scholarly journals Genetic tools to improve reproduction traits in dairy cattle

2015 ◽  
Vol 27 (1) ◽  
pp. 14 ◽  
Author(s):  
A. Capitan ◽  
P. Michot ◽  
A. Baur ◽  
R. Saintilan ◽  
C. Hozé ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Genetic Research ◽  
Detection Methods ◽  
Whole Genome ◽  
Sequencing Data ◽  
Lethal Mutations ◽  
Embryonic Lethal ◽  
Embryonic Lethal Mutations

Fertility is a major concern in the dairy cattle industry and has been the subject of numerous studies over the past 20 years. Surprisingly, most of these studies focused on rough female phenotypes and, despite their important role in reproductive success, male- and embryo-related traits have been poorly investigated. In recent years, the rapid and important evolution of technologies in genetic research has led to the development of genomic selection. The generalisation of this method in combination with the achievements of the AI industry have led to the constitution of large databases of genotyping and sequencing data, as well as refined phenotypes and pedigree records. These resources offer unprecedented opportunities in terms of fundamental and applied research. Here we present five such examples with a focus on reproduction-related traits: (1) detection of quantitative trait loci (QTL) for male fertility and semen quality traits; (2) detection of QTL for refined phenotypes associated with female fertility; (3) identification of recessive embryonic lethal mutations by depletion of homozygous haplotypes; (4) identification of recessive embryonic lethal mutations by mining whole-genome sequencing data; and (5) the contribution of high-density single nucleotide polymorphism chips, whole-genome sequencing and imputation to increasing the power of QTL detection methods and to the identification of causal variants.

scholarly journals A comprehensive benchmarking of WGS-based structural variant callers

2020 ◽  
Author(s):  
Varuni Sarwal ◽  
Sebastian Niehus ◽  
Ram Ayyala ◽  
Sei Chang ◽  
Angela Lu ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Gold Standard ◽  
Detection Methods ◽  
Whole Genome ◽  
Sequencing Data ◽  
Structural Variant ◽  
Gold Standard Dataset ◽  
Low Pass ◽  
Standard Set

AbstractAdvances in whole genome sequencing promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from whole genome sequencing (WGS) data presents a substantial number of challenges and a plethora of SV-detection methods have been developed. Currently, there is a paucity of evidence which investigators can use to select appropriate SV-detection tools. In this paper, we evaluated the performance of SV-detection tools using a comprehensive PCR-confirmed gold standard set of SVs. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of SV-detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance, as the SV-detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV-detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low and ultra-low pass sequencing data.

scholarly journals Evaluation of variant identification methods for whole genome sequencing data in dairy cattle

BMC Genomics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 948 ◽  
Author(s):  
Christine F Baes ◽  
Marlies A Dolezal ◽  
James E Koltes ◽  
Beat Bapst ◽  
Eric Fritz-Waters ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Identification Methods ◽  
Variant Identification

scholarly journals Monitoring the Antimicrobial Resistance Dynamics of Salmonella enterica in Healthy Dairy Cattle Populations at the Individual Farm Level Using Whole-Genome Sequencing

2021 ◽  
Author(s):  
Laura M Carroll ◽  
Ariel J Buehler ◽  
Ahmed Gaballa ◽  
Julie D Siler ◽  
Kevin J Cummings ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Dairy Cattle ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
In Silico ◽  
Salmonella Enterica ◽  
Detection Methods ◽  
Whole Genome ◽  
The Individual ◽  
Individual Farm

Livestock represent a possible reservoir for facilitating the transmission of the zoonotic foodborne pathogen Salmonella enterica to humans; there is also concern that strains can acquire resistance to antimicrobials in the farm environment. Here, we use whole-genome sequencing (WGS) to characterize Salmonella strains (n = 128) isolated from healthy dairy cattle and their associated environments on 13 New York State farms to assess the diversity and microevolution of this important pathogen at the level of the individual herd. Additionally, the accuracy and concordance of multiple in silico tools are assessed, including: (i) two in silico serotyping tools, (ii) combinations of five antimicrobial resistance (AMR) determinant detection tools and one to five AMR determinant databases, and (iii) one antimicrobial minimum inhibitory concentration (MIC) prediction tool. For the isolates sequenced here, in silico serotyping methods outperformed traditional serotyping and resolved all un-typable and/or ambiguous serotype assignments. Serotypes assigned in silico showed greater congruency with the Salmonella whole-genome phylogeny than traditional serotype assignments, and in silico methods showed high concordance (99% agreement). In silico AMR determinant detection methods additionally showed a high degree of concordance, regardless of the pipeline or database used (≥98% agreement between susceptible/resistant assignments for all pipeline/database combinations). For AMR detection methods that relied exclusively on nucleotide BLAST, accuracy could be maximized by using a range of minimum nucleotide identity and coverage thresholds, with thresholds of 75% nucleotide identity and 50-60% coverage adequate for most pipeline/database combinations. In silico characterization of the microevolution and AMR dynamics of each of six serotype groups (S. Anatum, Cerro, Kentucky, Meleagridis, Newport, Typhimurium/Typhimurium variant Copenhagen) revealed that some lineages were strongly associated with individual farms, while others were distributed across multiple farms. Numerous AMR determinant acquisition and loss events were identified, including the recent acquisition of cephalosporin resistance-conferring blaCMY- and blaCTX-M-type beta-lactamases. The results presented here provide high-resolution insight into the temporal dynamics of AMR Salmonella at the scale of the individual farm and highlight both the strengths and limitations of WGS in tracking zoonotic pathogens and their associated AMR determinants at the livestock-human interface.

scholarly journals mity: A highly sensitive mitochondrial variant analysis pipeline for whole genome sequencing data

2019 ◽  
Author(s):  
Clare Puttick ◽  
Kishore R Kumar ◽  
Ryan L Davis ◽  
Mark Pinese ◽  
David M Thomas ◽  
...  
Keyword(s):  
Mitochondrial Genome ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Mitochondrial Diseases ◽  
Detection Methods ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Phenotype Heterogeneity

AbstractMotivationMitochondrial diseases (MDs) are the most common group of inherited metabolic disorders and are often challenging to diagnose due to extensive genotype-phenotype heterogeneity. MDs are caused by mutations in the nuclear or mitochondrial genome, where pathogenic mitochondrial variants are usually heteroplasmic and typically at much lower allelic fraction in the blood than affected tissues. Both genomes can now be readily analysed using unbiased whole genome sequencing (WGS), but most nuclear variant detection methods fail to detect low heteroplasmy variants in the mitochondrial genome.ResultsWe present mity, a bioinformatics pipeline for detecting and interpreting heteroplasmic SNVs and INDELs in the mitochondrial genome using WGS data. In 2,980 healthy controls, we observed on average 3,166× coverage in the mitochondrial genome using WGS from blood. mity utilises this high depth to detect pathogenic mitochondrial variants, even at low heteroplasmy. mity enables easy interpretation of mitochondrial variants and can be incorporated into existing diagnostic WGS pipelines. This could simplify the diagnostic pathway, avoid invasive tissue biopsies and increase the diagnostic rate for MDs and other conditions caused by impaired mitochondrial function.Availabilitymity is available from https://github.com/KCCG/mityunder an MIT [email protected], [email protected], [email protected]

scholarly journals Monitoring the Microevolution of Salmonella enterica in Healthy Dairy Cattle Populations at the Individual Farm Level Using Whole-Genome Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
Laura M. Carroll ◽  
Ariel J. Buehler ◽  
Ahmed Gaballa ◽  
Julie D. Siler ◽  
Kevin J. Cummings ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
In Silico ◽  
Salmonella Enterica ◽  
Detection Methods ◽  
Nucleotide Identity ◽  
Whole Genome ◽  
The Individual ◽  
Individual Farm

Livestock represent a possible reservoir for facilitating the transmission of the zoonotic foodborne pathogen Salmonella enterica to humans; there is also concern that strains can acquire resistance to antimicrobials in the farm environment. Here, whole-genome sequencing (WGS) was used to characterize Salmonella strains (n = 128) isolated from healthy dairy cattle and their associated environments on 13 New York State farms to assess the diversity and microevolution of this important pathogen at the level of the individual herd. Additionally, the accuracy and concordance of multiple in silico tools are assessed, including: (i) two in silico serotyping tools, (ii) combinations of five antimicrobial resistance (AMR) determinant detection tools and one to five AMR determinant databases, and (iii) one antimicrobial minimum inhibitory concentration (MIC) prediction tool. For the isolates sequenced here, in silico serotyping methods outperformed traditional serotyping and resolved all un-typable and/or ambiguous serotype assignments. Serotypes assigned in silico showed greater congruency with the Salmonella whole-genome phylogeny than traditional serotype assignments, and in silico methods showed high concordance (99% agreement). In silico AMR determinant detection methods additionally showed a high degree of concordance, regardless of the pipeline or database used (≥98% agreement among susceptible/resistant assignments for all pipeline/database combinations). For AMR detection methods that relied exclusively on nucleotide BLAST, accuracy could be maximized by using a range of minimum nucleotide identity and coverage thresholds, with thresholds of 75% nucleotide identity and 50–60% coverage adequate for most pipeline/database combinations. In silico characterization of the microevolution and AMR dynamics of each of six serotype groups (S. Anatum, Cerro, Kentucky, Meleagridis, Newport, Typhimurium/Typhimurium variant Copenhagen) revealed that some lineages were strongly associated with individual farms, while others were distributed across multiple farms. Numerous AMR determinant acquisition and loss events were identified, including the recent acquisition of cephalosporin resistance-conferring blaCMY- and blaCTX–M-type beta-lactamases. The results presented here provide high-resolution insight into the temporal dynamics of AMR Salmonella at the scale of the individual farm and highlight both the strengths and limitations of WGS in tracking zoonotic pathogens and their associated AMR determinants at the livestock-human interface.

scholarly journals High-precision and cost-efficient sequencing for real-time COVID-19 surveillance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Yong Park ◽  
Gina Faraci ◽  
Pamela M. Ward ◽  
Jane F. Emerson ◽  
Ha Youn Lee
Keyword(s):  
Los Angeles ◽  
Whole Genome Sequencing ◽  
Real Time ◽  
Genome Sequencing ◽  
High Precision ◽  
High Throughput Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Public Health Response ◽  
Cost Efficient

AbstractCOVID-19 global cases have climbed to more than 33 million, with over a million total deaths, as of September, 2020. Real-time massive SARS-CoV-2 whole genome sequencing is key to tracking chains of transmission and estimating the origin of disease outbreaks. Yet no methods have simultaneously achieved high precision, simple workflow, and low cost. We developed a high-precision, cost-efficient SARS-CoV-2 whole genome sequencing platform for COVID-19 genomic surveillance, CorvGenSurv (Coronavirus Genomic Surveillance). CorvGenSurv directly amplified viral RNA from COVID-19 patients’ Nasopharyngeal/Oropharyngeal (NP/OP) swab specimens and sequenced the SARS-CoV-2 whole genome in three segments by long-read, high-throughput sequencing. Sequencing of the whole genome in three segments significantly reduced sequencing data waste, thereby preventing dropouts in genome coverage. We validated the precision of our pipeline by both control genomic RNA sequencing and Sanger sequencing. We produced near full-length whole genome sequences from individuals who were COVID-19 test positive during April to June 2020 in Los Angeles County, California, USA. These sequences were highly diverse in the G clade with nine novel amino acid mutations including NSP12-M755I and ORF8-V117F. With its readily adaptable design, CorvGenSurv grants wide access to genomic surveillance, permitting immediate public health response to sudden threats.

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