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Background and Purpose: Genetic factors play an important role in the pathogenesis of stroke(S). This study aimed to screen the loci associated with S risk in northwestern Chinese population by genome-wide association analysis (GWAS)., Methods: A total of 1394 subjects, including 682 S patients and 692 controls, were enrolled in this study. SPSS 25.0 software was used for statistical analysis, and the independent sample t-test as well as Chi-square test were used to analyze the differences in age and gender between the case and control groups. The Precision Medicine Diversity Array (PMDA) genotyping chip was used in this study. The genotyping platform was the Gene Titan multi-channel instrument, and the Axiom Analysis Suite 6.0 software was used for the data analyzing. Besides, the LASSO analysis, SNP-SNP and GO/KEGG analysis were conducted to analyze the association between significant loci and S risk., Results: A total of 30 SNPs were found to be associated with the S risk based on additive model (p < 5 × 10 -8 ). After the LASSO screening, 22 SNPs showed the diagnostic value in S. The SNPs interaction analysis further screened the SNP-SNP interaction groups associated with the S risk(p < 0.05). Finally, the GO/KEGG analysis discovered the suggestive significance loci could be involved in the S development mainly by immune-related functions and pathways., Conclusion: This study discovered 30 S related SNPs and analyzed the potential pathways associated with genes located on the 30 SNPs, which were beneficial for enriching the genetic mechanism analysis of S in northwestern Chinese population., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background: Previous studies have demonstrated that asthma is closely associated with bronchiectasis, however, the causal relationship between asthma and bronchiectasis has not been investigated in depth. Therefore, this study aims to explore the causal relationship and to identify potential factors that mediate between these two diseases., Method: All the necessary summarized information were obtained from publicly available genome-wide association study (GWAS). Two-sample Mendelian randomization (two-sample MR) was employed to explore the causal relationship between asthma and bronchiectasis, with an additional dataset used for validation. Heterogeneity and pleiotropy analyses were utilized to verify the robustness of the results. Subsequently, mediation MR analyses were performed to identify potential mediating factors. Lastly, a retrospective observational study was conducted to validate the findings., Result: Preliminary inverse-variance weighted (IVW) results indicated there was a causal effect of asthma on bronchiectasis (odds ratio [OR] = 1.228, 95% confidence interval [CI]: 1.077-1.400, P = 0.002). Repetition validation yielded a consistent result. Mediation MR analysis demonstrated that the presence of nasal polyps (OR = 1.063, 95% CI: 1.015-1.113, mediation ratio = 30.492%, P = 0.009), acute sinusitis (OR = 1.062, 95% CI: 1.009-1.118, mediation ratio = 30.157%, P = 0.018), chronic sinusitis (OR = 1.085, 95% CI: 1.024-1.150, mediation ratio = 40.677%, P = 0.005), and peripheral eosinophil counts (OR = 1.013, 95% CI: 1.000-1.026, mediation ratio = 6.514%, P = 0.042) served as significant mediators in the occurrence and development of bronchiectasis induced by asthma. Furthermore, a retrospective observational study observed that bronchiectasis patients with asthma had a higher prevalence of sinusitis (5.043% vs 2.971%, P < 0.001), nasal polyps (0.536% vs 0.152%, P < 0.001), and rhinitis (13.197% vs 1.860%, P < 0.001). The ratio (1.950 (0.500, 5.600) vs 1.500 (0.500, 2.600), P = 0.006) and counts (0.125 (0.040, 0.363) vs 0.090 (0.030, 0.160), P < 0.001) of peripheral blood eosinophils were also elevated in bronchiectasis patients with asthma., Conclusion: The MR analysis uncovered a notable genetic association between asthma and bronchiectasis, which was partially mediated by sinusitis, nasal polyps, and eosinophils. A subsequent retrospective study provided further evidence by demonstrating that bronchiectasis patients with asthma had a higher prevalence of sinusitis, nasal polyps, an elevated proportion of eosinophils, and higher eosinophil counts., Competing Interests: Declarations. Ethics approval and consent to participate: Ethical approval for this study was obtained from the Ethics Committees of Shanghai Pulmonary Hospital, Tongji University (No. K18-167). The anonymous nature of the data allowed the requirement for informed consent from the patients to be waived. Consent for publication: Not applicable. Competing interests: All authors declare no competing interests., (© 2024. The Author(s).)

Background: DNA methylation is a critical regulatory mechanism of gene expression, influencing various human diseases and traits. While traditional expression quantitative trait loci (eQTL) studies have helped elucidate the genetic regulation of gene expression, there is a growing need to explore environmental influences on gene expression. Existing methods such as PrediXcan and FUSION focus on genotype-based associations but overlook the impact of environmental factors. To address this gap, we present MOSES (methylation-based gene association), a novel approach that utilizes DNA methylation to identify environmentally regulated genes associated with traits or diseases without relying on measured gene expression., Results: MOSES involves training, imputation, and association testing. It employs elastic-net penalized regression models to estimate the influence of CpGs and SNPs (if available) on gene expression. We developed and compared four MOSES versions incorporating different methylation and genetic data: (1) cis-DNA methylation within 1 Mb of promoter regions, (2) both cis-SNPs and cis-CpGs, 3) both cis- and a part of trans- CpGs (±5Mb away) from promoter regions), and 4) long-range DNA methylation (±10 Mb away) from promoter regions. Our analysis using nasal epithelium and white blood cell data from the Epigenetic Variation and Childhood Asthma in Puerto Ricans (EVA-PR) study demonstrated that MOSES, particularly the version incorporating long-range CpGs (MOSES-DNAm 10 M), significantly outperformed existing methods like PrediXcan, MethylXcan, and Biomethyl in predicting gene expression. MOSES-DNAm 10 M identified more differentially expressed genes (DEGs) associated with atopic asthma, particularly those involved in immune pathways, highlighting its superior performance in uncovering environmentally regulated genes. Further application of MOSES to lung tissue data from idiopathic pulmonary fibrosis (IPF) patients confirmed its robustness and versatility across different diseases and tissues., Conclusion: MOSES represents an innovative advancement in gene association studies, leveraging DNA methylation to capture the influence of environmental factors on gene expression. By incorporating long-range CpGs, MOSES-DNAm 10 M provides superior predictive accuracy and gene association capabilities compared to traditional genotype-based methods. This novel approach offers valuable insights into the complex interplay between genetics and the environment, enhancing our understanding of disease mechanisms and potentially guiding therapeutic strategies. The user-friendly MOSES R package is publicly available to advance studies in various diseases, including immune-related conditions like asthma., Competing Interests: Declarations Ethical approval and consent to participate The Epigenetic Variation and Childhood Asthma in Puerto Ricans (EVA-PR) study was approved by the institutional review boards of the University of Puerto Rico (San Juan, PR) and the University of Pittsburgh (Pittsburgh, PA). Written parental consent and participant assent were obtained for individuals younger than 18, while consent was obtained from participants 18 years and older. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here, we introduce Enformer Celltyping, a deep learning model which incorporates distal effects of DNA interactions, up to 100,000 base-pairs away, to predict epigenetic signals in previously unseen cell types. Using DNA and chromatin accessibility data for epigenetic imputation, Enformer Celltyping outperforms current best-in-class approaches and generalises across cell types and biological regions. Moreover, we propose a framework for evaluating models on genetic variant effect prediction using regulatory quantitative trait loci mapping studies, highlighting current limitations in genomic deep learning models. Despite this, Enformer Celltyping can also be used to study cell type-specific genetic enrichment of complex traits., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Background: Genotype imputation is a cost-effective method for obtaining sequence genotypes for downstream analyses such as genome-wide association studies (GWAS). However, low imputation accuracy can increase the risk of false positives, so it is important to pre-filter data or at least assess the potential limitations due to imputation accuracy. In this study, we benchmarked three different imputation programs (Beagle 5.2, Minimac4 and IMPUTE5) and compared the empirical accuracy of imputation with the software estimated accuracy of imputation (Rsq soft ). We also tested the accuracy of imputation in cattle for autosomal and X chromosomes, SNP and INDEL, when imputing from either low-density or high-density genotypes., Results: The accuracy of imputing sequence variants from real high-density genotypes was higher than from low-density genotypes. In our software benchmark, all programs performed well with only minor differences in accuracy. While there was a close relationship between empirical imputation accuracy and the imputation Rsq soft , this differed considerably for Minimac4 compared to Beagle 5.2 and IMPUTE5. We found that the Rsq soft threshold for removing poorly imputed variants must be customised according to the software and this should be accounted for when merging data from multiple studies, such as in meta-GWAS studies. We also found that imposing an Rsq soft filter has a positive impact on genomic regions with poor imputation accuracy due to large segmental duplications that are susceptible to error-prone alignment. Overall, our results showed that on average the imputation accuracy for INDEL was approximately 6% lower than SNP for all software programs. Importantly, the imputation accuracy for the non-PAR (non-Pseudo-Autosomal Region) of the X chromosome was comparable to autosomal imputation accuracy, while for the PAR it was substantially lower, particularly when starting from low-density genotypes., Conclusions: This study provides an empirically derived approach to apply customised software-specific Rsq soft thresholds for downstream analyses of imputed variants, such as needed for a meta-GWAS. The very poor empirical imputation accuracy for variants on the PAR when starting from low density genotypes demonstrates that this region should be imputed starting from a higher density of real genotypes., Competing Interests: Declarations Ethics approval and consent to participate. Not applicable. Consent for publication The authors all give their consent for publication. Competing interests The authors declare that they have no competing interests., (© 2024. The Author(s).)

Background: Systemic lupus erythematosus (SLE) is an autoimmune disease with protean manifestations. There is little understanding of why some organs are specifically impacted in patients and the mechanisms of disease persistence remain unclear. While much work has been done characterizing the DNA methylation status in SLE, there is less information on histone modifications, a more dynamic epigenetic feature. This study identifies two histone marks of activation and the binding of p300 genome-wide in three cell types and three clinical subsets to better understand cell-specific effects and differences across clinical subsets., Results: We examined 20 patients with SLE and 8 controls and found that individual chromatin marks varied considerably across T cells, B cells, and monocytes. When pathways were examined, there was far more concordance with conservation of TNF, IL-2/STAT5, and KRAS pathways across multiple cell types and ChIP data sets. Patients with cutaneous lupus and lupus nephritis generally had less dramatically altered chromatin than the general SLE group. Signals also demonstrated significant overlap with GWAS signals in a manner that did not implicate one cell type more than the others., Conclusions: The pathways identified by altered histone modifications and p300 binding are pathways known to be important from RNA expression studies and recognized pathogenic mechanisms of disease. NFκB and classical inflammatory pathways were strongly associated with increased peak heights across all cell types but were the highest-ranking pathway for all three antibodies in monocytes according to fgsea analysis. IL-6 Jak/STAT3 signaling was the most significant pathway association in T cells marked by H3K27ac change. Therefore, each cell type experiences the disease process distinctly although in all cases there was a strong theme of classical inflammatory pathways. Importantly, this NFκB pathway, so strongly implicated in the patients with generalized SLE, was much less impacted in monocytes when cutaneous lupus was compared to the general SLE cohort and also less impacted in lupus nephritis compared to general SLE. These studies define important cell type differences and emphasize the breadth of the inflammatory effects in SLE., Competing Interests: Declarations Ethical approval and consent to participate The Johns Hopkins University School of Medicine Institutional Review Board approved this study. IRB00216558. UPENN Institutional Review Board for healthy controls was 705906. Patient consent All patients consented for this study. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Meta-analysis of genome-wide association studies (GWAS) across diverse populations offers power gains to identify loci associated with complex traits and diseases. Often heterogeneity in effect sizes across populations will be correlated with genetic ancestry and environmental exposures (e.g. lifestyle factors). We present an environment-adjusted meta-regression model (env-MR-MEGA) to detect genetic associations by adjusting for and quantifying environmental and ancestral heterogeneity between populations. In simulations, env-MR-MEGA has similar or greater association power than MR-MEGA, with notable gains when the environmental factor has a greater correlation with the trait than ancestry. In our analysis of low-density lipoprotein cholesterol in ~19,000 individuals across twelve sex-stratified GWAS from Africa, adjusting for sex, BMI, and urban status, we identify additional heterogeneity beyond ancestral effects for seven variants. Env-MR-MEGA provides an approach to account for environmental effects using summary-level data, making it a useful tool for meta-analyses without the need to share individual-level data., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Background: This study aimed to identify DNA methylation biomarkers associated with myopia using summary-data-based Mendelian randomization (SMR)., Methods: A systematic search of the PubMed, Web of Science, Cochrane Library, and Embase databases was conducted up to March 27, 2024. SMR analyses were performed to integrate genome-wide association study (GWAS) with methylation quantitative trait loci (mQTL) and expression quantitative trait loci (eQTL) studies. The heterogeneity in the dependent instrument (HEIDI) test was utilized to distinguish pleiotropic associations from linkage disequilibrium., Results: The systematic review identified 26 DNA methylation biomarkers in five studies, with no overlap observed among those identified by different studies. After integrating GWAS with multi-omics data of mQTL and eQTL, six genes were significantly associated with myopia: PRMT6 (cg00944433 and cg15468180), SH3YL1 (cg03299269, cg11361895, and cg13354988), ZKSCAN4 (cg01192291), GATS (cg17830204), NPAT (cg04826772), and UBE2I (cg03545757 and cg08025960)., Conclusions: We identified six methylation biomarkers associated with the risk of myopia that may be helpful to elucidate the etiology mechanisms of myopia. Further experimental validation studies are required to corroborate these findings., Competing Interests: Declarations Ethics approval and consent to participate Given that this study represents a reevaluation of previously collected and published data, no additional ethical approval or informed consent from the subjects was needed. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

DNA methylation is a molecular process that mediates gene-environment interactions. Epigenome-wide association studies (EWAS) using the Illumina Human Methylation BeadChip are powerful tools for quantifying the relationship between DNA methylation and phenotypes. Recently, the Illumina Methylation EPICv2 BeadChip (EPICv2) was released, which includes new features, such as duplicated probes and changed probe names. Several published algorithms have been updated to address these features in EPICv2. However, appropriate EPICv2 preprocessing and integration with previous microarray versions remain complex. Therefore, MethylCallR, an open-source R package designed to provide standard procedures for performing EWAS using Illumina methylation microarrays including EPICv2, was developed. MethylCallR can be used to control duplicated probes in EPICv2, by using pre-set data implemented in MethylCallR or new customized data. MethylCallR includes a straightforward conversion function between different types of Illumina Human Methylation BeadChips. Using MethylCallR, potential outlier sample detection and statistical power estimation were conducted and used to select meaningful probes. Publicly available data was analyzed using MethylCallR and the findings were compared to that of a previous study., (© 2024. The Author(s).)

Balancing the tradeoff between quantity and quality of phenotypic data is critical in omics studies. Measurements below the limit of quantification (BLQ) are often tagged in quality control fields, but these flags are currently underutilized in human genetics studies. Extreme phenotype sampling is advantageous for mapping rare variant effects. We hypothesize that genetic drivers, along with environmental and technical factors, contribute to the presence of BLQ flags. Here, we introduce "hypometric genetics" (hMG) analysis and uncover a genetic basis for BLQ flags, indicating an additional source of genetic signal for genetic discovery, especially from phenotypic extremes. Applying our hMG approach to n = 227,469 UK Biobank individuals with metabolomic profiles, we reveal more than 5% heritability for BLQ flags and report biologically relevant associations, for example, at APOC3, APOA5, and PDE3B loci. For common variants, polygenic scores trained only for BLQ flags predict the corresponding quantitative traits with 91% accuracy, validating the genetic basis. For rare coding variant associations, we find an asymmetric 65.4% higher enrichment of metabolite-lowering associations for BLQ flags, highlighting the impact of putative loss-of-function variants with large effects on phenotypic extremes. Joint analysis of binarized BLQ flags and the corresponding quantitative metabolite measurements improves power in Bayesian rare variant aggregation tests, resulting in an average of 181% more prioritized genes. Our approach is broadly applicable to omics profiling. Overall, our results underscore the benefit of integrating quality control flags and quantitative measurements and highlight the advantage of joint analysis of population-based samples and phenotypic extremes in human genetics studies., Competing Interests: Declaration of interests Massachusetts Institute of Technology filed patent applications based on the findings and regarding the inclusive polygenic score approach used in the study. Y.T. and M.K. are designated as inventors of the applications., (Copyright © 2024 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

The expansive collection of genetic and phenotypic data within biobanks offers an unprecedented opportunity for biomedical research. However, the frequent occurrence of missing phenotypes presents a significant barrier to fully leveraging this potential. In our target application, on one hand, we have only a small and complete dataset with both genotypes and phenotypes to build a genetic prediction model, commonly called a polygenic (risk) score (PGS or PRS); on the other hand, we have a large dataset of genotypes (e.g. from a biobank) without the phenotype of interest. Our goal is to leverage the large dataset of genotypes (but without the phenotype) and a separate genome-wide association studies summary dataset of the phenotype to impute the phenotypes, which are then used as an individual-level dataset, along with the small complete dataset, to build a nonlinear model as PGS. More specifically, we trained some nonlinear models to 7 imputed and observed phenotypes from the UK Biobank data. We then trained an ensemble model to integrate these models for each trait, resulting in higher R2 values in prediction than using only the small complete (observed) dataset. Additionally, for 2 of the 7 traits, we observed that the nonlinear model trained with the imputed traits had higher R2 than using the imputed traits directly as the PGS, while for the remaining 5 traits, no improvement was found. These findings demonstrate the potential of leveraging existing genetic data and accounting for nonlinear genetic relationships to improve prediction accuracy for some traits., Competing Interests: Conflicts of interest The author(s) declare no conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, genomic markers can now be allocated to large sections of a genome. By analyzing allele sharing between individuals, one may calculate realized genomic relationships from single-nucleotide polymorphisms (SNPs) data rather than relying on known pedigree relationships under polygenic model. The traditional approaches in genome-wide prediction (GWP) of quantitative phenotypes utilize genomic relationships in fixed global covariance modeling, possibly with some nonlinear kernel mapping (for example Gaussian processes). On the other hand, the DL approaches proposed so far for GWP fail to take into account the non-Euclidean graph structure of relationships between individuals over several generations. In this paper, we propose one global convolutional neural network (GCN) and one local sub-sampling architecture (GCN-RS) that are specifically designed to perform regression analysis based on genomic relationship information. A GCN is tailored to non-Euclidean spaces and consists of several layers of graph convolutions. The GCN-RS architecture is designed to further improve the GCN's performance by sub-sampling the graph to reduce the dimensionality of the input data. Through these graph convolutional layers, the GCN maps input genomic markers to their quantitative phenotype values. The graphs are constructed using an iterative nearest neighbor approach. Comparisons show that the GCN-RS outperforms the popular Genomic Best Linear Unbiased Predictor method on one simulated and three real datasets from wheat, mice and pig with a predictive improvement of 4.4% to 49.4% in terms of test mean squared error. This indicates that GCN-RS is a promising tool for genomic predictions in plants and animals. Furthermore, GCN-RS is computationally efficient, making it a viable option for large-scale applications., Competing Interests: Conflicts of interest The author(s) declare no conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. We developed a network medicine methodology via integrating human brain multi-omics data to prioritize drug targets and repurposable treatments for ALS. We leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on human brain expression quantitative trait loci (QTL) (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Using a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in known ALS pathobiological pathways. Applying network proximity analysis of predicted ALS-associated genes and drug-target networks under the human protein-protein interactome (PPI) model, we identified potential repurposable drugs (i.e., Diazoxide and Gefitinib) for ALS. Subsequent validation established preclinical evidence for top-prioritized drugs. In summary, we presented a network-based multi-omics framework to identify drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied., (© 2024. The Author(s).)

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gholipourshahraki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Inflammation underlies many conditions causing excess morbidity and mortality among people with HIV (PWH). A handful of single-trait epigenome-wide association studies (EWAS) have suggested that inflammation is associated with DNA methylation (DNAm) among PWH. Multi-trait EWAS may further improve statistical power and reveal pathways in common between different inflammatory markers. We conducted single-trait EWAS of three inflammatory markers (soluble CD14, D-dimers and interleukin-6) in the Veterans Aging Cohort Study (n = 920). The study population was all male PWH with an average age of 51 years, and 82.3% self-reported as Black. We then applied two multi-trait EWAS methods-CPASSOC and OmniTest-to combine single-trait EWAS results. CPASSOC and OmniTest identified 189 and 157 inflammation-associated DNAm sites, respectively, of which 112 overlapped. Among the identified sites, 56% were not significant in any single-trait EWAS. Top sites were mapped to inflammation-related genes including IFITM1, PARP9 and STAT1. These genes were significantly enriched in pathways such as "type I interferon signaling" and "immune response to virus." We demonstrate that multi-trait EWAS can improve the discovery of inflammation-associated DNAm sites, genes and pathways. These DNAm sites might hold the key to addressing persistent inflammation in PWH., (© 2024. The Author(s).)

Background: Asthma, a complex respiratory disease, presents with inflammatory symptoms in the lungs, blood, and other tissues. We investigated the relationship between DNA methylation and 35 clinical markers of asthma., Methods: The Illumina Infinium EPIC v1 methylation array was used to evaluate 742,442 CpGs in whole blood from 319 participants from 94 families. They were part of the Netherlands Twin Register from families with at least one member suffering from severe asthma. Repeat blood samples were taken after 10 years from 182 individuals. Principal component analysis on the clinical asthma markers yielded ten principal components (PCs) that explained 92.8% of the total variance. We performed epigenome-wide association studies (EWAS) for each of the ten PCs correcting for familial structure and other covariates., Results: 221 unique CpGs reached genome-wide significance at timepoint 1 after Bonferroni correction. PC7, which correlated with loadings of eosinophil counts and immunoglobulin levels, accounted for the majority of associations (204). Enrichment analysis via the EWAS Atlas identified 190 of these CpGs to be previously identified in EWASs of asthma and asthma-related traits. Proximity assessment to previously identified SNPs associated with asthma identified 17 unique SNPs within 1 MB of two of the 221 CpGs. EWAS in 182 individuals with epigenetic data at a second timepoint identified 49 significant CpGs. EWAS Atlas enrichment analysis indicated that 4 of the 49 were previously associated with asthma or asthma-related traits. Comparing the estimates of all the significant associations identified across the two time points yielded a correlation of 0.81., Conclusion: We identified 270 unique CpGs that were associated with PC scores generated from 35 clinical markers of asthma, either cross-sectionally or 10 years later. A strong correlation was present between effect sizes at the 2 timepoints. Most associations were identified for PC7, which captured blood eosinophil counts and immunoglobulin levels and many of these CpGs have previous associations in earlier studies of asthma and asthma-related traits. The results point to a robust DNA methylation profile as a new, stable biomarker for asthma., (© 2024. The Author(s).)

Summary: Mendelian randomization is being utilized to assess causal effects of polygenic exposures, where many genetic instruments are subject to horizontal pleiotropy. Existing methods for detecting and correcting for horizontal pleiotropy have important assumptions that may not be fulfilled. Built upon the core gene hypothesis, we developed MR Corge for performing sensitivity analysis of Mendelian randomization. MR Corge identifies a small number of putative core instruments that are more likely to affect genes with a direct biological role in an exposure and obtains causal effect estimates based on these instruments, thereby reducing the risk of horizontal pleiotropy. Using positive and negative controls, we demonstrated that MR Corge estimates aligned with established biomedical knowledge and the results of randomized controlled trials. MR Corge may be widely applied to investigate polygenic exposure-outcome relationships., Availability and Implementation: An open-sourced R package is available at https://github.com/zhwm/MRCorge., (© The Author(s) 2024. Published by Oxford University Press.)

Polygenic risk scores (PRS) have emerged as a promising tool for predicting disease risk and treatment outcomes using genomic data. Thousands of genome-wide association studies (GWAS), primarily involving populations of European ancestry, have supported the development of PRS models. However, these models have not been adequately evaluated in non-European populations, raising concerns about their clinical validity and predictive power across diverse groups. Addressing this issue requires developing novel risk prediction frameworks that leverage genetic characteristics across diverse populations, considering host-microbiome interactions and a broad range of health measures. One of the key aspects in evaluating PRS is understanding the strengths and limitations of various methods for constructing them. In this review, we analyze strengths and limitations of different methods for constructing PRS, including traditional weighted approaches and new methods such as Bayesian and Frequentist penalized regression approaches. Finally, we summarize recent advances in PRS calculation methods development, and highlight key areas for future research, including development of models robust across diverse populations by underlining the complex interplay between genetic variants across diverse ancestral backgrounds in disease risk as well as treatment response prediction. PRS hold great promise for improving disease risk prediction and personalized medicine; therefore, their implementation must be guided by careful consideration of their limitations, biases, and ethical implications to ensure that they are used in a fair, equitable, and responsible manner., (© 2024. The Author(s).)

Linkage studies have successfully mapped loci underlying monogenic disorders, but mostly failed when applied to common diseases. Conversely, genome-wide association studies (GWASs) have identified replicable associations between thousands of SNPs and complex traits, yet capture less than half of the total heritability. In the present study we reconcile these two approaches by showing that linkage signals of height and body mass index (BMI) from 119,000 sibling pairs colocalize with GWAS-identified loci. Concordant with polygenicity, we observed the following: a genome-wide inflation of linkage test statistics; that GWAS results predict linkage signals; and that adjusting phenotypes for polygenic scores reduces linkage signals. Finally, we developed a method using recombination rate-stratified, identity-by-descent sharing between siblings to unbiasedly estimate heritability of height (0.76 ± 0.05) and BMI (0.55 ± 0.07). Our results imply that substantial heritability remains unaccounted for by GWAS-identified loci and this residual genetic variation is polygenic and enriched near these loci., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS), which uses sophisticated ML techniques to impute phenotypes and then performs GWAS on the imputed outcomes, have become increasingly common in complex trait genetics research. However, the validity of ML-assisted GWAS associations has not been carefully evaluated. Here, we report pervasive risks for false-positive associations in ML-assisted GWAS and introduce Post-Prediction GWAS (POP-GWAS), a statistical framework that redesigns GWAS on ML-imputed outcomes. POP-GWAS ensures valid and powerful statistical inference irrespective of imputation quality and choice of algorithm, requiring only GWAS summary statistics as input. We employed POP-GWAS to perform a GWAS of bone mineral density derived from dual-energy X-ray absorptiometry imaging at 14 skeletal sites, identifying 89 new loci and revealing skeletal site-specific genetic architecture. Our framework offers a robust analytic solution for future ML-assisted GWAS., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Whole-genome sequencing (WGS), whole-exome sequencing (WES) and array genotyping with imputation (IMP) are common strategies for assessing genetic variation and its association with medically relevant phenotypes. To date, there has been no systematic empirical assessment of the yield of these approaches when applied to hundreds of thousands of samples to enable the discovery of complex trait genetic signals. Using data for 100 complex traits from 149,195 individuals in the UK Biobank, we systematically compare the relative yield of these strategies in genetic association studies. We find that WGS and WES combined with arrays and imputation (WES + IMP) have the largest association yield. Although WGS results in an approximately fivefold increase in the total number of assayed variants over WES + IMP, the number of detected signals differed by only 1% for both single-variant and gene-based association analyses. Given that WES + IMP typically results in savings of lab and computational time and resources expended per sample, we evaluate the potential benefits of applying WES + IMP to larger samples. When we extend our WES + IMP analyses to 468,169 UK Biobank individuals, we observe an approximately fourfold increase in association signals with the threefold increase in sample size. We conclude that prioritizing WES + IMP and large sample sizes rather than contemporary short-read WGS alternatives will maximize the number of discoveries in genetic association studies., (© 2024. The Author(s).)

Background: Commercial genome-wide genotyping arrays have historically neglected coverage of genetic variation across populations., Objective: We aimed to create a multi-ancestry genome-wide array that would include a wide range of neuro-specific genetic content to facilitate genetic research in neurological disorders across multiple ancestral groups, fostering diversity and inclusivity in research studies., Methods: We developed the Illumina NeuroBooster Array (NBA), a custom high-throughput and cost-effective platform on a backbone of 1,914,934 variants from the Infinium Global Diversity Array and added custom content comprising 95,273 variants associated with more than 70 neurological conditions or traits, and we further tested its performance on more than 2000 patient samples. This novel platform includes approximately 10,000 tagging variants to facilitate imputation and analyses of neurodegenerative disease-related genome-wide association study loci across diverse populations., Results: In this article, we describe NBA's potential as an efficient means for researchers to assess known and novel disease genetic associations in a multi-ancestry framework. The NBA can identify rare genetic variants and accurately impute more than 15 million common variants across populations. Apart from enabling sample prioritization for further whole-genome sequencing studies, we envisage that NBA will play a pivotal role in recruitment for interventional studies in the precision medicine space., Conclusions: From a broader perspective, the NBA serves as a promising means to foster collaborative research endeavors in the field of neurological disorders worldwide. Ultimately, this carefully designed tool is poised to make a substantial contribution to uncovering the genetic etiology underlying these debilitating conditions. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA., (© 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Identifying associations between phenotype and genotype is the fundamental basis of genetic analyses. Inspired by frequentist probability and the work of R. A. Fisher, genome-wide association studies (GWAS) extract information using averages and variances from genotype-phenotype datasets. Averages and variances are legitimated upon creating distribution density functions obtained through the grouping of data into categories. However, as data from within a given category cannot be differentiated, the investigative power of such methodologies is limited. Genomic informational field theory (GIFT) is a method specifically designed to circumvent this issue. The way GIFT proceeds is opposite to that of GWAS. Although GWAS determines the extent to which genes are involved in phenotype formation (bottom-up approach), GIFT determines the degree to which the phenotype can select microstates (genes) for its subsistence (top-down approach). Doing so requires dealing with new genetic concepts, a.k.a. genetic paths, upon which significance levels for genotype-phenotype associations can be determined. By using different datasets obtained in Ovis aries related to bone growth ( dataset 1 ) and to a series of linked metabolic and epigenetic pathways ( dataset 2 ), we demonstrate that removing the informational barrier linked to categories enhances the investigative and discriminative powers of GIFT, namely that GIFT extracts more information than GWAS. We conclude by suggesting that GIFT is an adequate tool to study how phenotypic plasticity and genetic assimilation are linked. NEW & NOTEWORTHY The genetic basis of complex traits remains challenging to investigate using classic genome-wide association studies (GWASs). Given the success of gene editing technologies, this point needs to be addressed urgently since there can only be useful editing technologies whether precise genotype-phenotype mapping information is available initially. Genomic informational field theory (GIFT) is a new mapping method designed to increase the investigative power of biological/medical datasets suggesting, in turn, the need to rethink the conceptual bases of quantitative genetics.

Background: Observational studies have suggested a correlation between hyperuricemia and pulmonary arterial hypertension (PAH), yet the causal relationship remains uncertain. We aimed to establish this link using Mendelian Randomization (MR) methods., Objectives: Based on publicly accessible data, our study employs MR to determine the causal relationship between uric acid (UA) and PAH., Method: MR analysis was conducted among individuals of European descent. Genetic instruments linked to UA (p-value < 5 × 10 -8 ) were extracted from the Chronic Kidney Disease Genetic Consortium and genome-wide association study databases. PAH risk genetic associations were sourced separately. We employed four MR methods (MR-Egger, weighted median, inverse variance weighted, and weighted mode) with selected instrumental variables to assess the causal association between UA and PAH. MR-PRESSO was used to evaluate pleiotropy and outlier Single Nucleotide Polymorphisms (SNPs), while Cochran's Q test and funnel plot assessed SNP heterogeneity. Leave-one-out analysis examined SNP impacts on causal assessment., Result: Two-sample MR analysis revealed a positive, causal relationship between UA levels and PAH., Conclusion: Our MR analysis provides robust evidence of a causal link between serum UA and PAH, suggesting UA's potential as a biomarker and therapeutic target for PAH., Competing Interests: Declaration of competing interest The authors declare that there are no financial interests or personal relationships that could have appeared to influence the work reported in this paper. All authors have contributed to this research and concur with the content of the manuscript., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Age-related macular degeneration (AMD) is a complex eye disorder affecting millions worldwide. This article uses deep learning techniques to investigate the relationship between AMD, genetics and optical coherence tomography (OCT) scans., Methods: The cohort consisted of 332 patients, of which 235 were diagnosed with AMD and 97 were controls with no signs of AMD. The genome-wide association studies summary statistics utilized to establish the polygenic risk score (PRS) in relation to AMD were derived from the GERA European study. A PRS estimation based on OCT volumes for both eyes was performed using a proprietary convolutional neural network (CNN) model supported by machine learning models. The method's performance was assessed using numerical evaluation metrics, and the Grad-CAM technique was used to evaluate the results by visualizing the features learned by the model., Results: The best results were obtained with the CNN and the Extra Tree regressor (MAE = 0.55, MSE = 0.49, RMSE = 0.70, R 2  = 0.34). Extending the feature vector with additional information on AMD diagnosis, age and smoking history improved the results slightly, with mainly AMD diagnosis used by the model (MAE = 0.54, MSE = 0.44, RMSE = 0.66, R 2  = 0.42). Grad-CAM heatmap evaluation showed that the model decisions rely on retinal morphology factors relevant to AMD diagnosis., Conclusion: The developed method allows an efficient PRS estimation from OCT images. A new technique for analysing the association of OCT images with PRS of AMD, using a deep learning approach, may provide an opportunity to discover new associations between genotype-based AMD risk and retinal morphology., (© 2024 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.)

Despite numerous studies demonstrate that genetics and epigenetics factors play important roles on smoking behavior, our understanding of their functional relevance and coordinated regulation remains largely unknown. Here we present a multiomics study on smoking behavior for Chinese smoker population with the goal of not only identifying smoking-associated functional variants but also deciphering the pathogenesis and mechanism underlying smoking behavior in this under-studied ethnic population. After whole-genome sequencing analysis of 1329 Chinese Han male samples in discovery phase and OpenArray analysis of 3744 samples in replication phase, we discovered that three novel variants located near FOXP1 (rs7635815), and between DGCR6 and PRODH (rs796774020), and in ARVCF (rs148582811) were significantly associated with smoking behavior. Subsequently cis-mQTL and cis-eQTL analysis indicated that these variants correlated significantly with the differential methylation regions (DMRs) or differential expressed genes (DEGs) located in the regions where these variants present. Finally, our in silico multiomics analysis revealed several hub genes, like DRD2, PTPRD, FOXP1, COMT, CTNNAP2, to be synergistic regulated each other in the etiology of smoking., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes., (© 2024. The Author(s).)

Background: A key component of idiopathic pulmonary fibrosis (IPF) is the involvement of immune cells. However, the causal interaction between different immune cell signatures and IPF remain inconclusive., Objectives: Based on publicly accessible data, our study utilized the Mendelian randomization (MR) approach to determine the causative relevance of complex immune cell phenotypes in IPF., Methods: We deployed a two-sample Mendelian randomization approach to evaluate the causal interaction between immune cell markers and IPF. All data regarding 731 immune signatures and IPF were acquired from two genome-wide association studies (GWAS) that are accessible to the public. The original study adopted the inverse variance weighted (IVW) method, followed by sensitivity analyses aimed at eliminating heterogeneity and pleiotropy. Additionally, Multivariate Mendelian randomization (MVMR) was utilized to identify the independent risk factors in our study., Results: The summary dataset for IPF was accessed from the Finnish Genetic Consortium R9, comprising 2018 patients and 373,064 controls. And the dataset for immune signatures was conducted in 3,757 Sardinian individuals. By conducting IVW and extensive sensitivity analyses, univariate Mendelian randomization (UVMR) identified one immunophenotype that remained causally associated with IPF after false discovery rate (FDR) correction: CD39 on CD39 + CD8 + T cells (odd ratio [OR] = 0.850, 95 % confidence interval [CI] = 0.787-0.918, P = 3.68 × 10 -5 ). The causal association with IPF was further validated using MVMR., Conclusions: Based on rigorous MR analysis methods and FDR correction, our study demonstrated that CD39 on CD39 + CD8 + T cells showed a protective effect against IPF, providing effective insights for preventing and diagnosing pulmonary fibrosis., Competing Interests: Declaration of competing interest The authors assert that they do not have any conflicting interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

An important issue in the analysis of rare variant association studies is the ability to annotate nonsynonymous variants in terms of their likely importance as affecting protein function. To address this, AlphaMissense was recently released and was shown to have good performance using benchmarks based on variants causing severe disease and on functional assays. Here, we assess the performance of AlphaMissense across 18 genes which had previously demonstrated association between rare coding variants and hyperlipidaemia, hypertension or type 2 diabetes. The strength of evidence in favour of association, expressed as the signed log p value (SLP), was compared between AlphaMissense and 43 other annotation methods. The results demonstrated marked variability between genes regarding the extent to which nonsynonymous variants contributed to evidence for association and also between the performance of different methods of annotating the nonsynonymous variants. Although AlphaMissense produced the highest SLP on average across genes, it produced the maximum SLP for only 4 genes. For some genes, other methods produced a considerably higher SLP and there were examples of genes where AlphaMissense produced no evidence for association while another method performed well. The marked inconsistency across genes means that it is difficult to decide on an optimal method of analysis of sequence data. The fact that different methods perform well for different genes suggests that if one wished to use sequence data for individual risk prediction then gene-specific annotation methods should be used., Competing Interests: Competing interests The author declares no competing interests. Ethical approval UK Biobank had obtained ethics approval from the North West Multi-centre Research Ethics Committee which covers the UK (approval number: 11/NW/0382) and had obtained written informed consent from all participants. The UK Biobank approved an application for use of the data (ID 51119) and ethics approval for the analyses was obtained from the UCL Research Ethics Committee (11527/001)., (© 2024. The Author(s).)

Transcriptome-wide association studies (TWAS) have provided valuable insight in identifying genes that may impact cigarette smoking. Most of previous studies, however, mainly focused on European ancestry. Limited TWAS studies have been conducted across multiple ancestries to explore genes that may impact smoking behaviors. In this study, we used cis-eQTL data of cerebral cortex from multiple ancestries in MetaBrain, including European, East Asian, and African samples, as reference panels to perform multi-ancestry TWAS analyses on ancestry-matched GWASs of four smoking behaviors including smoking initiation, smoking cessation, age of smoking initiation, and number of cigarettes per day in GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN). Multiple-ancestry fine-mapping approach was conducted to identify credible gene sets associated with these four traits. Enrichment and module network analyses were further performed to explore the potential roles of these identified gene sets. A total of 719 unique genes were identified to be associated with at least one of the four smoking traits across ancestries. Among those, 249 genes were further prioritized as putative causal genes in multiple ancestry-based fine-mapping approach. Several well-known smoking-related genes, including PSMA4, IREB2, and CHRNA3, showed high confidence across ancestries. Some novel genes, e.g., TSPAN3 and ANK2, were also identified in the credible sets. The enrichment analysis identified a series of critical pathways related to smoking such as synaptic transmission and glutamate receptor activity. Leveraging the power of the latest multi-ancestry GWAS and eQTL data sources, this study revealed hundreds of genes and relevant biological processes related to smoking behaviors. These findings provide new insights for future functional studies on smoking behaviors., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders., (© 2024. The Author(s).)

A hallmark of fetal alcohol spectrum disorders (FASD) is neurobehavioral deficits that still do not have effective treatment. Here, we present that reduction of Apolipoprotein E (APOE) is critically involved in neurobehavioral deficits in FASD. We show that prenatal alcohol exposure (PAE) changes chromatin accessibility of Apoe locus, and causes reduction of APOE levels in both the brain and peripheral blood in postnatal mice. Of note, postnatal administration of an APOE receptor agonist (APOE-RA) mitigates motor learning deficits and anxiety in those mice. Several molecular and electrophysiological properties essential for learning, which are altered by PAE, are restored by APOE-RA. Our human genome-wide association study further reveals that the interaction of PAE and a single nucleotide polymorphism in the APOE enhancer which chromatin is closed by PAE in mice is associated with lower scores in the delayed matching-to-sample task in children. APOE in the plasma is also reduced in PAE children, and the reduced level is associated with their lower cognitive performance. These findings suggest that controlling the APOE level can serve as an effective treatment for neurobehavioral deficits in FASD., (© 2024. The Author(s).)

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Genetic risk loci for white matter hyperintensities (WMH), the most common MRI-marker of cSVD in older age, were recently shown to be significantly associated with white matter (WM) microstructure on diffusion tensor imaging (signal-based) in young adults. To provide new insights into these early changes in WM microstructure and their relation with cSVD, we sought to explore the genetic underpinnings of cutting-edge tissue-based diffusion imaging markers across the adult lifespan. We conducted a genome-wide association study of neurite orientation dispersion and density imaging (NODDI) markers in young adults (i-Share study: N = 1 758, (mean[range]) 22.1[18-35] years), with follow-up in young middle-aged (Rhineland Study: N = 714, 35.2[30-40] years) and late middle-aged to older individuals (UK Biobank: N = 33 224, 64.3[45-82] years). We identified 21 loci associated with NODDI markers across brain regions in young adults. The most robust association, replicated in both follow-up cohorts, was with Neurite Density Index (NDI) at chr5q14.3, a known WMH locus in VCAN. Two additional loci were replicated in UK Biobank, at chr17q21.2 with NDI, and chr19q13.12 with Orientation Dispersion Index (ODI). Transcriptome-wide association studies showed associations of STAT3 expression in arterial and adipose tissue (chr17q21.2) with NDI, and of several genes at chr19q13.12 with ODI. Genetic susceptibility to larger WMH volume, but not to vascular risk factors, was significantly associated with decreased NDI in young adults, especially in regions known to harbor WMH in older age. Individually, seven of 25 known WMH risk loci were associated with NDI in young adults. In conclusion, we identified multiple novel genetic risk loci associated with NODDI markers, particularly NDI, in early adulthood. These point to possible early-life mechanisms underlying cSVD and to processes involving remyelination, neurodevelopment and neurodegeneration, with a potential for novel approaches to prevention., (© 2024. The Author(s).)

Social isolation has been linked to a range of psychiatric issues, but the behavioral component that drives it is not well understood. Here, a genome-wide associations study (GWAS) was carried out to identify genetic variants that contribute specifically to social isolation behavior (SIB) in up to 449,609 participants from the UK Biobank. 17 loci were identified at genome-wide significance, contributing to a 4% SNP-based heritability estimate. Using the SIB GWAS, polygenic risk scores (PRS) were derived in ALSPAC, an independent, developmental cohort, and used to test for association with self-reported friendship scores, comprising items related to friendship quality and quantity, at age 12 and 18 to determine whether genetic predisposition manifests during childhood development. At age 18, friendship scores were associated with the SIB PRS, demonstrating that the genetic factors can predict related social traits in late adolescence. Linkage disequilibrium (LD) score correlation using the SIB GWAS demonstrated genetic correlations with autism spectrum disorder (ASD), schizophrenia, major depressive disorder (MDD), educational attainment, extraversion, and loneliness. However, no evidence of causality was found using a conservative Mendelian randomization approach between SIB and any of the traits in either direction. Genomic Structural Equation Modeling (SEM) revealed a common factor contributing to SIB, neuroticism, loneliness, MDD, and ASD, weakly correlated with a second common factor that contributes to psychiatric and psychotic traits. Our results show that SIB contributes a small heritable component, which is associated genetically with other social traits such as friendship as well as psychiatric disorders., (© 2024. Crown.)

Purpose: To investigate the number of rare missense variants observed in human genome sequences by ACMG/AMP PP3/BP4 evidence strength, following the ClinGen-calibrated PP3/BP4 computational recommendations., Methods: Missense variants from the genome sequences of 300 probands from the Rare Genomes Project with suspected rare disease were analyzed using computational prediction tools that were able to reach PP3&#95;Strong and BP4&#95;Moderate evidence strengths (BayesDel, MutPred2, REVEL, and VEST4). The numbers of variants at each evidence strength were analyzed across disease-associated genes and genome-wide., Results: From a median of 75.5 rare (≤1% allele frequency) missense variants in disease-associated genes per proband, a median of one reached PP3&#95;Strong, 3-5 PP3&#95;Moderate, and 3-5 PP3&#95;Supporting. Most were allocated BP4 evidence (median 41-49 per proband) or were indeterminate (median 17.5-19 per proband). Extending the analysis to all protein-coding genes genome-wide, the number of variants reaching PP3&#95;Strong score thresholds increased approximately 2.6-fold compared with disease-associated genes, with a median per proband of 1-3 PP3&#95;Strong, 8-16 PP3&#95;Moderate, and 10-17 PP3&#95;Supporting., Conclusion: A small number of variants per proband reached PP3&#95;Strong and PP3&#95;Moderate in 3424 disease-associated genes. Although not the intended use of the recommendations, this was also observed genome-wide. Use of PP3/BP4 evidence as recommended from calibrated computational prediction tools in the clinical diagnostic laboratory is unlikely to inappropriately contribute to the classification of an excessive number of variants as pathogenic or likely pathogenic by ACMG/AMP rules., Competing Interests: Conflict of Interest Leslie G. Biesecker receives royalties from Wolters-Kluwer for authorship of UpToDate, is a member of the Illumina Medical Ethics Committee, and receives research support from Merck, Inc. Anne O’Donnell-Luria oversees the Rare Genomes Project, which received research funding from Illumina Inc. Steven M. Harrison is an employee of Ambry Genetics. Vikas Pejaver and Predrag Radivojac participated in the development of some of the tools assessed in this study. Steven E. Brenner receives research support at UC Berkeley from Tata Consultancy Services. All other authors declare no conflicts of interest., (Copyright © 2024 American College of Medical Genetics and Genomics. All rights reserved.)

Background: Aging is an irreversible progressive decline in physical function. Graves' disease (GD) is a common cause of hyperthyroidism and is characterized by elevated levels of the thyroid hormone (TH). High TH levels are associated with aging and a shortened lifespan. The causal relationship between GD and aging has yet to be investigated., Methods: We used genome-wide association study (GWAS) datasets and Mendelian randomization (MR) analysis to explore the causal link between GD and aging. To assess the statistical power of instrumental variables (IVs), F-statistics and R 2 were used. MR analysis was conducted using inverse-variance weighting (IVW), MR-Egger, weighted median, and weighted mode. The odds ratio (OR) and 95% CI were calculated to estimate the relative risk of GD to the outcomes. The Cochran Q test, I 2 , MR-PRESSO test, and MR-Egger regression intercept were calculated using statistical and leave-one-out analyses to test the heterogeneity, horizontal pleiotropy, and stability of the IVs on the outcomes., Results: F-statistics of the five IVs were greater than 10, and the R 2 values ranged from 0.033 to 0.156 (R 2  > 0.01). According to the results of the IVW analysis, GD had no causal effect on facial aging (p = 0.189), age-related macular degeneration (p = 0.346), and Alzheimer's disease (p = 0.479). There was a causal effect of GD on the remaining outcomes: telomere length (TL) (OR = 0.982; 95%CI:0.969-0.994; p = 0.004), senile cataract (OR = 1.031; 95%CI:1.002-1.060; p = 0.033), age-related hearing impairment (OR = 1.009; 95%CI:1.004-1.014; p = 0.001), chronic obstructive pulmonary disease (COPD) (OR = 1.055; 95%CI:1.008-1.103; p = 0.020), and sarcopenia (OR = 1.027; 95%CI:1.009-1.046; p = 0.004)., Conclusions: GD accelerates the occurrence of age-related phenotypes including TL, senile cataracts, age-related hearing impairment, COPD, and sarcopenia. In contrast, there are no causal linkages between GD and facial aging, age-related macular degeneration, or Alzheimer's disease. Further experimental studies could be conducted to elucidate the mechanisms by which GD facilitates aging, which could help slow down the progress of aging., (© 2024. The Author(s).)

Classical gene-by-environment interaction (GxE) analysis can be used to characterize genetic effect heterogeneity but has a high multiple testing burden in the context of genome-wide association studies (GWAS). We adapt a colocalization method, SharePro, to account for effect heterogeneity in fine-mapping and identify candidates for GxE analysis with reduced multiple testing burden. SharePro demonstrates improved power for both fine-mapping and GxE analysis compared to existing methods as well as well-controlled false type I error in simulations. Using smoking status stratified GWAS summary statistics, we identify genetic effects on lung function modulated by smoking status that are not identified by existing methods. Additionally, using sex stratified GWAS summary statistics, we characterize sex differentiated genetic effects on fat distribution. In summary, we have developed an analytical framework to account for effect heterogeneity in fine-mapping and subsequently improve power for GxE analysis. The SharePro software for GxE analysis is openly available at https://github.com/zhwm/SharePro&#95;gxe ., (© 2024. The Author(s).)

Background: Efficient management of environmental stresses is essential for sustainable crop production. Calcium (Ca²⁺) signaling plays a crucial role in regulating responses to both biotic and abiotic stresses, particularly during host-pathogen interactions. In Arabidopsis thaliana, calmodulin-binding protein 60 (CBP60) family members, such as AtCBP60g, AtCBP60a, and AtSARD1, have been well characterized for their involvement in immune regulation. However, a comprehensive understanding of CBP60 genes in major crops remains limited., Methods: In this study, we utilized the Phytozome v12.1 database to identify and analyze CBP60 genes in agriculturally important crops. Expression patterns of a Oryza sativa (rice) CBP60 gene, OsCBP60bcd-1, were assessed in resistant and susceptible rice genotypes in response to infection by the bacterial pathogen Xanthomonas oryzae. Localization of CBP60 proteins was analyzed to predict their functional roles, and computational promoter analysis was performed to identify stress-responsive cis-regulatory elements., Results: Phylogenetic analysis revealed that most CBP60 genes in crops belong to the immune-related clade. Expression analysis showed that OsCBP60bcd-1 was significantly upregulated in the resistant rice genotype upon pathogen infection. Subcellular localization studies suggested that the majority of CBP60 proteins are nuclear-localized, indicating a potential role as transcription factors. Promoter analysis identified diverse stress-responsive cis-regulatory elements in the promoters of CBP60 genes, highlighting their regulatory potential under stress conditions., Conclusion: The upregulation of OsCBP60bcd-1 in response to Xanthomonas oryzae and the presence of stress-responsive elements in its promoter underscore the importance of CBP60 genes in pathogen defense. These findings provide a basis for further investigation into the functional roles of CBP60 genes in crop disease resistance, with implications for enhancing stress resilience in agricultural species., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the "quantitative omnigenic model" (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans- , break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of nontranscriptional (including environmental) effects., Competing Interests: Competing interests statement:The authors declare no competing interest.

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies., Competing Interests: Competing interests statement:The authors declare no competing interest.

PWAS (proteome-wide association study) is an innovative genetic association approach that complements widely used methods like GWAS (genome-wide association study). The PWAS approach involves consecutive phases. Initially, machine learning modeling and probabilistic considerations quantify the impact of genetic variants on protein-coding genes' biochemical functions. Secondly, for each individual, aggregating the variants per gene determines a gene-damaging score. Finally, standard statistical tests are activated in the case-control setting to yield statistically significant genes per phenotype. The PWAS Hub offers a user-friendly interface for an in-depth exploration of gene-disease associations from the UK Biobank (UKB). Results from PWAS cover 99 common diseases and conditions, each with over 10,000 diagnosed individuals per phenotype. Users can explore genes associated with these diseases, with separate analyses conducted for males and females. For each phenotype, the analyses account for sex-based genetic effects, inheritance modes (dominant and recessive), and the pleiotropic nature of associated genes. The PWAS Hub showcases its usefulness for asthma by navigating through proteomic-genetic analyses. Inspecting PWAS asthma-listed genes (a total of 27) provide insights into the underlying cellular and molecular mechanisms. Comparison of PWAS-statistically significant genes for common diseases to the Open Targets benchmark shows partial but significant overlap in gene associations for most phenotypes. Graphical tools facilitate comparing genetic effects between PWAS and coding GWAS results, aiding in understanding the sex-specific genetic impact on common diseases. This adaptable platform is attractive to clinicians, researchers, and individuals interested in delving into gene-disease associations and sex-specific genetic effects., (© 2024 Kelman et al.; Published by Cold Spring Harbor Laboratory Press.)

Background: Observational studies have indicated that breakfast skipping and gut microbiome dysbiosis are associated with a higher risk of major depressive disorder (MDD). However, it remains unknown whether the alteration of gut microbes is implicated in the associations between breakfast skipping and MDD., Methods: Leveraging genome-wide association studies (GWAS) on breakfast skipping, gut microbes, and MDD, we conducted a two-step Mendelian randomization (MR) study to determine the causal associations between breakfast skipping (N = 193,860) and MDD (N = 1,815,091), and evaluate the role of gut microbes (N = 18,340). Genetic variants with a P-value less than 5E-08 were selected as instrumental variables (IVs). The false discovery rate (FDR) method was employed to correct the P-values for multiple tests in gut microbes., Results: Breakfast skipping was associated with an increased risk of MDD (odds ratio [OR] = 1.36, 95%CI = 1.12-1.65, P = 0.002), but no effect of MDD on breakfast skipping was observed (β per doubling odds of MDD =-0.001, 95%CI=-0.024 to 0.023, P = 0.957). After adjusting for multiple comparisons, the MR analysis provided little evidence for an association between breakfast skipping and the abundance of any gut microbes (P FDR >0.05). Among the 21 gut microbes with IVs available, only the abundance of Class Actinobacteria was causally associated with a reduced risk of MDD (OR = 0.85, 95%CI = 0.75-0.97, P FDR =0.015)., Conclusions: Our findings demonstrated that breakfast skipping was associated with an increased risk of MDD, but provided little evidence supporting the role of the abundance of gut microbes in it. Further efforts with a large sample size are warranted to clarify the findings., (© 2024. The Author(s).)

Background: Epigenome-wide association studies have identified multiple DNA methylation sites (CpGs) associated with alcohol consumption, an important lifestyle risk factor for cardiovascular diseases. This study aimed to test the hypothesis that an alcohol consumption epigenetic risk score (ERS) is associated with blood pressure (BP) traits., Results: We implemented an ERS based on a previously reported epigenetic signature of 144 alcohol-associated CpGs in meta-analysis of participants of European ancestry. We found a one-unit increment of ERS was associated with eleven drinks of alcohol consumed per day, on average, across several cohorts (p < 0.0001). We examined the association of the ERS with systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension (HTN) in 3,898 Framingham Heart Study (FHS) participants. Cross-sectional analyses in FHS revealed that a one-unit increment of the ERS was associated with 1.93 mm Hg higher SBP (p = 4.64E-07), 0.68 mm Hg higher DBP (p = 0.006), and an odds ratio of 1.78 for HTN (p < 2E-16). Meta-analysis of the cross-sectional association of the ERS with BP traits in eight independent external cohorts (n = 11,544) showed similar relationships with BP levels, i.e., a one-unit increase in ERS was associated with 0.74 mm Hg (p = 0.002) higher SBP and 0.50 mm Hg (p = 0.0006) higher DBP, but not with HTN. Longitudinal analyses in FHS (n = 3260) and five independent external cohorts (n = 4021) showed that the baseline ERS was not associated with a change in BP over time or with incident HTN., Conclusions: Our findings demonstrate that the ERS has potential clinical utility in assessing lifestyle factors related to cardiovascular risk, especially when self-reported behavioral data (e.g., alcohol consumption) are unreliable or unavailable., (© 2024. The Author(s).)

Perinatal traits are influenced by fetal and maternal genomes. We investigate the performance of three strategies to detect loci in maternal and fetal genome-wide association studies (GWASs) of the same quantitative trait: (i) the traditional strategy of analysing maternal and fetal GWASs separately; (ii) a two-degree-of-freedom test which combines information from maternal and fetal GWASs; and (iii) a one-degree-of-freedom test where signals from maternal and fetal GWASs are meta-analysed together conditional on estimated sample overlap. We demonstrate that the optimal strategy depends on the extent of sample overlap, correlation between phenotypes, whether loci exhibit fetal and/or maternal effects, and whether these effects are directionally concordant. We apply our methods to summary statistics from a recent GWAS meta-analysis of birth weight. Both the two-degree-of-freedom and meta-analytic approaches increase the number of genetic loci for birth weight relative to separately analysing the scans. Our best strategy identifies an additional 62 loci compared to the most recently published meta-analysis of birth weight. We conclude that whilst the two-degree-of-freedom test may be useful for the analysis of certain perinatal phenotypes, for most phenotypes, a simple meta-analytic strategy is likely to perform best, particularly in situations where maternal and fetal GWASs only partially overlap., (© 2024. The Author(s).)

Genome-wide association studies typically considers epistatic interactions as a crucial factor in exploring complex diseases. However, the current methods primarily concentrate on the detection of two-order epistatic interactions, with flaws in accuracy. In this work, we introduce a novel method called Epi-SSA, which can be better utilized to detect high-order epistatic interactions. Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions. To evaluate its performance, we conducted a comprehensive comparison between Epi-SSA and seven other methods using five simulation datasets: DME 100, DNME 100, DME 1000, DNME 1000 and DNME3 100. The DME 100 dataset encompasses eight second-order epistasis disease models with marginal effects, each comprising 100 simulated data instances, featuring 100 SNPs per instance, alongside 800 case and 800 control samples. The DNME 100 encompasses eight second-order epistasis disease models without marginal effects and retains other properties consistent with DME 100. Experiments on the DME 100 and DNME 100 datasets were designed to evaluate the algorithms' capacity to detect epistasis across varying disease models. The DME 1000 and DNME 1000 datasets extend the complexity with 1000 SNPs per simulated data instance, while retaining other properties consistent with DME 100 and DNME 100. These experiments aimed to gauge the algorithms' adaptability in detecting epistasis as the number of SNPs in the data increases. The DNME3 100 dataset introduces a higher level of complexity with six third-order epistasis disease models, otherwise paralleling the structure of DNME 100, serving to test the algorithms' proficiency in identifying higher-order epistasis. The highest average F-measures achieved by the seven other existing methods on the five datasets are 0.86, 0.86, 0.41, 0.56, and 0.79 respectively, while the average F-measures of Epi-SSA on the five datasets are 0.92, 0.97, 0.79, 0.86, and 0.97 respectively. The experimental results demonstrate that the Epi-SSA algorithm outperforms other methods in a variety of epistasis detection tasks. As the number of SNPs in the data set increases and the order of epistasis rises, the advantages of the Epi-SSA algorithm become increasingly pronounced. In addition, we applied Epi-SSA to the analysis of the WTCCC dataset, uncovering numerous genes and gene pairs that might play a significant role in the pathogenesis of seven complex diseases. It is worthy of note that some of these genes have been relatedly reported in the Comparative Toxicogenomics Database (CTD). Epi-SSA is a potent tool for detecting epistatic interactions, which aids us in further comprehending the pathogenesis of common and complex diseases. The source code of Epi-SSA can be obtained at https://osf.io/6sqwj/., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

As more multi-ancestry GWAS summary data become available, we have developed a comprehensive trans-ancestry pathway analysis framework that effectively utilizes this diverse genetic information. Within this framework, we evaluated various strategies for integrating genetic data at different levels-SNP, gene, and pathway-from multiple ancestry groups. Through extensive simulation studies, we have identified robust strategies that demonstrate superior performance across diverse scenarios. Applying these methods, we analyzed 6,970 pathways for their association with schizophrenia, incorporating data from African, East Asian, and European populations. Our analysis identified over 200 pathways significantly associated with schizophrenia, even after excluding genes near genome-wide significant loci. This approach substantially enhances detection efficiency compared to traditional single-ancestry pathway analysis and the conventional approach that amalgamates single-ancestry pathway analysis results across different ancestry groups. Our framework provides a flexible and effective tool for leveraging the expanding pool of multi-ancestry GWAS summary data, thereby improving our ability to identify biologically relevant pathways that contribute to disease susceptibility., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

The properties of cell-free DNA (cfDNA) are intensely studied for their potential as non-invasive biomarkers. We explored the effect of common genetic variants on the concentration and fragmentation properties of cfDNA using a genome-wide association study (GWAS) based on low-coverage whole-genome sequencing data of 140,000 Dutch non-invasive prenatal tests (NIPTs). Our GWAS detects many genome-wide significant loci, functional enrichments for phagocytes, liver, adipose tissue, and macrophages, and genetic correlations with autoimmune and cardiovascular disease. A common (7%) missense variant in DNASE1L3 (p.Arg206Cys) strongly affects all cfDNA properties. It increases the size of fragments, lowers cfDNA concentrations, affects the distribution of cleave-site motifs, and increases the fraction of circulating fetal DNA during pregnancy. For the application of NIPT, and potentially other cfDNA-based tests, this variant has direct clinical consequences, as it increases the odds of inconclusive results and impairs the sensitivity of NIPT by causing predictors to overestimate the fetal fraction., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Genome-wide association studies have advanced our understanding of complex traits, but studying how a GWAS variant can affect a specific trait in the human population remains challenging due to environmental variability. Drosophila melanogaster is in this regard an excellent model organism for studying the relationship between genetic and phenotypic variation due to its simple handling, standardized growth conditions, low cost, and short lifespan. The Drosophila Genetic Reference Panel (DGRP) in particular has been a valuable tool for studying complex traits, but proper harmonization and indexing of DGRP phenotyping data is necessary to fully capitalize on this resource. To address this, we created a web tool called DGRPool (dgrpool.epfl.ch), which aggregates phenotyping data of 1034 phenotypes across 135 DGRP studies in a common environment. DGRPool enables users to download data and run various tools such as genome-wide (GWAS) and phenome-wide (PheWAS) association studies. As a proof-of-concept, DGRPool was used to study the longevity phenotype and uncovered both established and unexpected correlations with other phenotypes such as locomotor activity, starvation resistance, desiccation survival, and oxidative stress resistance. DGRPool has the potential to facilitate new genetic and molecular insights of complex traits in Drosophila and serve as a valuable, interactive tool for the scientific community., Competing Interests: VG, RB, FD, ER, RR, BD No competing interests declared, (© 2023, Gardeux et al.)

Background: Previous studies have established a genetic link between gut microbiota and hypertension, but whether blood cell count plays a mediating role in this remains unknown. This study aims to explore genetic associations and causal factors involving the gut microbiome, peripheral blood cell count, and blood pressure., Methods: We utilized summary statistics derived from genome-wide association studies to conduct a two-sample mediation Mendelian randomization analysis (https://gwas.mrcieu.ac.uk/). We applied inverse variance weighted (IVW) estimation method as the primary method, along with MR Egger, Weighted median, Simple mode and Weighted mode as complementary methods. To ensure the robustness of the results, several sensitivity analyses were conducted., Results: Genetic variants significantly associated with the microbiome, blood pressure, or peripheral blood cell counts were selected as instrumental variables. Fourteen microbial taxa were found to have suggestive associations with diastolic blood pressure (DBP), while fifteen microbial taxa showed suggestive associations with systolic blood pressure (SBP). Meanwhile, red blood cell count, lymphocyte count, and platelet count were identified to mediate the influence of the gut microbiome on blood pressure. Specifically, red cell count was identified to mediate the effects of the phylum Cyanobacteria on DBP (mediated proportion: 8.262 %). Lymphocyte count was found mediate the effects of the genus Subdoligranulum (mediated proportion: 2.642 %) and genus Collinsella (mediated proportion: 2.749 %) on SBP. Additionally, platelet count was found to mediate the relationship between the genus Eubacterium ventriosum group and SBP, explaining 3.421 % of the mediated proportion., Conclusions: Our findings highlighted that gut microbiota may have causal influence on the blood pressure by modulating blood cell counts, which sheds new light on the pathogenesis and potential clinical interventions through the intricate axis of gut microbiome, blood cell counts, and blood pressure., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Goat milk has gained global attention for its unique nutritional properties and potential health benefits. Advancements in functional genomic technologies have significantly progressed genetic research on milk composition traits in dairy goats., Results: This review summarizes various research methodologies applied in this field. Genome-wide association studies (GWAS) have identified genomic regions associated with major milk components, with the diacylglycerol acyltransferase 1 ( DGAT1 ) gene and casein gene cluster consistently linked to milk composition traits. Transcriptomics has revealed gene expression patterns in mammary tissue across lactation stages, while the role of non-coding RNAs (such as miRNAs and circRNAs) in regulating milk composition has been confirmed. Proteomic and metabolomic studies have not only helped us gain a more comprehensive understanding of goat milk composition characteristics but have also provided crucial support for the functional validation of genes related to milk components. The integration of multi-omics data has emerged as an effective strategy for elucidating complex regulatory networks from a systems biology perspective., Conclusions: Despite progress, challenges remain, including refining reference genomes, collecting large-scale phenotypic data, and conducting functional validations. Future research should focus on improving reference genomes, expanding study populations, investigating functional milk components, exploring epigenetic regulation and non-coding RNAs, and studying microbiome-host genome interactions. These efforts will inform more precise genomic and marker-assisted selection strategies, advancing genetic improvements in milk composition traits in dairy goats.