Methods for Functional Characterization of Genetic Polymorphisms of Non-Coding Regulatory Regions of the Human Genome.

Currently, numerous associations between genetic polymorphisms and various diseases have been characterized through the Genome-Wide Association Studies. Majority of the clinically significant polymorphisms are localized in non-coding regions of the genome. While modern bioinformatic resources make it possible to predict molecular mechanisms that explain influence of the non-coding polymorphisms on gene expression, such hypotheses require experimental verification. This review discusses the methods for elucidating molecular mechanisms underlying dependence of the disease pathogenesis on specific genetic variants within the non-coding sequences. A particular focus is on the methods for identification of transcription factors with binding efficiency dependent on polymorphic variations. Despite remarkable progress in bioinformatic resources enabling prediction of the impact of polymorphisms on the disease pathogenesis, there is still the need for experimental approaches to investigate this issue.

Genetic mapping of regions associated with root system architecture in rice using MutMap QTL-seq.

The root system architecture is an important complex trait in rice. With changing climatic conditions and soil nutrient deficiencies, there is an immediate need to breed nutrient-use-efficient rice varieties with robust root system architectural (RSA) traits. To map the genomic regions associated with crucial component traits of RSA viz. root length and root volume, a biparental F2 mapping population was developed using TI-128, an Ethyl Methane Sulphonate (EMS) mutant of a mega variety BPT-5204 having high root length (RL) and root volume (RV) with wild type BPT-5204. Extreme bulks having high RL and RV and low RL and RV were the whole genome re-sequenced along with parents. Genetic mapping using the MutMap QTL-Seq approach elucidated two genomic intervals on Chr.12 (3.14-3.74 Mb, 18.11-20.85 Mb), and on Chr.2 (23.18-23.68 Mb) as potential regions associated with both RL and RV. The Kompetitive Allele Specific PCR (KASP) assays for SNPs with delta SNP index near 1 were associated with higher RL and RV in the panel of sixty-two genotypes varying in root length and volume. The KASP_SNPs viz. Chr12_S4 (C→T; Chr12:3243938), located in the 3' UTR region of LOC_Os12g06670 encoding a protein kinase domain-containing protein and Chr2_S6 (C→T; Chr2:23181622) present upstream in the regulator of chromosomal condensation protein LOC_Os2g38350. Validation of these genes using qRT-PCR and in-silico studies using various online tools and databases revealed higher expression in TI-128 as compared to BPT- 5204 at the seedling and panicle initiation stages implying the functional role in enhancing RL and RV.

Unraveling the genetic basis of methane emission in dairy cattle: a comprehensive exploration and breeding approach to lower methane emissions.

Ruminant animals, such as dairy cattle, produce CH4, which contributes to global warming emissions and reduces dietary energy for the cows. While the carbon foot print of milk production varies based on production systems, milk yield and farm management practices, enteric fermentation, and manure management are major contributors togreenhouse gas emissions from dairy cattle. Recent emerging evidence has revealed the existence of genetic variation for CH4 emission traits among dairy cattle, suggests their potential inclusion in breeding goals and genetic selection programs. Advancements in high-throughput sequencing technologies and analytical techniques have enabled the identification of potential metabolic biomarkers, candidate genes, and SNPs linked to methane emissions. Indeed, this review critically examines our current understanding of carbon foot print in milk production, major emission sources, rumen microbial community and enteric fermentation, and the genetic architecture of methane emission traits in dairy cattle. It also emphasizes important implications for breeding strategies aimed at halting methane emissions through selective breeding, microbiome driven breeding, breeding for feed efficiency, and breeding by gene editing.

Assessing myBaits Target Capture Sequencing Methodology Using Short-Read Sequencing for Variant Detection in Oat Genomics and Breeding.

The integration of target capture systems with next-generation sequencing has emerged as an efficient tool for exploring specific genetic regions with a high resolution and facilitating the rapid discovery of novel alleles. Despite these advancements, the application of targeted sequencing methodologies, such as the myBaits technology, in polyploid oat species remains relatively unexplored. In this study, we utilized the myBaits target capture method offered by Daicel Arbor Biosciences to detect variants and assess their reliability for variant detection in oat genomics and breeding. Ten oat genotypes were carefully chosen for targeted sequencing, focusing on specific regions on chromosome 2A to detect variants. The selected region harbors 98 genes. Precisely designed baits targeting the genes within these regions were employed for the target capture sequencing. We employed various mappers and variant callers to identify variants. After the identification of variants, we focused on the variants identified via all variants callers to assess the applicability of the myBaits sequencing methodology in oat breeding. In our efforts to validate the identified variants, we focused on two SNPs, one deletion and one insertion identified via all variant callers in the genotypes KF-318 and NOS 819111-70 but absent in the remaining eight genotypes. The Sanger sequencing of targeted SNPs failed to reproduce target capture data obtained through the myBaits technology. Similarly, the validation of deletion and insertion variants via high-resolution melting (HRM) curve analysis also failed to reproduce target capture data, again suggesting limitations in the reliability of the myBaits target capture sequencing using short-read sequencing for variant detection in the oat genome. This study shed light on the importance of exercising caution when employing the myBaits target capture strategy for variant detection in oats. This study provides valuable insights for breeders seeking to advance oat breeding efforts and marker development using myBaits target capture sequencing, emphasizing the significance of methodological sequencing considerations in oat genomics research.

Comparative Bioinformatic Analysis Reveals Conserved Regions in SARS-CoV-2 Genome for RAPID Pandemic Response.

In the face of the SARS-CoV-2 pandemic, characterized by the virus's rapid mutation rates, developing timely and targeted therapeutic and diagnostic interventions presents a significant challenge. This study utilizes bioinformatic analyses to pinpoint conserved genomic regions within SARS-CoV-2, offering a strategic advantage in the fight against this and future pathogens. Our approach has enabled the creation of a diagnostic assay that is not only rapid, reliable, and cost-effective but also possesses a remarkable capacity to detect a wide array of current and prospective variants with unmatched precision. The significance of our findings lies in the demonstration that focusing on these conserved genomic sequences can significantly enhance our preparedness for and response to emerging infectious diseases. By providing a blueprint for the development of versatile diagnostic tools and therapeutics, this research paves the way for a more effective global pandemic response strategy.

Effect of genotyping density on the detection of runs of homozygosity and heterozygosity in cattle.

Runs of homozygosity (ROHom) are contiguous stretches of homozygous regions of the genome. In contrast, runs of heterozygosity (ROHet) are heterozygosity-rich regions. The detection of these two types of genomic regions (ROHom and ROHet) is influenced by the parameters involved in their identification and the number of available single-nucleotide polymorphisms (SNPs). The present study aimed to test the effect of chip density in detecting ROHom and ROHet in the Italian Simmental cattle breed. A sample of 897 animals were genotyped at low density (50k SNP; 397 individuals), medium density (140k SNP; 348 individuals), or high density (800k SNP; 152 individuals). The number of ROHom and ROHet per animal (nROHom and nROHet, respectively) and their average length were calculated. ROHom or ROHet shared by more than one animal and the number of times a particular SNP was inside a run were also computed (SNPROHom and SNPROHet). As the chip density increased, the nROHom increased, whereas their average length decreased. In contrast, the nROHet decreased and the average length increased as the chip density increased. The most repeated ROHom harbored no genes, whereas in the most repeated ROHet four genes (SNRPN, SNURF, UBE3A, and ATP10A) previously associated with reproductive traits were found. Across the 3 datasets, 31 SNP, located on Bos taurus autosome (BTA) 6, and 37 SNP (located on BTA21) exceeded the 99th percentile in the distribution of the SNPROHom and SNPROHet, respectively. The genomic region on BTA6 mapped the SLIT2, PACRGL, and KCNIP4 genes, whereas 19 and 18 genes were mapped on BTA16 and BTA21, respectively. Interestingly, most of genes found through the ROHet analysis were previously reported to be related to health, reproduction, and fitness traits. The results of the present study confirm that the detection of ROHom is more reliable when the chip density increases, whereas the ROHet trend seems to be the opposite. Genes and quantitative trait loci (QTL) mapped in the highlighted regions confirm that ROHet can be due to balancing selection, thus related to fitness traits, health, and reproduction, whereas ROHom are mainly involved in production traits. The results of the present study strengthened the usefulness of these parameters in analyzing the genomes of livestock and their biological meaning.

Runs of homozygosity (ROHom), continuous stretches of homozygous loci, and runs of heterozygosity (ROHet), continuous stretches of heterozygous loci, may be due to directional (ROHom) or balancing selection (ROHet) and are interesting to analyze those shared among animals within a population and the genes they harbor. The detection of both types of genomic regions is influenced by genotyping density and involved parameters. Thus, this work aimed to study the impact of the BeadChip density on the ROHom and ROHet detection in the Italian Simmental cattle breed. Results showed that the ROHom detection is more reliable as the density increases, whereas a more cryptic pattern was observed for ROHet. Interestingly, the hypothesis on how these two types of runs arise was supplied by the results of this study. The genes mapped on the highlighted ROHet were mainly associated with fitness traits, health, and reproduction, whereas those found in the ROHom were associated with production traits.

Dissecting Selective Signatures and Candidate Genes in Grandparent Lines Subject to High Selection Pressure for Broiler Production and in a Local Russian Chicken Breed of Ushanka.

Breeding improvements and quantitative trait genetics are essential to the advancement of broiler production. The impact of artificial selection on genomic architecture and the genetic markers sought remains a key area of research. Here, we used whole-genome resequencing data to analyze the genomic architecture, diversity, and selective sweeps in Cornish White (CRW) and Plymouth Rock White (PRW) transboundary breeds selected for meat production and, comparatively, in an aboriginal Russian breed of Ushanka (USH). Reads were aligned to the reference genome bGalGal1.mat.broiler.GRCg7b and filtered to remove PCR duplicates and low-quality reads using BWA-MEM2 and bcftools software; 12,563,892 SNPs were produced for subsequent analyses. Compared to CRW and PRW, USH had a lower diversity and a higher genetic distinctiveness. Selective sweep regions and corresponding candidate genes were examined based on ZFST, hapFLK, and ROH assessment procedures. Twenty-seven prioritized chicken genes and the functional projection from human homologs suggest their importance for selection signals in the studied breeds. These genes have a functional relationship with such trait categories as body weight, muscles, fat metabolism and deposition, reproduction, etc., mainly aligned with the QTLs in the sweep regions. This information is pivotal for further executing genomic selection to enhance phenotypic traits.

Weighted single-step genome-wide association study and functional enrichment analyses for gastrointestinal nematode resistance traits in Santa Ines sheep.

This study aimed to identify genomic regions, pathways, and putative candidate genes associated with resistance to gastrointestinal nematode in Santa Ines sheep. The phenotypic information comprised 5529 records from 1703 naturally infected animals. After genomic data quality control, 37,511 SNPs from 589 animals were available. The weighted single-step approach for genome-wide association study was performed to estimate the SNP effects and variances accounted by 10-SNP sliding windows. Confirming the polygenic nature of the studied traits, 20, 22, 21, and 19 genomic windows that explained more than 0.5% of the additive genetic variance were identified for fecal egg counts (FEC), Famacha© (FAM), packed cell volume (PCV), and total plasma protein (TPP), respectively. A total of 81, 122, 106, and 101 protein-coding genes were found in windows associated with FEC, FAM, PCV, and TPP, respectively. Several protein-coding genes related to the immune system and inflammatory response functions were identified within those genomic regions, such as ADCY9, ADRB2, BRAF, CADM1, CCL20, CD70, CREBBP, FNBP1, HTR4, IL16, IL22, IL26, MAPK8, NDFIP1, NLRC3, PAK5, PLCB1, PLCB4, ROCK1, TEK, TNFRSF12A, and VAV1. Functional enrichment analysis by DAVID tool also revealed many significant (P < 0.05) pathways and Gene Ontology terms that could be related to resistance to gastrointestinal nematode in Santa Ines sheep, such as chemokine signaling pathway (oas04062), cAMP signaling pathway (oas04024), cGMP-PKG signaling pathway (Oas04022), platelet activation (Oas04611), Rap1 signaling pathway (oas04015), and oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen (GO:0016705). These results contribute to improving the knowledge of the genetic architecture of resistance to gastrointestinal nematode in Santa Ines sheep.

Candidate genes associated with heat stress and breeding strategies to relieve its effects in dairy cattle: a deeper insight into the genetic architecture and immune response to heat stress.

The need for food products of animal origin is increasing worldwide. Satisfying these needs in a way that has minimal impact on the environment requires cutting-edge technologies and techniques to enhance the genetic quality of cattle. Heat stress (HS), in particular, is affecting dairy cattle with increasing frequency and severity. As future climatic challenges become more evident, identifying dairy cows that are more tolerant to HS will be important for breeding dairy herds that are better adapted to future environmental conditions and for supporting the sustainability of dairy farming. While research into the genetics of HS in the context of the effect of global warming on dairy cattle is gaining momentum, the specific genomic regions involved in heat tolerance are still not well documented. Advances in omics information, QTL mapping, transcriptome profiling and genome-wide association studies (GWAS) have identified genomic regions and variants associated with tolerance to HS. Such studies could provide deeper insights into the genetic basis for response to HS and make an important contribution to future breeding for heat tolerance, which will help to offset the adverse effects of HS in dairy cattle. Overall, there is a great interest in identifying candidate genes and the proportion of genetic variation associated with heat tolerance in dairy cattle, and this area of research is currently very active worldwide. This review provides comprehensive information pertaining to some of the notable recent studies on the genetic architecture of HS in dairy cattle, with particular emphasis on the identified candidate genes associated with heat tolerance in dairy cattle. Since effective breeding programs require optimal knowledge of the impaired immunity and associated health complications caused by HS, the underlying mechanisms by which HS modulates the immune response and renders animals susceptible to various health disorders are explained. In addition, future breeding strategies to relieve HS in dairy cattle and improve their welfare while maintaining milk production are discussed.

Linked candidate genes of different functions for white mold resistance in common bean (Phaseolus vulgaris L) are identified by multiple QTL mapping approaches.

White mold (WM) is a major disease in common bean (Phaseolus vulgaris L.), and its complex quantitative genetic control limits the development of WM resistant cultivars. WM2.2, one of the nine meta-QTL with a major effect on WM tolerance, explains up to 35% of the phenotypic variation and was previously mapped to a large genomic interval on Pv02. Our objective was to narrow the interval of this QTL using combined approach of classic QTL mapping and QTL-based bulk segregant analysis (BSA), and confirming those results with Khufu de novo QTL-seq. The phenotypic and genotypic data from two RIL populations, 'Raven'/I9365-31 (R31) and 'AN-37'/PS02-029C-20 (Z0726-9), were used to select resistant and susceptible lines to generate subpopulations for bulk DNA sequencing. The QTL physical interval was determined by considering overlapping interval of the identified QTL or peak region in both populations by three independent QTL mapping analyses. Our findings revealed that meta-QTL WM2.2 consists of three regions, WM2.2a (4.27-5.76 Mb; euchromatic), WM 2.2b (12.19 to 17.61 Mb; heterochromatic), and WM2.2c (23.01-25.74 Mb; heterochromatic) found in both populations. Gene models encoding for gibberellin 2-oxidase 8, pentatricopeptide repeat, and heat-shock proteins are the likely candidate genes associated with WM2.2a resistance. A TIR-NBS-LRR class of disease resistance protein (Phvul.002G09200) and LRR domain containing family proteins are potential candidate genes associated with WM2.2b resistance. Nine gene models encoding disease resistance protein [pathogenesis-related thaumatin superfamily protein and disease resistance-responsive (dirigent-like protein) family protein etc] found within the WM2.2c QTL interval are putative candidate genes. WM2.2a region is most likely associated with avoidance mechanisms while WM2.2b and WM2.2c regions trigger physiological resistance based on putative candidate genes.

OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations.

BACKGROUND: Modern genome sequencing leads to an ever-growing collection of genomic annotations. Combining these elements with a set of input regions (e.g. genes) would yield new insights in genomic associations, such as those involved in gene regulation. The required data are scattered across different databases making a manual approach tiresome, unpractical, and prone to error. Semi-automatic approaches require programming skills in data parsing, processing, overlap calculation, and visualization, which most biomedical researchers lack. Our aim was to develop an automated tool providing all necessary algorithms, benefiting both bioinformaticians and researchers without bioinformatic training. RESULTS: We developed overlapping annotated genomic regions (OGRE) as a comprehensive tool to associate and visualize input regions with genomic annotations. It does so by parsing regions of interest, mining publicly available annotations, and calculating possible overlaps between them. The user can thus identify location, type, and number of associated regulatory elements. Results are presented as easy to understand visualizations and result tables. We applied OGRE to recent studies and could show high reproducibility and potential new insights. To demonstrate OGRE's performance in terms of running time and output, we have conducted a benchmark and compared its features with similar tools. CONCLUSIONS: OGRE's functions and built-in annotations can be applied as a downstream overlap association step, which is compatible with most genomic sequencing outputs, and can thus enrich pre-existing analyses pipelines. Compared to similar tools, OGRE shows competitive performance, offers additional features, and has been successfully applied to two recent studies. Overall, OGRE addresses the lack of tools for automatic analysis, local genomic overlap calculation, and visualization by providing an easy to use, end-to-end solution for both biologists and computational scientists.

Genetic mapping identified three hotspot genomic regions and candidate genes controlling heat tolerance-related traits in groundnut.

Groundnut productivity and quality have been impeded by rising temperatures in semi-arid environments. Hence, understanding the effects and molecular mechanisms of heat stress tolerance will aid in tackling yield losses. In this context, a recombinant inbred line (RIL) population was developed and phenotyped for eight seasons at three locations for agronomic, phenological, and physiological traits under heat stress. A genetic map was constructed using genotyping-by-sequencing with 478 single-nucleotide polymorphism (SNP) loci spanning a map distance of 1,961.39 cM. Quantitative trait locus (QTL) analysis using phenotypic and genotypic data identified 45 major main-effect QTLs for 21 traits. Intriguingly, three QTL clusters (Cluster-1-Ah03, Cluster-2-Ah12, and Cluster-3-Ah20) harbor more than half of the major QTLs (30/45, 66.6%) for various heat tolerant traits, explaining 10.4%-38.6%, 10.6%-44.6%, and 10.1%-49.5% of phenotypic variance, respectively. Furthermore, important candidate genes encoding DHHC-type zinc finger family protein (arahy.J0Y6Y5), peptide transporter 1 (arahy.8ZMT0C), pentatricopeptide repeat-containing protein (arahy.4A4JE9), Ulp1 protease family (arahy.X568GS), Kelch repeat F-box protein (arahy.I7X4PC), FRIGIDA-like protein (arahy.0C3V8Z), and post-illumination chlorophyll fluorescence increase (arahy.92ZGJC) were the underlying three QTL clusters. The putative functions of these genes suggested their involvement in seed development, regulating plant architecture, yield, genesis and growth of plants, flowering time regulation, and photosynthesis. Our results could provide a platform for further fine mapping, gene discovery, and developing markers for genomics-assisted breeding to develop heat-tolerant groundnut varieties.

PCR based early detection and antibiotic resistance pattern of Salmonella Gallinarum isolates from Pakistan poultry.

The poultry industry in developing countries is still combating mortality and economic loss due to Salmonella contamination. Salmonella Gallinarum is a common pathogen of poultry birds, being the etiologic agent of fowl typhoid, which specifically infects adult birds via the oral-fecal route. Timely detection of S. Gallinarum in poultry flocks can allow early treatment intervention leading to a decrease in economic losses. Detection of S. Gallinarum is challenging, while its PCR-based detection is a promising strategy, however, due to its high genomic similarity with other commonly existing Salmonella spp., identification of S. Gallinarum from poultry samples with high specificity is still a challenge. The current study was conducted to isolate S. Gallinarum from different districts of Pakistan, assess their antibiotic susceptibility profile, and develop a method for its early detection. A total of 20 strains were isolated using buffer peptone water, selenite cysteine broth, and Xylose Lysine Tergitol-4 (XLT-4) agar supplemented with tergitol and characterized by biochemical procedures. The antibiotic sensitivity profile highlighted the highest resistance of isolates towards novobiocin and nalidixic acid, commonly used antibiotics in Pakistan Poultry production. The primers designed to amplify a unique genomic region of S. Gallinarum, showed successful detection of twenty S. Gallinarum strains, while no amplification with genomic DNA from other common Salmonella spp. The reported method can be utilized to detect S. Gallinarum from tissue samples of infected birds in a short time leading to early diagnosis and timely treatment intervention.

CRISPR-Cas9 enrichment, a new strategy in microbial metagenomics to investigate complex genomic regions: The case of an environmental integron.

Environmental integrons are ubiquitous in natural microbial communities, but they are mostly uncharacterized and their role remains elusive. Thus far, research has been hindered by methodological limitations. Here, we successfully used an innovative approach combining CRISPR-Cas9 enrichment with long-read nanopore sequencing to target, in a complex microbial community, a putative adaptive environmental integron, InOPS, and to unravel its complete structure and genetic context. A contig of 20 kb was recovered containing the complete integron from the microbial metagenome of oil-contaminated coastal sediments. InOPS exhibited typical integron features. The integrase, closely related to integrases of marine Desulfobacterota, possessed all the elements of a functional integron integrase. The gene cassettes harboured mostly unknown functions hampering inferences about their ecological importance. Moreover, the putative InOPS host, likely a hydrocarbonoclastic marine bacteria, raises questions as to the adaptive potential of InOPS in response to oil contamination. Finally, several mobile genetic elements were intertwined with InOPS highlighting likely genomic plasticity, and providing a source of genetic novelty. This case study showed the power of CRISPR-Cas9 enrichment to elucidate the structure and context of specific DNA regions for which only a short sequence is known. This method is a new tool for environmental microbiologists working with complex microbial communities to target low abundant, large or repetitive genetic structures that are difficult to obtain by classical metagenomics. More precisely, here, it offers new perspectives to comprehensively assess the eco-evolutionary significance of environmental integrons.

Les intégrons environnementaux sont omniprésents dans les communautés microbiennes naturelles, mais la plupart ne sont pas caractérisés et leur rôle reste obscur. A ce jour, les limitations méthodologiques ont restreint leur étude. Ici, nous avons utilisé avec succès une approche innovante, combinant l'enrichissement par CRISPR-Cas9 et le séquençage nanopore longs-fragments, pour cibler, dans une communauté microbienne complexe, un intégron environnemental potentiellement adaptatif, InOPS, et pour révéler sa structure complète et son contexte génétique. Un contig de 20 kb contenant l'intégron complet a été obtenu à partir du métagénome microbien de sédiments côtiers contaminés par du pétrole. InOPS présente les caractéristiques typiques d'un intégron. Son intégrase, proche des intégrases des Desulfobacterota marines, possède tous les éléments d'une intégrase d'intégron fonctionnelle. Les cassettes de gène ont des fonctions pour la plupart inconnues, ce qui empêche d'inférer leur importance écologique. De plus, l'hôte présumé d'InOPS, probablement une bactérie marine hydrocarbonoclaste, interroge sur le potentiel adaptatif d'InOPS en réponse à la contamination par le pétrole. En outre, la présence de plusieurs éléments génétiques mobiles dans le contig met en évidence une probable plasticité génomique qui pourrait être source de remaniements génétiques. Cette étude de cas a montré la puissance de l'enrichissement par CRISPR-Cas9 pour élucider la structure et le contexte de régions d'ADN spécifiques pour lesquelles seule une courte séquence est connue. Cette méthode fournit un nouvel outil aux microbiologistes environnementaux travaillant avec des communautés microbiennes complexes pour cibler des structures génétiques peu abondantes, larges ou répétées, qui sont difficiles à obtenir par métagénomique classique. Plus précisément, elle offre ici de nouvelles perspectives pour évaluer de manière exhaustive l'importance éco-évolutive des intégrons environnementaux.

Genome-Wide Association Analysis Reveals Novel Loci Related with Visual Score Traits in Nellore Cattle Raised in Pasture-Based Systems.

Body conformation traits assessed based on visual scores are widely used in Zebu cattle breeding programs. The aim of this study was to identify genomic regions and biological pathways associated with body conformation (CONF), finishing precocity (PREC), and muscling (MUSC) in Nellore cattle. The measurements based on visual scores were collected in 20,807 animals raised in pasture-based systems in Brazil. In addition, 2775 animals were genotyped using a 35 K SNP chip, which contained 31,737 single nucleotide polymorphisms after quality control. Single-step GWAS was performed using the BLUPF90 software while candidate genes were identified based on the Ensembl Genes 69. PANTHER and REVIGO platforms were used to identify key biological pathways and STRING to create gene networks. Novel candidate genes were revealed associated with CONF, including ALDH9A1, RXRG, RAB2A, and CYP7A1, involved in lipid metabolism. The genes associated with PREC were ELOVL5, PID1, DNER, TRIP12, and PLCB4, which are related to the synthesis of long-chain fatty acids, lipid metabolism, and muscle differentiation. For MUSC, the most important genes associated with muscle development were SEMA6A, TIAM2, UNC5A, and UIMC1. The polymorphisms identified in this study can be incorporated in commercial genotyping panels to improve the accuracy of genomic evaluations for visual scores in beef cattle.

A brief survey of tools for genomic regions enrichment analysis.

Functional enrichment analysis or pathway enrichment analysis (PEA) is a bioinformatics technique which identifies the most over-represented biological pathways in a list of genes compared to those that would be associated with them by chance. These biological functions are found on bioinformatics annotated databases such as The Gene Ontology or KEGG; the more abundant pathways are identified through statistical techniques such as Fisher's exact test. All PEA tools require a list of genes as input. A few tools, however, read lists of genomic regions as input rather than lists of genes, and first associate these chromosome regions with their corresponding genes. These tools perform a procedure called genomic regions enrichment analysis, which can be useful for detecting the biological pathways related to a set of chromosome regions. In this brief survey, we analyze six tools for genomic regions enrichment analysis (BEHST, g:Profiler g:GOSt, GREAT, LOLA, Poly-Enrich, and ReactomePA), outlining and comparing their main features. Our comparison results indicate that the inclusion of data for regulatory elements, such as ChIP-seq, is common among these tools and could therefore improve the enrichment analysis results.

Meta-Analysis of SNPs Determining Litter Traits in Pigs.

Nearly 2000 SNPs associated with pig litter size traits have been reported based on genome-wide association studies (GWASs). The aims of this study were to gather and integrate previously reported associations between SNPs and five litter traits: total number born (TNB), number born alive (NBA), number of stillborn (SB), litter birth weight (LWT), and corpus luteum number (CLN), in order to evaluate their common genetic background and to perform a meta-analysis (MA) of GWASs for total number born (TNB) recorded for animals from five pig populations. In this study, the genes with the largest number of associations with evaluated litter traits were GABRG3, RBP7, PRKD1, and STXBP6. Only 21 genes out of 233 associated with the evaluated litter traits were reported in more than one population or for more than one trait. Based on this evaluation, the most interesting candidate gene is PRKD1, which has an association with SB and TNB traits. Based on GO term analysis, PRKD1 was shown to be involved in angiogenesis as well. As a result of the MA, two new genomic regions, which have not been previously reported, were found to be associated with the TNB trait. One SNP was located on Sus scrofa chromosome (SSC) 14 in the intron of the FAM13C gene. The second SNP was located on SSC9 within the intron of the AGMO gene. Functional analysis revealed a strong candidate causal gene underlying the QTL on SSC9. The third best hit and the most promising candidate gene for litter size was found within the SOSTDC1 gene, associated with lower male fertility in rats. We showed that litter traits studied across pig populations have only a few genomic regions in common based on candidate gene comparison. PRKD1 could be an interesting candidate gene with a wider association with fertility. The MA identified new genomic regions on SSC9 and SSC14 associated with TNB. Further functional analysis indicated the most promising gene was SOSTDC1, which was confirmed to affect male fertility in other mammals. This is an important finding, as litter traits are by default linked with females rather than males.

Genome-Wide Association Study of Parasite Resistance to Gastrointestinal Nematodes in Corriedale Sheep.

Selection of genetically resistant animals is one alternative to reduce the negative impact of gastrointestinal nematodes (GIN) on sheep production. The aim of this study was to identify genomic regions associated with GIN resistance in Corriedale sheep by single-step genome-wide association studies (ssGWAS) using 170, 507 and 50K single nucleotide polymorphisms (SNPs). Analysis included 19,547 lambs with faecal egg counts (FEC) records, a pedigree file of 40,056 animals and 454, 711 and 383 genotypes from 170, 507 and 50K SNPs, respectively. Genomic estimated breeding values (GEBV) were obtained with single-step genomic BLUP methodology (ssGBLUP), using a univariate animal model, which included contemporary group, type of birth and age of dam as class fixed effects and age at FEC recording as covariate. The SNP effects as wells as p-values were estimated with POSTGSF90 program. Significance level was defined by a chromosome-wise False Discovery Rate of 5%. Significant genomic regions were identified in chromosomes 1, 3, 12 and 19 with the 170 SNP set, in chromosomes 7, 12 and 24 using the 507 SNP chip and only in chromosome 7 with the 50K SNP chip. Candidate genes located in these regions, using Oar_v4.0 as reference genome, were TIMP3, TLR5, LEPR and TLR9 (170 SNPs), SYNDIG1L and MGRN1 (507 SNP chip) and INO80, TLN2, TSHR and EIF2AK4 (50K SNP chip). These results validate genomic regions associated with FEC previously identified in Corriedale and other breeds and report new candidate regions for further investigation.

Exploring Genes and Phenotypes Within Chromosomal Regions Using OMIM's GeneScout.

Structural variation in genomes, such as copy number variants (CNVs), is under scrutiny for its contribution to phenotypic expression and evolution. Regions of homozygosity (ROH) are ripe for phenotype-gene discovery. Determining the genes and related phenotypes within genomic regions is key to studying potential functional and phenotypic consequences. Because individuals have multiple CNVs and ROHs in their genome, identifying genomic regions that are phenotypically significant is challenging. GeneScout is a web-based tool that can be used to search genomic regions to display and filter the genes and their associated phenotypes within regions of interest. Phenotypes and their associated gene(s) can then be filtered to show only the genes with phenotypes that have a particular inheritance pattern and/or specific clinical feature(s). Phenotypes can then be selected to compare the clinical synopses side-by-side in Online Mendelian Inheritance in Man (OMIM® ). Additionally, two coordinate sets can be compared to determine either the regions of overlap or the unique regions (subtraction). The resulting coordinate ranges are displayed on the results page, and the results table displays only the genes and phenotypes present within the coordinate ranges. The interactive table includes gene-specific links to external resources such as ClinVar, ClinGen validity, ClinGen dosage, and gnomAD, and a diamond symbol next to the gene name indicates a gene that spans the start or end of a coordinate range. Searches and comparisons may be performed for coordinates in assemblies GRCh37 (hg19) and GRCh38 (hg38). The results page offers the option to liftover coordinates entered in GRCh37 to GRCh38 and updates the results table to display the gene content based on assembly GRCh38. The search coordinates and results table can be downloaded in a tab-delimited or Excel file. © 2022 Wiley Periodicals LLC. Basic Protocol: Searching GeneScout.