Improving Dairy Cattle by the Continuous Use of the Proved Sire

Graves, R. R.

doi:10.3168/jds.s0022-0302(25)93972-0

Cited by 3 publications

(3 citation statements)

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“…Meade (1921) evaluated the performance of Guernsey sires and their transmitting ability and concluded that the best method for selection may be to consider the percentage production of all advanced registry daughters based on standardized requirements according to age. The male line received the greatest attention in selection because the sire's heredity was most accurately indicated by his daughters' production, more so than the dam based on her own production record (Graves, 1925). A recognized fault of production records in an advanced registry for selection purposes was that records included only daughters that were put on test and met advanced registry standards.…”

Section: Productionmentioning

confidence: 99%

A 100-Year Review: Identification and genetic selection of economically important traits in dairy cattle

Miglior

Fleming

Malchiodi

et al. 2017

Journal of Dairy Science

348

259

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Over the past 100 yr, the range of traits considered for genetic selection in dairy cattle populations has progressed to meet the demands of both industry and society. At the turn of the 20th century, dairy farmers were interested in increasing milk production; however, a systematic strategy for selection was not available. Organized milk performance recording took shape, followed quickly by conformation scoring. Methodological advances in both genetic theory and statistics around the middle of the century, together with technological innovations in computing, paved the way for powerful multitrait analyses. As more sophisticated analytical techniques for traits were developed and incorporated into selection programs, production began to increase rapidly, and the wheels of genetic progress began to turn. By the end of the century, the focus of selection had moved away from being purely production oriented toward a more balanced breeding goal. This shift occurred partly due to increasing health and fertility issues and partly due to societal pressure and welfare concerns. Traits encompassing longevity, fertility, calving, health, and workability have now been integrated into selection indices. Current research focuses on fitness, health, welfare, milk quality, and environmental sustainability, underlying the concentrated emphasis on a more comprehensive breeding goal. In the future, on-farm sensors, data loggers, precision measurement techniques, and other technological aids will provide even more data for use in selection, and the difficulty will lie not in measuring phenotypes but rather in choosing which traits to select for.

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Section: Productionmentioning

confidence: 99%

A 100-Year Review: Identification and genetic selection of economically important traits in dairy cattle

Miglior

Fleming

Malchiodi

et al. 2017

Journal of Dairy Science

348

259

View full text Add to dashboard Cite

show abstract

“…Several female fertility traits in dairy cattle showed reduced genetic correlations among milk production traits because of low or moderate heritabilities [ 4 , 128 ]. The male lines received the greatest attention in selection because of accurate prediction of sire's heritability [ 129 ]. Estimated breeding values of AI bulls are considered to be more reliable for various predictions [ 130 ].…”

Section: Discussionmentioning

confidence: 99%

Population demographic history and population structure for Pakistani Nili-Ravi breeding bulls based on SNP genotyping to identify genomic regions associated with male effects for milk yield and body weight

et al. 2020

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The domestic Nili-Ravi water buffalo (Bubalus bubalis) is the best dairy animal contributing 68% to total milk production in Pakistan. In this study, we identified genome-wide single nucleotide polymorphisms (SNPs) to estimate various population genetic parameters such as diversity, pairwise population differentiation, linkage disequilibrium (LD) distribution and for genome-wide association study for milk yield and body weight traits in the Nili-Ravi dairy bulls that they may pass on to their daughters who are retained for milking purposes. The genotyping by sequencing approach revealed 13,039 reference genome-anchored SNPs with minor allele frequency of 0.05 among 167 buffalos. Population structure analysis revealed that the bulls were grouped into two clusters (K = 2), which indicates the presence of two different lineages in the Pakistani Nili-Ravi water buffalo population, and we showed the extent of admixture of these two lineages in our bull collection. LD analysis revealed 4169 significant SNP associations, with an average LD decay of 90 kb for these buffalo genome. Genome-wide association study involved a multi-locus mixed linear model for milk yield and body weight to identify genome-wide male effects. Our study further illustrates the utility of the genotyping by sequencing approach for identifying genomic regions to uncover additional demographic complexity and to improve the complex dairy traits of the Pakistani Nili-Ravi water buffalo population that would provide the lot of economic benefits to dairy industry.

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“…The idea of comparing a cow's milk production with that of her dam emerged near the turn of the 20th century. Several indices were proposed for this purpose (Davidson, 1925;Graves, 1925;Yapp, 1925;Goodale, 1927;Gowen, 1930;Bonnier, 1936;Allen, 1944) and their relative accuracy was compared by Edwards (1932). In practice, the earliest known daughter-dam differences in the United States were computed by individual bull associations around 1915, based on a handful of sires with a few offspring apiece-this was the first serious attempt to improve dairy cattle by selection.…”

Section: Daughter-dam Comparisonmentioning

confidence: 99%

A 100-Year Review: Methods and impact of genetic selection in dairy cattle—From daughter–dam comparisons to deep learning algorithms

Weigel

VanRaden

Norman

et al. 2017

Journal of Dairy Science

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In the early 1900s, breed society herdbooks had been established and milk-recording programs were in their infancy. Farmers wanted to improve the productivity of their cattle, but the foundations of population genetics, quantitative genetics, and animal breeding had not been laid. Early animal breeders struggled to identify genetically superior families using performance records that were influenced by local environmental conditions and herd-specific management practices. Daughter-dam comparisons were used for more than 30 yr and, although genetic progress was minimal, the attention given to performance recording, genetic theory, and statistical methods paid off in future years. Contemporary (herdmate) comparison methods allowed more accurate accounting for environmental factors and genetic progress began to accelerate when these methods were coupled with artificial insemination and progeny testing. Advances in computing facilitated the implementation of mixed linear models that used pedigree and performance data optimally and enabled accurate selection decisions. Sequencing of the bovine genome led to a revolution in dairy cattle breeding, and the pace of scientific discovery and genetic progress accelerated rapidly. Pedigree-based models have given way to whole-genome prediction, and Bayesian regression models and machine learning algorithms have joined mixed linear models in the toolbox of modern animal breeders. Future developments will likely include elucidation of the mechanisms of genetic inheritance and epigenetic modification in key biological pathways, and genomic data will be used with data from on-farm sensors to facilitate precision management on modern dairy farms.

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Improving Dairy Cattle by the Continuous Use of the Proved Sire

Cited by 3 publications

References 0 publications

A 100-Year Review: Identification and genetic selection of economically important traits in dairy cattle

A 100-Year Review: Identification and genetic selection of economically important traits in dairy cattle

Population demographic history and population structure for Pakistani Nili-Ravi breeding bulls based on SNP genotyping to identify genomic regions associated with male effects for milk yield and body weight

A 100-Year Review: Methods and impact of genetic selection in dairy cattle—From daughter–dam comparisons to deep learning algorithms

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