168 Use of Genomic Tools to Improve Production Efficiency, Health Resilience and Carbon Footprint of Beef Production

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 90-91
Author(s):  
John A Basarab ◽  
Changxi Li ◽  
Paul Stothard ◽  
Carolyn J Fitzsimmons ◽  
Graham Plastow
Keyword(s):  
Carbon Footprint ◽  
Validation Studies ◽  
Feed Efficiency ◽  
Mate Selection ◽  
Association Studies ◽  
Carbon Intensity ◽  
Genome Wide Association Studies ◽  
Beef Production ◽  
Dna Pooling ◽  
Genomic Tools

Abstract The aim is to present validation studies that demonstrate the benefits of genomic retained heterozygosity, genomic enhanced expected progeny differences (gEPDs) for feed efficiency and carcass traits, as well as DNA pooling technologies, to the beef industry. Team members of Livestock Gentec are global leaders in beef genomics research as evidenced by their leadership roles on the Canadian Cattle Genome Project, 1,000 Bull Genomes Project, gEPDs for Commercial Cattle Project and the Functional Annotation of ANimal Genomes initiative. These large-scale projects have created databases of 380 whole bovine sequence genomes, >24,000 cattle genotypes imputed to sequence variants using Run 6 genotypes from 1000 Bull Genomes project, and >20,000 cattle with associated phenotypes for feed efficiency, carcass quality, cow fertility and methane emissions. The use of admixture analysis, genome wide association studies, and genomic prediction have resulted in new genomic tools that aid in mate selection, improve herd heterosis, female fertility, lifetime productivity and health resilience, and improve accuracy (acc. >0.36) of gEPDs for 18 traits in crossbred cattle. Genomic retained heterozygosity has a benefit of $161/female over five parities while decreasing morbidity of calves and improving the carbon intensity of beef production. Multi-trait selection studies using gEPDs for residual feed intake (acc. > 0.35) have demonstrated annual rates of genetic progress of 0.7%. Validation studies have reported that sires with superior gEPDs for increased marbling, decreased grade fat, increased ribeye and increased carcass weight (acc. > 0.45) produced progeny with improved AAA retail cut yield (59.9 vs 56.7%). DNA pooling shows potential for cheaper genotyping while providing information on pooled records related to sire contribution, heterosis and performance as influenced by genetics. The application of these genomic tools has potential to improve calf crop percentage, health resilience, and retail cut yield while decreasing the carbon footprint of beef production.

scholarly journals Climate-Resilient Dairy Cattle Production: Applications of Genomic Tools and Statistical Models

2021 ◽  
Vol 8 ◽  
Author(s):  
Mullakkalparambil Velayudhan Silpa ◽  
Sven König ◽  
Veerasamy Sejian ◽  
Pradeep Kumar Malik ◽  
Mini Ravi Reshma Nair ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Dairy Cows ◽  
Milk Production ◽  
Statistical Models ◽  
Association Studies ◽  
Farm Animals ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Climate Trend ◽  
Genomic Tools

The current changing climate trend poses a threat to the productive efficacy and welfare of livestock across the globe. This review is an attempt to synthesize information pertaining to the applications of various genomic tools and statistical models that are available to identify climate-resilient dairy cows. The different functional and economical traits which govern milk production play a significant role in determining the cost of milk production. Thus, identification of these traits may revolutionize the breeding programs to develop climate-resilient dairy cattle. Moreover, the genotype–environment interaction also influences the performance of dairy cattle especially during a challenging situation. The recent advancement in molecular biology has led to the development of a few biotechnological tools and statistical models like next-generation sequencing (NGS), microarray technology, whole transcriptome analysis, and genome-wide association studies (GWAS) which can be used to quantify the molecular mechanisms which govern the climate resilience capacity of dairy cows. Among these, the most preferred option for researchers around the globe was GWAS as this approach jointly takes into account all the genotype, phenotype, and pedigree information of farm animals. Furthermore, selection signatures can also help to demarcate functionally important regions in the genome which can be used to detect potential loci and candidate genes that have undergone positive selection in complex milk production traits of dairy cattle. These identified biomarkers can be incorporated in the existing breeding policies using genomic selection to develop climate-resilient dairy cattle.

Aggressive prostate cancer phenotype and genome-wide association studies: where are we now?

2020 ◽  
Vol 21 (7) ◽  
pp. 487-503
Author(s):  
Ana R Pinto ◽  
Jani Silva ◽  
Ricardo Pinto ◽  
Rui Medeiros
Keyword(s):  
Prostate Cancer ◽  
Validation Studies ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Functional Consequences ◽  
Cancer Phenotype ◽  
Positive Results ◽  
Worse Prognosis

The majority of prostate cancer (PCa) is indolent, however, a percentage of patients are initially diagnosed with metastatic disease, for which there is a worse prognosis. There is a lack of biomarkers to identify men at greater risk for developing aggressive PCa. Genome-wide association studies (GWAS) scan the genome to search associations of SNPs with specific traits, like cancer. To date, eight GWAS have resulted in the reporting of 16 SNPs associated with aggressive PCa (p < 5.00 × 10-2). Still, validation studies need to be conducted to confirm the obtained results as GWAS can generate false-positive results. Furthermore, post-GWAS studies provide a better understanding of the functional consequences.

scholarly journals The carbon footprint of bioinformatics

2021 ◽  
Author(s):  
Jason Grealey ◽  
Loïc Lannelongue ◽  
Woei-Yuh Saw ◽  
Jonathan Marten ◽  
Guillaume Meric ◽  
...  
Keyword(s):  
Carbon Footprint ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Association Studies ◽  
Processing Unit ◽  
Genome Wide Association Studies ◽  
Central Processing ◽  
Bioinformatic Tools ◽  
Software Upgrades ◽  
Efficient Data

AbstractBioinformatic research relies on large-scale computational infrastructures which have a non-zero carbon footprint. So far, no study has quantified the environmental costs of bioinformatic tools and commonly run analyses. In this study, we estimate the bioinformatic carbon footprint (in kilograms of CO2 equivalent units, kgCO2e) using the freely available Green Algorithms calculator (www.green-algorithms.org). We assess (i) bioinformatic approaches in genome-wide association studies (GWAS), RNA sequencing, genome assembly, metagenomics, phylogenetics and molecular simulations, as well as (ii) computation strategies, such as parallelisation, CPU (central processing unit) vs GPU (graphics processing unit), cloud vs. local computing infrastructure and geography. In particular, for GWAS, we found that biobank-scale analyses emitted substantial kgCO2e and simple software upgrades could make GWAS greener, e.g. upgrading from BOLT-LMM v1 to v2.3 reduced carbon footprint by 73%. Switching from the average data centre to a more efficient data centres can reduce carbon footprint by ~34%. Memory over-allocation can be a substantial contributor to an algorithm’s carbon footprint. The use of faster processors or greater parallelisation reduces run time but can lead to, sometimes substantially, greater carbon footprint. Finally, we provide guidance on how researchers can reduce power consumption and minimise kgCO2e. Overall, this work elucidates the carbon footprint of common analyses in bioinformatics and provides solutions which empower a move toward greener research.

scholarly journals Identification of genomic regions affecting production traits in pigs divergently selected for feed efficiency

2020 ◽  
Author(s):  
Emilie Delpuech ◽  
Amir Aliakbari ◽  
Yann Labrune ◽  
Katia Fève ◽  
Yvon Billon ◽  
...  
Keyword(s):  
Feed Efficiency ◽  
Production Systems ◽  
Agronomic Traits ◽  
Association Studies ◽  
Pedigree Information ◽  
Strong Impact ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Production Traits ◽  
Genomic Regions

AbstractBackgroundFeed efficiency is a major driver of the sustainability of pig production systems. Understanding biological mechanisms underlying these agronomic traits is an important issue whether for environment and farms economy. This study aimed at identifying genomic regions affecting residual feed intake (RFI) and other production traits in two pig lines divergently selected for RFI during 9 generations (LRFI, low RFI; HRFI, high RFI).ResultsWe built a whole dataset of 570,447 single nucleotide polymorphisms (SNPs) in 2,426 pigs with records for 24 production traits after both imputation and prediction of genotypes using pedigree information. Genome-wide association studies (GWAS) were performed including both lines (Global-GWAS) or each line independently (LRFI-GWAS and HRFI-GWAS). A total of 54 chromosomic regions were detected with the Global-GWAS, whereas 37 and 61 regions were detected in LRFI-GWAS and HRFI-GWAS, respectively. Among those, only 15 regions were shared between at least two analyses, and only one was common between the three GWAS but affecting different traits. Among the 12 QTL detected for RFI, some were close to QTL detected for meat quality traits and 9 pinpointed novel genomic regions for some harbored candidate genes involved in cell proliferation and differentiation processes of gastrointestinal tissues or lipid metabolism-related signaling pathways. Detection of mostly different QTL regions between the three designs suggests the strong impact of the dataset on the detection power, which could be due to the changes of allelic frequencies during the line selection.ConclusionsBesides efficiently detecting known and new QTL regions for feed efficiency, the combination of GWAS carried out per line or simultaneously using all individuals highlighted the identification of chromosomic regions under selection that affect various production traits.

scholarly journals Advances in biotechnology and propagation of nut trees in Iran

2020 ◽  
Vol 25 ◽  
pp. 01003
Author(s):  
Kourosh Vahdati ◽  
Mohammad Mehdi Arab ◽  
Saadat Sarikhani
Keyword(s):  
High Throughput ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
High Genetic Diversity ◽  
Breeding Programs ◽  
Genome Wide ◽  
Genomic Tools ◽  
Nut Production ◽  
New Varieties ◽  
Next Generation Sequencing Ngs

As one of the main origin centers of nut trees, Iran is the fourth leading nut crops producer in the world (6% of total nut production). Due to the high genetic diversity, development of new varieties and rootstocks with desirable characteristics have been highly considered by fruit breeders in Iran. In this regard, molecular breeders concentrate on filling the gaps in the conventional breeding with the aim of accelerating breeding programs. Recent advancements in molecular breeding such as next-generation sequencing (NGS) techniques, high-throughput genotyping platforms and genomics-based approaches including genome wide association studies (GWAS), and genomic selection (GS) have opened up new avenues to enhance the efficiency of nut trees breeding. Over the past decades, Iranian nut crops breeders have successfully used advanced molecular and genomic tools such as molecular markers, genetic transformations and high-throughput genotyping to explore the genetic basis of the desired traits and eventually to develop new varieties and rootstocks. Due to a broad international cooperation, a clear perspective is envisaged for the nut breeding programs in Iran, especially based on new biotechnology techniques. The propagation of nut trees in Iran have also been dramatically improved. Different types of grafting and tissue culture (micropropagation or somatic embryogenesis) techniques for propagation of nut crops have been studied intensively in the last 30 years in Iran and the successful techniques have been commercialized. Several certified nurseries are producing grafted and micropropagation plants of walnut, pistachio and other nut crops commercially. A part of the grafted and micropropagaited plants of nut crops in Iran is being exported to the other countries. Establishing modern orchards of nut crops using new cultivars and rootsocks is presently being advised by professional consultants.

scholarly journals Identification of genomic regions affecting production traits in pigs divergently selected for feed efficiency

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Emilie Delpuech ◽  
Amir Aliakbari ◽  
Yann Labrune ◽  
Katia Fève ◽  
Yvon Billon ◽  
...  
Keyword(s):  
Feed Efficiency ◽  
Agronomic Traits ◽  
Association Studies ◽  
Pedigree Information ◽  
Strong Impact ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Production Traits ◽  
Allelic Frequencies ◽  
Genomic Regions

Abstract Background Feed efficiency is a major driver of the sustainability of pig production systems. Understanding the biological mechanisms that underlie these agronomic traits is an important issue for environment questions and farms' economy. This study aimed at identifying genomic regions that affect residual feed intake (RFI) and other production traits in two pig lines divergently selected for RFI during nine generations (LRFI, low RFI; HRFI, high RFI). Results We built a whole dataset of 570,447 single nucleotide polymorphisms (SNPs) in 2426 pigs with records for 24 production traits after both imputation and prediction of genotypes using pedigree information. Genome-wide association studies (GWAS) were performed including both lines (global-GWAS) or each line independently (LRFI-GWAS and HRFI-GWAS). Forty-five chromosomal regions were detected in the global-GWAS, whereas 28 and 42 regions were detected in the HRFI-GWAS and LRFI-GWAS, respectively. Among these 45 regions, only 13 were shared between at least two analyses, and only one was common between the three GWAS but it affects different traits. Among the five quantitative trait loci (QTL) detected for RFI, two were close to QTL for meat quality traits and two pinpointed novel genomic regions that harbor candidate genes involved in cell proliferation and differentiation processes of gastrointestinal tissues or in lipid metabolism-related signaling pathways. In most cases, different QTL regions were detected between the three designs, which suggests a strong impact of the dataset structure on the detection power and could be due to the changes in allelic frequencies during the establishment of lines. Conclusions In addition to efficiently detecting known and new QTL regions for feed efficiency, the combination of GWAS carried out per line or simultaneously using all individuals highlighted chromosomal regions that affect production traits and presented significant changes in allelic frequencies across generations. Further analyses are needed to estimate whether these regions correspond to traces of selection or result from genetic drift.

TLR1 and PRKAA1 Gene Polymorphisms in the Development of Atrophic Gastritis and Gastric Cancer

2018 ◽  
Vol 27 (4) ◽  
pp. 363-369 ◽  
Author(s):  
Gintare Dargiene ◽  
Greta Streleckiene ◽  
Jurgita Skieceviciene ◽  
Marcis Leja ◽  
Alexander Link ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Atrophic Gastritis ◽  
Genetic Polymorphisms ◽  
Association Studies ◽  
Control Group ◽  
Similar Distribution ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Infection Susceptibility ◽  
H Pylori

Background & Aims: Previous genome-wide association studies showed that genetic polymorphisms in toll-like receptor 1 (TLR1) and protein kinase AMP-activated alpha 1 catalytic subunit (PRKAA1) genes were associated with gastric cancer (GC) or increased Helicobacter pylori (H. pylori) infection susceptibility. The aim of this study was to evaluate the association between TLR1 and PRKAA1 genes polymorphisms and H.pylori infection, atrophic gastritis (AG) or GC in the European population.Methods: Single-nucleotide polymorphisms (SNPs) were analysed in 511 controls, 340 AG patients and 327 GC patients. TLR1 C>T (rs4833095) and PRKAA1 C>T (rs13361707) were genotyped by the real-time polymerase chain reaction. H. pylori status was determined by testing for anti-H. pylori IgG antibodies in the serum.Results: The study included 697 (59.2%) H. pylori positive and 481 (40.8%) H. pylori negative cases. We observed similar distribution of TLR1 and PRKAA1 alleles and genotypes in H. pylori positive and negative cases. TLR1 and PRKAA1 SNPs were not linked with the risk of AG. TC genotype of TLR1 gene was more prevalent in GC patients compared to the control group (29.7% and 22.3% respectively, p=0.002). Carriers of TC genotype had a higher risk of GC (aOR=1.89, 95% CI: 1.26–2.83, p=0.002). A similar association was observed in a dominant inheritance model for TLR1 gene SNP, where comparison of CC+TC vs. TT genotypes showed an increased risk of GC (aOR=1.86, 95% CI: 1.26–2.75, p=0.002). No association between genetic polymorphism in PRKAA1 gene and GC was observed.Conclusions: TLR1 rs4833095 SNP was associated with an increased risk of GC in a European population, while PRKAA1 rs13361707 genetic variant was not linked with GC. Both genetic polymorphisms were not associated with H. pylori infection susceptibility or the risk of AG.

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