Polygenic Background Research Articles

Dinucleotide composition representation -based deep learning to predict scoliosis-associated Fibrillin-1 genotypes

IntroductionScoliosis is a pathological spine structure deformation, predominantly classified as “idiopathic” due to its unknown etiology. However, it has been suggested that scoliosis may be linked to polygenic backgrounds. It is crucial to identify potential Adolescent Idiopathic Scoliosis (AIS)-related genetic backgrounds before scoliosis onset.MethodsThe present study was designed to intelligently parse, decompose and predict AIS-related variants in ClinVar database. Possible AIS-related variant records downloaded from ClinVar were parsed for various labels, decomposed for Dinucleotide Compositional Representation (DCR) and other traits, screened for high-risk genes with statistical analysis, and then learned intelligently with deep learning to predict high-risk AIS genotypes.ResultsResults demonstrated that the present framework is composed of all technical sections of data parsing, scoliosis genotyping, genome encoding, machine learning (ML)/deep learning (DL) and scoliosis genotype predicting. 58,000 scoliosis-related records were automatically parsed and statistically analyzed for high-risk genes and genotypes, such as FBN1, LAMA2 and SPG11. All variant genes were decomposed for DCR and other traits. Unsupervised ML indicated marked inter-group separation and intra-group clustering of the DCR of FBN1, LAMA2 or SPG11 for the five types of variants (Pathogenic, Pathogeniclikely, Benign, Benignlikely and Uncertain). A FBN1 DCR-based Convolutional Neural Network (CNN) was trained for Pathogenic and Benign/ Benignlikely variants performed accurately on validation data and predicted 179 high-risk scoliosis variants. The trained predictor was interpretable for the similar distribution of variant types and variant locations within 2D structure units in the predicted 3D structure of FBN1.DiscussionIn summary, scoliosis risk is predictable by deep learning based on genomic decomposed features of DCR. DCR-based classifier has predicted more scoliosis risk FBN1 variants in ClinVar database. DCR-based models would be promising for genotype-to-phenotype prediction for more disease types.

Implementing the Risk Stratification and Clinical Management of Breast Cancer Families Using Polygenic Risk Score Evaluation: A Pilot Study.

The identification of women at high risk of breast cancer (BC) is crucial for personalized screening strategies. Pathogenic and likely pathogenic variants (PVs/LPVs) in susceptibility risk genes explain part of the individual risk. Moreover, a polygenic background, summarized as a polygenic risk score (PRS), contributes to the risk of BC and may modify the individual risk in carrier and non-carrier members of BC families. We performed a retrospective pilot study evaluating PRS in women from a subset of high- (BRCA1 and BRCA2) and moderate-risk (PALB2 and ATM) BC families. We included PVs/LPVs carriers and non-carriers and evaluated a PRS based on 577,113 BC-associated variants. Using BOADICEA, we calculated the adjusted lifetime BC risk. Our data showed that in BRCA1/BRCA2 carriers, PVs have a major role in stratifying the lifetime risk, while PRS improves risk estimation in non-carriers of these families. A different scenario may be observed in PALB2 and ATM families where PRS combined with PV/LPV carrier status gives a more informative lifetime risk. This study showed that in BC families, the PRS might help to quantify the weight of the genetic familial background, improving the individual risk stratification and contributing to personalized clinical management for carrier and non-carrier women.

A New Method for Conditional Gene-Based Analysis Effectively Accounts for the Regional Polygenic Background.

Gene-based association analysis is a powerful tool for identifying genes that explain trait variability. An essential step of this analysis is a conditional analysis. It aims to eliminate the influence of SNPs outside the gene, which are in linkage disequilibrium with intragenic SNPs. The popular conditional analysis method, GCTA-COJO, accounts for the influence of several top independently associated SNPs outside the gene, correcting the z statistics for intragenic SNPs. We suggest a new TauCOR method for conditional gene-based analysis using summary statistics. This method accounts the influence of the full regional polygenic background, correcting the genotype correlations between intragenic SNPs. As a result, the distribution of z statistics for intragenic SNPs becomes conditionally independent of distribution for extragenic SNPs. TauCOR is compatible with any gene-based association test. TauCOR was tested on summary statistics simulated under different scenarios and on real summary statistics for a 'gold standard' gene list from the Open Targets Genetics project. TauCOR proved to be effective in all modelling scenarios and on real data. The TauCOR's strategy showed comparable sensitivity and higher specificity and accuracy than GCTA-COJO on both simulated and real data. The method can be successfully used to improve the effectiveness of gene-based association analyses.

Identification of genetic susceptibility for Chinese migraine with depression using machine learning.

Migraine is a common primary headache that has a significant impact on patients' quality of life. The co-occurrence of migraine and depression is frequent, resulting in more complex symptoms and a poorer prognosis. The evidence suggests that depression and migraine comorbidity share a polygenic genetic background. The aim of this study is to identify related genetic variants that contribute to genetic susceptibility to migraine with and without depression in a Chinese cohort. In this case-control study, 263 individuals with migraines and 223 race-matched controls were included. Eight genetic polymorphism loci selected from the GWAS were genotyped using Sequenom's MALDI-TOF iPLEX platform. In univariate analysis, ANKDD1B rs904743 showed significant differences in genotype and allele distribution between migraineurs and controls. Furthermore, a machine learning approach was used to perform multivariate analysis. The results of the Random Forest algorithm indicated that ANKDD1B rs904743 was a significant risk factor for migraine susceptibility in China. Additionally, subgroup analysis by the Boruta algorithm showed a significant association between this SNP and migraine comorbid depression. Migraineurs with depression have been observed to have worse scores on the Beck Anxiety Inventory (BAI) and the Migraine Disability Assessment Scale (MIDAS). The study indicates that there is an association between ANKDD1B rs904743 and susceptibility to migraine with and without depression in Chinese patients.

FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.

Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.

The SLC6A1 Mutation Schizophrenia case — A Comprehensive Case Study With iPSC Generation

IntroductionThe main finding of a large-scale collaborative study (Rees et al. Nat Neurosci 2020;23(2) 179-184), which focused on de novo mutations in schizophrenia, was the discovery of an enrichment of these mutations in the SLC6A1 gene. This gene encodes the gamma-aminobutyric acid (GABA) transporter GAT1, thereby encouraging further research into novel schizophrenia targets within the GABA pathway. However, the gene was not highlighted in recent schizophrenia genetic studies, while typically pathogenic SLC6A1 mutations result in epilepsy, motor dysfunction, autistic spectrum disorder (ASD) and developmental delay. The absence of genetic replication for SLC6A1’s involvement in schizophrenia and the differing clinical spectrum for SLC6A1 mutations led us to study in depth one of the only three original probands from the Rees et al. 2020 study.ObjectivesIn our comprehensive case study, we delved deep into the relationship between the SLC6A1 mutation and schizophrenia.MethodsOur subject, a patient who first presented with acute mania symptoms at age 15 and was later diagnosed with schizophrenia, carried the SLC6A1 Arg211Cys mutation. Over a detailed 25-year follow-up, we conducted an array of assessments and tests, including cognitive testing, personality assessments, EEG, and 1H-MRS.ResultsNotably, we discovered abnormal GABA levels, potentially indicating a dysfunction in GABA reuptake, adding a new layer of complexity to our understanding. Further analysis revealed a significant correlation between the patient’s clinical picture and a polygenic background, rather than the SLC6A1 mutation. Despite having a high polygenic risk score for bipolar disorder, the dominant features of his condition were more representative of schizophrenia. Interestingly, neither the patient nor his father, who also showed a higher BP PRS, had a diagnosis of bipolar disorder. The pathogenic significance of the mutation warrants investigation in cells of neuronal origin. We generated induced pluripotent stem cells (iPSC) from the patient and his parents. This approach provides us with a platform for future investigations into the pathogenic significance of the mutation in neuronal cells. The Human Pluripotent Stem Cell Registry accession numbers of those cells are MHRCCGi001-A (patient), MHRCCGi005-A (mother) and MHRCCGi004-A (father).ConclusionsIn the presented case the clinical picture is rather explained by the polygenic background than by the SLC6A1 Arg211Cys mutation. The study is supported by Russian Science Foundation, grant 21-15-00124 (https://rscf.ru/project/21-15-00124)Disclosure of InterestNone Declared

Assessing the efficacy of target adaptive sampling long-read sequencing through hereditary cancer patient genomes

Innovations in sequencing technology have led to the discovery of novel mutations that cause inherited diseases. However, many patients with suspected genetic diseases remain undiagnosed. Long-read sequencing technologies are expected to significantly improve the diagnostic rate by overcoming the limitations of short-read sequencing. In addition, Oxford Nanopore Technologies (ONT) offers adaptive sampling and computationally driven target enrichment technology. This enables more affordable intensive analysis of target gene regions compared to standard non-selective long-read sequencing. In this study, we developed an efficient computational workflow for target adaptive sampling long-read sequencing (TAS-LRS) and evaluated it through application to 33 genomes collected from suspected hereditary cancer patients. Our workflow can identify single nucleotide variants with nearly the same accuracy as the short-read platform and elucidate complex forms of structural variations. We also newly identified several SINE-R/VNTR/Alu (SVA) elements affecting the APC gene in two patients with familial adenomatous polyposis, as well as their sites of origin. In addition, we demonstrated that off-target reads from adaptive sampling, which is typically discarded, can be effectively used to accurately genotype common single-nucleotide polymorphisms (SNPs) across the entire genome, enabling the calculation of a polygenic risk score. Furthermore, we identified allele-specific MLH1 promoter hypermethylation in a Lynch syndrome patient. In summary, our workflow with TAS-LRS can simultaneously capture monogenic risk variants including complex structural variations, polygenic background as well as epigenetic alterations, and will be an efficient platform for genetic disease research and diagnosis.

Inherited polygenic effects on common hematological traits influence clonal selection on JAK2V617F and the development of myeloproliferative neoplasms

Myeloproliferative neoplasms (MPNs) are chronic cancers characterized by overproduction of mature blood cells. Their causative somatic mutations, for example, JAK2V617F, are common in the population, yet only a minority of carriers develop MPN. Here we show that the inherited polygenic loci that underlie common hematological traits influence JAK2V617F clonal expansion. We identify polygenic risk scores (PGSs) for monocyte count and plateletcrit as new risk factors for JAK2V617F positivity. PGSs for several hematological traits influenced the risk of different MPN subtypes, with low PGSs for two platelet traits also showing protective effects in JAK2V617F carriers, making them two to three times less likely to have essential thrombocythemia than carriers with high PGSs. We observed that extreme hematological PGSs may contribute to an MPN diagnosis in the absence of somatic driver mutations. Our study showcases how polygenic backgrounds underlying common hematological traits influence both clonal selection on somatic mutations and the subsequent phenotype of cancer.

Autoimmune primary adrenal insufficiency -current diagnostic approaches and future perspectives.

The adrenal glands are small endocrine glands located on top of each kidney, producing hormones regulating important functions in our body like metabolism and stress. There are several underlying causes for adrenal insufficiency, where an autoimmune attack by the immune system is the most common cause. A number of genes are known to confer early onset adrenal disease in monogenic inheritance patterns, usually genetic encoding enzymes of adrenal steroidogenesis. Autoimmune primary adrenal insufficiency is usually a polygenic disease where our information recently has increased due to genome association studies. In this review, we go through the physiology of the adrenals before explaining the different reasons for adrenal insufficiency with a particular focus on autoimmune primary adrenal insufficiency. We will give a clinical overview including diagnosis and current treatment, before giving an overview of the genetic causes including monogenetic reasons for adrenal insufficiency and the polygenic background and inheritance pattern in autoimmune adrenal insufficiency. We will then look at the autoimmune mechanisms underlying autoimmune adrenal insufficiency and how autoantibodies are important for diagnosis. We end with a discussion on how to move the field forward emphasizing on the clinical workup, early identification, and potential targeted treatment of autoimmune PAI.

Polygenic Background Modifies Risk of Coronary Artery Disease Among Individuals With Heterozygous Familial Hypercholesterolemia

BackgroundHeterozygous familial hypercholesterolemia (HeFH) is a monogenic disorder characterized by increased circulating low-density lipoprotein cholesterol and accelerated atherosclerosis. Even among this high-risk group, prior studies note considerable variability in risk of coronary artery disease (CAD). ObjectivesThe purpose of this study was to evaluate the cumulative impact of many common DNA variants—as quantified by a polygenic score—on incident CAD among individuals carrying a HeFH variant. MethodsWe analyzed data from a prospective cohort study of 1,315 individuals who carried a HeFH variant and 1,315 matched family noncarriers derived from a nationwide screening program in the Netherlands, with subsequent replication in 151,009 participants of the UK Biobank. ResultsDespite identification and lipid management within the Dutch screening program, 84 (6.4%) of HeFH variant carriers developed CAD as compared to 45 (3.4%) of matched family members (median follow-up 10.2 years, HR 1.88, 95% CI: 1.31-2.70). Among HeFH variant carriers, a polygenic score was associated with CAD with an effect size similar to low-density lipoprotein cholesterol - HR of 1.35 (95% CI: 1.07-1.70) and 1.41 (95% CI: 1.17-1.70) per standard deviation increase, respectively. When compared to noncarriers, CAD risk increased from 1.24-fold (95% CI: 0.64-2.34) to 3.37-fold (95% CI: 2.11-5.36) across quintiles of the polygenic score. A similar risk gradient, 1.36-fold (95% CI: 0.65-2.85) to 2.88-fold (95% CI: 1.59-5.20), was observed in 429 carriers in the UK Biobank. ConclusionsIn 2 cohort studies involving 1,744 individuals with genetically confirmed HeFH - the largest study to date - risk of CAD varied according to polygenic background, in some cases approaching the risk observed in noncarriers.

Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures.

Large-effect loci-those statistically significant loci discovered by genome-wide association studies or linkage mapping-associated with key traits segregate amidst a background of minor, often undetectable, genetic effects in wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model analysis is vital for selecting superior progeny and parents in plant and animal breeding, gene therapy, and medical genetics in humans. Marker-assisted prediction and its successor, genomic prediction, have many advantages for selecting superior individuals and understanding disease risk. However, these two approaches are less often integrated to study complex traits with different genetic architectures. This simulation study demonstrates that the average semivariance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms simultaneously and yields accurate estimates of the variance explained for all relevant variables. Our previous research focused on large-effect loci and polygenic variance separately. This work aims to synthesize and expand the average semivariance framework to various genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.

Gene B5 in Cotton Confers High and Broad Resistance to Bacterial Blight and Conditions High Amounts of Sesquiterpenoid Phytoalexins.

Bacterial blight resistance gene B5 has received little attention since it was first described in 1950. A near-isogenic line (NIL) of Gossypium hirsutum cotton, AcB5, was generated in an otherwise bacterial-blight-susceptible 'Acala 44' background. The introgressed locus B5 in AcB5 conferred strong and broad-spectrum resistance to bacterial blight. Segregation patterns of test crosses under Oklahoma field conditions indicated that AcB5 is likely homozygous for resistance at two loci with partial dominance gene action. In controlled-environment conditions, two of the four copies of B5 were required for effective resistance. Contrary to expectations of gene-for-gene theory, AcB5 conferred high resistance toward isogenic strains of Xanthomonas citri subsp. malvacearum carrying cloned avirulence genes avrB4, avrb7, avrBIn, avrB101, and avrB102, respectively, and weaker resistance toward the strain carrying cloned avrb6. The hypothesis that each B gene, in the absence of a polygenic complex, triggers sesquiterpenoid phytoalexin production was tested by measurement of cadalene and lacinilene phytoalexins during resistant responses in five NILs carrying different B genes, four other lines carrying multiple resistance genes, as well as susceptible Ac44E. Phytoalexin production was an obvious, but variable, response in all nine resistant lines. AcB5 accumulated an order of magnitude more of all four phytoalexins than any of the other resistant NILs. Its total levels were comparable to those detected in OK1.2, a highly resistant line that possesses several B genes in a polygenic background.

Genomic-assisted breeding for climate-smart coffee.

Coffee is a universal beverage that drives a multi-industry market on a global basis. Today, the sustainability of coffee production is threatened by accelerated climate changes. In this work, we propose the implementation of genomic-assisted breeding for climate-smart coffee in Coffea canephora. This species is adapted to higher temperatures and is more resilient to biotic and abiotic stresses. After evaluating two populations, over multiple harvests, and under severe drought weather condition, we dissected the genetic architecture of yield, disease resistance, and quality-related traits. By integrating genome-wide association studies and diallel analyses, our contribution is four-fold: (i) we identified a set of molecular markers with major effects associated with disease resistance and post-harvest traits, while yield and plant architecture presented a polygenic background; (ii) we demonstrated the relevance of nonadditive gene actions and projected hybrid vigor when genotypes from different geographically botanical groups are crossed; (iii) we computed medium-to-large heritability values for most of the traits, representing potential for fast genetic progress; and (iv) we provided a first step toward implementing molecular breeding to accelerate improvements in C. canephora. Altogether, this work is a blueprint for how quantitative genetics and genomics can assist coffee breeding and support the supply chain in the face of the current global changes.

Clinically relevant combined effect of polygenic background, rare pathogenic germline variants, and family history on colorectal cancer incidence

Background and aimsSummarised in polygenic risk scores (PRS), the effect of common, low penetrant genetic variants associated with colorectal cancer (CRC), can be used for risk stratification.MethodsTo assess the combined impact of the PRS and other main factors on CRC risk, 163,516 individuals from the UK Biobank were stratified as follows: 1. carriers status for germline pathogenic variants (PV) in CRC susceptibility genes (APC, MLH1, MSH2, MSH6, PMS2), 2. low (< 20%), intermediate (20–80%), or high PRS (> 80%), and 3. family history (FH) of CRC. Multivariable logistic regression and Cox proportional hazards models were applied to compare odds ratios and to compute the lifetime incidence, respectively.ResultsDepending on the PRS, the CRC lifetime incidence for non-carriers ranges between 6 and 22%, compared to 40% and 74% for carriers. A suspicious FH is associated with a further increase of the cumulative incidence reaching 26% for non-carriers and 98% for carriers. In non-carriers without FH, but high PRS, the CRC risk is doubled, whereas a low PRS even in the context of a FH results in a decreased risk. The full model including PRS, carrier status, and FH improved the area under the curve in risk prediction (0.704).ConclusionThe findings demonstrate that CRC risks are strongly influenced by the PRS for both a sporadic and monogenic background. FH, PV, and common variants complementary contribute to CRC risk. The implementation of PRS in routine care will likely improve personalized risk stratification, which will in turn guide tailored preventive surveillance strategies in high, intermediate, and low risk groups.

Which mouse multiparental population is right for your study? The Collaborative Cross inbred strains, their F1 hybrids, or the Diversity Outbred population.

Multiparental populations (MPPs) encompass greater genetic diversity than traditional experimental crosses of two inbred strains, enabling broader surveys of genetic variation underlying complex traits. Two such mouse MPPs are the Collaborative Cross (CC) inbred panel and the Diversity Outbred (DO) population, which are descended from the same eight inbred strains. Additionally, the F1 intercrosses of CC strains (CC-RIX) have been used and enable study designs with replicate outbred mice. Genetic analyses commonly used by researchers to investigate complex traits in these populations include characterizing how heritable a trait is, i.e. its heritability, and mapping its underlying genetic loci, i.e. its quantitative trait loci (QTLs). Here we evaluate the relative merits of these populations for these tasks through simulation, as well as provide recommendations for performing the quantitative genetic analyses. We find that sample populations that include replicate animals, as possible with the CC and CC-RIX, provide more efficient and precise estimates of heritability. We report QTL mapping power curves for the CC, CC-RIX, and DO across a range of QTL effect sizes and polygenic backgrounds for samples of 174 and 500 mice. The utility of replicate animals in the CC and CC-RIX for mapping QTLs rapidly decreased as traits became more polygenic. Only large sample populations of 500 DO mice were well-powered to detect smaller effect loci (7.5-10%) for highly complex traits (80% polygenic background). All results were generated with our R package musppr, which we developed to simulate data from these MPPs and evaluate genetic analyses from user-provided genotypes.

Polygenic risk scores for complex diseases: Where are we now?

Polygenic risk scores for complex diseases: Where are we now?

Selectively bred rodent models for studying the etiology of type 2 diabetes: Goto-Kakizaki rats and Oikawa-Nagao mice.

Type 2 diabetes (T2D) is a polygenic disease and studies to understand the etiology of the disease have required selectively bred animal models with polygenic background. In this review, we present two models; the Goto-Kakizaki (GK) rat and the Oikawa-Nagao Diabetes-Prone (ON-DP) and Diabetes-Resistant (ON-DR) mouse. The GK rat was developed by continuous selective breeding for glucose tolerance from the outbred Wistar rat around 50 years ago. The main cause of spontaneous hyperglycemia in this model is insulin secretion deficiency from pancreatic β-cells and mild insulin resistance in insulin target organs. A disadvantage of the GK rat is that environmental factors have not been considered in the selective breeding. Hence, the GK rat may not be suitable for elucidating predisposition to diabetes under certain environmental conditions, such as a high-fat diet. Therefore, we recently established two mouse lines with different susceptibilities to diet-induced diabetes, which are prone and resistant to the development of diabetes, designated as the ON-DP and ON-DR mouse, respectively. The two ON mouse lines were established by continuous selective breeding for inferior and superior glucose tolerance after high-fat diet feeding in hybrid mice of three inbred strains. Studies of phenotypic differences between ON-DP and ON-DR mice and their underlying molecular mechanisms will shed light on predisposing factors for the development of T2D in the modern obesogenic environment. This review summarizes the background and the phenotypic differences and similarities of GK rats and ON mice and highlights the advantages of using selectively bred rodent models in diabetes research.

Clinical Significance of Decreased TIPE2 Expression in Peripheral Blood Mononuclear Cells of Patients with Psoriasis Vulgaris

Psoriasis is an immune system disorder induced by the interaction of the polygenic background and environmental factors. Tumor necrosis factor‐α (TNF‐α)‐induced protein 8‐like 2 (TNFAIP8L2, TIPE2), a regulator of immunity, is differentially expressed in tumors, inflammation, and autoimmune diseases. However, to our knowledge, no study has evaluated TIPE2 expression in patients with psoriasis vulgaris. The present case‐control study aimed to determine the expression levels of TIPE2 mRNA in the peripheral blood mononuclear cells (PBMCs) of patients with psoriasis vulgaris and analyze the correlation between TIPE2 and other inflammatory cytokine variations in the pathogenesis of psoriasis vulgaris. Thirty‐three patients with psoriasis vulgaris and thirty‐one healthy volunteers were enrolled from October 2021 to March 2022. The mRNA expression levels of TIPE2, RELA (NF‐κb p65), TNF‐α, IL‐10, IL‐6, and IL‐1β in PBMCs were determined using qPCR. The severity of psoriasis vulgaris was determined according to the Psoriasis Area and Severity Index (PASI) scores. TIPE2 mRNA expression significantly decreased, while the expression of RELA, TNF‐α, IL‐10, IL‐6, and IL‐1β increased in patients with psoriasis vulgaris compared with that in the healthy control group. In addition, the expression levels of TIPE2, RELA, TNF‐α, and IL‐1β positively correlated with the PASI score. TIPE2 mRNA expression negatively correlated with IL‐6 and positively correlated with TNF‐α. Moreover, TIPE2 mRNA expression was higher in the active stage than that in the stationary stage. Notably, TNF‐α expression levels were higher in patients with psoriasis vulgaris combined with a history of respiratory infection. TIPE2 may contribute to the pathogenesis of psoriasis and be a potential biomarker of psoriasis vulgaris.

Genetic insights of all‐cause and vascular dementia through genome‐wide association studies

AbstractBackgroundGenome‐wide association studies (GWAS) have identified more than 40 genetic loci associated with Alzheimer’s disease (AD). Although vascular dementia (VaD) is the second most common type of dementia after AD, the genetic contribution to VaD is understudied. We hypothesize that common forms of dementia will share genetic risk factors. We conducted the largest trans‐ancestral GWAS of "all‐cause dementia" (ACD), VaD, and examined the underlying biological mechanismsMethodDonors from 16 population‐based CHARGE cohorts, two national case‐control consortia (ADGC, MEMENTO), and the UKBB contributed 46,533 and 4,078 cases of ACD and VaD, respectively. The overall sample (475,577) included European, African, Asian, and Hispanic ancestry. We conducted ancestry‐specific and trans‐ancestral meta‐analyses using METAL and MR‐MEGA, respectively. We explored the shared genetics of ACD with related disease traits and risk factors. Using a Bayesian approach, the level of polygenicity is explored across dementia and closely related traits, followed by a multi‐trait GWAS including ACD with traits of identical polygenic background. Finally, genome‐wide (GW) signals were functionally prioritized using a TWAS study.ResultFor ACD, we replicated ten known AD loci, including regions near APOE and BIN1. We found novel suggestive loci near SEMA4D, ANO3, AJAP1, HBEGF, and RBFFOX1. These loci were previously associated with energy transport throughout the brain (SEMA4D), neuronal excitability (ANO3), amyloid plaques (RBFOX1), and cerebral small vessel disease (‐cSVD‐, HBEGF). For VaD, one locus near APOE reached GW significance along with 22 suggestive, including SPRY2, FOXA2, AJAP1, and PSMA3, previously associated with hypertension, diabetes, and neuron maintenance. In addition to the genetic overlap with neurodegenerative processes, genetic risk loci for ACD exhibited overlap with vascular risk factors (T2D, blood pressure, lipid levels) and MRI markers of cSVD. Adjusting for SNP effects from traits with similar polygenic backgrounds revealed risk loci implicated in regulating cholesterol metabolism and maintaining neuronal mRNA levelsConclusionWe leveraged data from 19 cohorts and population‐based studies to assess the genetic contribution to ACD and VaD. GW suggestive signals included genes implicated in various brain activities. Bioinformatic parsing of the identified loci pointed to a genetic overlap of ACD with vascular risk factors and MRI markers of cSVD.

The best of both worlds: Combining population genetic and quantitative genetic models

Numerous traits under migration–selection balance are shown to exhibit complex patterns of genetic architecture with large variance in effect sizes. However, the conditions under which such genetic architectures are stable have yet to be investigated, because studying the influence of a large number of small allelic effects on the maintenance of spatial polymorphism is mathematically challenging, due to the high complexity of the systems that arise. In particular, in the most simple case of a haploid population in a two-patch environment, while it is known from population genetics that polymorphism at a single major-effect locus is stable in the symmetric case, there exist no analytical predictions on how this polymorphism holds when a polygenic background also contributes to the trait. Here we propose to answer this question by introducing a new eco-evo methodology that allows us to take into account the combined contributions of a major-effect locus and of a quantitative background resulting from small-effect loci, where inheritance is encoded according to an extension to the infinitesimal model. In a regime of small variance contributed by the quantitative loci, we justify that traits are concentrated around the major alleles, according to a normal distribution, using new convex analysis arguments. This allows a reduction in the complexity of the system using a separation of time scales approach. We predict an undocumented phenomenon of loss of polymorphism at the major-effect locus despite strong selection for local adaptation, because the quantitative background slowly disrupts the rapidly established polymorphism at the major-effect locus, which is confirmed by individual-based simulations. Our study highlights how segregation of a quantitative background can greatly impact the dynamics of major-effect loci by provoking migrational meltdowns. We also provide a comprehensive toolbox designed to describe how to apply our method to more complex population genetic models.