BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization identifies 9 circulating proteins associated with heart failure or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of heart failure, and identify circulating proteins that may represent cardiomyopathy treatment targets.Heart failure is a leading cause of cardiovascular morbidity and mortality. However, the contribution of common genetic variation to heart failure risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We report a multi-ancestry genome-wide association study meta-analysis of all-cause heart failure including up to 115,150 cases and 1,550,331 controls of diverse genetic ancestry, identifying 47 risk loci. We also perform multivariate genome-wide association studies that integrate heart failure with related cardiac magnetic resonance imaging endophenotypes, identifying 61 risk loci. Gene-prioritization analyses including colocalization and transcriptome-wide association studies identify known and previously unreported candidate cardiomyopathy genes and cellular processes, which we validate in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provide convergent evidence for the roles of  Heart failure is a major cause of cardiovascular morbidity and mortality. Here, the authors report results of a genome-wide association study meta-analysis, characterizing the role of common genetic variants in heart failure, finding overlap with common cardiovascular risk factors and imaging measures of cardiac structure/function. HF affects >38 million individuals globally, with rapidly growing prevalence, and is a major cause of cardiovascular morbidity, mortality, hospitalization, and healthcare costs5. Despite the prevalence of HF, the role of common genetic variation in HF risk remains poorly understood. In comparison to other common cardiometabolic traits like coronary artery disease (CAD), myocardial infarction, diabetes, blood pressure, and obesity, where hundreds of genetic loci have been associated with disease risk, discovery of common genetic sequence variants associated with HF has been modest, with only 11 genomic loci identified in the largest genome-wide association study (GWAS) of HF to date6.Heart failure (HF) is a common cardiovascular syndrome characterized by symptoms including shortness of breath, volume-overload, and functional limitation that result from structural or functional impairment of ventricular filling or ejection of blood7, recently demonstrated with applications to CAD and blood lipids9. Refining clinical phenotypes is another effective approach to improve detection of common variant associations. This approach may be particularly relevant, as HF is a heterogeneous clinical syndrome10. For example, in prior work parsing dilated cardiomyopathy from all-cause HF using diagnosis and procedure codes facilitated the identification of common-genetic variation in genes associated with Mendelian forms of cardiomyopathy11. More recently, statistical methods that jointly consider related phenotypes have been developed. These multi-trait GWAS methods leverage the shared genetic relationships between related traits, and have been shown to improve power for genetic discovery across a range of diseases14. For example, a recent multi-trait analysis jointly considered hypertrophic cardiomyopathy, dilated cardiomyopathy, and cardiac imaging traits, successfully identifying novel common genetic variants associated with these traits15. Because structural cardiac abnormalities and dysfunction of the left ventricle are key contributors to the development of clinical HF, and these traits share genetic architecture, we hypothesized that jointly considering HF and related continuous quantitative cardiac imaging phenotypes within a multi-trait GWAS framework may similarly improve power for genetic discovery.Several strategies have been described to improve power for GWAS locus discovery. One approach, particularly useful for improving the generalizability of GWAS findings, is to improve the genetic diversity of participants included in the study. Simulation and empiric studies have identified power gains of multi-ancestry GWASIn this study, we report a multi-ancestry meta-analysis of HF genome-wide association studies to estimate the effect of common genetic sequence variants on all-cause HF risk. We integrate GWAS of cardiac imaging endophenotypes and HF using multivariate GWAS methods to further improve power for locus discovery. Finally, we evaluate the genetic evidence for these associations using colocalization, transcriptome-wide association, gene-expression profiling, and Mendelian randomization. In summary, this study identifies previously unreported HF risk variants and putative effector genes, prioritizes relevant tissues, highlights roles for common genetic sequence variation in the pathogenesis of HF and related traits, and identifies circulating proteins associated with HF and cardiovascular imaging phenotypes.19 (Supplementary Data p < 5 × 10−8) , and the remaining two were significant at a Bonferroni-adjusted threshold (p < 0.05/12). Of the 47 loci we identified, 34 were located >500 kb from the lead variants reported by the largest published GWAS of all-cause HF6. Consistent with prior reports6, the strongest association with HF was found at the PITX2 locus   Fig. . Of the cus Fig. . There w20 and Mass General Brigham Biobank . Of 47 genome-wide significant risk loci in our analysis, 44 were available for replication (Supplementary Data p = 8.1 × 10−10), and 27 were associated with HF at a Bonferroni-adjusted significance threshold (p < 0.05/44 = 0.001). In all, 37/44 loci were associated with HF at a nominal significance threshold (p < 0.05) with concordant direction of effect. In a combined discovery + replication meta-analysis, 39/44 loci reached the genome-wide significance threshold (p < 5 × 10−8).We sought replication in data from the VA Million Veteran Program https://gwas.mrcieu.ac.uk/). The lead variants at the novel HF loci shared pleiotropic associations with a wide variety of traits (Supplementary Data SH2B3 (7 cardiometabolic traits) and rs11066188 near HECTD4 (7 traits). Although the HF-increasing alleles at these loci are associated with favorable markers of metabolic health including lower body mass index, total-, and LDL-cholesterol, these loci are also associated with increased blood pressure and coronary artery disease, which may explain the association with increased HF-risk.To determine associations between the HF loci and other traits, we queried summary-level results from 34,513 GWAS collected by the MRC-IEU OpenGWAS Project (4. Cross-trait linkage disequilibrium score regression (LDSC)22 was performed to estimate the genetic correlation of HF with previously-reported cardiac imaging-derived measures of cardiac structure and function including: cardiac MRI-derived measures of left-ventricular end-diastolic volume (LVEDVMRI), left-ventricular end-systolic volume (LVESVMRI), volumes indexed for body surface area (LVEDViMRI and LVESViMRI), and left-ventricular ejection fraction (LVEFMRI) obtained from a GWAS of cardiac MRI traits among 36,041 healthy UK Biobank participants23. HF was significantly correlated with all imaging endophenotypes except LVEDViMRI, with the strongest correlation between HF and LVESVMRI  HF lead risk variants were associated with a cardiac MRI parameter at a genome-wide level of significance, and 10 additional loci were associated with MRI measures at a more liberal FDR .05 Fig. . While m14. These methods make different assumptions about the shared relationships and heritability among the input traits, but are robust to scenarios where SNP-trait associations are derived from overlapping samples, and have been previously been demonstrated to improve power for genetic discovery14. This analysis utilized the results of the multi-ancestry HF meta-analysis performed above, along with the previously-published cardiac MRI GWAS23. Given the stronger genetic correlations and larger number of shared risk loci between HF and cardiac MRI measures that were not indexed for body surface area, we focused our primary analysis on HF, LVEF, LVEDVMRI, and LVESVMRI. We additionally considered MRI measures indexed for body surface area in a sensitivity analysis.Having established significant genetic correlations and shared risk variants between HF and cardiac imaging phenotypes suggestive of common genetic etiology, we applied multi-trait GWAS methods , and a common factor model specified within the Genomic Structural Equation Modeling framework) to improve the power to discover additional associated genetic variantsACTN2, ALPK3, BAG3, FLNC, PLN, TTN), representing significant enrichment (hypergeometric p = 6.01 × 10−11). In a sensitivity analysis considering HF, LVEF, LVEDV and LVESV indexed for body surface area, or in a combined analysis including both indexed and unindexed ventricular volumes, we similarly identified significant enrichment of loci located near known cardiomyopathy genes . Genome-wide significant variants from these analyses are reported in Supplementary Data Across the three multi-trait methods, we identified 61 independent loci Fig. . Of the 24. This method simultaneously evaluates the probability of a shared causal variant at a locus across multiple traits. We found evidence for colocalization across two or more HF/imaging traits at 58 of the 61 loci, suggesting the multivariate GWAMA results represent discovery of shared genetic etiologies among the input traits , previously identified an association in this region with expression of PRKCA in the human left ventricle, with zebrafish and in vitro reporter assays suggestive of cardiac-specific enhancer activity at this locus27. In humans, the PRKCA locus has previously been associated with electrocardiographic measures of left ventricular mass at genome-wide significance27, and nominally associated with echocardiographic traits and dilated cardiomyopathy27, now reaching genome-wide significance for HF. We detected another association at rs6915002, an intronic variant near MLIP that encodes the Muscular LMNA Interacting Protein. This highly-conserved gene has been established as a key cardiac sensitizer to stress, regulating morphologic adaptation (hypertrophy and dilation) in a series of murine overexpression and deletion experiments29. Another strong novel association was rs3820888, an intronic variant located near the SPATS2L gene, which has previously been implicated in atrial fibrillation and QT-interval30. Many of the novel loci in the multivariate analysis have been previously associated with known HF risk factors including measures of blood pressure and diabetes (hemoglobin A1c), markers of arrhythmias , blood cell traits, as well as anthropometric traits such as height and body fat/mass-related traits  is expressed in the heart, and has previously been linked to beta-adrenergic-induced cardiac hypertrophy in mice and congenital heart disease in humans32. FAF1 (rs12096443) encodes a member of the Fas death-induced signaling complex, and contributes to stress-induced apoptosis of cardiomyocytes33. While TCF7L2 is an important risk locus for diabetes34, the specific variant we identified (rs34943800) has not been previously linked to diabetes. In human, rat, and murine HF, TCF7L2 has been identified as a binding partner of β-catenin, acting to mediate Wnt signaling leading to cardiac hypertrophy35.Additional loci uniquely prioritized when considering MRI measures indexed for body surface area included intronic variants located near 36. We detected significant associations (p < 0.05) with gene expression (GTEx) and chromatin marks (ROADMAP and ENTEX) in several tissues. The strongest association with gene expression was in the left ventricle (p = 8.5 × 10−4), and the strongest association with chromatin marks was with H3K36me3 in psoas muscle  dataset from the Myocardial Applied Genomics Network (MAGNet), derived from 313 human hearts 39. In total, genetic loci linked to expression of 32 genes colocalized with GWAS signals for HF and/or cardiac imaging traits  degradation) has been implicated in adverse cardiac remodeling and HF45; ABO has been linked to myocardial infarction;46ALPK3 is an established cardiomyopathy gene associated with both dilated and hypertrophic cardiomyopathies48; and PROM1 is a marker of fibroblast progenitor cells that has been previously linked to cardiac fibrosis49.The genes with strongest evidence for colocalization included BCKDHA expression in healthy hearts, failing hearts, LVEDVMRI and LVESVMRI (posterior probability 0.98). The heart is a major source of BCAA catabolism50, and BCAA have been linked to HF phenotypes in several model systems45. BCKDHA expression was also increased in failing compared to healthy hearts . Given the colocalization evidence for a shared genetic signal influencing BCKDHA expression and LVEDVMRI and LVESVMRI, we performed Mendelian randomization to determine whether circulating branch-chain amino acid  levels may be causally associated with LVEDVMRI and LVESVMRI. Using genetic instruments derived from a GWAS of circulating metabolites among up to 24,925 participants of ten European studies51, Increased circulating levels of isoleucine and leucine were significantly associated with decreased LVEDVMRI  and LVSEVMRI , with no significant associations identified for valine , and the findings remained robust when using the weighted-median MR method, which makes different assumptions about the presence of pleiotropy , to identify genes where tissue-specific expression levels (eQTL) or transcript splicing events (sQTL) may be relevant to HF. Across all tissues, we identified 36 distinct genes representing 73 gene-tissue pairs where gene expression was significantly associated with HF after Bonferroni adjustment for multiple testing   (hypergeometric p = 0.049).Next, we performed TWASs integrating gene expression and splicing data from the Genotype-Tissue Expression (GTEx) project with the results of our HF GWASrs) Fig. . We alsors) Fig. . The setCLCNKA gene expression in kidney . CLCNKA encodes the Ka renal chloride channel (ClC-Ka), with a prior candidate-variant study identifying a suggestive association between the common coding variant rs10927887 and HF56. Further functional characterization of this variant revealed loss-of-function in the ClC-Ka chloride channel, implicating a Bartter syndrome-like cardio-renal axis in HF56. Although other genes in this region including HSPB757 and ZBTB1758 have been implicated in HF, our results provide support for a role of CLCNKA. Other highly prioritized genes included CDKN1A , an important cell-cycle regulator of cardiomyocyte proliferation during terminal differentiation;60SYNPO2L , a Z-disc protein previously linked to atrial fibrillation62.Among the most highly prioritized TWAS associations was p < 0.05) among 166 healthy  and 166 failing  hearts from MAGNet. Of 49 genes with available expression data, we identified 34 genes where expression significantly differed between healthy and failing hearts after Bonferroni adjustment for multiple testing (p < 0.05 adjusting for 49 genes)  Fig. .63 among the TWAS-prioritized genes. Cellular Component analysis was notable for enrichment of several sarcomere components including the Z-disc, I-band, sarcomere overall, and myofibrils. , and muscle cell development/assembly/organization  , with increasing levels associated with increased risk of HF (OR 1.04 per 1−SD increase in lipoprotein(a), 95% CI 1.03 to 1.05, p = 1.6 × 10−16). Lipoprotein(a) is a known risk factor for coronary artery disease that has been previously associated in observational studies with incident HF and associated hospitalization65. To further evaluate this association, we considered an additional genetic instrument previously reported to explain >40% of the variation in circulating Lp(a) levels across multiple cohorts66. Using this genetic instrument, each 10 mg/dL increase in Lp(a) was associated with a small but significant increased risk of HF . This association was no longer significant in multivariable MR accounting for the association of these genetic variants with coronary artery disease , indicating CAD may mediate the effects of lipoprotein(a) in the pathogenesis of HF. These findings are consistent with prior identification of LPA as a HF GWAS locus6, and with observational findings which suggest the effects of Lp(a) on incident HF are likely attributable to coronary artery disease67.Finally, we performed an unbiased proteome-wide Mendelian randomization analysis using high-confidence genetic instruments for 725 circulating proteins to identify their contribution to each of the HF endophenotypes. We identified 17 significant (FDR < 0.05) protein–trait associations, across 9 distinct circulating proteins Fig. . Among tNMB was associated with increased LVEF, decreased ventricular volumes, and decreased risk of HF. Other proteins associated with multiple traits included SPON1, FKBP7, and B3GNT8.  demonstrating improved power for novel locus discovery for this collection of traits, (2) implicating both known and previously unreported variants in HF pathogenesis, and (3) prioritizing genes, pathways, and circulating proteins for future study in the pathogenesis of HF.8, we found that multi-ancestry analysis improved power for discovery, identifying 47 HF loci . Similarly, incorporating HF endophenotypes in multi-trait analyses further improved power for discovery. Across both analyses, we identified many variants which had previously been associated with cardiometabolic and anthropometric traits, which are themselves HF risk factors. With the growth of institutional biobanks that link rich electronic health records, laboratory, and imaging findings with genetic data, more refined phenotyping efforts  may further enable genetic discovery within this multi-trait paradigm.First, these findings highlight the value of multi-ancestry and multi-trait genome wide analyses to improve genetic discovery. Although GWAS of HF have been performed largely in populations of European ancestry, HF is a global disease associated with high morbidity and mortality. Consistent with GWAS of other cardiovascular traits like CADFBN171, GWAS of electrocardiographic traits find long-QT syndrome-associated genes like SCN5A are associated with variation in many other subclinical electrocardiographic traits72, and GWAS of both left- and right-heart structure/function among healthy individuals identify Mendelian cardiomyopathy genes74. We demonstrate an example of linking GWAS of a common, complex trait like HF, with GWAS of detailed cardiac structure/function endophenotypes, and recapitulate known Mendelian cardiomyopathy genes and sarcomere components. Incorporating detailed phenotyping of biomarkers and imaging traits among smaller, healthy cohorts, particularly of diverse genetic ancestry, may be an important opportunity for further improve genetic discovery.Importantly, our findings highlight the value of integrating large-scale GWAS of common, complex traits with smaller, more focused studies of specific quantitative endophenotypes. GWAS of many cardiovascular endophenotypes have been previously reported, including among largely healthy populations. A common finding across these studies has been the observation that the genes and pathways identified by studying variation in cardiovascular traits among largely disease-free populations recapitulate known disease-associated genes and pathways. For example, recent GWAS of thoracic aorta diameters among healthy individuals implicate known Mendelian aortopathy genes like STRN. This gene encodes striatin, a calmodulin binding protein which has been linked to the intercalated disc of cardiac myocytes, colocalizes with desmosomal proteins, and associates with spontaneous arrhythmogenic cardiomyopathy in canines75. Other highly prioritized genes across multiple lines of evidence include Mendelian cardiomyopathy genes . We also detected significant enrichment of genes associated with cellular contractile machinery and cardiomyocyte development and structure. Overall, these findings highlight the utility of integrative analyses that draw on genetic association, gene expression, chromatin modification, and tissue/cell-type-specific datasets to prioritize disease-associated genes. These findings suggest that while HF has pleiotropic contributors, similar genes, tissues, and cellular components associated with Mendelian forms of HF may be important for common manifestations as well.Second, our findings implicate known and previously unreported HF effector genes and pathways. We found convergent evidence for the influence of common variants across several secondary analyses, summarized in Supplemental Fig. 76. Future work investigating the interactions between common and rare HF variants, as well as genetic determinants of common HF risk factors may be useful in clarifying the pathways that determine the heterogeneous clinical presentations.Although our findings implicate some genes and pathways with putative links to cardiomyocyte structure/function, other loci appear to have pleiotropic effects, and are linked to HF risk factors like atrial fibrillation or blood pressure. HF is a heterogeneous disease, varying in etiology, severity, and age of onset, among other factors. As we studied a broad “all-cause” definition of HF, these findings are not surprising. Future studies of more homogeneous HF definitions may help clarify the relevance of specific pathways to HF subtypes, help resolve pleiotropy, and clarify whether some variants may be associated with diseases that phenocopy HF-like chronic lung diseases and obesity. Prior studies have also broadly suggested that polygenic risk may modify the impact of rare but more severely damaging variants77. Our colocalization and MR analyses identified strong evidence of a shared genetic etiology and potentially causal relationship between circulating leucine and isoleucine levels and left ventricular volumes, although the pathologic relevance to human HF specifically requires further investigation.Finally, we observed potentially causal links between circulating metabolites and proteins with HF and related imaging traits. Elevated circulating levels of branch chain amino acids have been previously implicated as a risk factor for incident HF and adverse cardiac remodeling in a murine model of myocardial infarction11, and statistical methods to account for misclassification78 may also be helpful in ensuring GWAS of HF identify bona fide risk loci, rather than variants that may be primarily associated with phenocopies like obesity and chronic lung disease. Third, these results represent the findings of a selection of multi-trait GWAS methods which make different assumptions about disease heritability and genetic relatedness. No gold-standard multi-trait GWAS method exists, and whether locus discovery or biologic relevance may be further improved with other methods requires further study. Similarly, no gold-standard gene prioritization framework exits. We applied several methods which provided biologically plausible candidate genes supported by prior literature; however, other bioinformatic or functional approaches may prioritize different candidate genes. Functional assessment of HF-associated risk variants, genes, and pathways in model systems will be important to validate their role in HF. Finally, although incidence of HF is similar among men and women, epidemiologic differences have been noted, and sex-specific subsets of HF exist 79. Here, we did not perform sex-stratified analyses, or analyses focused specifically on identifying associations on the sex chromosomes. Whether sex-specific genetic associations exist will be an important area of future study.This study has several possible limitations. First, although we performed the largest multi-ancestry GWAS of HF to-date, these analyses included a large number of participants of European ancestry. We were able to replicate the majority of our genetic associations, but replication was limited to cohorts of European ancestry. As the global burden of HF is increasing, future GWAS of HF and related traits (particularly quantitative imaging traits) in other diverse populations is warranted. Future multi-ancestry analyses will hopefully further improve our understanding of the true breadth of the common genetic basis of these traits. In downstream colocalization, TWAS, and gene expression profiling analyses we included multi-ancestry cohorts , which should overall improve generalizability of our findings. Second, the definition of HF in many cohorts was based on diagnosis codes, which may have resulted in phenotype misclassification. We included highly correlated imaging traits with biologically plausible connections to HF to maximize interpretability of our multi-trait analysis and enhance identification of cardiac-specific loci. Including other cardiovascular and HF endophenotypes  may further improve discovery. Efforts to develop specific HF phenotyping definitionsIn summary, these analyses highlight similarities and differences among HF and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of HF, and identify circulating proteins and metabolites that may represent cardiomyopathy treatment targets.19. All-cause HF was defined using cohort-specific definitions  . Details of study-specific genotyping and quality control are available in the Supplemental Methods, and included standard local controls for missingness, sex discordance, variant-level factors including missingness, Hardy–Weinberg equilibrium, and imputation accuracy. Analyses were performed separately by cohort and ancestry, adjusted for age, sex, and population structure. Prior to meta-analysis, GWASinspector81 was used to perform study-level quality control using default settings to evaluate for test statistic inflation, skewness, kurtosis, allele frequency mismatches, and perform allele harmonization. Fixed-effects meta-analysis was performed using METAL82 using the inverse-variance weighted (standard error) method within and across ancestries to generate ancestry-specific and multi-ancestry meta-analysis summary statistics. Fine-grained ancestry estimation from summary statistics was performed using bigsnpr84. Independent significant genomic risk loci were defined using the “–clump” command in PLINK85 and the 1000 Genomes Phase 3 reference panel . Lead variants (or proxies identified using LDlinkR86) at each independent risk locus were carried forward for replication. For replication, fixed effects meta-analysis was performed to combine data from the VA Million Veteran Program and Mass General Brigham Biobank . Discovery + replication phase data for each independent risk locus was combined in a final fixed effects meta-analysis. All genomic positions are reported using coordinates from the GRCh37 build of the human genome.In the discovery phase, GWAS summary statistics for HF were obtained from non-overlapping analyses of six separate cohorts/consortia rg) between HF, and cardiac MRI traits 22. LDSC is a computationally efficient method which utilizes GWAS summary statistics to estimate heritability and genetic correlation between polygenic traits while accounting for sample overlap.Cross-trait linkage-disequilibrium score regression (LDSC) was performed to estimate genetic correlation (rg), quantify heritability, estimate dependence between traits, and account for up to complete sample overlap22. This approach is flexible, allowing the user to use systems of equations to model proposed relationships between the observed traits and latent variables. To estimate SNP-level effects, the genetic covariance and sampling covariance matrices (estimated using LD score regression) are expanded to include SNPs, which are then individually regressed on parameters specified by each structural model. We specified a common factor model, as well as models corresponding to MTAG and N-weighted multivariate GWAMA frameworks14. Details of model specifications are available in the Supplemental Methods. All multi-trait analyses were implemented using the GenomicSEM package in R using the diagonally-weighted least squares estimator13.Genome-wide association study summary statistics were obtained for HF and cardiac MRI  traits. The primary analysis included HF and LVEF, LVEDV, LVESV, while MRI measures indexed for body surface area were included in a sensitivity analysis. Variants were filtered to include common (MAF > 0.01) variants present in the 1000 Genomes Phase 3 reference panel. Genomic Structural Equation Modeling (SEM) is a framework which uses GWAS summary statistics to model the genetic covariance structure of complex traits. Genomic-SEM leverages a multivariate extension of cross-trait linkage-disequilibrium score regression (LDSC) to estimate genetic correlation  dataset from the Myocardial Applied Genomics Network (MAGNet), derived from 313 human hearts  obtained at time of organ procurement (control hearts) or heart transplant (failing hearts)39. Evidence for colocalization was determined based on the default variant specific regional and alignment priors Statistical colocalization is a method to assess shared genetic etiology between traits. We used HyPrColoc36 to test for enrichment of disease heritability by integrating our GWAMA summary statistics with gene expression88, chromatin90, and cardiac-specific cell-type37 datasets. We applied false discovery rate correction separately across each dataset  to account for multiple testing, with FDR < 0.05 considered significant.We implemented LDSC-SEGp < 5 × 10−8) were identified from a previously reported GWAS meta-analysis including up to 24,925 participants of ten European studies51 Participants underwent genotyping and NMR profiling of circulating metabolites, and GWAS was performed to understand the contribution of common genetic variation to circulating metabolite levels. Genetic instruments were constructed from independent  variants associated with each BCAA at genome-wide significance. The corresponding SNP effects were identified in GWAS summary statistics for LVEDVMRI and LVSEVMRI, harmonized to consistent effect alleles, and two-sample inverse variance weighted Mendelian randomization with random effects was performed using the TwoSampleMR package in R91. Sensitivity analysis was performed using the weighted median method, which remains robust when up to 50% of the weight of the genetic instrument is invalid92.Genetic variants associated with branch chain amino acids  at genome wide significance  were obtained from http://predictdb.org/. Bonferroni adjustment was performed to account for multiple testing , with adjusted p < 0.05 considered significant. We tested for enrichment of TWAS-prioritized genes among Mendelian cardiomyopathy-associated genes using the hypergeometric distribution to yield a one-tailed p value.S-PrediXcan was used to integrate gene expression and splicing data from GTEx version 8 and GWAS summary statistics from the HF GWAS to identify genes associated with all-cause HF93. Briefly, human cardiac samples were obtained from failing hearts collected at time of heart transplantation, and from healthy donor hearts that were suitable for transplant but logistically did not reach recipients. RNA gene expression profiling on cardiac tissue samples obtained from the left ventricle was performed as previously described39. Gene expression profiling was performed using Affymetrix expression arrays, log-transformed, normalized to reference probes, batch normalized, and adjusted for gender, age, and collection site. Gene expression data was queried to determine whether significant expression differences existed between healthy and failing hearts for genes prioritized by TWAS, with significant fold-changes differences determined by Bonferroni-adjusted p < 0.05 to account for multiple testing.The Myocardial Applied Genetics Consortium (MAGnet) is a multicenter, institutional review board-approved consortium designed to explore the genetic underpinnings of cardiac gene expression63. Based on a set of input genes, the application tests for enrichment among prespecified gene sets based on the hypergeometric distribution followed by false discovery rate correction for multiple testing. Pathways or components with FDR q < 0.05 were considered significant.Biological pathway and cellular component analysis was performed using ShinyGO, an online platform for gene enrichment analysishttp://www.epigraphdb.org/pqtl/)94. Briefly, we identified high-confidence (tier 1) cis-acting genetic instruments for 725 circulating proteins, which passed previously defined consistency and pleiotropy tests and had available corresponding SNP effects from our HF meta-analysis and/or the cardiac MRI GWAS. When multiple SNPs were available for an exposure–outcome pair, inverse-variance weighted MR was performed as the primary analysis, with Wald-ratio MR performed when only one SNP was available for the exposure-outcome pair. FDR correction was applied to account for multiple testing, with q < 0.05 considered significant. For the Mendelian Randomization analysis of lipoprotein(a), a secondary genetic instrument was constructed using genetic variants previously reported to explain >40% of the variation in circulating Lp(a) levels. Of 43 previously reported genetic variants, 27 were available in our HF GWAS. Inverse-variance weighted MR was performed as the primary analysis, with MR-Egger and weighted median methods applied as sensitivity analyses, as these make different assumptions about the presence of invalid instruments and pleiotropy95. Multivariable MR was performed to evaluate whether the effects of Lp(a) on were attenuated by the effects of Lp(a) on coronary artery disease. Summary data on coronary artery disease was obtained from ref. 91.Proteome-wide Mendelian randomization was performed as previously described .The UK Biobank obtained IRB approval from the North West Multi-centre Research Ethics Committee , and participants provided informed consent. The BioBank Japan Project was approved by the research ethics committees at the Institute of Medical Science, the University of Tokyo, the RIKEN Yokohama Institute, and cooperating hospitals; participants gave written informed consent. FinnGen participants provided informed consent for biobank research, and the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS) approved the FinnGen Study protocol No. HUS/990/2017. The Penn Medicine BioBank is approved by the University of Pennsylvania, and participants gave written informed consent.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-18Reporting Summary


Genome-wide association studies (GWASs) have demonstrated that most common diseases have a strong genetic component from many genetic variants each with a small effect size. GWAS summary statistics have allowed the construction of polygenic scores (PGSs) estimating part of the individual risk for common diseases. Here, we propose to improve PGS-based risk estimation by incorporating genetic ancestry derived from genome-wide genotyping data. Our method involves three cohorts: a base (or discovery) for association studies, a target for phenotype/risk prediction, and a map for ancestry mapping; successively, (1) it generates for each individual in the base and target cohorts a set of principal components based on the map cohort—called mapped PCs, (2) it associates in the base cohort the phenotype with the mapped-PCs, and (3) it uses the mapped PCs in the target cohort to generate a phenotypic predictor called the ancestry score. We evaluated the ancestry score by comparing a predictive model using a PGS with one combining a PGS and an ancestry score. First, we performed simulations and found that the ancestry score has a greater impact on traits that correlate with ancestry-specific variants. Second, we showed, using UK Biobank data, that the ancestry score improves genetic prediction for our nine phenotypes to very different degrees. Third, we performed simulations and found that the more heterogeneous the base and target cohorts, the more beneficial the ancestry score is. Finally, we validated our approach under realistic conditions with UK Biobank as the base cohort and Swiss individuals from the CoLaus|PsyCoLaus study as the target cohort. It can be useful for biomedical applicationsGenetically, ancestry can be estimated via principal-component analysis (PCA) of genome-wide genotyping data to obtain the genetically inferred ancestry. The PCA produces a series of ordered axes, the first ones of which empirically correspond to the genetic ancestry.In genome-wide association studies (GWASs), such genetically inferred ancestry can be used to correct for population stratification. Specifically, the coordinates of the relevant principal-component axes can be included as covariates in the association models together with other demographic or clinical risk parameters, such as age or sex. Subpopulation-specific variants will be responsible for the We here propose a method that improves PGS-based prediction of complex diseases using additional information about genetic ancestry derived from PCA. Through both simulation and real-life testing in large cohorts, we show that the addition of an ancestry score (AS) based on principal components, which takes into account the Association studies, such as a GWASs, are performed in what we call a base cohort. The “individual risk scores” or “phenotypic predictions” are calculated on what we call a target cohort. When we run a PCA on the base cohort, the populations separated with each principal component (PC) is cohort specific. Therefore, if in the base cohort we run an associations study between a set of PCs and a phenotype, it is necessary to have a similar set of PC variables in the target cohort if we want to use it for phenotypic predictions. Unfortunately, the genetic information of the base cohort is often not accessible due to privacy issues, and thus direct transformation between the two PC spaces can be impractical. Here, we propose a method to circumvent this problem with an intermediate map cohort that must be accessible to all stakeholders and have sufficient diversity to separate the populations present in the base and target cohorts.(1)r2 = 0.5 and a maximum base pair in the sliding window of kb = 250);map-PC SNP-loadings L, a p times p representing the number of PCs retained (we decided to retain 40 PCs). These map-PC SNP-loadings are used to produce set of PCs that we call mapped PCs for the base cohort such thatMapping the base cohort to the map cohort. First, the base cohort must be mapped to the PC space of the map cohort. To do this, the PC space of the map cohort is based on an optimal set of SNPs selected with respect to the base- and map-cohorts such that (1) only SNPs present in both are retained; (2) on the base-cohort side, a first filtering of SNPs to be kept in the analysis can be done, but we suggest not to discriminate on the basis of minor allele frequency (MAF) and linkage disequilibrium (LD) at this level ; on the map-cohort side, SNPs are filtered for MAF (we use MAF >0.01) and pruned (we use an LD threshold based on an R coefficient of p and (2)Associations analyses on the base cohort. Second, we perform two association studies for the phenotype of interest on the base cohort: a GWAS  and whatThe different steps of the method can be visualized in s, c, with c as the number of covariates, and γ is the corresponding column vector of effect sizes. The outcome of the GWAS is the “GWAS summary statistics.”Let GWAS estimating the e-4 and INFO >0.8), we run the GWAS with BOLT-LMM.In our case, after quality control for imputation and MAFs Defining PGS design. Third, to design the best PGS formula, a calibration cohort is needed to determine the SNPs to include in the PGS formula. Practically, it would be a small hold-out subset of the base cohort. We define the calibration cohort with the genotype data In our case, for both association studies, the covariate (4)Calibration of the predictive model. Fourth, the calculation of phenotypic predictions involves a calibrated predictive model in which the coefficients of different parameters such as age, sexm and other relevant covariates, but also here the PGS p, and Computation of ASs and PGSs on the target. Fifth, similarly, Finally, we calibrate our predictive model, a multiple linear regression modelINFO × r2).(6)Phenotypic predictions. Finally, the calibrated predictive model can be used to calculate the phenotypic predictions on the target samples, such that the phenotypic prediction for individual j isBut for AS to get the mapped PC of the target cohort, 2 regression score (coefficient of determination) between the prediction and the actual phenotypes.PGSs and ASs are two composite variables derived from the same genome-wide genotyping data. Therefore, in order to estimate the predictive value of the AS, it is necessary to evaluate it together with the PGS. Two predictive models are calibrated, one with PGS alone and the other with both the PGS and AS . The twoYˆPGS+AS . The res,We generated genotyping and phenotypic data for a base cohort, a calibration cohort, and a target cohort, each based on a mixture of individuals from three genetically equidistant populations A, B, and C. The SNPs present in the simulated dataset can be non-specific (evenly distributed in the three populations), population specific (exclusive to one population), or stratified (present at different frequencies between populations). The alternative allele frequency for each population G, a matrix of size m times s with m as the number of samples, and s, the number of SNPs, respectively, with genotypes 0 , 1 (heterozygous), or 2  were assigned for a given population P with probabilities Genotype data were generated based on Y were generated based on heritability m, the genetic basis s SNPs selected randomly as 10% of SNPs per category and associated with an effect size Phenotypes C and i effect size. On the calibration cohort, we determined the best set of SNP We computed a PCA on the base-cohort-generated genotyping data  publicly available online.We constructed base and target cohorts from the UK Biobank (UKB) based on all individuals  or white Britons only . The recruitment process was described previously.,Genotyping and imputation of participants in the UKB study were fully described by Bycroft et al.Phenotypes were selected based on their high degree of differentiation between populations as characterized by the Global Distribution of Genetic Traits (GADGET).As external target cohort, we used the Cohorte lausannoise (CoLaus|PsyCoLaus), a population-based research study launched in 2003 in Lausanne, Switzerland, as an additional independent target cohort. It includes a total of 4,781 unrelated individuals of European ancestry after filtering out participants whose genetic sex did not match the reported sex or whose missing genotype rate was excessive (>5%). Participants ranged in age from 35 to 75 years at enrollment (mean https://imputation.sanger.ac.uk).Genotype imputation was performed using two independent reference panels: the HRC reference panel and the merged 1000 Genomes phase 3 and UK10K reference panel.We used standing height, body mass index (BMI), and diastolic blood pressure as phenotypic outcomes. Phenotypes were normalized on the basis of the parameters used in the UKB phenotype normalization.As a first step, we mapped individuals from both the base and target cohorts to the PC space of the map cohort. We characterized the resulting mapped PCs by assessing the correspondence between the position of the mapped PCs from UKB or CoLaus|PsyCoLaus and the PCs from the 1KG samples and, second, by testing whether the mapped PCs and the “regular” PCs explain the phenotypic variance with similar magnitude.UKB-all and 1KG overlap widely, showing the diversity present in both cohorts. UKB-WBO and the European 1KG samples. PC5 and PC7 are the axes discriminating the most samples of different European ancestry. As expected, there is a significant overlap of UKB-WBO with the British cluster (Great Britain [GBR]). Similar to CoLaus|PsyCoLaus, in We first jointly plot the mapped PCs of UKB/CoLaus|PsyCoLaus and the corresponding PCs of the map cohort (1KG). In ,target-UKB-all) and CoLaus|PsyCoLaus with a 10-fold cross-validation. The results in We then compared the PVE by the mapped PCs and by the regular PCs, which comes from a PCA done directly on the cohort (in CoLaus|PsyCoLaus) or provided (in UKB).We evaluated the performance of the AS, the composite variable we created from mapped PCs that captures the association between phenotype and ancestry.To characterize scenarios where the use of ASs would lead to a gain in predictive power, we simulated the scenarios based on the different genetic architectures as shown in The results of the different scenarios are shown in To evaluate the transferability of AS, we simulated scenarios based on the different cohort composition as shown in The results of the different scenarios are presented in base/target-UKB-WBO—and the other with samples of all ancestries—base/target-UKB-all. The split between the base and target cohorts was 90/10. The calibration cohort was generated multiple times based on a 10-times cross-validation from the target cohort.We then evaluated our method on UKB. Two sets of base and target cohorts were generated: one with samples of White British ancestry only—,Heritability is estimated by genome-based restricted maximum likelihood (GREML) method on the target cohort.base/target-UKB-all are shown in The results based on base/target-UKB-WBO in Results based on Note that our exploration on the UKB data does not exploit the gain due to heterogeneity between cohorts as seen in the simulations (we are in a similar case to scenario 1).base/calibration-UKB-WBO and base/calibration-UKB-all, based on a 90/10 split.We also evaluated our method using CoLaus|PsyCoLaus as an external target cohort. From UKB, two sets of base and calibration cohorts were generated, base/target-UKB-all are shown in base/target-UKB-WBO are shown in Results based on base/target-UKB-WBO, the PGS alone for height remains suboptimal, with a PVE of 0.223 compared with the model with the PGS and AS based on base/target-UKB-all.Note that with These results show the generalizability of our method to an independent cohort. Of note, the best results were obtained by including all UKB individuals in the base cohort, even if the CoLaus|PsyCoLaus cohort consists exclusively of individuals of European ancestry.Here, we propose a method allowing for the inclusion of an ancestry parameter derived from genetic data into phenotype prediction scores without having to manually categorize individuals. We show that the inclusion of an AS improves prediction, especially for admixed populations. In addition, because our approach emphasizes the inclusion of all individuals, it tends to increase the statistical power and the production of summary statistics that generalize better to diverse populations. Our method could therefore promote a much-needed increase in population diversity for human genomic research.,,So far, human genomic research has been disproportionately performed in populations of European ancestry, which might cause genomic-based medicine to exacerbate health disparities.As a result of our simulations, we can hypothesize that the traits that show a higher gain in prediction upon inclusion of an AS are the ones that are influenced by more population-specific alleles with a phenotype that is also differentiated between populations. These population-specific alleles—fixed in one ancestry, absent in others—will only be detected as variables in a multi-ethnic GWAS and are perfectly correlated with ancestry. In such a context, they can be thought of as perfectly correlated variants that are distributed across the genome independently of their physical distances. In a GWAS, such variants are usually discarded because of their collinearity with a covariate controlling for ancestry. As a result, the corresponding GWAS summary statistics miss them entirely, which precludes their inclusion in downstream polygenic risk prediction. Here, we correct this bias by adding a separate ancestry term in the predictive risk model.trans-ethnic cases due to (1) variants in the target that are absent in the base GWAS, (2) different effect sizes for the same causal variant between two populations due to pleiotropic effect, and (3) unmatched tagging SNPs due to different LD structures between populations.There are still limitations when doing predictions in a multi-ethnic setting. The PGS-based prediction will remain limited for ,Non-genetic factors influence phenotypes in complex ways and must be carefully considered in genetic studies to avoid confounding and false genetic associations. In particular, behavioral phenotypes are correlated with socio-economic and cultural factors, including racial and ethnic categoriesSince genetic ancestry and ethnicity are closely related, it is necessary to draw the line between these two concepts. Ethnicity or race is a social construct that classifies people independently of the genetic component. Its meaning changes over time and between societies. Genetic ancestry—or genetically inferred ancestry—can be operationally defined as the systematic difference in allelic frequencies between subpopulations. Until now, reporting of ancestry has been mostly based on the Self-Identification of Race and Ethnicity (SIRE) method, which has important limitations,Here, we have shown that clinical risk models can benefit from a risk parameter, the AS, derived from mapped PCs, which allows each individual to be fitted to its phenotypic baseline value based on ancestry. The use of this fitting parameter makes it possible to directly apply the predictive models to individuals from underrepresented populations and of mixed ancestry.The ClinGen Complex Disease Working Group has defined a standard method for reporting risk models based on PGSsWe are at a pivotal moment for genomic-based medicine: large-scale personal data can begin to be used effectively to develop more individualized approaches to disease prevention and treatment. Ensuring equitable access to new approaches and technology is a major responsibility for the biomedical research community. We have introduced a method that aims to foster predictive models based on PGS and promotes the inclusion of more diverse populations in GWAS.


Over the last decade, advances in high-throughput omics technologies and methods have enabled researchers to measure multiple biological data modalities simultaneously and accurately or to integrate multi-omics data from different sources and modalities. Numerous datasets are being rapidly generated encompassing genomics, transcriptomics, proteomics, metabolomics, phenomics, radiomics, cutting-edge 3D spatial omics, and single-cell omics data. This represents an unprecedented opportunity for knowledge discovery in disease biology, including the identification of biomarkers, functional modules, causal pathways, or regulatory networks implicated in disease, thus having also the potential to bolster current therapeutic pipelines.In parallel, a wide-array of statistical methods have been developed to leverage availability of these data, from genome-wide association studies (GWAS) to transcription-wide association studies (TWAS), methylome-wide association studies (MWAS), molecular quantitative trait loci (molQTL) analysis, or summary-based two-sample Mendelian Randomization. However, the ability to integrate different features of existing methods is still insufficient, limiting the power for knowledge discovery. Thus, advances in data mining, or statistical and machine learning techniques are urgently needed to perform cross-modal data integration and modeling. Here, we present a Research Topic on “Data Mining and Statistical Methods for Knowledge Discovery in Diseases Based on Multimodal Omics” to showcase studies that leverage these techniques to enable discovery of disease-related knowledge and illuminate molecular mechanisms of complex diseases. After rigorous peer-review, a total of 14 outstanding articles were selected for this topic collection. Below we highlighted six of them.Huang et al. explored the causal effects of insomnia on bipolar disorder, major depression, and schizophrenia in the European population using a two-sample Mendelian randomization approach. They first collected GWAS summary datasets for each trait and conducted meta-analyses for each trait to increase statistical power. The results of Mendelian randomization were further evaluated using extensive complementarity and sensitivity analysis. Among these psychiatric disorders, they found insomnia is causally associated with an increased risk of major depression, with an odds ratio estimated as 1.408 : 1.210–1.640, p = 1.03E-05) in the European population. No causal association was observed for other traits. The study provides new evidence to support the causal effect of insomnia on major depression and adds to a better understanding of the relationship between sleep and psychiatric disorders.Hamidi et al. proposed a machine learning framework to explore miRNA biomarkers and prediction for Ovarian cancer. miRNAs play an important role in cancer progression. In this study, the authors first used LASSO and Elastic Net for miRNA feature selection. They found 10 miRNA’s as potential biomarkers by comparing the expression levels in ovarian serum cancer samples and normal samples. Furthermore, they used multiple machine learning classifiers, including logistic regression, random forest, artificial neural network, XGBoost, and decision trees for ovarian cancer prediction. Experiments demonstrated the accuracy of their proposed model. The performance of the proposed models was further evaluated in external datasets.Liu et al. proposed a network representation learning (NRL)-based method to identify the disease-related genes of cerebral IS. The proposed method includes three key components: capturing the topological information of the PPI network, denoising the gene feature, and optimizing a support vector machine (SVM) classifier to identify IS-related genes. The evaluation showed that the proposed method performs better than existing methods on IS-related gene prediction. In addition, the case study also shows that the proposed method can identify IS-related genes.Cerebral ischemic stroke (IS) is a complex disease caused by multiple factors, including vascular risk, genetic, and environmental factors. Identifying the genes associated with IS critical for understanding the biological mechanisms underlying the disease. Xu et al. proposed an imputation method based on semi-supervised autoencoders named AdImpute. The method applies the cost function with imputation weights to learn the latent information in the data to achieve a more accurate imputation. The evaluation indicates that AdImpute is more accurate than the other four publicly available scRNA-seq imputation methods on the simulated and real data sets.Recently, single-cell RNA sequencing (scRNA-seq) technology has been used to measure RNA levels at single-cell resolution to study biological functions. Yang et al. tackled the issue of systematic selection bias in Mendelian randomization. The authors proposed a new approach that uses control exposures based on subject-matter knowledge to triangulate the estimated causal effects vulnerable to selection bias. The proposed approach can be used to assess credible MR estimates in the presence of selection bias from selection of survivors. The authors illustrate the application of their method by validating MR estimates through a real example investigating the potential association of transferrin with stroke (including ischemic and cardioembolic stroke).Park et al. developed an innovative approach for integrative pathway analysis that leverages genome-wide association studies summary statistics to construct genetic metabolomic scores (GMSs) that are then used as components of pathways in a hierarchical model that considers the structural relationships of SNPs, metabolites, pathways, and phenotypes. The authors applied their method to identify pathways associated with type 2 diabetes in the Korean population.second edition of this Research Topic which is currently open for submissions.All the contributions in this special issue have been peer-reviewed by no less than two professional domain experts. We believe that the final compilation includes high-quality publications that represent significant scientific progress that will impact the relevant research communities. On this basis, we have launched a


Livestock rearing is the main component of global food production systems and contributes to nearly half of global agricultural production. The emerging demand for animal products is considered a food revolution in developing countries. Although livestock farming is an essential component of the global economy but faces an immense challenge to meet out the increased demand of the exploding human population and other environmental factors. As a result, understanding animal health and production is becoming mandatory. Advances in genetic and genomic technologies have played a key role in improving animal welfare and productivity for decades. Genetic development in livestock, both in terms of productivity and other functional trait complexes associated with health and animal welfare, has been greatly aided by genomic selection, which is regarded as a success story. Genomic breeding programmes offer the potential to improve cattle productivity through the utilization of molecular genetics, the detection of markers and chromosomal areas that contain quantitative trait loci, and genome mapping technology. Present breeding strategies not only account for phenotypic variance in traits but also other factors like epigenetics. Epigenetic mechanisms  have been strongly found to influence livestock traits like growth, development, and other phenotypic effects. Technologies related to omics are developing more frequently in the area of animal production. Other omics topics like phosphoproteomics, peptidomics, or lipidomics are presently used in livestock production and disease management in addition to omics methodologies such as transcriptomics, genomics, proteomics, and metabolomics. Animal breeding systems already in place now have additional dimensions due to the introduction of revolutionary ideas like animal cloning and genetic engineering. It provides a faster way to increase the frequency of desirable alleles in an animal population using genetically modified animals than traditional breeding procedures. With this background, the aim of the current research topic was to collect research data on the role of omics technologies in livestock improvement from selection to breeding decisions.The special edition of Frontiers in Genetics i.e., “Omics Technologies in Livestock Improvement: From Selection to Breeding Decisions” published 26 original research articles, 2 review papers, and 01 methodology paper. All 29 articles were focused mainly on understanding the complex interplay of genes in mediating important traits, elucidating livestock genome/transcriptome, and improving livestock yield, nutrition values, and animal welfare.Chakraborty et al.).Omics technologies are effective tools, especially when used in conjunction with advanced molecular and breeding methods. Genomic research in particular has the potential to improve the precision and efficacy of traditional breeding and advanced breeding approaches by enhancing consistency and predictability. Indeed, the information provided by genomics and other omics-technologies like epigenomics and proteomics is crucial for understanding genes and their function. Additionally, summarized the role of omics technologies in improving livestock . The regulatory structure of circRNA in testis development and spermatogenesis in cattle bulls was evaluated using RNA sequencing by Khan et al., in immature and mature Wandong cattle bull testes. In calf and bull testes, 579 of the 17,013 circRNAs were elevated and 103 were downregulated. Bull spermatogenesis and the genes ATM, GSK3B, CCNA1, KMT2C, NSD2, KMT2E, QKI, SUCLG2, HOMER1, and SNAP91 were discovered to be strongly correlated. Through genetic selection, this information may aid in enhancing bulls’ reproductive productivity .Male infertility still poses a significant problem for animals, despite significant increases in reproductive efficiency brought about by artificial insemination procedures. Recent research indicates that the spermatozoa of fertile and infertile males have different populations of RNA. Studies have also shown that spermatozoal RNA (spRNA) is essential for spermatogenesis, fertilisation, and the early stages of embryonic development. Updated various spRNA species in livestock animals, including protein-coding and non-coding RNAs and their possible impact on quality of sperms, especially motility, freezability, and fertility . There were discovered to be 684 (464 SNPs and 220 INDELs) and 607 (442 SNPs and 169 INDELs) high-impact variations unique to Kashmir and Jersey cattle, respectively. The RNA sequencing-based SNP research revealed a considerable difference between Kashmiri and Jersey cattle in terms of milk production attributes. The high-impact SNP variations in Kashmiri cattle contributed to adaptive immunity and tolerance to infectious diseases of the mammary gland. Contrarily, in Jersey cattle, enhanced pathways were mostly implicated in production and lactation maintenance. These findings offer information on breed-specific genetic diversity that can be applied to the genomic selection of animals. There are many studies like RNA sequencing leads and high-throughput genomic DNA data, which have identified the genes related to lipid storage in broilers. Using RNA-Seq and microarray data techniques, Farzad Ghafouri et al. identified and categorised candidate genes and miRNAs involved in lipid metabolism. Based on the amount of abdominal fat present, two broiler groups were selected: high and low. In the analysis, 34 genes and 19 miRNAs were detected as common. A total of seven genes were revealed as common, three being most important viz., REBF1, SREBF2, SCD, and FASN. These have a significant impact on fat metabolism, storage, and signalling pathways of endocrine glands that are triggered by PPAR, AMPK, and adipokines. This method improved our comprehension of the biological mechanisms affecting adipose tissues . In animal food industry, the genetics and physiological mechanisms that govern skeletal muscle mass production are of prime importance to study. Satellite cells are crucial for skeletal muscle growth and regeneration. scRNA-seq was used by Lyu et al. to analyse the makeup of bovine satellite cells. According to the findings, bovine satellite cells may be divided into subpopulations with different transcriptional statuses, rates of proliferation, and myogenic potential. The current study by Lyu et al. also indicates the existence of FAP cells in bovine skeletal muscle, which may help researchers devise new methods for enhancing these features or find the DNA sequences and variants linked to them in cattle .Deep RNA sequencing was utilised to discover probable single nucleotide polymorphisms (SNP) in mammary epithelial cells from two different cow breeds (Jersey and Kashmiri) in order to study the alterations in coding regions that affect milk output differences. Ahmad et al., work seeks to find high-impact SNPs in Jersey and Kashmiri cows in order to uncover the main pathways controlling milk production features in each breed using RNA-Seq data . Lameness in an animal incurs financial burden on the animal owner, making it the third largest cause of culling an animal after mastitis and infertility. This condition is caused by foot lesions which can be either infectious or non-infectious in nature. Digital dermatitis (DD), WLD and SU are the most frequent causes. A study was performed by Lai et al. to find the loci influencing the association between the genetic causes of lameness and other prevalent health conditions. Estimates of the genetic connection between DD and SU, WLD and SU were all substantially different from zero (p < 0.05), whereas estimates of the genetic association between mastitis and DD, SU with metritis, and DD with milk fever were just suggestive (p < 0.1). These estimations of the five genetic correlations were all positive. Lai et al., observed that selection against disorders of foot may also reduce vulnerability to other health disorders because of the significant genetic association estimations between foot disorders and other illnesses .Lameness in cattle is frequently brought on by two non-infectious claw lesions: sole ulcers (SU) and white line disease (WLD). Sensitivity to SU and WLD is influenced by both hereditary and non-genetic variables, and protection can be accomplished by genetic methods and herd management. Genome-wide association studies (GWAS) were conducted using generalised linear mixed model (GLMM) regression, random forest (RF) and a chunk-based association testing (CBAT) technique to find susceptibility loci for WLD, SU, SU and/or WLD, and any sort of non-infectious claw damage. Potential genes for skeletal growth and mineralization, keratin, adipose tissue, wound healing and skin lesions were present in the linked areas on BTA8 and BTA13. The abundance of correlations found in this study conducted by Lai et al., as well as previous studies that showed the intricacy of the genetic background underlying non-infectious claw lesion vulnerability . Singh et al., conducted a GWAS to find substantially related SNPs between proteins in milk and minerals in cattle. Identifying potential genes connected to milk minerals and milk protein percentage in Vrindavani cattle was the goal of their study. Protein percentage Ca, Cu, Zn, P and Fe were found to have a strong association with BTA 7, 2, 3, 14, and 2, respectively . Further, consumers mostly judge the quality of meat based on its colour. In order to aid in pig breeding programs, Liu et al. conducted a study to identify the genes associated with meat colour in Suhuai pigs. Additionally, it calculates the genetic correlation and heredity of meat colour. . Using GWAS and FST testing on Suhuai pigs, six potential genes  and 39 possible SNPs associated to flesh colour were found. These findings pave the way for genetic modification of pig flesh colour since they have functional implications for muscle growth, lipid binding and phosphatidylinositol phosphorylation. For sheep breeding industry, ewe productivity is considered the most important economic trait. Genes influencing fertility and lamb growth following parturition in Iranian Baluchi sheep were discovered using GWAS methods and gene set enrichment analysis (GSEA). The notable SNPs within or close to the genes RDX, ARHGAP20, FDX1, THBS1, ZC3H12C, and EPG5 were linked to composite features at birth. The identified pathways and genes have roles relevant to pregnancy, such as autophagy in the placenta, calcium ion transport, placental development and progesterone release and maternal immunological response. Genes NR2C1, HSD17B4, RSU1, VEZT, CUBN, PRLR, VIM. and FTH were discovered to be associated with composite features at weaning. According to Esmaeili-Fard et al., the findings imply that calcium ion transport throughout pregnancy and milk feeding lambs following parturition had the greatest influence on weight gain in comparison to other maternal origin effects . Liao et al. analysed the selectively bred group of meat rabbits and used a non-linear mixed model to fit growth curves and measure the effects of SNPs throughout the entire genome. The genetic organization of growth parameters, which is useful for applying genome selection, was also disclosed in this study, which is the first report of GWAS based on single-step NMM for longitudinal traits in rabbits. The logistic model, comprised of 87,704 SNPs in rabbits, was ideally selected and subjected to GWAS using this approach. The two growth indices mature weight (A) and maturity rate (K) were shown to be simultaneously impacted by a total of 45 important SNPs spread across 5 chromosomes. Seven positional genes were proposed as potential candidates influencing meat rabbit growth, including GBA3, KCNIP4, LDB2, PPARGC1A, GNA13, SHISA3 and FGF10 .GWAS provide opportunities for comprehensive improvement of buffalo by finding genetic variants connected with lactation and reproductive features. The work conducted by Vohra et al., intends to find novel SNPs linked with milk output, lactation consistency, milk composition and fertility features at the genomic level in Murrah buffalo using the genotype-by-sequencing technique. To run GWAS on fat percentages and SNF percentages independently, more than 38,000 SNPs were employed. GWAS was also conducted on 305 days’ worth of milk output to test lactation consistency. SNPs were found to be substantially linked with the first principal component, which explained the greatest fraction of variability in milk output. Breeding effectiveness, post-partum breeding interval, and age at sexual maturity were all taken into account as fertility features. Additionally, certain putative genetic areas that may play a part in controlling milk production and fertility in Murrah were found (p > 0.05) between the prediction accuracies produced from these genomic approaches in each scenario, ssGBLUP and BayesC techniques often demonstrated the maximum predictive performance and unbiasedness, respectively .One of the most important economic traits in swine industry which determines its profitability is the meat quality and genetics has an important role in determining such traits. Ardestani et al., conducted a study to evaluate the accuracy of standard BLUP prediction with several prominent genetic assessment techniques such as GBLUP, ssGBLUP, and BayesC for back-fat thickness (BFT), average daily gain (ADG) and loin muscle depth (LMD) parameters in Canadian swine populations. Despite the absence of any significant differences .The milk cattle breed known as Sahiwal is indigenous to the Indian subcontinent. They are renowned for their high milk production, exceptional ability to adjust to the hot, humid conditions that prevails in their native territory, and resilience to parasites, ticks, and tropical diseases. This study uses a genotyping INDUS chip to investigate the signature of selection in the genome of Sahiwal cattle. The potential signs of selection in Sahiwal cattle were discovered in 14 important regions of the genome. The most prominent locations were mapped onto BTA 6, 20, and 23, and the Pollott et al.). The Pashmina goat genome published in the current edition by Bhat et al., may offers an opportunity to understand the biology of development of this pricy and luxurious fiber . This study is one of the first attempts at providing Pashmina genomics and transcriptomic information. Using the Illumina HiSeq 2500 sequencer, a total of 294.8 GB (>100X coverage) of the whole-genome sequence data was generated from a 26 months old male Changthangi Pashmina goat. Moreover, the genomes of a wild goat and a Pashmina goat were compared, and the results showed a total of 2,823 high impact single nucleotide variants as well as minor insertions and deletions that may be related to the evolution of Pashmina goats. The complex traits of the Pashmina goat, such as annual fibre cycling, defence mechanisms against hypoxia, a method of surviving in extremely cold temperatures, adjustment to a meagre diet, and distinctions of Pashmina fibre from other fibres to avoid marketing practises, can also be understood with the aid of this study.Bioinformatic techniques were used to streamline and accelerate the process into a single step in order to identify the causative variants of full penetrance recessive genetic diseases using only nine whole genome sequenced animals. A novel mutation causing prenatal mortality in Irish moiled calves was found utilising whole genome sequencing of only three situations and six carriers. The variant call format (VCF) data file of these 9 animals was examined using four different techniques: autozygosity-by-difference (ABD), genotyping criteria (GCR), variant prediction scoring, and enrolled SNP information. Only one site (Chr4: g.77173487A>T (ARS-UCD1.2 (GCF 002263795.1)) out of around ten million variants in the VCF file was recognised by all these techniques. The glucokinase gene contained a splice acceptor variation at this location (GCK). This study by Pollott et al., showed that only a limited number of cases and controls are needed, and that controls should exceed cases by a ratio of 2:1 and should be less closely related to cases . Vaughn et al., used a mixed linear model in fixed effects and random effects to investigate the expression of Actinin-3 (ACTN3) gene, which encodes a muscle-specific structural protein, in correlation to the feeding efficiency phenotype in Bos taurus - Bos indicus crossbred steers . It was assumed that animals with a higher proportion of fast-twitch muscle fibres are comparatively feed inefficient because the ACTN3 is exclusive to fast-twitch muscle fibres and absent from slow-twitch muscle fibres.The majority of the effort in genomic selection has focused on computational efficiency and prediction accuracy, but computing restrictions are becoming an increasingly critical factor that must be considered. A Bayes-based genomic selection model named FMixFN was created in a study by Xu et al. It combines consistent prediction ability and computational effectiveness. The needs of large breeding enterprises or combined breeding programs could be satisfied by the reliable, big data-oriented genomic selection approach known as FMixFN. The FMixFN technique is publicly available at Tao et al.). Yak, a unique reservoir of genetic material that primarily lives on the Qinghai-Tibet Plateau. A clear heterosis in production performance may be shown in cattle-yak, the hybrid offspring of yak (Bos grunniens) and cattle (Bos taurus). Through RNA sequencing, 7,126 mRNAs, 791 lncRNAs, and 1,057 circRNAs that differ in expression between yaks and cattle-yaks in the longissimus dorsi muscle were discovered. The ncRNAs were discovered to be associated with the proliferation and differentiation of myoblast cells, skeletal development, and signalling pathway of muscle growth using bioinformatics techniques. Huang et al. claim that this study can be utilised as a benchmark to establish the molecular framework for understanding muscle growth . The blue egg is biologically interesting as well as economically significant for consumers, egg sellers, and scientists. Studies conducted in the past mostly focused on protein-coding genes to understand the genetic pathways behind pigment deposition. The underlying mechanism by which non-coding RNAs affect the pigment deposition in various eggshell colours is still unknown. Profiled the uterine gland transcriptome (lncRNA and mRNA) of 15 Changshun blue eggshell layers using RNA sequencing. 8 and 22 lncRNA-gene pairings were predicted by combining analyses of lncRNA and mRNA profiles. According to mRNA sequencing, the majority of pathways were mostly focused on lipid-related metabolisms. The found lncRNAs influence immunological and lipid-related metabolisms, as well as pigment disposition, to exert similar effects on colour creation . The integrated co-expressed transcriptomes, i.e., mRNA and miRNA, were investigated in primary bovine myoblasts following Resveratrol treatment. According to Hao et al., this study improved our knowledge of the functions of RSV in inducing miRNA, the features of DE miRNAs in the important co-expressed component that regulate mRNAs, and it discovered new potential transcription factors and miRNAs for traits associated with meat quality .The ovulation rate, a critical reproductive characteristic for goats, determines the top limit of the female’s litter size. The rate of ovulation in the ovary is influenced by follicle growth and development. To anticipate how lncRNAs and miRNAs would interact, Tao et al., sequenced and analysed the mRNA expression profiles of pre-ovulatory follicles from goats. Out of the 895 lncRNAs that were found, 88 displayed a clear difference in expression, suggesting important impacts on the ovarian follicles in goats. LncRNA XR_311113.2 might operate as a chi-miR-424-5p sponge. This research demonstrates that LncRNAs may be a valuable new area of investigation in the context of ovarian follicular development . Heat stress in cattle exhibit a direct impact on rumen health by affecting the processes of fermentation and metabolism of rumen papillae proliferating the rumen papillae. It may adversely affect the physical membrane of the rumen epithelium to a significant degree by increasing corneum loss. HS up-regulated biological processes such sister chromatid segregation while down-regulating MAPK and NF-kB cell signalling pathways, according to an examination of the rumen papillae’s transcriptome profile. These pathways were linked to DNA replication and repair, amino acid metabolism, and other pathways that were problematic. TLR4 or Tight junction protein signalling expression did not change specifically in response to heat stress, according to Guo et al., indicating that HS had a limited detrimental impact on the ruminal epithelium’s physical barrier but did not completely destroy it. The development of mitigation techniques as well as the productivity of lactation cows is both impacted by the increase in amino acid metabolism in rumen papillae .He et al., investigated the link between ACSF3 expression and cellular synthesis of triglycerides (TG) by silencing and over-expression of ACSF3. As a result of the discovery that ACSF3 regulates cytoplasmic triacylglycerol and long-chain PUFA levels, polymorphism may be used as a diagnostic biomarker for forthcoming marker-assisted selection in the production of elevated lipid accumulation traits in beef cattle . Khan et al. developed the first comprehensive buffalo user-friendly web genomic resource (BuffGR). It contained genetic research on five buffalo breeds with major commercial importance: the Mediterranean, Egyptian, Bangladeshi, Jaffrarabadi, and Murrah . The website’s database contains information on 4504691 SSR markers across all breeds, 1458 distinct circRNAs, 37712 lncRNAs, and 938 miRNAs from the genomic sequences of the Mediterranean breed of buffalo. This data could be utilised to research the genetic diversity of many buffalo breeds. It is also possible to research post-transcriptional regulation and its function in several bovine diseases. The BuffGR can be found at http://backlin.cabgrid.res.in/buffgr/.A basic description of the inheritance patterns of alternative splicing in broiler and layer chickens is provided by Qi et al. to best explain post-transcriptional regulation during hybridization. The White Leghorn and Cornish Game chicken breeds, which have markedly different body types and reproductive characteristics, were crossed in an experiment, and the muscle, brain, and liver tissues were then sequenced to determine the inheritance patterns. High tissue and strain specificity is suggested by alternative splicing profiles. The majority of the alternative splicing genes had patterns that were conserved across all three organs, according to a study between parental strains and hybrid crossings. This study gives an overview of the alternative splicing inheritance patterns in these chicken breeds . Variable selection ssGBLUP, especially XGBoost, has prediction accuracy that is higher than other ssGBLUP methods without the exaggerated bias and dispersion that come with weighted ssGBLUP. In this study, machine learning algorithms are used, which may provide a solution to the problem of genomic selection in small populations, such as the local cow population. The techniques described in his study may help preserve indigenous cow breeds, study them, and boost their economic competitiveness. For carcass evaluation in meat industry, rib eye area is an important index used. Since this indicator is inherited and possesses genetic diversity, it can be used to improve the genetic makeup of sheep. This study conducted KASPar genotyping on five SNPs, demonstrating that the rib eye region is highly linked with SNPs in LOC105611989, DPP6, and COL12A1. These SNPs could be used as genetic markers for rib eye area molecular breeding. The findings published by Zhao et al., offer genetic factors evaluated on the rib eye region and guidance for breeding Hu sheep based on carcass features .Precision when population size is small is a significant barrier to their genomic selection; during the past several years, variable selection approaches with diverse variance have been proposed to increase breeding value accuracy. While these models might be more accurate than conventional and genetic forecasts, they also carry a correspondingly higher breeding value bias and dispersion. Mancin et al., used a number of diverse techniques in his study to increase the precision of genomic selection in a small population. They were chosen using a variety of algorithms, including XGBoost, penalised regression, and recursive feature eliminations (