Polygenic Effects Research Articles

Comparing genomic studies in animal breeding and human genetics: Focus on disease-related traits in livestock.

Genomic studies of diseases can be divided into two types: (1) analyses that reveal causal genes by focusing on linkage disequilibrium between observed and causal variants and (2) those that simultaneously assess numerous genetic markers to estimate the polygenic effects of a particular genomic region or entire genome. The field of human genetics has emphasized the discovery of causal genes, but these represent only a fraction of the total genetic variance. Therefore, alternative approaches, such as the polygenic risk score, which estimates the genetic risk for a given trait or disease on the basis of all genetic markers (rather than on known causal variants only), have begun to garner attention. In many respects, these human genetic methods are similar to those originally developed for the estimation of breeding values (i.e., total additive genetic effects) in livestock. However, despite these similarities in methods, the fields of human and animal genetics still differ markedly in terms of research objectives, target populations, and other characteristics. For example, livestock populations have continually been selected and inbred throughout their history; consequently, their effective population size has shrunk and preferred genes (such as those influencing disease resistance and production traits) have accumulated in the modern breeding populations. By examining the characteristics of these two fields, particularly from the perspectives of disease and disease resistance, this review aims to improve understanding of the intrinsic differences between genomic studies using human compared with livestock populations.

Polygenic risk and rare variant gene clustering enhance cancer risk stratification for breast and prostate cancers

Polygenic risk score (PRS) and rare monogenic variant screening are valuable tools for predicting cancer risk and identifying individuals at high risk. Integrating both common and rare genetic variants is crucial for accurate risk assessment. However, estimating the impacts of rare variants on cancer and combining them with PRS remains challenging. Here, we analyze 454,711 exome sequencing and 487,409 array UK Biobank samples, focusing on breast and prostate cancers. We introduce an expanded PRS (EPRS) approach, yielding a systematic model for more effective risk stratification. By prioritizing and clustering genes with cancer-specific rare variants based on odds ratios and population-attributable fraction, we refine risk stratification by combining both monogenic and polygenic effects. Individuals in high-PRS groups with rare high-impact gene variants show up to 15- and 22-fold higher risk for breast and prostate cancers, respectively, compared to those in the intermediate-PRS groups without rare variants. Combined risk profiles vary across distinct rare variant clusters within the same PRS group for both cancers. Our EPRS approach enhances risk stratification for breast and prostate cancers, offering important insights for future research and potential applications to other cancer types.

Estimation of Direct and Indirect Polygenic Effects and Gene-Environment Interactions using Polygenic Scores in Case-Parent Trio Studies.

Family-based studies provide a unique opportunity to characterize genetic risks of diseases in the presence of population structure, assortative mating, and indirect genetic effects. We propose a novel framework, PGS-TRI, for the analysis of polygenic scores (PGS) in case-parent trio studies for estimation of the risk of an index condition associated with direct effects of inherited PGS, indirect effects of parental PGS, and gene-environment interactions. Extensive simulation studies demonstrate the robustness of PGS-TRI in the presence of complex population structure and assortative mating compared to alternative methods. We apply PGS-TRI to multi-ancestry trio studies of autism spectrum disorders (N trio = 1,517) and orofacial clefts (N trio = 1,904) to establish the first transmission-based estimates of risk associated with pre-defined PGS for these conditions and other related traits. For both conditions, we further explored offspring risk associated with polygenic gene-environment interactions, and direct and indirect effects of genetically predicted levels of gene expression and metabolite traits.

Forensic height estimation using polygenic score in Korean population.

Height is known to be a classically heritable trait controlled by complex polygenic factors. Numerous height-associated genetic variants across the genome have been identified so far. It is also a representative of externally visible characteristics (EVC) for predicting appearance in forensic science. When biological evidence at a crime scene is deficient in identifying an individual, the examination of forensic DNA phenotyping using some genetic variants could be considered. In this study, we aimed to predict 'height', a representative forensic phenotype, by using a small number of genetic variants when short tandem repeat (STR) analysis is hard with insufficient biological samples. Our results not only replicated previous genetic signals but also indicated an upward trend in polygenic score (PGS) with increasing height in the validation and replication stages for both genders. These results demonstrate that the established SNP sets in this study could be used for height estimation in the Korean population. Specifically, since the PGS model constructed in this study targets only a small number of SNPs, it contributes to enabling forensic DNA phenotyping even at crime scenes with a minimal amount of biological evidence. To the best of our knowledge, this was the first study to evaluate a PGS model for height estimation in the Korean population using GWAS signals. Our study offers insight into the polygenic effect of height in East Asians, incorporating genetic variants from non-Asian populations.

Modeling QTL-by-environment interactions for multi-parent populations.

Multi-parent populations (MPPs) are attractive for genetic and breeding studies because they combine genetic diversity with an easy-to-control population structure. Most methods for mapping QTLs in MPPs focus on the detection of QTLs in single environments. Little attention has been given to mapping QTLs in multienvironment trials (METs) and to detecting and modeling QTL-by-environment interactions (QEIs). We present mixed model approaches for the detection and modeling of consistent versus environment-dependent QTLs, i.e., QTL-by-environment interaction (QEI). QTL effects are assumed to be normally distributed with variances expressing consistency or dependence on environments and families. The entries of the corresponding design matrices are functions of identity-by-descent (IBD) probabilities between parents and offspring and follow from the parental origin of offspring DNA. A polygenic effect is added to the models to account for background genetic variation. We illustrate the wide applicability of our method by analyzing several public MPP datasets with observations from METs. The examples include diallel, nested association mapping (NAM), and multi-parent advanced inter-cross (MAGIC) populations. The results of our approach compare favorably with those of previous studies that used tailored methods.

FedGMMAT: Federated generalized linear mixed model association tests.

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for confounding fixed and additive polygenic random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.

Schoolhouse risk: Can we mitigate the polygenic Pygmalion effect?

BackgroundAlthough limited in predictive accuracy, polygenic scores (PGS) for educational outcomes are currently available to the public via direct-to-consumer genetic testing companies. Further, there is a growing movement to apply PGS in educational settings via ‘precision education.’ Prior scholarship highlights the potentially negative impacts of such applications, as disappointing results may give rise a “polygenic Pygmalion effect.” In this paper two studies were conducted to identify factors that may mitigate or exacerbate negative impacts of PGS. MethodsTwo studies were conducted. In each, 1188 students were randomized to one of four conditions: Low-percentile polygenic score for educational attainment (EA-PGS), Low EA-PGS + Mitigating information, Low EA-PGS + Exacerbating information, or Control. Regression analyses were used to examine differences between conditions. ResultsIn Study 1, participants randomized to Control reported significantly higher on the Rosenberg Self-Esteem Scale (RSES), Competence Scale (CS), Academic Efficacy Scale (AES) and Educational Potential Scale (EPS). CS was significantly higher in the Low EA-PGS + Mitigating information condition. CS and AES were significantly lower in the Low EA-PGS + Exacerbating information condition compared to the Low EA-PGS + Mitigating information condition. In Study 2, participants randomized to Control reported significantly higher CS and AES. Pairwise comparisons did not show significant differences in CS and AES. Follow-up pairwise comparisons using Tukey P-value correction did not find significant associations between non-control conditions. ConclusionThese studies replicated the polygenic Pygmalion effect yet were insufficiently powered to detect significant effects of mitigating contextual information. Regardless of contextual information, disappointing EA-PGS results were significantly associated with lower assessments of self-esteem, competence, academic efficacy, and educational potential.

Population-specific validation and comparison of the performance of 77- and 313-variant polygenic risk scores for breast cancer risk prediction.

The polygenic risk score (PRS) allows the quantification of the polygenic effect of many low-penetrance alleles on the risk of breast cancer (BC). This study aimed to evaluate the performance of two sets comprising 77 or 313 low-penetrance loci (PRS77 and PRS313) in patients with BC in the Czech population. In a retrospective case-control study, variants were genotyped from both the PRS77 and PRS313 sets in 1329 patients with BC and 1324 noncancer controls, all women without germline pathogenic variants in BC predisposition genes. Odds ratios (ORs) were calculated according to the categorical PRS in individual deciles. Weighted Cox regression analysis was used to estimate the hazard ratio (HR) per standard deviation (SD) increase in PRS. The distributions of standardized PRSs in patients and controls were significantly different (p < 2.2×10-16) with both sets. PRS313 outperformed PRS77 in categorical and continuous PRS analyses. For patients in the highest 2.5% of PRS313, the risk reached an ORof3.05 (95% CI,1.66-5.89; p=1.76×10-4). The continuous risk was estimated as an HRper SDof1.64 (95% CI,1.49-1.81; p<2.0×10-16), which resulted in an absolute risk of 21.03% at age 80 years for individuals in the 95th percentile of PRS313. Discordant categorization into PRS deciles was observed in 248 individuals (9.3%). Both PRS77 and PRS313 are able to stratify individuals according to their BC risk in the Czech population. PRS313 shows better discriminatory ability. The results support the potential clinical utility of using PRS313 in individualized BC risk prediction.

Breast Cancer Polygenic Risk Score Validation and Effects of Variable Imputation.

Breast cancer (BC) is a complex disease affecting one in eight women in the USA. Advances in population genomics have led to the development of polygenic risk scores (PRSs) with the potential to augment current risk models, but replication is often limited. We evaluated 2 robust PRSs with 313 and 3820 SNPs and the effects of multiple genotype imputation replications in BC cases and control populations. Biological samples from BC cases and cancer-free controls were drawn from three European ancestry cohorts. Genotyping on the Illumina Global Screening Array was followed by stringent quality control measures and 20 genotype imputation replications. A total of 468 unrelated cases and 4337 controls were scored, revealing significant differences in mean PRS percentiles between cases and controls (p < 0.001) for both SNP sets (313-SNP PRS: 52.81 and 48.07; 3820-SNP PRS: 55.45 and 49.81), with receiver operating characteristic curve analysis showing area under the curve values of 0.596 and 0.603 for the 313-SNP and 3820-SNP PRS, respectively. PRS fluctuations (from ~2-3% up to 9%) emerged across imputation iterations. Our study robustly reaffirms the predictive capacity of PRSs for BC by replicating their performance in an independent BC population and showcases the need to average imputed scores for reliable outcomes.

Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae.

Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics.

Sex-specific and polygenic effects underlying resting heart rate and associated risk of cardiovascular disease.

Resting heart rate (RHR) is associated with cardiovascular disease (CVD) and mortality. This study aimed to identify genetic loci associated with RHR, develop a genome-wide polygenic risk score (PRS) for RHR, and assess associations between the RHR PRS and CVD outcomes, to better understand the biological mechanisms linking RHR to disease. Sex-specific analyses were conducted to potentially elucidate different pathways between the sexes. We performed a genome-wide meta-analysis of RHR (n = 550 467) using two independent study populations, The Trøndelag Health Study (HUNT) and the UK Biobank (UKB), comprising 69 155 and 481 312 participants, respectively. We also developed a genome-wide PRS for RHR using UKB and tested for association between the PRS and 13 disease outcomes in HUNT. We identified 403, 253, and 167 independent single nucleotide polymorphisms (SNPs) significantly associated with RHR in the total population, women, and men, respectively. The sex-specified analyses indicated differences in the genetic contribution to RHR and revealed loci significantly associated with RHR in only one of the sexes. The SNPs were mapped to genes enriched in heart tissue and cardiac conduction pathways, as well as disease-pathways, including dilated cardiomyopathy. The PRS for RHR was associated with increased risk of hypertension and dilated cardiomyopathy, and decreased risk of atrial fibrillation. Our findings provide insight into the pleiotropic effects of the RHR variants, contributing towards an improved understanding of mechanisms linking RHR and disease. In addition, the sex-specific results might contribute to a more refined understanding of RHR as a risk factor for the different diseases.

Polygenic effects on the risk of Alzheimer’s disease in the Japanese population

BackgroundPolygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer’s disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans.MethodsIn this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD.ResultsThe PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (β estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results.ConclusionsWe showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences.

Polygenic effects on brain functional endophenotype for deficit and non-deficit schizophrenia

Deficit schizophrenia (DS) is a subtype of schizophrenia (SCZ). The polygenic effects on the neuroimaging alterations in DS still remain unknown. This study aims to calculate the polygenic risk scores for schizophrenia (PRS-SCZ) in DS, and further explores the potential associations with functional features of brain. PRS-SCZ was calculated according to the Whole Exome sequencing and Genome-wide association studies (GWAS). Resting-state fMRI, as well as biochemical features and neurocognitive data were obtained from 33 DS, 47 NDS and 41 HCs, and association studies of genetic risk with neuroimaging were performed in this sample. The analyses of amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were performed to detect the functional alterations between DS and NDS. In addition, correlation analysis was used to investigate the relationships between functional features (ALFF, ReHo, FC) and PRS-SCZ. The PRS-SCZ of DS was significantly lower than that in NDS and HC. Compared to NDS, there was a significant increase in the ALFF of left inferior temporal gyrus (ITG.L) and left inferior frontal gyrus (IFG.L) and a significant decrease in the ALFF of right precuneus (PCUN.R) and ReHo of right middle frontal gyrus (MFG.R) in DS. FCs were widely changed between DS and NDS, mainly concentrated in default mode network, including ITG, PCUN and angular gyrus (ANG). Correlation analysis revealed that the ALFF of left ITG, the ReHo of right middle frontal gyrus, the FC value between insula and ANG, left ITG and right corpus callosum, left ITG and right PCUN, as well as the scores of Trail Making Test-B, were associated with PRS-SCZ in DS. The present study demonstrated the differential polygenic effects on functional changes of brain in DS and NDS, providing a potential neuroimaging-genetic perspective for the pathogenesis of schizophrenia.

Interpreting polygenic score effects in sibling analysis.

Researchers often claim that sibling analysis can be used to separate causal genetic effects from the assortment of biases that contaminate most downstream genetic studies (e.g. polygenic score predictors). Indeed, typical results from sibling analysis show large (>50%) attenuations in the associations between polygenic scores and phenotypes compared to non-sibling analysis, consistent with researchers' expectations about bias reduction. This paper explores these expectations by using family (quad) data and simulations that include indirect genetic effect processes and evaluates the ability of sibling analysis to uncover direct genetic effects of polygenic scores. We find that sibling analysis, in general, fail to uncover direct genetic effects; indeed, these models have both upward and downward biases that are difficult to sign in typical data. When genetic nurture effects exist, sibling analysis creates "measurement error" that attenuates associations between polygenic scores and phenotypes. As the correlation between direct and indirect effect changes, this bias can increase or decrease. Our findings suggest that interpreting results from sibling analysis aimed at uncovering direct genetic effects should be treated with caution.

Role of Skewed X-Chromosome Inactivation in Common Variable Immunodeficiency.

The term common variable immunodeficiency (CVID) encompasses a clinically diverse group of disorders, mainly characterized by hypogammaglobulinemia, insufficient specific antibody production, and recurrent infections. The genetics of CVID is complex, and monogenic defects account for only a portion of cases, typically <30%. Other proposed mechanisms include digenic, oligogenic, or polygenic inheritance and epigenetic dysregulation. In this study, we aimed to assess the role of skewed X-chromosome inactivation (XCI) in CVID. Within our cohort of 131 genetically analyzed CVID patients, we selected female patients with rare variants in CVID-associated genes located on the X-chromosome. Four patients harboring heterozygous variants in BTK (n = 2), CD40LG (n = 1), and IKBKG (n = 1) were included in the study. We assessed XCI status using the HUMARA assay and an NGS-based method to quantify the expression of the 2 alleles in mRNA. Three of the 4 patients (75%) exhibited skewed XCI, and the mutated allele was predominantly expressed in all cases. Patient 1 harbored a hypomorphic variant in BTK (p.Tyr418His), patient 3 had a pathogenic variant in CD40LG (c.288+1G>A), and patient 4 had a hypomorphic variant in IKBKG (p.Glu57Lys) and a heterozygous splice variant in TNFRSF13B (TACI) (c.61+2T>A). Overall, the analysis of our cohort suggests that CVID in a small proportion of females (1.6% in our cohort) is caused by skewed XCI and highly penetrant gene variants on the X-chromosome. Additionally, skewed XCI may contribute to polygenic effects (3.3% in our cohort). These results indicate that skewed XCI may represent another piece in the complex puzzle of CVID genetics.

Long-read sequencing-based transcriptomic landscape in longissimus dorsi and transcriptome-wide association studies for growth traits of meat rabbits.

Rabbits are an attractive meat livestock species that can efficiently convert human-indigestible plant biomass, and have been commonly used in biological and medical researches. Yet, transcriptomic landscape in muscle tissue and association between gene expression level and growth traits have not been specially studied in meat rabbits. In this study Oxford Nanopore Technologies (ONT) long-read sequencing technology was used for comprehensively exploring transcriptomic landscape in Longissimus dorsi for 115 rabbits at 84 days of age, and transcriptome-wide association studies (TWAS) were performed for growth traits, including body weight at 84 days of age and average daily gain during three growth periods. The statistical analysis of TWAS was performed using a mixed linear model, in which polygenic effect was fitted as a random effect according to gene expression level-based relationships. A total of 18,842 genes and 42,010 transcripts were detected, among which 35% of genes and 47% of transcripts were novel in comparison with the reference genome annotation. Furthermore, 45% of genes were widely expressed among more than 90% of individuals. The proportions (±SE) of phenotype variance explained by genome-wide gene expression level ranged from 0.501 ± 0.216 to 0.956 ± 0.209, and the similar results were obtained when explained by transcript expression level. In contrast, neither gene nor transcript was detected by TWAS to be statistically significantly associated with these growth traits. In conclusion, these novel genes and transcripts that have been extensively profiled in a single muscle tissue using long-read sequencing technology will greatly improve our understanding on transcriptional diversity in rabbits. Our results with a relatively small sample size further revealed the important contribution of global gene expression to phenotypic variation on growth performance, but it seemed that no single gene has an outstanding effect; this knowledge is helpful to include intermediate omics data for implementing genetic evaluation of growth traits in meat rabbits.

Genome-Wide Association Study of Milk Composition in Karachai Goats.

This study is first to perform a genome-wide association study (GWAS) to investigate the milk quality traits in Karachai goats. The objective of the study was to identify candidate genes associated with milk composition traits based on the identification and subsequent analysis of all possible SNPs, both genome-wide (high-confidence) and suggestive (subthreshold significance). To estimate the milk components, 22 traits were determined, including several types of fatty acids. DNA was extracted from ear tissue or blood samples. A total of 167 Karachai goats were genotyped using an Illumina GoatSNP53K BeadChip panel (Illumina Inc., San Diego, CA, USA). Overall, we identified 167 highly significant and subthreshold SNPs associated with the milk components of Karachai goats. A total of 10 SNPs were located within protein-coding genes and 33 SNPs in close proximity to them (±0.2 Mb). The largest number of genome-wide significant SNPs was found on chromosomes 2 and 8 and some of them were associated with several traits. The greatest number of genome-wide significant SNPs was identified for crude protein and lactose (6), and the smallest number-only 1 SNP-for freezing point depression. No SNPs were identified for monounsaturated and polyunsaturated fatty acids. Functional annotation of all 43 SNPs allowed us to identify 66 significant candidate genes on chromosomes 1, 2, 3, 4, 5, 8, 10, 13, 16, 18, 21, 23, 25, 26, and 27. We considered these genes potential DNA markers of the fatty acid composition of Karachai goat milk. Also, we found 12 genes that had a polygenic effect: most of them were simultaneously associated with the dry matter content and fatty acids (METTL, SLC1A 8, PHACTR1, FMO2, ECI1, PGP, ABCA3, AMDHD2). Our results suggest that the genes identified in our study affecting the milk components in Karachai goats differed from those identified in other breeds of dairy goats.

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.

Effects of polygenes, parent-child relationship and frustration on junior high school students' aggressive behaviors.

The effects of the interaction between polygenes and the parent-child relationship on junior high school students' aggressive behaviors were explored through the frameworks of gene-endophenotype-behavior and neurophysiological basis. A total of 892 junior high school students participated in this study. They were asked to complete self-reported questionnaires, and saliva samples were collected. Results showed that 5-HTTLPR, MAOA-uVNTR, COMT (rs4680), and Taq1 (rs1800497) of the DRD2 gene affected students' aggressive behaviors in an accumulative way. The polygenic risk score explained 3.4% of boys' aggression and 1.1% of girls' aggression. The interactions between polygenic risk score and parent-child conflict significantly affected the aggressive behaviors of male students, but did not show any significant effect on those of female students. The interactional effect of polygenic risk score and parent-child conflict on junior high school students' aggressive behaviors was completely mediated by frustration. However, the interaction effect of polygenic risk score and parent-child affinity on aggression was not affected by frustration. This study helps us better understand junior high school students' aggressive behaviors and promotes the prevention and correction of adolescents' problem behaviors.

Omics analysis of Alzheimer’s disease stratified by the microglial polygenic effect

AbstractBackgroundRecent single‐cell omics analyses have revealed that microglia change into reactive microglia when Aβ accumulates in the brain and exhibit Aβ phagocytosis. However, reactive microglia are less likely to be induced in TREM2 mutation carriers. This microglia‐centred pathological mechanism may be considered one of the pathologies of Alzheimer’s disease (AD). However, a single gene mutation, such as a TREM2 mutation, can rarely explain the pathology of AD. Therefore, polygenic effects involving multiple gene mutations have been proposed. In this study, we propose a polygenic risk score (PRS) to quantify the microglial polygenic effect. We stratified patient groups by this PRS and examined differences in pathogenesis based on omics analysis using postmortem brains.MethodWhole‐genome sequencing and bulk brain tissue RNA‐seq of the frontal cortex were performed using postmortem brain samples from 100 subjects with Braak senile plaque (SP) and neurofibrillary tangle (NFT) staging information. In addition, single‐nuclei RNA‐seq (snRNA‐seq) was performed on 15 of these subjects (healthy subjects: 8 subjects, AD patients: 7 subjects).ResultWe divided AD patients into low and high groups by microglial PRS and performed bulk brain tissue RNA‐seq analysis. We identified 112 differentially expressed genes and found that those genes were related to autophagy and inflammation. To further validate these findings at the single‐cell level, we identified cell clusters containing microglia based on snRNA‐seq. Gene expression analysis of microglia in 3 AD patients with low PRS and 4 AD patients with high PRS suggested altered transcriptional profiles of immune cells and phagocytosis. The expression of APOE and TYROBP, signature genes of reactive microglia, was significantly upregulated in the AD patients with low PRS compared to the AD patients with high PRS and healthy subjects. In contrast, CX3CR1 and P2RY12, signature genes of homeostatic microglia, were downregulated.ConclusionThe stratification analysis of AD by PRS suggests that microglial reactivity differs among AD patient groups. Future detailed microglial observations in postmortem brains from AD patients with low PRS and those with high PRS will further elaborate our results.