The battle of the sexes in humans is highly polygenic.

Sex-differential selection (SDS), which occurs when the fitness effects of alleles differ between males and females, can have profound impacts on the maintenance of genetic variation, disease risk, and other key aspects of natural populations. Because the sexes mix their autosomal genomes each generation, quantifying SDS is not possible using conventional population genetic approaches. Here, we introduce a method that exploits subtle sex differences in haplotype frequencies resulting from SDS acting in the current generation. Using data from 300K individuals in the UK Biobank, we estimate the strength of SDS throughout the genome. While only a handful of loci under SDS are individually significant, we uncover highly polygenic signals of genome-wide SDS for both viability and fecundity. Selection coefficients of [Formula: see text] may be typical. Despite its ubiquity, SDS may impose a mortality load of less than 1%. An interesting life-history tradeoff emerges: Alleles that increase viability more strongly in females than males tend to increase fecundity more strongly in males than in females. Finally, we find marginal evidence of SDS on fecundity acting on alleles affecting arm fat-free mass. Taken together, our findings connect the long-standing evidence of SDS acting on human phenotypes with its impact on the genome.

Multiancestry transferability of a polygenic risk score for diverticulitis.

OBJECTIVE: Polygenic risk scores (PRS) for diverticular disease must be evaluated in diverse cohorts. We sought to explore shared genetic predisposition across the phenome and to assess risk stratification in individuals genetically similar to European, African and Admixed-American reference samples. METHODS: A 44-variant PRS was applied to the All of Us Research Program. Phenome-wide association studies (PheWAS) identified conditions linked with heightened genetic susceptibility to diverticular disease. To evaluate the PRS in risk stratification, logistic regression models for symptomatic and for severe diverticulitis were compared with base models with covariates of age, sex, body mass index, smoking and principal components. Performance was assessed using area under the receiver operating characteristic curves (AUROC) and Nagelkerke's R2. RESULTS: The cohort comprised 181 719 individuals for PheWAS and 50 037 for risk modelling. PheWAS identified associations with diverticular disease, connective tissue disease and hernias. Across ancestry groups, one SD PRS increase was consistently associated with greater odds of severe (range of ORs (95% CI) 1.60 (1.27 to 2.02) to 1.86 (1.42 to 2.42)) and of symptomatic diverticulitis ((95% CI) 1.27 (1.10 to 1.46) to 1.66 (1.55 to 1.79)) relative to controls. European models achieved the highest AUROC and Nagelkerke's R2 (AUROC (95% CI) 0.78 (0.75 to 0.81); R2 0.25). The PRS provided a maximum R2 increase of 0.034 and modest AUROC improvement. CONCLUSION: Associations between a diverticular disease PRS and severe presentations persisted in diverse cohorts when controlling for known risk factors. Relative improvements in model performance were observed, but absolute change magnitudes were modest.

Improving on polygenic scores across complex traits using select and shrink with summary statistics (S4) and LDpred2.

BACKGROUND: As precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer. Here, we applied S4 PGS to 12 phenotypes for UK Biobank participants, and compared it with the LDpred2 and a combined S4 + LDpred2 method. RESULTS: The S4 + LDpred2 method provided overall improved PGS accuracy across a variety of phenotypes for UK Biobank participants. Additionally, the S4 + LDpred2 method had the best estimated PGS accuracy in Finnish and Japanese populations. We also addressed the challenge of limited genotype level data by developing the PGS models using only GWAS summary statistics. CONCLUSIONS: Taken together, the S4 + LDpred2 method represents an improvement in overall PGS accuracy across multiple phenotypes and populations.

Identification of gene co-expression modules from zebrafish brain data: Applications in psychiatry illustrated through alcohol-related traits.

Cumulative evidence suggests that zebrafish is a useful model in psychiatric research. Weighted Gene Co-expression Network Analysis (WGCNA) enables the reduction of genome-wide expression data to modules of highly co-expressed genes, which are hypothesized to interact within molecular networks. In this study, we first applied WGCNA to zebrafish brain expression data across different experimental conditions. Then, we characterized the different co-expression modules by gene-set enrichment analysis and hub gene-phenotype association. Finally, we analyzed association of polygenic risk scores (PRSs) based on genes of some interesting co-expression modules with alcohol dependence in 524 patients and 729 controls from Galicia, using competitive tests. Our approach revealed 34 co-expression modules in the zebrafish brain, with some showing enrichment in human synaptic genes, brain tissues, or brain developmental stages. Moreover, certain co-expression modules were enriched in psychiatry-related GWAS and comprised hub genes associated with psychiatry-related traits in both human GWAS and zebrafish models. Expression patterns of some co-expression modules were associated with the tested experimental conditions, mainly with substance withdrawal and cold stress. Notably, a PRS based on genes from co-expression modules exclusively associated with substance withdrawal in zebrafish showed a stronger association with human alcohol dependence than PRSs based on randomly selected brain-expressed genes. In conclusion, our analysis led to the identification of co-expressed gene modules that may model human brain gene networks involved in psychiatry-related traits. Specifically, we detected a cluster of co-expressed genes whose expression was exclusively associated with substance withdrawal in zebrafish, which significantly contributed to alcohol dependence susceptibility in humans.

Association of Polygenic Risk Score With Lifetime Risk of Developing Multiple Sclerosis in a Population-Based Birth-Year Cohort.

BACKGROUND AND OBJECTIVES: More than 200 genetic variants have been associated with multiple sclerosis (MS) susceptibility. However, it is unclear to what extent genetic factors influence lifetime risk of MS. Using a population-based birth-year cohort, we investigate the effect of genetics on lifetime risk of MS. METHODS: In the Project Y study, we tracked down almost all persons with MS (pwMS) from birth year 1966 in the Netherlands. As control participants, we included non-MS participants from the Project Y cohort (born 1965-1967 in the Netherlands) and non-MS participants from the Amsterdam Dementia Cohort born between 1963 and 1969. Genetic variants associated with MS were determined in pwMS and control participants using genotyping or imputation methods. Polygenic risk scores (PRSs) based on variants and weights from the largest genetic study in MS were calculated for each participant and assigned into deciles based on the PRS distribution in the control participants. We examined the lifetime risk for each decile and the association between PRS and MS disease variables, including age at onset and time to secondary progression. RESULTS: MS-PRS was calculated for 285 pwMS (mean age 53.0 ± 0.9 years, 72.3% female) and 267 control participants (mean age 51.8 ± 3.2 years, 58.1% female). Based on the lifetime risk estimation, we observed that 1:2,739 of the women with the lowest 30% genetic risk developed MS, whereas 1:92 of the women with the top 10% highest risk developed MS. For men, only 1:7,900 developed MS in the lowest 30% genetic risk group, compared with 1:293 men with the top 10% genetic risk. The PRS was not significantly associated with age at onset and time to secondary progression in both sexes. DISCUSSION: Our results show that the lifetime risk of MS is strongly influenced by genetic factors. Our findings have the potential to support diagnostic certainty in individuals with suspected MS: a high PRS could strengthen a diagnosis, but especially a PRS from the lowest tail of the PRS distribution should be considered a red flag and could prevent misdiagnosing conditions that mimic MS.

Polygenic, autosomal, and stable spirotetramat resistance in Chrysoperla carnea resulting in increased fitness.

Green lacewing, Chrysoperla carnea (Stephens) is a generalist predator used as a biological control agent in agro ecosystems. In order to use chemical and biological control in an integrated way, it is advantageous to know about natural enemy resistance response to a selected chemical. To determine C. carnea spirotetramat resistance potential, a population collected from the field was selected in the laboratory. Then we determined how spirotetramat resistance was inherited and how much it impacts the fitness of C. carnea. After eighteen selections with spirotetramat, the selected population (Spiro-Sel) of C. carnea had a 47-fold of resistance when compared to an UNSEL population. Inheritance results showed that spirotetramat resistance was inherited as an autosomal, incompletely dominant and polygenic trait. The values of effective dominance decreased from 0.87 (incomplete dominant) to 0.00 (complete recessive) as the concentration of spirotetramat increased from 625 mg/L to 10000 mg/L. The Spiro-Sel strain had no cross resistance to chlorfenapyr (1.10-fold), deltamethrin (1.26-fold) and chlorpyrifos (1.27-fold). After 7 generations without selection pressure resistance to all experimental insecticides in the Spiro-Sel strain was stable. Fitness data of the Spiro-Sel, Cross A, Cross B, UNSEL and susceptible strains of C. carnea showed that spirotetramat resistance increased the fitness of the selected green lacewing population. Life history parameters like fecundity, net reproductive rate, and relative fitness of the Spiro-Sel strain significantly increased when compared to the susceptible or unselected strains of C. carnea. These findings show that C. carnea is a perfect candidate for integrated pest management (IPM) programmes that combine biological control methods with selective pesticide applications to manage a variety of insect pests. Additionally, it would reduce the possibility of pests developing pesticide resistance despite repeated applications. It would be an excellent choice for widespread releases and be effective in most spray programs.

Polygenic risk of high LDL cholesterol and ischemic heart disease in the general population.

BACKGROUND AND AIMS: We tested the association of polygenic risk scores (PRS) for low-density lipoprotein cholesterol (LDL-C) and coronary artery disease (CAD) with LDL-C and risk of ischemic heart disease (IHD) in the Danish general population. METHODS: We included a total of 21,485 individuals from the Copenhagen General Population Study and Copenhagen City Heart Study. For everyone, LDL-PRS and CAD-PRS were calculated, each based on >400,000 variants. We also genotyped four rare variants in LDLR or APOB known to cause familial hypercholesterolemia (FH). RESULTS: Heterozygous carriers of FH-causing variants in APOB or LDLR had a mean LDL-C of 5.40 and 6.09 mmol/L, respectively, and an odds ratio for IHD of 2.27 (95 % CI 1.43-3.51) when compared to non-carriers. The LDL-PRS explained 13.8 % of the total variation in LDL-C in the cohort. Individuals in the lowest and highest 1 % of LDL-PRS had a mean LDL-C of 2.49 and 4.75 mmol/L, respectively. Compared to those in the middle 20-80 %, those in the lowest and highest 1 % of LDL-PRS had odds ratios for IHD of 0.58 (95 % CI, 0.38-0.88) and 1.83 (95 % CI, 1.33-2.53). The corresponding odds ratios for CAD-PRS were 0.61 (95 % CI, 0.41-0.92) and 2.06 (95 % CI, 1.49-2.85). CONCLUSIONS: The top 1 % of LDL-PRS and CAD-PRS conferred effects on LDL-C and risk of IHD comparable to those seen for carriers of rare FH-causing variants in APOB or LDLR. These results highlight the potential value of implementing such PRS clinically.

A systematic evaluation of the performance and properties of the UK Biobank Polygenic Risk Score (PRS) Release.

We assess the UK Biobank (UKB) Polygenic Risk Score (PRS) Release, a set of PRSs for 28 diseases and 25 quantitative traits that has been made available on the individuals in UKB, using a unified pipeline for PRS evaluation. We also release a benchmarking software tool to enable like-for-like performance evaluation for different PRSs for the same disease or trait. Extensive benchmarking shows the PRSs in the UKB Release to outperform a broad set of 76 published PRSs. For many of the diseases and traits we also validate the PRS algorithms in a separate cohort (100,000 Genomes Project). The availability of PRSs for 53 traits on the same set of individuals also allows a systematic assessment of their properties, and the increased power of these PRSs increases the evidence for their potential clinical benefit.

Disease prediction with multi-omics and biomarkers empowers case-control genetic discoveries in the UK Biobank.

The emergence of biobank-level datasets offers new opportunities to discover novel biomarkers and develop predictive algorithms for human disease. Here, we present an ensemble machine-learning framework (machine learning with phenotype associations, MILTON) utilizing a range of biomarkers to predict 3,213 diseases in the UK Biobank. Leveraging the UK Biobank's longitudinal health record data, MILTON predicts incident disease cases undiagnosed at time of recruitment, largely outperforming available polygenic risk scores. We further demonstrate the utility of MILTON in augmenting genetic association analyses in a phenome-wide association study of 484,230 genome-sequenced samples, along with 46,327 samples with matched plasma proteomics data. This resulted in improved signals for 88 known (P < 1 × 10-8) gene-disease relationships alongside 182 gene-disease relationships that did not achieve genome-wide significance in the nonaugmented baseline cohorts. We validated these discoveries in the FinnGen biobank alongside two orthogonal machine-learning methods built for gene-disease prioritization. All extracted gene-disease associations and incident disease predictive biomarkers are publicly available ( http://milton.public.cgr.astrazeneca.com ).

Complex trait associations in rare diseases and impacts on Mendelian variant interpretation.

Emerging evidence implicates common genetic variation - aggregated into polygenic scores (PGS) - in the onset and phenotypic presentation of rare diseases. Here, we comprehensively map individual polygenic liability for 1102 open-source PGS in a cohort of 3059 probands enrolled in the Genomic Answers for Kids (GA4K) rare disease study, revealing widespread associations between rare disease phenotypes and PGSs for common complex diseases and traits, blood protein levels, and brain and other organ morphological measurements. Using this resource, we demonstrate increased polygenic liability in probands with an inherited candidate disease variant (VUS) compared to unaffected carrier parents. Further, we show an enrichment for large-effect rare variants in putative core PGS genes for associated complex traits. Overall, our study supports and expands on previous findings of complex trait associations in rare diseases, implicates polygenic liability as a potential mechanism underlying variable penetrance of candidate causal variants, and provides a framework for identifying novel candidate rare disease genes.

Commonly used genomic arrays may lose information due to imperfect coverage of discovered variants for autism spectrum disorder.

BACKGROUND: Common genetic variation has been shown to account for a large proportion of ASD heritability. Polygenic scores generated for autism spectrum disorder (ASD-PGS) using the most recent discovery data, however, explain less variance than expected, despite reporting significant associations with ASD and other ASD-related traits. Here, we investigate the extent to which information loss on the target study genome-wide microarray weakens the predictive power of the ASD-PGS. METHODS: We studied genotype data from three cohorts of individuals with high familial liability for ASD: The Early Autism Risk Longitudinal Investigation (EARLI), Markers of Autism Risk in Babies-Learning Early Signs (MARBLES), and the Infant Brain Imaging Study (IBIS), and one population-based sample, Study to Explore Early Development Phase I (SEED I). Individuals were genotyped on different microarrays ranging from 1 to 5 million sites. Coverage of the top 88 genome-wide suggestive variants implicated in the discovery was evaluated in all four studies before quality control (QC), after QC, and after imputation. We then created a novel method to assess coverage on the resulting ASD-PGS by correlating a PGS informed by a comprehensive list of variants to a PGS informed with only the available variants. RESULTS: Prior to imputations, None of the four cohorts directly or indirectly covered all 88 variants among the measured genotype data. After imputation, the two cohorts genotyped on 5-million arrays reached full coverage. Analysis of our novel metric showed generally high genome-wide coverage across all four studies, but a greater number of SNPs informing the ASD-PGS did not result in improved coverage according to our metric. LIMITATIONS: The studies we analyzed contained modest sample sizes. Our analyses included microarrays with more than 1-million sites, so smaller arrays such as Global Diversity and the PsychArray were not included. Our PGS metric for ASD is only generalizable to samples of European ancestries, though the coverage metric can be computed for traits that have sufficiently large-sized discovery findings in other ancestries. CONCLUSIONS: We show that commonly used genotyping microarrays have incomplete coverage for common ASD variants, and imputation cannot always recover lost information. Our novel metric provides an intuitive approach to reporting information loss in PGS and an alternative to reporting the total number of SNPs included in the PGS. While applied only to ASD here, this metric can easily be used with other traits.

Causal interpretations of family GWAS in the presence of heterogeneous effects.

Family-based genome-wide association studies (GWASs) are often claimed to provide an unbiased estimate of the average causal effects (or average treatment effects; ATEs) of alleles, on the basis of an analogy between the random transmission of alleles from parents to children and a randomized controlled trial. We show that this claim does not hold in general. Because Mendelian segregation only randomizes alleles among children of heterozygotes, the effects of alleles in the children of homozygotes are not observable. This feature will matter if an allele has different average effects in the children of homozygotes and heterozygotes, as can arise in the presence of gene-by-environment interactions, gene-by-gene interactions, or differences in linkage disequilibrium patterns. At a single locus, family-based GWAS can be thought of as providing an unbiased estimate of the average effect in the children of heterozygotes (i.e., a local average treatment effect; LATE). This interpretation does not extend to polygenic scores (PGSs), however, because different sets of SNPs are heterozygous in each family. Therefore, other than under specific conditions, the within-family regression slope of a PGS cannot be assumed to provide an unbiased estimate of the LATE for any subset or weighted average of families. In practice, the potential biases of a family-based GWAS are likely smaller than those that can arise from confounding in a standard, population-based GWAS, and so family studies remain important for the dissection of genetic contributions to phenotypic variation. Nonetheless, their causal interpretation is less straightforward than has been widely appreciated.

Evaluating the cost-effectiveness of polygenic risk score-stratified screening for abdominal aortic aneurysm.

As the heritability of abdominal aortic aneurysm (AAA) is high and AAA partially shares genetic architecture with other cardiovascular diseases, genetic information could help inform AAA screening strategies. Exploiting pleiotropy and meta-analysing summary data from large studies, we construct a polygenic risk score (PRS) for AAA. Leveraging related traits improves PRS performance (R2) by 22.7%, relative to using AAA alone. Compared with the low PRS tertile, intermediate and high tertiles have hazard ratios for AAA of 2.13 (95%CI 1.61, 2.82) and 3.70 (95%CI 2.86, 4.80) respectively, adjusted for clinical risk factors. Using simulation modelling, we compare PRS- and smoking-stratified screening with inviting men at age 65 and not inviting women (current UK strategy). In a futuristic scenario where genomic information is available, our modelling suggests inviting male current smokers with high PRS earlier than 65 and screening female smokers with high/intermediate PRS at 65 and 70 respectively, may improve cost-effectiveness.

Genetic Theory behind the Molar Incisor Hypomineralisation.

Much effort has been spent in the identification of an environmental factor that explains the occurrence of molar incisor hypomineralisation (MIH). The best explanation, however, is accepting that MIH has a complex or multifactorial mode of inheritance. This chapter provides an analysis of how multifactorial inheritance operates in determining MIH, an explanation that accommodates the variation seen in the phenotype and the frequency of the problem around the world.

An observational and genetic investigation into the association between psoriasis and risk of malignancy.

The relationship between psoriasis and site-specific cancers remains unclear. Here, we aim to investigate whether psoriasis is causally associated with site-specific cancers. We use observational and genetic data from the UK Biobank, obtaining GWAS summary data, eQTL analysis data, TCGA data, and GTEx data from public datasets. We perform PheWAS, polygenic risk score analysis, and one-sample and two-sample Mendelian randomization analyses to investigate the potential causal associations between psoriasis and cancers. In the unselected PheWAS analysis, psoriasis is associated with higher risks of 16 types of cancer. Using one-sample Mendelian randomization analyses, it is found that genetically predicted psoriasis is associated with higher risks of anal canal cancer, breast cancer, follicular non-Hodgkin's lymphoma and nonmelanoma skin cancer in women; and lung cancer and kidney cancer in men. Our two-sample Mendelian randomization analysis indicates that psoriasis is causally associated with breast cancer and lung cancer. Gene annotation shows that psoriasis-related genes, such as ERAP1, are significantly different in lung and breast cancer tissues. Taken together, clinical attention to lung cancer and breast cancer may be warranted among patients with psoriasis.

Investigating the shared genetic architecture between depression and subcortical volumes.

Depression, a widespread and highly heritable mental health condition, profoundly affects millions of individuals worldwide. Neuroimaging studies have consistently revealed volumetric abnormalities in subcortical structures associated with depression. However, the genetic underpinnings shared between depression and subcortical volumes remain inadequately understood. Here, we investigate the extent of polygenic overlap using the bivariate causal mixture model (MiXeR), leveraging summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 14 subcortical volumetric phenotypes (N = 33,224). Additionally, we identify shared genomic loci through conditional/conjunctional FDR analyses. MiXeR shows that subcortical volumetric traits share a substantial proportion of genetic variants with depression, with 44 distinct shared loci identified by subsequent conjunctional FDR analysis. These shared loci are predominantly located in intronic regions (58.7%) and non-coding RNA intronic regions (25.4%). The 269 protein-coding genes mapped by these shared loci exhibit specific developmental trajectories, with the expression level of 55 genes linked to both depression and subcortical volumes, and 30 genes linked to cognitive abilities and behavioral symptoms. These findings highlight a shared genetic architecture between depression and subcortical volumetric phenotypes, enriching our understanding of the neurobiological underpinnings of depression.

Shared genetic architecture between gastro-esophageal reflux disease, asthma, and allergic diseases.

The aim is to investigate the evidence for shared genetic architecture between each of asthma, allergic rhinitis and eczema with gastro-esophageal reflux disease (GERD). Structural equation models (SEM) and polygenic risk score (PRS) analyses are applied to three Swedish twin cohorts (n = 46,582) and reveal a modest genetic correlation between GERD and asthma of 0.18 and bidirectional PRS and phenotypic associations ranging between OR 1.09-1.14 and no correlations for eczema and allergic rhinitis. Linkage disequilibrium score regression is applied to summary statistics of recently published GERD and asthma/allergic disease genome wide association studies and reveals a genetic correlation of 0.48 for asthma and GERD, and Genomic SEM supports a single latent factor. A gene-/gene-set analysis using MAGMA reveals six pleiotropic genes (two at 12q13.2) associated with asthma and GERD. This study provides evidence that there is a common genetic architecture unique to asthma and GERD that may explain comorbidity and requires further investigation.

Identification of Sweetness Preference-Related Single-Nucleotide Polymorphisms for Polygenic Risk Scores Associated with Obesity.

Our study aimed to identify sweetness preference-associated single-nucleotide polymorphisms (SNPs), characterize the related genetic loci, and develop SNP-based polygenic risk scores (PRS) to analyze their associations with obesity. For genotyping, we utilized a pooled genome-wide association study (GWAS) dataset of 18,499 females and 10,878 males. We conducted genome-wide association analyses, functional annotation, and employed the weighted method to calculate the levels of PRS from 677 sweetness preference-related SNPs. We used Cox proportional hazards modeling with time-varying covariates to estimate age-adjusted and multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) for obesity incidence. We also tested the correlation between PRS and environmental factors, including smoking and dietary components, on obesity. Our results showed that in males, the TT genotype of rs4861982 significantly increased obesity risk compared to the GG genotype in the Health Professionals Follow-up Study (HPFS) cohort (HR = 1.565; 95% CI, 1.122-2.184; p = 0.008) and in the pooled analysis (HR = 1.259; 95% CI, 1.030-1.540; p = 0.025). Protein tyrosine phosphatase receptor type O (PTPRO) was identified as strongly associated with sweetness preference, indicating a positive correlation between sweetness preference and obesity risk. Moreover, each 10 pack-year increment in smoking was significantly associated with an increased risk of obesity in the HPFS cohort (HR = 1.024; 95% CI, 1.000-1.048) in males but not in females. In conclusion, significant associations between rs4861982, sweetness preference, and obesity were identified, particularly among males, where environmental factors like smoking are also correlated with obesity risk.

Interplay between polygenic risk score and solar insolation: Implication for systemic lupus erythematosus diagnosis and pathogenesis.

OBJECTIVES: This research elucidates the correlation between solar radiation insolation, polygenic risk score (PRS), and systemic lupus erythematosus (SLE) diagnosis, utilizing genomic, environmental, and clinical data. METHODS: We included 1,800 SLE participants and 1,800 controls from the Taiwan Precision Medicine Initiative, genotyped via the Affymetrix Genome-Wide TWB 2.0 SNP Array. The study employed a SLE-PRS tailored for individuals of Taiwanese ancestry, comprising 27 single nucleotide polymorphisms (SNPs). QGIS computed solar radiation insolation from participants' residences. We employed logistic regression to investigate the associations between SLE-PRS, solar insolation susceptibility, and SLE. Additive and multiplicative interactions were utilized to assess the interactions between solar insolation and SLE-PRS regarding the risk of SLE. RESULTS: SLE patients showed decreased solar insolation (p < 0.001). The highest decile of SLE-PRS exhibited a statistically significant lower solar insolation 1, 3, 6, and 12 months prior to diagnosis as compared to the lowest decile. Specifically, there were significant differences observed at 1 and 12 months (p = 0.025 and p = 0.004, respectively). It suggests that higher SLE-PRS correlated with reduced solar insolation tolerance. We observed an increase in SLE risk across ascending SLE-PRS percentiles exclusively in the high solar insolation group, not in the low solar insolation group. However, the interaction effect of SLE-PRS and solar insolation on SLE risk is not statistically significant. Compared to the lowest decile, the highest SLE-PRS decile showed a 10.98-fold increase in SLE risk (95 % CI, 3.773-31.952, p < 0.001). High SLE-PRS scores in conjunction with high solar insolation contribute to SLE incidence. CONCLUSIONS: Our study unveils the intertwined nature of UV insolation and polygenic risks in SLE. Future studies should explore the preventative potential of robust solar radiation protection for high-risk individuals before the disease onset.