Using divergent selection and genomics to uncover genetic variation underlying larkspur tolerance and susceptibility in cattle

In the Rocky Mountain region of western US, selection for larkspur tolerance would reduce mortality of cattle from larkspur poisoning and increase opportunity to utilize pastures at peak nutrient availability resulting in increased sustainability of beef production. Previous research indicated that...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of animal science 2016-10, Vol.94, p.860-860
Hauptverfasser: Keele, J W, McDaneld, T G, Kuehn, L A, Snelling, W M, Tait, R G, Welch, K D, Green, B T
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 860
container_issue
container_start_page 860
container_title Journal of animal science
container_volume 94
creator Keele, J W
McDaneld, T G
Kuehn, L A
Snelling, W M
Tait, R G
Welch, K D
Green, B T
description In the Rocky Mountain region of western US, selection for larkspur tolerance would reduce mortality of cattle from larkspur poisoning and increase opportunity to utilize pastures at peak nutrient availability resulting in increased sustainability of beef production. Previous research indicated that there are breed differences for tolerance to toxic larkspur. Our objective was to estimate heritability for larkspur tolerance within breed and evaluate the potential for increasing larkspur tolerance through artificial selection. Larkspur challenge was administered to 141 yearling steers (32 Angus, 13 Brahman, 49 Line 1 Hereford, 33 Holstein, and 14 Jersey) with a standardized dose of dried ground larkspur suspended in water and gavaged directly into the rumen. Larkspur tolerance was measured at 24 h after dosing as the length of time (up to 40 min maximum) in which the animal could sustain walking at 6.44 km/h while being led behind a tractor on a circular track. High-density SNP arrays (770,000 or 30,000 SNP) were used to genotype each steer and genotypes were used to compute the genomic relationship matrix which is a precursor to estimating heritability. Larkspur tolerance heritability estimates were similar whether estimated with REML (0.36 ± 0.30; P = 0.10) or Bayesian Monte Carlo Markov chain (MCMC) (0.42 ± 0.23; MCMC posterior distribution 2.5, 25, 50, 75, and 97.5th percentiles were 0.035, 0.24, 0.40, 0.59, and 0.90). To evaluate the potential for using our larkspur challenge data to calculate EBV of an untested population, we computed genomic relationship coefficients between 190 previously genotyped (but untested for larkspur tolerance and comprising the same 5 breeds in this study) cattle and the 141 steers tested for larkspur tolerance. Because of uncertainty in the heritability estimate, EBV were computed for each iteration of the MCMC to average over all possible values for heritability and weight by the appropriate posterior density. The most extreme EBV were for target animals with the strongest genetic ties to tested animals. Simulations indicated that divergent selection of parents can more than double the power for estimating heritability. Our results indicate that selection for larkspur tolerance should be effective. The rate of selection response will critically depend on challenging and testing animals with strong genetic ties to candidates for selection. Genetic ties can either be estimated from SNP genotypes or computed from common ances
doi_str_mv 10.2527/jam2016-1768
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2046728374</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2046728374</sourcerecordid><originalsourceid>FETCH-proquest_journals_20467283743</originalsourceid><addsrcrecordid>eNqNjE1OwzAQha0KpIafXQ8wEuuA7dRJWCMQB6DryjhDNcG1U49dqRvOTlpxAFZP-t73nhArJR-10d3TaPdaqrZWXdsvRKWMNnWj2uZKVFJqVfe90ktxwzxKqbR5NpX42TCFHQx0xLTDkIHRo8sUA9gwwIzinhxDjlCCi7N1ZpjJwdEmshezhAGTP52PvE3fPJU0DzwmGxxefriwwynTJ3nKJ6AAzubs8U5cf1nPeP-Xt-Lh7fXj5b2eUjwU5LwdY0lhrrZarttO9023bv5n_QKeDle4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2046728374</pqid></control><display><type>article</type><title>Using divergent selection and genomics to uncover genetic variation underlying larkspur tolerance and susceptibility in cattle</title><source>Oxford University Press Journals All Titles (1996-Current)</source><creator>Keele, J W ; McDaneld, T G ; Kuehn, L A ; Snelling, W M ; Tait, R G ; Welch, K D ; Green, B T</creator><creatorcontrib>Keele, J W ; McDaneld, T G ; Kuehn, L A ; Snelling, W M ; Tait, R G ; Welch, K D ; Green, B T</creatorcontrib><description>In the Rocky Mountain region of western US, selection for larkspur tolerance would reduce mortality of cattle from larkspur poisoning and increase opportunity to utilize pastures at peak nutrient availability resulting in increased sustainability of beef production. Previous research indicated that there are breed differences for tolerance to toxic larkspur. Our objective was to estimate heritability for larkspur tolerance within breed and evaluate the potential for increasing larkspur tolerance through artificial selection. Larkspur challenge was administered to 141 yearling steers (32 Angus, 13 Brahman, 49 Line 1 Hereford, 33 Holstein, and 14 Jersey) with a standardized dose of dried ground larkspur suspended in water and gavaged directly into the rumen. Larkspur tolerance was measured at 24 h after dosing as the length of time (up to 40 min maximum) in which the animal could sustain walking at 6.44 km/h while being led behind a tractor on a circular track. High-density SNP arrays (770,000 or 30,000 SNP) were used to genotype each steer and genotypes were used to compute the genomic relationship matrix which is a precursor to estimating heritability. Larkspur tolerance heritability estimates were similar whether estimated with REML (0.36 ± 0.30; P = 0.10) or Bayesian Monte Carlo Markov chain (MCMC) (0.42 ± 0.23; MCMC posterior distribution 2.5, 25, 50, 75, and 97.5th percentiles were 0.035, 0.24, 0.40, 0.59, and 0.90). To evaluate the potential for using our larkspur challenge data to calculate EBV of an untested population, we computed genomic relationship coefficients between 190 previously genotyped (but untested for larkspur tolerance and comprising the same 5 breeds in this study) cattle and the 141 steers tested for larkspur tolerance. Because of uncertainty in the heritability estimate, EBV were computed for each iteration of the MCMC to average over all possible values for heritability and weight by the appropriate posterior density. The most extreme EBV were for target animals with the strongest genetic ties to tested animals. Simulations indicated that divergent selection of parents can more than double the power for estimating heritability. Our results indicate that selection for larkspur tolerance should be effective. The rate of selection response will critically depend on challenging and testing animals with strong genetic ties to candidates for selection. Genetic ties can either be estimated from SNP genotypes or computed from common ancestry. USDA is an equal opportunity provider and employer.</description><identifier>ISSN: 0021-8812</identifier><identifier>EISSN: 1525-3163</identifier><identifier>DOI: 10.2527/jam2016-1768</identifier><language>eng</language><publisher>Champaign: Oxford University Press</publisher><subject>Agricultural equipment ; Animals ; Bayesian analysis ; Beef ; Beef cattle ; Bovidae ; Cattle ; Computation ; Computer simulation ; Delphinium ; Estimation ; Genetic diversity ; Genotypes ; Heritability ; Iterative methods ; Markov chains ; Mathematical analysis ; Monte Carlo simulation ; Nutrient availability ; Parents ; Pasture ; Single-nucleotide polymorphism ; Studies ; Sustainability</subject><ispartof>Journal of animal science, 2016-10, Vol.94, p.860-860</ispartof><rights>Copyright Oxford University Press, UK Oct 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Keele, J W</creatorcontrib><creatorcontrib>McDaneld, T G</creatorcontrib><creatorcontrib>Kuehn, L A</creatorcontrib><creatorcontrib>Snelling, W M</creatorcontrib><creatorcontrib>Tait, R G</creatorcontrib><creatorcontrib>Welch, K D</creatorcontrib><creatorcontrib>Green, B T</creatorcontrib><title>Using divergent selection and genomics to uncover genetic variation underlying larkspur tolerance and susceptibility in cattle</title><title>Journal of animal science</title><description>In the Rocky Mountain region of western US, selection for larkspur tolerance would reduce mortality of cattle from larkspur poisoning and increase opportunity to utilize pastures at peak nutrient availability resulting in increased sustainability of beef production. Previous research indicated that there are breed differences for tolerance to toxic larkspur. Our objective was to estimate heritability for larkspur tolerance within breed and evaluate the potential for increasing larkspur tolerance through artificial selection. Larkspur challenge was administered to 141 yearling steers (32 Angus, 13 Brahman, 49 Line 1 Hereford, 33 Holstein, and 14 Jersey) with a standardized dose of dried ground larkspur suspended in water and gavaged directly into the rumen. Larkspur tolerance was measured at 24 h after dosing as the length of time (up to 40 min maximum) in which the animal could sustain walking at 6.44 km/h while being led behind a tractor on a circular track. High-density SNP arrays (770,000 or 30,000 SNP) were used to genotype each steer and genotypes were used to compute the genomic relationship matrix which is a precursor to estimating heritability. Larkspur tolerance heritability estimates were similar whether estimated with REML (0.36 ± 0.30; P = 0.10) or Bayesian Monte Carlo Markov chain (MCMC) (0.42 ± 0.23; MCMC posterior distribution 2.5, 25, 50, 75, and 97.5th percentiles were 0.035, 0.24, 0.40, 0.59, and 0.90). To evaluate the potential for using our larkspur challenge data to calculate EBV of an untested population, we computed genomic relationship coefficients between 190 previously genotyped (but untested for larkspur tolerance and comprising the same 5 breeds in this study) cattle and the 141 steers tested for larkspur tolerance. Because of uncertainty in the heritability estimate, EBV were computed for each iteration of the MCMC to average over all possible values for heritability and weight by the appropriate posterior density. The most extreme EBV were for target animals with the strongest genetic ties to tested animals. Simulations indicated that divergent selection of parents can more than double the power for estimating heritability. Our results indicate that selection for larkspur tolerance should be effective. The rate of selection response will critically depend on challenging and testing animals with strong genetic ties to candidates for selection. Genetic ties can either be estimated from SNP genotypes or computed from common ancestry. USDA is an equal opportunity provider and employer.</description><subject>Agricultural equipment</subject><subject>Animals</subject><subject>Bayesian analysis</subject><subject>Beef</subject><subject>Beef cattle</subject><subject>Bovidae</subject><subject>Cattle</subject><subject>Computation</subject><subject>Computer simulation</subject><subject>Delphinium</subject><subject>Estimation</subject><subject>Genetic diversity</subject><subject>Genotypes</subject><subject>Heritability</subject><subject>Iterative methods</subject><subject>Markov chains</subject><subject>Mathematical analysis</subject><subject>Monte Carlo simulation</subject><subject>Nutrient availability</subject><subject>Parents</subject><subject>Pasture</subject><subject>Single-nucleotide polymorphism</subject><subject>Studies</subject><subject>Sustainability</subject><issn>0021-8812</issn><issn>1525-3163</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqNjE1OwzAQha0KpIafXQ8wEuuA7dRJWCMQB6DryjhDNcG1U49dqRvOTlpxAFZP-t73nhArJR-10d3TaPdaqrZWXdsvRKWMNnWj2uZKVFJqVfe90ktxwzxKqbR5NpX42TCFHQx0xLTDkIHRo8sUA9gwwIzinhxDjlCCi7N1ZpjJwdEmshezhAGTP52PvE3fPJU0DzwmGxxefriwwynTJ3nKJ6AAzubs8U5cf1nPeP-Xt-Lh7fXj5b2eUjwU5LwdY0lhrrZarttO9023bv5n_QKeDle4</recordid><startdate>20161001</startdate><enddate>20161001</enddate><creator>Keele, J W</creator><creator>McDaneld, T G</creator><creator>Kuehn, L A</creator><creator>Snelling, W M</creator><creator>Tait, R G</creator><creator>Welch, K D</creator><creator>Green, B T</creator><general>Oxford University Press</general><scope>3V.</scope><scope>7RQ</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8AF</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M2P</scope><scope>M7P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope><scope>U9A</scope></search><sort><creationdate>20161001</creationdate><title>Using divergent selection and genomics to uncover genetic variation underlying larkspur tolerance and susceptibility in cattle</title><author>Keele, J W ; McDaneld, T G ; Kuehn, L A ; Snelling, W M ; Tait, R G ; Welch, K D ; Green, B T</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_20467283743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Agricultural equipment</topic><topic>Animals</topic><topic>Bayesian analysis</topic><topic>Beef</topic><topic>Beef cattle</topic><topic>Bovidae</topic><topic>Cattle</topic><topic>Computation</topic><topic>Computer simulation</topic><topic>Delphinium</topic><topic>Estimation</topic><topic>Genetic diversity</topic><topic>Genotypes</topic><topic>Heritability</topic><topic>Iterative methods</topic><topic>Markov chains</topic><topic>Mathematical analysis</topic><topic>Monte Carlo simulation</topic><topic>Nutrient availability</topic><topic>Parents</topic><topic>Pasture</topic><topic>Single-nucleotide polymorphism</topic><topic>Studies</topic><topic>Sustainability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Keele, J W</creatorcontrib><creatorcontrib>McDaneld, T G</creatorcontrib><creatorcontrib>Kuehn, L A</creatorcontrib><creatorcontrib>Snelling, W M</creatorcontrib><creatorcontrib>Tait, R G</creatorcontrib><creatorcontrib>Welch, K D</creatorcontrib><creatorcontrib>Green, B T</creatorcontrib><collection>ProQuest Central (Corporate)</collection><collection>Career &amp; Technical Education Database</collection><collection>Agricultural Science Collection</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Journal of animal science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Keele, J W</au><au>McDaneld, T G</au><au>Kuehn, L A</au><au>Snelling, W M</au><au>Tait, R G</au><au>Welch, K D</au><au>Green, B T</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using divergent selection and genomics to uncover genetic variation underlying larkspur tolerance and susceptibility in cattle</atitle><jtitle>Journal of animal science</jtitle><date>2016-10-01</date><risdate>2016</risdate><volume>94</volume><spage>860</spage><epage>860</epage><pages>860-860</pages><issn>0021-8812</issn><eissn>1525-3163</eissn><abstract>In the Rocky Mountain region of western US, selection for larkspur tolerance would reduce mortality of cattle from larkspur poisoning and increase opportunity to utilize pastures at peak nutrient availability resulting in increased sustainability of beef production. Previous research indicated that there are breed differences for tolerance to toxic larkspur. Our objective was to estimate heritability for larkspur tolerance within breed and evaluate the potential for increasing larkspur tolerance through artificial selection. Larkspur challenge was administered to 141 yearling steers (32 Angus, 13 Brahman, 49 Line 1 Hereford, 33 Holstein, and 14 Jersey) with a standardized dose of dried ground larkspur suspended in water and gavaged directly into the rumen. Larkspur tolerance was measured at 24 h after dosing as the length of time (up to 40 min maximum) in which the animal could sustain walking at 6.44 km/h while being led behind a tractor on a circular track. High-density SNP arrays (770,000 or 30,000 SNP) were used to genotype each steer and genotypes were used to compute the genomic relationship matrix which is a precursor to estimating heritability. Larkspur tolerance heritability estimates were similar whether estimated with REML (0.36 ± 0.30; P = 0.10) or Bayesian Monte Carlo Markov chain (MCMC) (0.42 ± 0.23; MCMC posterior distribution 2.5, 25, 50, 75, and 97.5th percentiles were 0.035, 0.24, 0.40, 0.59, and 0.90). To evaluate the potential for using our larkspur challenge data to calculate EBV of an untested population, we computed genomic relationship coefficients between 190 previously genotyped (but untested for larkspur tolerance and comprising the same 5 breeds in this study) cattle and the 141 steers tested for larkspur tolerance. Because of uncertainty in the heritability estimate, EBV were computed for each iteration of the MCMC to average over all possible values for heritability and weight by the appropriate posterior density. The most extreme EBV were for target animals with the strongest genetic ties to tested animals. Simulations indicated that divergent selection of parents can more than double the power for estimating heritability. Our results indicate that selection for larkspur tolerance should be effective. The rate of selection response will critically depend on challenging and testing animals with strong genetic ties to candidates for selection. Genetic ties can either be estimated from SNP genotypes or computed from common ancestry. USDA is an equal opportunity provider and employer.</abstract><cop>Champaign</cop><pub>Oxford University Press</pub><doi>10.2527/jam2016-1768</doi></addata></record>
fulltext fulltext
identifier ISSN: 0021-8812
ispartof Journal of animal science, 2016-10, Vol.94, p.860-860
issn 0021-8812
1525-3163
language eng
recordid cdi_proquest_journals_2046728374
source Oxford University Press Journals All Titles (1996-Current)
subjects Agricultural equipment
Animals
Bayesian analysis
Beef
Beef cattle
Bovidae
Cattle
Computation
Computer simulation
Delphinium
Estimation
Genetic diversity
Genotypes
Heritability
Iterative methods
Markov chains
Mathematical analysis
Monte Carlo simulation
Nutrient availability
Parents
Pasture
Single-nucleotide polymorphism
Studies
Sustainability
title Using divergent selection and genomics to uncover genetic variation underlying larkspur tolerance and susceptibility in cattle
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-10T09%3A12%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Using%20divergent%20selection%20and%20genomics%20to%20uncover%20genetic%20variation%20underlying%20larkspur%20tolerance%20and%20susceptibility%20in%20cattle&rft.jtitle=Journal%20of%20animal%20science&rft.au=Keele,%20J%20W&rft.date=2016-10-01&rft.volume=94&rft.spage=860&rft.epage=860&rft.pages=860-860&rft.issn=0021-8812&rft.eissn=1525-3163&rft_id=info:doi/10.2527/jam2016-1768&rft_dat=%3Cproquest%3E2046728374%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2046728374&rft_id=info:pmid/&rfr_iscdi=true