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One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bacterial genomes contain signatures of natural evolution and adaptive markers that can be exploited to better understand the behavior of pathogen in the food industry. The monitoring of foodborne strains can therefore be facilitated by the use of these genomic markers capable of rapidly providing essential information on isolated strains, such as the source of contamination, risk of illness, potential for biofilm formation, and tolerance or resistance to biocides. The increasing availability of large genome datasets is enhancing the understanding of the genetic basis of complex traits such as host adaptation, virulence, and persistence. Genome-wide association studies have shown very promising results in the discovery of genomic markers that can be integrated into rapid detection tools. In addition, machine learning has successfully predicted phenotypes and classified important traits. Genome-wide association and machine learning tools have therefore the potential to support decision-making circuits intending at reducing the burden of foodborne diseases. The aim of this chapter review is to provide knowledge on the use of these two methods in food microbiology and to recommend their use in the field.

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Preeclampsia (PE) is a major cause of maternal and neonatal morbidity and mortality worldwide, with the placenta playing a central role in disease pathophysiology. This review synthesizes recent advancements in understanding the molecular mechanisms underlying PE, focusing on placental genes, proteins, and genetic variants identified through multi-omic approaches. Transcriptomic studies in bulk placental tissue have identified many dysregulated genes in the PE placenta, including the PE signature gene, Fms-like tyrosine kinase 1 (FLT1). Emerging single-cell level transcriptomic data have revealed key cell types and molecular signatures implicated in placental dysfunction and PE. However, the considerable variability among studies underscores the need for standardized methodologies and larger sample sizes to enhance the reproducibility of results. Proteomic profiling of PE placentas has identified numerous PE-associated proteins, offering insights into potential biomarkers and pathways implicated in PE pathogenesis. Despite significant progress, challenges such as inconsistencies in study findings and lack of validation persist. Recent fetal genome-wide association studies have identified multiple genetic loci associated with PE, with ongoing efforts to elucidate their impact on placental gene expression and function. Future directions include the integration of multi-omic data, validation of findings in diverse PE populations and clinical subtypes, and the development of analytical approaches and experimental models to study the complex interplay of placental and maternal factors in PE etiology. These insights hold promise for improving risk prediction, diagnosis, and management of PE, ultimately reducing its burden on maternal and neonatal health.

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OBJECTIVES: Recent studies suggest immunophenotypes may play a role in asthma, but their causal relationship has not been thoroughly examined. METHODS: We used single nucleotide polymorphism (SNP)-derived instrumental variables. Summary data from 731 immune cell profiles and asthma cases were analyzed from genome-wide association studies (GWAS) of European populations. Mendelian Randomization (MR) analyses included inverse variance weighted (IVW), weighted median, and MR-Egger methods. Pleiotropy was assessed using the MR-Egger intercept and MR pleiotropy residual sum and outlier (MR-PRESSO) tests. Reverse MR analysis explored bidirectional causation between asthma and immunophenotypes. All statistical analyses were conducted using R software. RESULTS: MR analysis identified 108 immune signatures potentially contributing to asthma. Two immunophenotypes were significantly associated with asthma risk: CD4+ secreting Treg cells in allergic asthma (ORIVW = 1.078; 95% CI: 1.036-1.122; PIVW = 0.0002) and IgD + CD38- %lymphocyte cells in non-allergic asthma (ORIVW = 1.123; 95% CI: 1.057-1.194; PIVW = 0.0002). CONCLUSIONS: This study highlights the causal associations between specific immunophenotypes and asthma risk, providing new insights into asthma pathogenesis.

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Activin A, a cytokine belonging to the transforming growth factor-beta (TGF-ß) superfamily, mediates a multifunctional signaling pathway that is essential for embryonic development, cell differentiation, metabolic regulation, and physiological equilibrium. Biomedical research using diabetes-based model organisms and cellular cultures reports evidence of different activin A levels between diabetic and control groups. Activin A is highly conserved across species and universally expressed among disparate tissues. A systematic review of published literatures on human populations reveals association of plasma activin A levels with diabetic patients in some (7) but not in others (5) of the studies. With summarized data from publicly available genome-wide association studies (GWASs), a two-sample Mendelian randomization (TSMR) analysis is conducted on the causality between the exposure and the outcome. Wald ratio estimates from single instruments are predominantly non-significant. In contrast to positive controls between diabetes and plasma cholesterol levels, inverse-variance-weighted (IVW), Egger, weighted median, and weighted mode MR methods all lead to no observed causal link between diabetes (type 1 and type 2) and plasma activin A levels. Unavailability of strong instruments prevents the reversal MR analysis of activin A on diabetes. In summary, further research is needed to confirm or deny the potential association between diabetes and plasma activin A, and to elucidate the temporal incidence of these traits in human populations. At this stage, no causality has been found between diabetes and plasma activin A based on TSMR analysis.

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Genome-wide association studies (GWASs) have revealed numerous loci associated with breast cancer risk, yet the precise causal variants, their impact on molecular mechanisms, and the affected genes often remain elusive. We hypothesised that specific variants exert their influence by affecting cis-regulatory alternative splice elements. An analysis of splicing quantitative trait loci (sQTL) in healthy breast tissue from female individuals identified multiple variants linked to alterations in splicing ratios. Through colocalisation analysis, we pinpointed 43 variants within twelve genes that serve as candidate causal links between sQTL and GWAS findings. In silico splice analysis highlighted a potential mechanism for three genes-FDPS, SGCE, and MRPL11-where variants in proximity to or on the splice site modulate usage, resulting in alternative splice transcripts. Further in vitro/vivo studies are imperative to fully understand how these identified changes contribute to breast oncogenesis. Moreover, investigating their potential as biomarkers for breast cancer risk could enhance screening strategies and early detection methods for breast cancer.

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The study investigated the causal relationships between spermidine levels and CVD risk factors using a bi-directional MR approach. Employing genetic variants from extensive GWAS datasets as IVs, the study aimed to determine whether spermidine levels can influence CVD risk factors such as blood pressure, blood glucose, and lipid profiles, and vice versa. The findings suggest a protective role of elevated spermidine levels against hypertension, elevated blood glucose, and lipid profiles (LDL-C and HDL-C). Specifically, increased spermidine levels were significantly associated with lower risk of hypertension (IVW beta = -0.0013453913, p = 0.01597648) and suppression risk of elevated blood glucose (IVW beta = -0.08061330, p = 0.02450205). Additionally, there was a notable association with lipid modulation, showing a decrease in LDL-C (IVW beta = -0.01849161, p = 0.01086728) and an increase in HDL-C (IVW beta = 0.0044608332, P = 0.01760051). Conversely, the influence of CVD risk factors on spermidine levels was minimal, with the exception that elevated blood glucose levels resulted in reduced spermidine levels. (IVW beta = -0.06714391, P = 0.01096123). These results underline the potential of spermidine as a modifiable dietary target for the prevention and management of cardiovascular diseases. Further investigations are warranted to explore the underlying biological mechanisms and the applicability of these findings in broader and diverse populations.

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Background: Recent studies have observed the relationships of circulatory and dietary intake of branched-chain amino acids (BCAAs) with long-term risk of certain cancers. However, the exact causality of BCAA with lung cancer (LUCA) and its pathological subtypes remains obscure. The aim of this study is to investigate the association between BCAA metabolism and risk of LUCA. Methods: Here we conducted Mendelian randomization (MR) and observational epidemiological analyses to investigate the association between BCAA and risk of LUCA. With single nucleotide polymorphism (SNP)-phenotype association data extracted from genome-wide association studies (GWAS), we performed univariate and multivariate MR analyses to infer the causal effect of circulatory BCAA concentrations on LUCA. We further investigated the effects of several potential mediators and quantified the mediation effects. Population-level analyses were performed in the National Health and Nutrition Examination Survey (NHANES) III. Results: Our results demonstrated that genetically predicted circulatory valine concentrations causally increased the risk of overall LUCA [odds ratio (OR) =1.324, 95% confidence interval (CI): 1.058-1.658, P=0.01]. For pathological subgroups, elevated levels of leucine, isoleucine, valine, and total BCAA were founded to be significantly associated with a higher risk of squamous cell lung cancer (LUSC); however, they did not significantly affect lung adenocarcinoma (LUAD). Moreover, body mass index (BMI) mediated approximately 3.91% (95% CI: 1.22-7.18%) of the total effect of leucine on LUSC. In the NHANES III population, dietary total BCAA intake was significantly associated with BMI ≥30 kg/m2, while no non-linear relationships were observed. Conclusions: This study provides genetic evidence for the histology-specific causality of BCAA on LUCA and implies the mediation role of BMI in this relationship. Further studies are needed to confirm these findings and elucidate the underlying mechanisms.

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Cadmium (Cd) is a dangerous environmental contaminant. Jute (Corchorus sp.) is an important natural fiber crop with strong absorption and excellent adaptability to metal-stressed environments, used in the phytoextraction of heavy metals. Understanding the genetic and molecular mechanisms underlying Cd tolerance and accumulation in plants is essential for efficient phytoremediation strategies and breeding novel Cd-tolerant cultivars. Here, machine learning (ML) and hyperspectral imaging (HSI) combining genome-wide association studies (GWAS) and RNA-seq reveal the genetic basis of Cd resistance and absorption in jute. ML needs a small number of plant phenotypes for training and can complete the plant phenotyping of large-scale populations with efficiency and accuracy greater than 90%. In particular, a candidate gene for Cd resistance (COS02g_02406) and a candidate gene (COS06g_03984) associated with Cd absorption are identified in isoflavonoid biosynthesis and ethylene response signaling pathways. COS02g_02406 may enable plants to cope with metal stress by regulating isoflavonoid biosynthesis involved in antioxidant defense and metal chelation. COS06g_03984 promotes the binding of Cd2+ to ETR/ERS, resulting in Cd absorption and tolerance. The results confirm the feasibility of high-throughput phenotyping for studying plant Cd tolerance by combining HSI and ML approaches, facilitating future molecular breeding.

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This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. We used 11,818 individuals (T2D cases: 2,210) with genotype (649,932 SNPs) and pedigree information from the ongoing periodic cohort study of the Iranian population project. We considered three different familial structure scenarios, including (i) all families, (ii) all families with ≥ 1 generation, and (iii) families with ≥ 1 generation in which both case and control individuals are presented. Comprehensive simulation strategies were implemented to quantify the difference between estimates of [Formula: see text] and [Formula: see text]. A proportion of still-missing heritability in T2D could be explained by overestimation of pedigree-based heritability due to the presence of families with individuals having only one of the two disease statuses. Our research findings underscore the significance of including families with only case/control individuals in cohort studies. The presence of such family structures (as observed in scenarios i and ii) contributes to a more accurate estimation of disease heritability, addressing the underestimation that was previously overlooked in prior research. However, when predicting disease risk, the absence of these families (as seen in scenario iii) can yield the highest prediction accuracy and the strongest correlation with Polygenic Risk Scores. Our findings represent the first evidence of the important contribution of familial structure for heritability estimations and genomic prediction studies in T2D.

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BACKGROUND: Few rare variants have been identified in genetic loci from genome-wide association studies (GWAS) of Alzheimer's disease (AD), limiting understanding of mechanisms, risk assessment, and genetic counseling. METHODS: Using genome sequencing data from 197 families in the National Institute on Aging Alzheimer's Disease Family Based Study and 214 Caribbean Hispanic families, we searched for rare coding variants within known GWAS loci from the largest published study. RESULTS: Eighty-six rare missense or loss-of-function (LoF) variants completely segregated in 17.5% of families, but in 91 (22.1%) families Apolipoprotein E (APOE)-𝜀4 was the only variant segregating. However, in 60.3% of families, APOE 𝜀4, missense, and LoF variants were not found within the GWAS loci. DISCUSSION: Although APOE 𝜀4and several rare variants were found to segregate in both family datasets, many families had no variant accounting for their disease. This suggests that familial AD may be the result of unidentified rare variants. HIGHLIGHTS: Rare coding variants from GWAS loci segregate in familial Alzheimer's disease. Missense or loss of function variants were found segregating in nearly 7% of families. APOE-𝜀4 was the only segregating variant in 29.7% in familial Alzheimer's disease. In Hispanic and non-Hispanic families, different variants were found in segregating genes. No coding variants were found segregating in many Hispanic and non-Hispanic families.

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This study offers insights into the genetic and biological connections between nine common metabolic diseases using data from genome-wide association studies. Our goal is to unravel the genetic interactions and biological pathways of these complex diseases, enhancing our understanding of their genetic architecture. We employed a range of advanced analytical techniques to explore the genetic correlations and shared genetic variants of these diseases. These methods include Linked Disequilibrium Score Regression, High-Definition Likelihood (HDL), genetic analysis combining multiplicity and annotation (GPA), two-sample Mendelian randomization analyses, analysis under the multiplicity-complex null hypothesis (PLACO), and Functional mapping and annotation of genetic associations (FUMA). Additionally, Bayesian co-localization analyses were used to examine associations of specific loci across traits. Our study discovered significant genomic correlations and shared loci, indicating complex genetic interactions among these metabolic diseases. We found several shared single nucleotide variants and risk loci, notably highlighting the role of the immune system and endocrine pathways in these diseases. Particularly, rs2476601 and its associated gene PTPN22 appear to play a crucial role in the connection between type 2 diabetes mellitus, hypothyroidism/mucous oedema and hypoglycaemia. These findings enhance our understanding of the genetic underpinnings of these diseases and open new potential avenues for targeted therapeutic and preventive strategies. The results underscore the importance of considering pleiotropic effects in deciphering the genetic architecture of complex diseases, especially metabolic ones.

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Soybean Glycine max L., paleopolyploid genome, poses challenges to its genetic improvement. However, the development of reference genome assemblies and genome sequencing has completely changed the field of soybean genomics, allowing for more accurate and successful breeding techniques as well as research. During the single-cell revolution, one of the most advanced sequencing tools for examining the transcriptome landscape is single-cell RNA sequencing (scRNA-seq). Comprehensive resources for genetic improvement of soybeans may be found in the SoyBase and other genomics databases. CRISPR-Cas9 genome editing technology provides promising prospects for precise genetic modifications in soybean. This method has enhanced several soybean traits, including as yield, nutritional value, and resistance to both biotic and abiotic stresses. With base editing techniques that allow for precise DNA modifications, the use of CRISPR-Cas9 is further increased. With the availability of the reference genome for soybeans and the following assembly of wild and cultivated soybeans, significant chromosomal rearrangements and gene duplication events have been identified, offering new perspectives on the complex genomic structure of soybeans. Furthermore, major single nucleotide polymorphisms (SNPs) linked to stachyose and sucrose content have been found through genome-wide association studies (GWAS), providing important tools for enhancing soybean carbohydrate profiles. In order to open up new avenues for soybean genetic improvement, future research approaches include investigating transcriptional divergence processes, enhancing genetic resources, and incorporating CRISPR-Cas9 technologies.

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Infections with the coronavirus disease 2019 (COVID-19) and disorders of the heart and blood vessels are causally related. To ascertain the causal relationship between COVID-19 and cardiovascular disease (CVD), we carried out a Mendelian randomization (MR) study through a method known as inverse variance weighting (IVW). When analyzing multiple SNPs, MR can meta-aggregate the effects of multiple loci by using IVW meta-pooling method. The weighted median (WM) is the median of the distribution function obtained by ranking all individual SNP effect values according to their weights. WM yields robust estimates when at least 50% of the information originates from valid instrumental variables (IVs). Directed gene pleiotropy in the included IVs is permitted because MR-Egger does not require a regression straight line through the origin. For MR estimation, IVW, WM and MR-Egger were employed. Sensitivity analysis was conducted using funnel plots, Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis. SNPs related to exposure to COVID-19 and CVD were compiled. CVD for COVID-19 infection, COVID-19 laboratory/self-reported negative, and other very severe respiratory diagnosis and population were randomly assigned using MR. The COVID-19 laboratory/self-reported negative results and other very severe respiratory confirmed cases versus MR analysis of CVD in the population (p â€‹> â€‹0.05); COVID-19 infection to CVD (p â€‹= â€‹0.033, OR â€‹= â€‹1.001, 95%CI: 1.000-1.001); and the MR-Egger results indicated that COVID-19 infection was associated with CVD risk. This MR study provides preliminary evidence for the validity of the causal link between COVID-19 infection and CVD.

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Genotyping single nucleotide polymorphisms (SNPs) is fundamental to disease research, as researchers seek to establish links between genetic variation and disease. Although significant advances in genome technology have been made with the development of bead-based SNP genotyping and Genome Studio software, some SNPs still fail to be genotyped, resulting in "no-calls" that impede downstream analyses. To recover these genotypes, we introduce Cluster Buster, a genotyping neural network and visual inspection system designed to improve the quality of neurodegenerative disease (NDD) research. Concordance analysis with whole genome sequencing (WGS) and imputed genotypes validated the reliability of predicted genotypes, with dozens of high-performing SNPs across LRRK2, APOE, and GBA loci achieving at least 90% concordance per SNP location. Further analysis of concordance between Genome Studio genotypes and imputed and WGS genotypes revealed discrepancies between the genotyping technologies, highlighting the need for selective application of Cluster Buster on SNP locations based on concordance rates. Cluster Buster's implementation significantly reduces manual labor for recovering no-call SNPs, refining genotype quality for the Global Parkinson's Genetics Program (GP2). This system facilitates better imputation and GWAS outcomes, ultimately contributing to a deeper understanding of genetic factors in NDDs.

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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 GWAS 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 demonstrates the potential of leveraging existing genetic data and accounting for nonlinear genetic relationships to improve prediction accuracy for some traits.

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BACKGROUND: Observational studies have reported associations between primary aldosteronism (PA) and cardiovascular outcomes, including coronary artery diseases (CAD), congestive heart failure (CHF), and stroke. However, establishing causality remains a challenge due to the lack of randomized controlled trial data on this topic. We thus aimed to investigate the causal relationship between PA and the risk of developing CAD, CHF, and stroke. METHODS AND RESULTS: Cross-ancestry meta-analysis of genome-wide association studies combining East Asian and European ancestry (1560 PA cases and 742 139 controls) was conducted to identify single-nucleotide variants that are associated with PA. Then, using the identified genetic variants as instrumental variables, we conducted the 2-sample Mendelian randomization analysis to investigate the causal relationship between PA and incident CAD, CHF, and stroke among both East Asian and European ancestry. Summary association results were extracted from large genome-wide association studies consortia. Our cross-ancestry meta-analysis of East Asian and European populations identified 7 genetic loci significantly associated with the risk of PA, for which the genes nearest to the lead variants were CASZ1, WNT2B, HOTTIP, LSP1, TBX3, RXFP2, and NDP. Among the East Asian population, the pooled odds ratio estimates using these 7 genetic instruments of PA were 1.07 (95% CI, 1.03-1.11) for CAD, 1.10 (95% CI, 1.01-1.20) for CHF, and 1.13 (95% CI, 1.09-1.18) for stroke. The results were consistent among the European population. CONCLUSIONS: Our 2-sample Mendelian randomization study revealed that PA had increased risks of CAD, CHF, and stroke. These findings highlight that early and active screening of PA is critical to prevent future cardiovascular events.

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BACKGROUND: In the face of contemporary climatic vulnerabilities and escalating global temperatures, the prevalence of maydis leaf blight (MLB) poses a potential threat to maize production. This study endeavours to discern marker-trait associations and elucidate the candidate genes that underlie resistance to MLB in maize by employing a diverse panel comprising 336 lines. The panel was screening for MLB across four environments, employing standard artificial inoculation techniques. Genome-wide association studies (GWAS) and haplotype analysis were conducted utilizing a total of 128,490 SNPs obtained from genotyping-by-sequencing (GBS). RESULTS: GWAS identified 26 highly significant SNPs associated with MLB resistance, among the markers examined. Seven of these SNPs, reported in novel chromosomal bins (9.06, 5.01, 9.01, 7.04, 4.06, 1.04, and 6.05) were associated with genes: bzip23, NAGS1, CDPK7, aspartic proteinase NEP-2, VQ4, and Wun1, which were characterized for their roles in diminishing fungal activity, fortifying defence mechanisms against necrotrophic pathogens, modulating phyto-hormone signalling, and orchestrating oxidative burst responses. Gene mining approach identified 22 potential candidate genes associated with SNPs due to their functional relevance to resistance against necrotrophic pathogens. Notably, bin 8.06, which hosts five SNPs, showed a connection to defense-regulating genes against MLB, indicating the potential formation of a functional gene cluster that triggers a cascade of reactions against MLB. In silico studies revealed gene expression levels exceeding ten fragments per kilobase million (FPKM) for most genes and demonstrated coexpression among all candidate genes in the coexpression network. Haplotype regression analysis revealed the association of 13 common significant haplotypes at Bonferroni ≤ 0.05. The phenotypic variance explained by these significant haplotypes ranged from low to moderate, suggesting a breeding strategy that combines multiple resistance alleles to enhance resistance to MLB. Additionally, one particular haplotype block (Hap_8.3) was found to consist of two SNPs (S8_152715134, S8_152460815) identified in GWAS with 9.45% variation explained (PVE). CONCLUSION: The identified SNPs/ haplotypes associated with the trait of interest contribute to the enrichment of allelic diversity and hold direct applicability in Genomics Assisted Breeding for enhancing MLB resistance in maize.

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Background: Previous epidemiological studies have reported an association between Sjögren's syndrome (SS) and Parkinson's disease (PD); however, the causality and direction of this relationship remain unclear. In this study, we aimed to investigate the causal relationship between genetically determined SS and the risk of PD using bidirectional Mendelian randomization (MR). Methods: Summary statistics for Sjögren's syndrome used as exposure were obtained from the FinnGen database, comprising 1,290 cases and 213,145 controls. The outcome dataset for PD was derived from the United Kingdom Biobank database, including 6,998 cases and 415,466 controls. Various MR methods, such as inverse variance weighted (IVW), Mendelian randomization Egger regression (MR-Egger), weighted median (WM), simple mode, weighted mode, MR-pleiotropy residual sum and outlier (MR-PRESSO), and robust adjusted profile score (RAPS), were employed to investigate the causal effects of SS on PD. Instrumental variable strength evaluation and sensitivity analyses were conducted to ensure the reliability of the results. In addition, reverse MR analysis was performed to examine the causal effects of PD on SS. Results: The WM, IVW, RAPS and MR-PRESSO methods demonstrated a significant association between genetically predicted SS and reduced risk of PD (odds ratio ORWM = 0.9988, ORIVW = 0.9987, ORRAPS = 0.9987, ORMR-PRESSO = 0.9987, respectively, P < 0.05). None of the MR analyses showed evidence of horizontal pleiotropy (P > 0.05) based on the MR-Egger and MR-PRESSO tests, and there was no statistical heterogeneity in the test results of the MR-Egger and IVW methods. The leave-one-out sensitivity analysis confirmed the robustness of the causal relationship between SS and PD. Furthermore, reverse MR analysis did not support any causal effects of PD on SS. Conclusion: Our MR study supports a potential causal association between SS and a reduced risk of PD. Further extensive clinical investigations and comprehensive fundamental research are warranted to elucidate the underlying mechanisms linking SS and PD.

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The objective of this study was to identify genomic regions and candidate genes associated with productive traits using a total of 37,099 productive records and 6,683 single nucleotide polymorphism (SNP) data obtained from five Great-Grand-Parents (GGP) farms in Landrace. The estimated of heritabilities for days to 105 kg (AGE), average daily gain (ADG), backfat thickness (BF), and eye muscle area (EMA) were 0.49, 0.49, 0.56, and 0.23, respectively. We identified a genetic window that explained 2.05%-2.34% for each trait of the total genetic variance. We observed a clear partitioning of the four traits into two groups, and the most significant genomic region for AGE and ADG were located on the Sus scrofa chromosome (SSC) 1, while BF and EMA were located on SSC 2. We conducted Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), which revealed results in three biological processes, four cellular component, three molecular function, and six KEGG pathway. Significant SNPs can be used as markers for quantitative trait loci (QTL) investigation and genomic selection (GS) for productive traits in Landrace pig.

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Cattle are regarded as highly valuable animals because of their milk, beef, dung, fur, and ability to draft. The scientific community has tried a number of strategies to improve the genetic makeup of bovine germplasm. To ensure higher returns for the dairy and beef industries, researchers face their greatest challenge in improving commercially important traits. One of the biggest developments in the last few decades in the creation of instruments for cattle genetic improvement is the discovery of the genome. Breeding livestock is being revolutionized by genomic selection made possible by the availability of medium- and high-density single nucleotide polymorphism (SNP) arrays coupled with sophisticated statistical techniques. It is becoming easier to access high-dimensional genomic data in cattle. Continuously declining genotyping costs and an increase in services that use genomic data to increase return on investment have both made a significant contribution to this. The field of genomics has come a long way thanks to groundbreaking discoveries such as radiation-hybrid mapping, in situ hybridization, synteny analysis, somatic cell genetics, cytogenetic maps, molecular markers, association studies for quantitative trait loci, high-throughput SNP genotyping, whole-genome shotgun sequencing to whole-genome mapping, and genome editing. These advancements have had a significant positive impact on the field of cattle genomics. This manuscript aimed to review recent advances in genomic technologies for cattle breeding and future prospects in this field.