencies in global populations. To deliver a mechanistic explanation for the pathogenicity of selected variants, we also performed molecular modeling studies. Our modeling studies concluded that the small structural distortions observed in the local structures of the protein may lead to the destabilization of the global structure. To the best of our knowledge, ATP7A Clinical Genetics Resource is one of the most comprehensive compendium of variants in the gene providing clinically relevant annotations in gene.

Here, 622 imputations were conducted with 394 customized reference panels for Han Chinese and European populations. Besides validating the fact that imputation accuracy could always benefit from the increased panel size when the reference panel was population specific, the results brought two new thoughts. First, when the haplotype size of the reference panel was fixed, the imputation accuracy of common and low-frequency variants (Minor Allele Frequency (MAF) > 0.5%) decreased while the population diversity of the reference panel increased, but for rare variants (MAF 5%) for the European population could always benefit from the expanding sample size. However, for the Han Chinese population, the accuracy of all imputed variants reached the highest when reference panel contained a fraction of an extra diverse sample (8–21%). In addition, we evaluated the imputation performances in the existing reference panels, such as the Haplotype Reference Consortium (HRC), 1000 Genomes Project Phase 3 and the China, Oxford and Virginia Commonwealth University Experimental Research on Genetic Epidemiology (CONVERGE). For the European population, the HRC panel showed the best performance in our analysis. For the Han Chinese population, we proposed an optimum imputation reference panel constituent ratio if researchers would like to customize their own sequenced reference panel, but a high-quality and large-scale Chinese reference panel was still needed. Our findings could be generalized to the other populations with conservative genome; a tool was provided to investigate other populations of interest (https://github.com/Abyss-bai/reference-panel-reconstruction).

Venous thromboembolism (VTE) is a significant cause of mortality1, yet its genetic determinants remain incompletely defined. We performed a discovery genome-wide association study in the Million Veteran Program and UK Biobank testing ~13 million DNA sequence variants for association with VTE (26,066 cases; 624,053 controls) and meta-analyzed both studies, followed by independent replication with up to 17,672 VTE cases and 167,295 controls. We identified 22 novel loci, bringing the total number of VTE-associated loci to 33 and subsequently fine-mapped these associations. We developed a genome-wide polygenic risk score for VTE that identifies 5% of the population at equivalent incident VTE risk to carriers of the established F5 Leiden (p.R506Q) and prothrombin G20210A mutations. Our data provide new mechanistic insights into the genetic epidemiology of VTE and suggest a greater overlap among venous and arterial cardiovascular disease than previously suggested., Editorial summary Genome-wide analysis of venous thromboembolism identifies 22 new risk loci and facilitates construction of a polygenic risk score. Comparison to arterial vascular disease highlights shared pathophysiology and potential therapeutic strategies.

Structured summary Background A characteristic feature of SARS-CoV-2 is its ability to transmit from pre- or asymptomatic patients, complicating the tracing of infection pathways and causing outbreaks. Despite several reports that whole genome sequencing (WGS) and haplotype networks are useful for epidemiologic analysis, little is known about their use in nosocomial infections. Aim We aimed to demonstrate the advantages of genetic epidemiology in identifying the link in nosocomial infection by comparing single nucleotide variations (SNVs) of isolates from patients associated with an outbreak in Showa University Hospital. Methods We used specimens from 32 patients in whom COVID-19 had been diagnosed using clinical reverse transcription-polymerase chain reaction tests. RNA of SARS-CoV-2 from specimens was reverse-transcribed and analysed using WGS. SNVs were extracted and used for lineage determination, phylogenetic tree analysis, and median-joining analysis. Findings The lineage of SARS-CoV-2 that was associated with outbreak in Showa University Hospital was B.1.1.214, which was consistent with that found in the Kanto metropolitan area during the same period. Consistent with canonical epidemiological observations, haplotype network analysis was successful for the classification of patients. Additionally, phylogenetic tree analysis revealed three independent introductions of the virus into the hospital during the outbreak. Further, median-joining analysis indicated that four patients were directly infected by any of the others in the same cluster. Conclusion Genetic epidemiology with WGS and haplotype networks is useful for tracing transmission and optimizing prevention strategies in nosocomial outbreaks.

Objectives Hereditary hearing loss exhibits high degrees of genetic and clinical heterogeneity. To elucidate the population-specific and age-related genetic and clinical spectra of hereditary hearing loss, we investigated the sequencing data of causally associated hearing loss genes in a large cohort of hearing-impaired probands with a balanced age distribution from a single center in Southwest Germany. Design Genetic testing was applied to 305 hearing-impaired probands/families with a suspected genetic hearing loss etiology and a balanced age distribution over a period of 8 years (2011-2018). These individuals were representative of the regional population according to age and sex distributions. The genetic testing workflow consisted of single-gene screening (n = 21) and custom-designed hearing loss gene panel sequencing (n = 284) targeting known nonsyndromic and syndromic hearing loss genes in a diagnostic setup. Retrospective reanalysis of sequencing data was conducted by applying the current American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines. Results A genetic diagnosis was established for 75 (25%) of the probands that involved 75 causal variants in 35 genes, including 16 novel causal variants and 9 medically significant variant reclassifications. Nearly half of the solved cases (47%; n = 35) were related to variants in the five most frequently affected genes: GJB2 (25%), MYO15A, WFS1, SLC26A4, and COL11A1 (all 5%). Nearly one-quarter of the cases (23%; n = 17) were associated with variants in seven additional genes (TMPRSS3, COL4A3, LOXHD1, EDNRB, MYO6, TECTA, and USH2A). The remaining one-third of single cases (33%; n = 25) were linked to variants in 25 distinct genes. Diagnostic rates and gene distribution were highly dependent on phenotypic characteristics. A positive family history of autosomal-recessive inheritance in combination with early onset and higher grades of hearing loss significantly increased the solve rate up to 60%, while late onset and lower grades of hearing loss yielded significantly fewer diagnoses. Regarding genetic diagnoses, autosomal-dominant genes accounted for 37%, autosomal-recessive genes for 60%, and X-linked genes for 3% of the solved cases. Syndromic/nonsyndromic hearing loss mimic genes were affected in 27% of the genetic diagnoses. Conclusions The genetic epidemiology of the largest German cohort subjected to comprehensive targeted sequencing for hereditary hearing loss to date revealed broad causal gene and variant spectra in this population. Targeted hearing loss gene panel analysis proved to be an effective tool for ensuring an appropriate diagnostic yield in a routine clinical setting including the identification of novel variants and medically significant reclassifications. Solve rates were highly sensitive to phenotypic characteristics. The unique population-adapted and balanced age distribution of the cohort favoring late hearing loss onset uncovered a markedly large contribution of autosomal-dominant genes to the diagnoses which may be a representative for other age balanced cohorts in other populations.

The study was performed using archival data from the laboratory of genetic epidemiology of the Research Center for Medical Genetics. Of the 65 747 marriage records for Udmurtia, Chuvashia, Mari El, Bashkiria, and Tatarstan dating back to the end of the 20th century, 15 804 were selected in which at least one of the spouses was Russian. Russians make up almost 270 000 people out of the 1.2 million population of the surveyed areas. The intensity of crossbreeding of the Russian population was 52.5%. The marital ethnic assortativity was 2.3. Data is provided for all five republics.

In the largest DNA-based study of domestic cats to date, 11,036 individuals (10,419 pedigreed cats and 617 non-pedigreed cats) were genotyped via commercial panel testing elucidating the distribution and frequency of known disease, blood type, and physical trait associated genetic variants across cat breeds. This study provides allele frequencies for many disease-associated variants for the first time and provides updates on previously reported information with evidence suggesting that DNA testing has been effectively used to reduce disease associated variants within certain pedigreed cat populations over time. We identified 13 disease-associated variants in 47 breeds or breed types in which the variant had not previously been documented, highlighting the relevance of comprehensive genetic screening across breeds. Three disease-associated variants were discovered in non-pedigreed cats only. To investigate the causality of nine disease-associated variants in cats of different breed backgrounds our veterinarians conducted owner interviews, reviewed clinical records, and invited cats to have follow-up clinical examinations. Additionally, genetic variants determining blood types A, B and AB, which are relevant clinically and in cat breeding, were genotyped. Appearance-associated genetic variation in all cats is also discussed. Lastly, genome-wide SNP heterozygosity levels were calculated to obtain a comparable measure of the genetic diversity in different cat breeds. This study represents the first comprehensive exploration of informative Mendelian variants in felines by screening over 10,000 pedigreed cats. The results qualitatively contribute to the understanding of feline variant heritage and genetic diversity and demonstrate the clinical utility and importance of such information in supporting breeding programs and the research community. The work also highlights the crucial commitment of pedigreed cat breeders and registries in supporting the establishment of large genomic databases, that when combined with phenotype information can advance scientific understanding and provide insights that can be applied to improve the health and welfare of cats. Author summaryDomestic cats are one of the world's most popular companion animals, of which pedigreed cats represent small unique subpopulations. Genetic research on pedigreed cats has facilitated discoveries of heritable conditions resulting in the availability of DNA testing for studying and managing inherited disorders and traits in specific cat breeds. We have explored an extensive study cohort of 11,036 domestic cat samples representing pedigreed cats of 90 breeds and breed types. This work provided insight into the heritage of feline disease and trait alleles. We gained knowledge on the most common and relevant genetic markers for inherited disorders and physical traits, and the genetic determinants of the clinically relevant AB blood group system. We also used a measure of genetic diversity to compare inbreeding levels within and between breeds. This information can help support sustainable breeding goals within the cat fancy. Direct-to-consumer genetic tests help to raise awareness of various inherited single gene conditions in cats and provide information that owners can share with their veterinarians. In due course, ventures of this type will enable the genetics of common complex feline disease to be deciphered, paving the way for precision healthcare with the potential to ultimately improve welfare for all cats.

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We performed the largest genome-wide meta-analysis (GWAMA) (Max N=26,494) of the levels of 184 cardiovascular-related plasma protein levels to date and reported 592 independent loci (pQTL) associated with the level of at least one protein (1308 significant associations, median 6 per protein). We estimated that only between 8-37% of testable pQTL overlap with established expression quantitative trait loci (eQTL) using multiple methods, while 132 out of 1064 lead variants show evidence for transcription factor binding, and found that 75% of our pQTL are known DNA methylation QTL. We highlight the variation in genetic architecture between proteins and that proteins share genetic architecture with cardiometabolic complex traits. Using cis-instrument Mendelian randomisation (MR), we infer causal relationships for 11 proteins, recapitulating the previously reported relationship between PCSK9 and LDL cholesterol, replicating previous pQTL MR findings and discovering 16 causal relationships between protein levels and disease. Our MR results highlight IL2-RA as a candidate for drug repurposing for Crohn’s Disease as well as 2 novel therapeutic targets: IL-27 (Crohn’s disease) and TNFRSF14 (Inflammatory bowel disease, Multiple sclerosis and Ulcerative colitis). We have demonstrated the discoveries possible using our pQTL and highlight the potential of this work as a resource for genetic epidemiology.

There is an abundance of malaria genetic data being collected from the field, yet using these data to understand the drivers of regional epidemiology remains a challenge. A key issue is the lack of models that relate parasite genetic diversity to epidemiological parameters. Classical models in population genetics characterize changes in genetic diversity in relation to demographic parameters, but fail to account for the unique features of the malaria life cycle. In contrast, epidemiological models, such as the Ross-Macdonald model, capture malaria transmission dynamics but do not consider genetics. Here, we have developed an integrated model encompassing both parasite evolution and regional epidemiology. We achieve this by combining the Ross-Macdonald model with an intra-host continuous-time Moran model, thus explicitly representing the evolution of individual parasite genomes in a traditional epidemiological framework. Implemented as a stochastic simulation, we use the model to explore relationships between measures of parasite genetic diversity and parasite prevalence, a widely-used metric of transmission intensity. First, we explore how varying parasite prevalence influences genetic diversity at equilibrium. We find that multiple genetic diversity statistics are correlated with prevalence, but the strength of the relationships depends on whether variation in prevalence is driven by host- or vector-related factors. Next, we assess the responsiveness of a variety of statistics to malaria control interventions, finding that those related to mixed infections respond quickly (∼months) whereas other statistics, such as nucleotide diversity, may take decades to respond. These findings provide insights into the opportunities and challenges associated with using genetic data to monitor malaria epidemiology., Author Summary Knowledge of how the prevalence of P.falciparum malaria varies, either between regions or through time, is critical to the operation of malaria control programs. Yet obtaining this information through traditional methods presents many challenges. Parasite genetic data is increasingly accessible, and may provide an alternative means to estimate P.falciparum prevalence in the field. However, our understanding of how the genetic diversity of parasite populations relates to prevalence is limited, and suitable models to guide our understanding are largely lacking. Here, we merge two classical models—the Ross-Macondald and the Moran—to produce a framework in which the relationships between parasite genetic diversity and prevalence can be explored. We find that several genetic diversity statistics are correlated with prevalence, although to differing degrees, and over different time scales. Overall, statistics related to mixed infection are robustly and rapidly responsive to changes in prevalence, suggesting they may be a useful focal point for the development of malaria surveillance methods that harness genetic data.

Helicobacter pylori exhibits specific geographic distributions that related to the clinical outcomes. Despite the high infection rate of H. pylori throughout the world, the genetic epidemiology surveillance of H. pylori still needs to be improved. Here, we used single nucleotide polymorphisms (SNPs) profiling approach based on whole genome sequencing (WGS) that facilitates genomic population analyses of H. pylori and encourages the dissemination of microbial genotyping strategies worldwide. A total number of 1,211 public H. pylori genomes were downloaded and used to construct the typing tool, named as HPTT (H. pylori Typing Tool). Combined with the metadata, we developed two levels of genomic typing, including a continent scale and a country scale that nested in the continent scale. Results showed that Asia was the largest isolates source in our dataset, while isolates from Europe and Oceania were comparatively more widespread. More specifically, Switzerland and Australia are the main source of widespread isolates in their corresponding continents. To integrate all the typing information and enable researchers to compare their own dataset against the existing global database in an easy and rapid way, a user-friendly website (https://db.cngb.org/HPTT/) was developed with both genomic typing tool and visualization tool. To further confirm the validity of the website, ten newly assembled genomes were downloaded and tested precisely located on the branch as we expected. In summary, H. pylori typing tool (HPTT) is a novel genomic epidemiological tool that can achieve high resolution analysis of genomic typing and visualizing simultaneously, providing insights into the genetic population structure analysis, evolution analysis and epidemiological surveillance of H. pylori.

Etiological studies have shown genetic disorders to be a major cause of sensorineural hearing loss, but there are a limited number of comprehensive etiological reports based on genetic analysis. In the present study, the same platform using a diagnostic DNA panel carrying 63 deafness genes and the same filtering algorithm were applied to 10,047 samples obtained from social health insurance-based genetic testing of hearing loss. The most remarkable result obtained in this comprehensive study was that the data first clarified the genetic epidemiology from congenital/early-onset deafness to late-onset hearing loss. The overall diagnostic rate was 38.8%, with the rate differing for each age group; 48.6% for the congenital/early-onset group (~5y.o.), 33.5% for the juvenile/young adult-onset group, and 18.0% for the 40+ y.o. group. Interestingly, each group showed a different kind of causative gene. With regard to the mutational spectra, there are certain recurrent variants that may be due to founder effects or hot spots. A series of haplotype studies have shown many recurrent variants are due to founder effects, which is compatible with human migration. It should be noted that, regardless of differences in the mutational spectrum, the clinical characteristics caused by particular genes can be considered universal. This comprehensive review clarified the detailed clinical characteristics (onset age, severity, progressiveness, etc.) of hearing loss caused by each gene, and will provide useful information for future clinical application, including genetic counseling and selection of appropriate interventions.

Multi-host pathogens are particularly difficult to control, especially when at least one of the hosts acts as a hidden reservoir. Deep sequencing of densely sampled pathogens has the potential to transform this understanding, but requires analytical approaches that jointly consider epidemiological and genetic data to best address this problem. While there has been considerable success in analyses of single species systems, the hidden reservoir problem is relatively under-studied. A well-known exemplar of this problem is bovine Tuberculosis, a disease found in British and Irish cattle caused by Mycobacterium bovis, where the Eurasian badger has long been believed to act as a reservoir but remains of poorly quantified importance except in very specific locations. As a result, the effort that should be directed at controlling disease in badgers is unclear. Here, we analyse densely collected epidemiological and genetic data from a cattle population but do not explicitly consider any data from badgers. We use a simulation modelling approach to show that, in our system, a model that exploits available cattle demographic and herd-to-herd movement data, but only considers the ability of a hidden reservoir to generate pathogen diversity, can be used to choose between different epidemiological scenarios. In our analysis, a model where the reservoir does not generate any diversity but contributes to new infections at a local farm scale are significantly preferred over models which generate diversity and/or spread disease at broader spatial scales. While we cannot directly attribute the role of the reservoir to badgers based on this analysis alone, the result supports the hypothesis that under current cattle control regimes, infected cattle alone cannot sustain M. bovis circulation. Given the observed close phylogenetic relationship for the bacteria taken from cattle and badgers sampled near to each other, the most parsimonious hypothesis is that the reservoir is the infected badger population. More broadly, our approach demonstrates that carefully constructed bespoke models can exploit the combination of genetic and epidemiological data to overcome issues of extreme data bias, and uncover important general characteristics of transmission in multi-host pathogen systems., Author summary For single host pathogens, pathogen genetic data have been transformative for understanding the transmission and control of many diseases, particuarly rapidly evolving RNA viruses. However garnering similar insights where pathogens are multi-host is more challenging, particularly when the evolution of the pathogen is slower and pathogen sampling often heavily biased. This is the case for Mycobacterium bovis, the causative agent of bovine Tuberculosis (bTB) and for which the Eurasian badger plays an as yet poorly understood role in transmission and spread. Here we have developed a computational model that incorporates M. bovis genetic data from cattle only with a highly abstracted model of an unobserved reservoir. Our research shows that a model in which the reservoir does not contribute to pathogen diversity, but is a source of infection in spatially localised areas around each farm, better describes the patterns of outbreaks observed in a population-level sample of a single M. bovis genotype in Northern Ireland over a period of 15 years, compared to models in which either the reservoir has no role, disease spread is spatially extensive, or where they generate considerable diversity on their own. While this reservoir model is not explicitly a model of badgers, its characteristics are consistent with other data that would suggest a reservoir consisting of infected badgers that contribute substantially to cattle infection, but could not maintain disease on their own.

Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exposure effects in regressions controlling for increasingly powerful polygenic scores. Second, we use structural equation models to estimate genetic confounding using heritability estimates derived from both SNP-based and twin-based studies. We examine associations between maternal education and three developmental outcomes – child educational achievement, Body Mass Index, and Attention Deficit Hyperactivity Disorder. Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP- and twin-based heritability scenarios indicate that observed associations could be almost entirely explained by genetic confounding. Thus, caution is needed when interpreting associations from non-genetically informed epidemiology studies. Our approach, akin to a genetically informed sensitivity analysis can be applied widely., Author summary An objective shared across the life, behavioural, and social sciences is to identify factors that increase risk for a particular disease or trait. However, identifying true risk factors is challenging. Often, a risk factor is statistically associated with a disease even if it is not really relevant, meaning that even successfully improving the risk factor will not impact the disease. One reason for the existence of such misleading associations stems from genetic confounding. This is when genetic factors influence directly both the risk factor and the disease, which generates a statistical association even in the absence of a true effect of the risk factor. Here, we propose a method to estimate genetic confounding and quantify its effect on observed associations. We show that a large part of the associations between maternal education and three child outcomes—educational achievement, body mass index and Attention-Deficit Hyperactivity Disorder—is explained by genetic confounding. Our findings can be applied to better understand the role of genetics in explaining associations of key risk factors with diseases and traits.

Porcine transmissible gastroenteritis virus (TGEV) is a pathogenic agent responsible for high diarrhea-associated morbidity and mortality in suckling piglets. To understand the genomic characteristics and evolutionary trend of TGEV during nearly 70 years, we reanalyzed published TGEV whole genome sequences. The genomic sequences of 40 strains from different sources were downloaded from National Center for Biotechnology Information. The phylogenetic analysis was performed using both whole genome sequences and S gene sequences. The regional distribution of TGEV virus was obvious while the time distribution was relatively scattered. In the whole genome sequences, 2505 variable sites were found and 56% of them occurred more than once. In S gene sequences, 505 variable sites were detected, which generated 191 amino acid mutations.

Background Evidence from genetic epidemiology indicates that type 2 diabetes (T2D) has a strong genetic basis. Activated STAT4 has an inflammatory effect, and STAT4 is an important mediator of inflammation in diabetes. Our study aimed to study the association between STAT4 single nucleotide polymorphisms (SNPs) and T2D susceptibility in Chinese Han population. Methods We conducted a 'case–control' study among 500 T2D patients and 501 healthy individuals. 5 candidate STAT4 SNPs were successfully genotyped. The association between SNPs and T2D susceptibility under different genetic models was evaluated by logistic regression analysis. ‘SNP-SNP’ interaction was analyzed and completed by multi-factor dimensionality reduction (MDR). Finally, we evaluated the differences of clinical characteristics under different genotypes by one-factor analysis of variance. Results The overall results showed that STAT4 rs3821236 was associated with increasing T2D risk under allele (OR 1.23, p = 0.020), homozygous (OR 1.51, p = 0.025), dominant (OR 1.36, p = 0.029), and additive models (OR 1.23, p = 0.020). The results of stratified analysis showed that rs3821236, rs11893432, and rs11889341 were risk factors for T2D among participants ≤ 60 years old. Only rs11893432 was associated with increased T2D risk among female participants. There was also a potential association between rs3821236 and T2D with nephropathy risk. STAT4 rs11893432, rs7574865 and rs897200 were significantly associated with lysophosphatidic acid, cystatin C and thyroxine t4, respectively. Conclusion The genetic polymorphisms of STAT4 is potentially associated with T2D susceptibility of Chinese population. In particular, rs3821236 is significantly associated with T2D risk both in the overall and several subgroup analyses. Our study may provide new ideas for T2D individualized diagnosis/protection.

Although traditional models of epidemic spreading focus on the number of infected, susceptible and recovered individuals, a lot of attention has been devoted to integrate epidemic models with population genetics. Here we develop an individual-based model for epidemic spreading on networks in which viruses are explicitly represented by finite chains of nucleotides that can mutate inside the host. Under the hypothesis of neutral evolution we compute analytically the average pairwise genetic distance between all infecting viruses over time. We also derive a mean-field version of this equation that can be added directly to compartmental models such as SIR or SEIR to estimate the genetic evolution. We compare our results with the inferred genetic evolution of SARS-CoV-2 at the beginning of the epidemic in China and found good agreement with the analytical solution of our model. Finally, using genetic distance as a proxy for different strains, we use numerical simulations to show that the lower the connectivity between communities, e.g., cities, the higher the probability of reinfection.

Analysis of SARS-CoV-2 genetic diversity within infected hosts can provide insight into the generation and spread of new viral variants and may enable high resolution inference of transmission chains. However, little is known about temporal aspects of SARS-CoV-2 intrahost diversity and the extent to which shared diversity reflects convergent evolution as opposed to transmission linkage. Here we use high depth of coverage sequencing to identify within-host genetic variants in 325 specimens from hospitalized COVID-19 patients and infected employees at a single medical center. We validated our variant calling by sequencing defined RNA mixtures and identified viral load as a critical factor in variant identification. By leveraging clinical metadata, we found that intrahost diversity is low and does not vary by time from symptom onset. This suggests that variants will only rarely rise to appreciable frequency prior to transmission. Although there was generally little shared variation across the sequenced cohort, we identified intrahost variants shared across individuals who were unlikely to be related by transmission. These variants did not precede a rise in frequency in global consensus genomes, suggesting that intrahost variants may have limited utility for predicting future lineages. These results provide important context for sequence-based inference in SARS-CoV-2 evolution and epidemiology., Author summary Understanding the evolution and transmission of SARS-CoV-2 is important for designing public health interventions to prevent outbreaks. Viral genome sequencing has been widely used to reconstruct patterns of SARS-CoV-2 transmission through communities and to monitor the spread of new strains. However, because SARS-CoV-2 can transmit multiple times before a new mutation fixes, consensus sequences often cannot determine “who infected whom.” Identifying individuals who share the same viral genetic variants at low frequencies within each infection may help resolve this problem, but to do this we need to accurately identify within-host genetic variants and understand how they evolve and spread. We investigated within-host diversity of SARS-CoV-2 with samples collected in southeastern Michigan in March–May 2020. We show that there are relatively few genetic variants present in any given infection, and variants do not tend to accumulate in people over time. We also found that people who are not part of the same epidemic cluster can share the same within-host variants, due to chance or various evolutionary forces.

The results of genetic and epidemiological study of monogenic hereditary diseases (HD) among children of the Karachay-Cherkess Republics are presented. The surveyed population comprised 410 368 individuals (Cherkessk and ten rural areas), including 90 739 children (22.11%). The examination was performed following the original protocol for genetic-epidemiological studies (developed by the Research Centre for Medical Genetics) making it possible to register not less than 3500 HD and syndromes. One hundred eighty-three nosological forms of HD (922 patients from 764 families) were registered in the child population, including 100 patients with AD (459 patients from 366 families), 60 patients with AR (372 patients from 326 families), and 23 patients with X-linked inheritance (91 patients from 72 families). The structure of diversity is determined, and frequent HDs are described in accordance with the main classification by organ and systemic types of disease: neurological, ophthalmic, genodermatoses, skeletal, hereditary syndromes, and other pathology. There were 55 frequent HDs (30.05%)—the number of patients in this group was 739 (80.15% of sick children). The load of AD, AR, and X-linked pathology for urban and rural child population was calculated. Differences in HD load were identified. On the basis of correlations between the load and FST, the causes of the observed differentiation of subpopulations were proposed.

1.AbstractUnderstanding the relatedness of individuals within or between populations is a common goal in biology. Increasingly, relatedness features in genetic epidemiology studies of pathogens. These studies are relatively new compared to those in humans and other organisms, but are important for designing interventions and understanding pathogen transmission. Only recently have researchers begun to routinely apply relatedness to apicomplexan eukaryotic malaria parasites, and to date have used a range of different approaches on an ad hoc basis. It remains unclear how to compare different studies, therefore, and which measures to use. Here, we systematically compare measures based on identity-by-state and identity-by-descent using a globally diverse data set of malaria parasites,Plasmodium falciparumandPlasmodium vivax, and provide marker requirements for estimates based on identity-by-descent. We formally show that the informativeness of polyallelic markers for relatedness inference is maximised when alleles are equifrequent. Estimates based on identity-by-state are sensitive to allele frequencies, which vary across populations and by experimental design. For portability across studies, we thus recommend estimates based on identity-by-descent. To generate reliable estimates, we recommend approximately 200 biallelic or 100 polyallelic markers. Confidence intervals illuminate inference across studies based on different sets of markers. These marker requirements, unlike many thus far reported, are immediately applicable to haploid malaria parasites and other haploid eukaryotes. This is the first attempt to provide rigorous analysis of the reliability of, and requirements for, relatedness inference in malaria genetic epidemiology, and will provide a basis for statistically informed prospective study design and surveillance strategies.

BackgroundThe genetic profile of retinitis pigmentosa (RP) in East Asian populations has not been well characterised. Therefore, we conducted a large-scale sequencing study to investigate the genes and variants causing RP in a Japanese population.MethodsA total of 1209 Japanese patients diagnosed with typical RP were enrolled. We performed deep resequencing of 83 known causative genes of RP using next-generation sequencing. We defined pathogenic variants as those that were putatively deleterious or registered as pathogenic in the Human Gene Mutation Database or ClinVar database and had a minor allele frequency in any ethnic population of ≤0.5% for recessive genes or ≤0.01% for dominant genes as determined using population-based databases.ResultsWe successfully sequenced 1204 patients with RP and determined 200 pathogenic variants in 38 genes as the cause of RP in 356 patients (29.6%). Variants in six genes (EYS, USH2A, RP1L1, RHO, RP1 and RPGR) caused RP in 65.4% (233/356) of those patients. Among autosomal recessive genes, two known founder variants in EYS [p.(Ser1653fs) and p.(Tyr2935*)] and four East Asian-specific variants [p.(Gly2752Arg) in USH2A, p.(Arg658*) in RP1L1, p.(Gly2186Glu) in EYS and p.(Ile535Asn) in PDE6B] and p.(Cys934Trp) in USH2A were found in ≥10 patients. Among autosomal dominant genes, four pathogenic variants [p.(Pro347Leu) in RHO, p.(Arg872fs) in RP1, p.(Arg41Trp) in CRX and p.(Gly381fs) in PRPF31] were found in ≥4 patients, while these variants were unreported or extremely rare in both East Asian and non-East Asian population-based databases.ConclusionsEast Asian-specific variants in causative genes were the major causes of RP in the Japanese population.

The generalized results of the genetic and epidemiological study of the genetic structure of various ethnic groups of the Karachay-Cherkess Republic (KChR) through various genetic systems are presented: by the genes causing AD, AR, and X-linked hereditary diseases and by nonbiological parameters estimated by the methods of population statistics (random inbreeding FST, Malecot’s isolation-by-distance parameters, migration index, and Crow index and its components). The total size of the investigated population was 410 367 individuals (city of Cherkessk, Ust-Dzhegutinsky, Karachayevsky, Malokarachayevsky, Prikubansky, Khabezsky, Nogaysky, Adyge-Khablsky, Urupsky, and Zelenchuksky districts). On the basis of the study of correlations between different characteristics of the population genetic structure, it is assumed that the main cause of genetic differentiation of KChR subpopulations in terms of diversity and load of hereditary diseases (AD, AR, and X-linked) is genetic drift and migration under decreased influence of natural selection and mutational process.

In this paper, we give an overview of methodological issues related to the use of statistical learning approaches when analyzing high-dimensional genetic data. The focus is set on regression models and machine learning algorithms taking genetic variables as input and returning a classification or a prediction for the target variable of interest; for example, the present or future disease status, or the future course of a disease. After briefly explaining the basic motivation and principle of these methods, we review different procedures that can be used to evaluate the accuracy of the obtained models and discuss common flaws that may lead to over-optimistic conclusions with respect to their prediction performance and usefulness.

Replicable genetic association signals have consistently been found through genome-wide association studies in recent years. The recent dramatic expansion of study sizes improves power of estimation of effect sizes, genomic prediction, causal inference, and polygenic selection, but it simultaneously increases susceptibility of these methods to bias due to subtle population structure. Standard methods using genetic principal components to correct for structure might not always be appropriate and we use a simulation study to illustrate when correction might be ineffective for avoiding biases. New methods such as trans-ethnic modeling and chromosome painting allow for a richer understanding of the relationship between traits and population structure. We illustrate the arguments using real examples (stroke and educational attainment) and provide a more nuanced understanding of population structure, which is set to be revisited as a critical aspect of future analyses in genetic epidemiology. We also make simple recommendations for how problems can be avoided in the future. Our results have particular importance for the implementation of GWAS meta-analysis, for prediction of traits, and for causal inference.

Glioma incidence is highest in non-Hispanic Whites, and to date, glioma genome-wide association studies (GWAS) to date have only included European ancestry (EA) populations. African Americans and Hispanics in the US have varying proportions of EA, African (AA) and Native American ancestries (NAA). It is unknown if identified GWAS loci or increased EA is associated with increased glioma risk. We assessed whether EA was associated with glioma in African Americans and Hispanics. Data were obtained for 832 cases and 675 controls from the Glioma International Case-Control Study and GliomaSE Case-Control Study previously estimated to have

Genetic and environmental factors are involved in development of many diseases. The allelic frequencies may differ in different populations and in different ethnic groups. The aim of this study was to investigate the genotypes of MTHFR and factor VII polymorphisms and to identify biomarkers for thrombosis related diseases in Turkish Cypriot population. The lipid profiles and genotypes of MTHFR polymorphisms (rs1801133, rs1801131) and factor VII (rs6046) genes were investigated for the first time in the Turkish Cypriot population. The heterozygosity for MTHFR (rs1801133, rs1801131) and FVII (rs6046) polymorphisms is high in Turkish Cypriot population. The heterozygosity for MTHFR C677T was 38%, MTHFR A1298C was 40% and factor VII G353A was 37%, respectively. Allelic frequencies between males and females were similar. There were no correlations between the genotypes of polymorphisms and the lipid profiles. This study is the first genetic epidemiology study that investigated the allelic frequencies of MTHFR and FVII polymorphisms associated with metabolic syndromes. This study proves to be a crucial analysis in order to use these polymorphisms as a predictor of disease development in the Turkish Cypriot community.