This article was #24 of the Top 25 most well-read articles on www.progressivedairy.com in 2013. It was published in the May 30, 2013 Extra e-newsletter. Click here for the full list of the Top 25. In an effort to improve dairy cow fertility, Dr. Pablo J. Pinedo, CVM, of Texas A&M AgriLife Research and Texas A&M University, and Dr. Jose E.P. Santos of the Department of Animal Sciences at the University of Florida, are leading a team of scientists to carry out a five-year grant project looking at the heritability of reproduction traits.
They hope to identify SNPs that are associated with uterine health, resumption of postpartum ovulation, establishment and maintenance of pregnancy in cows, and providing a wider understanding of the genetic structure of fertility traits.
We asked José E.P. Santos,
Q. Are you finding this to be evident in your research thus far? How do you foresee the results of your findings being implemented at the farm level?
At this stage of our project, we do not have the genomic data analyzed. We are collecting data from 12,000 cows in 15 different herds located in six states in four regions of the U.S.
We are characterizing phenotypes linked to postpartum health and fertility of dairy cows, and we are collecting DNA for subsequent genotyping.
Our goal is to identify genomic markers through single nucleotide polymorphisms, the so-called SNPs or haplotypes (chunks of SNPs) that are transferred from one generation to another and are linked with some of the variation in observed postpartum health and fertility such that they can be incorporated into current platforms for genomic selection of sires and cows.
These data will only be finalized in the next few years, but we hope to have some answers for producers in late 2014. Our goal is to help producers make better decisions for genetic selection of cattle such that they improve the long-term sustainability of herds by enhancing production concurrent with better health and reproduction. PD
— Dr. Jose E.P. Santos, Department of Animal Sciences at the University of Florida
Technologies used in genetic studies have undergone major advances in recent decades. Decoding of the human genome took approximately 10 years and cost around $3 billion.
Similar technology is now available for a tiny fraction of that value, and it is routinely used in genetic selection programs in all A.I. studs as well as used by dairy producers for genomic selection of females.
This novel technology is not restricted to production traits and can be applied to health and reproduction. However, an issue with the current genomic selection is that phenotypic characterization for fertility traits and the respective DNA markers are limited, which poses constraints to future advances.
Aware of the limitations of the current genomic selection for fertility in dairy cattle, the National Institute of Food and Agriculture (NIFA), through its competitive research funding program in food security, has identified translational genomics as an area of investigation to improve fertility of animals.
A team of scientists from different U.S. institutions was recently awarded a five-year grant of $2.98 million to study genomic markers for selection of reproduction in dairy cattle. The emphases of the project are to characterize in detail phenotypes linked to uterine health, resumption of ovulation postpartum, conception and maintenance of pregnancy, and identify DNA markers linked to those phenotypic traits.
The team is led by Dr. Pablo Pinedo at the Texas A&M AgriLife Research and Texas A&M University CVM, and Dr. José E.P. Santos with the Department of Animal Sciences at the University of Florida.
The team is composed of researchers with areas of expertise in reproduction and health, genetics, bioinformatics, nutrition, economics, extension and education. See a full list of researchers at the end of the article.
The study aims to explore genomic markers, in this case single nucleotide polymorphisms, or SNPs, which are substitutions in the DNA of an animal that differ between individuals of the same species and are in proximity of genes linked with particular phenotypes of economic interest.
Sequences of DNA fragments can differ in their SNPs because of mutations in the so called nucleobases adenine (A), thymine (T), cytosine (C) and guanine (G). These mutations confer an animal particular characteristics related to its phenotype such as changes in production, health and reproduction.
Today, commercial application of the use of SNPs is common for identification of superior sires and cows for production traits, but also for traits linked to health and reproduction. Every genomically tested bull is evaluated using a 50,000 SNP platform whose markers are linked with predicted transmitting ability, or PTA, for milk production, fat and protein content and yield, productive life and daughter pregnancy rate, among others.
One of the challenges with genomic selection is the characterization of phenotypes of biological and economic importance that have genetic control and can be incorporated in the population through genetic selection.
In typical progeny-testing programs, animals have an initial genetic value based on the expected genes inherited by the parents, which is usually called the parental average because it is calculated based on the average proof of the parents.
Parental average predicts future performance with low reliability, typically less than 40 percent. Incorporation of genomic testing increases reliability of proofs to 65 to 70 percent. As offsprings are born and evaluated, then reliability in the proof can increase further.
The biggest advantage of genomic selection is that genetic markers linked to a particular trait can be identified such that genomic breeding values for that particular trait can be quantified.
Using such DNA markers allows for selection of superior bulls and cows without the need for traditional proof with progeny testing. This is particularly important for selection of females that typically have very limited number of offsprings and genetic proofs are limited to parental averages with restricted reliability.
It is known that fertility in dairy cows is suboptimal and has declined in the last decades. Current measures of fertility are influenced only partially by the genetic make-up of the bull and the cow, and environmental factors have dramatic impacts on establishment and maintenance of pregnancy in cattle.
Nevertheless, some physiological measures of fertility, such as resumption of ovarian cyclicity and pregnancy loss, have moderate heritabilities.
Consequently, these two phenotypic measures can be more easily selected because a greater portion of the phenotype variability is attributed to the genetic make-up of the individual.
This is the proposed approach taken by this project; cows will be phenotyped for uterine diseases and direct measures of fertility that are known to have genetic control. Currently, genomic selection for fertility in the U.S. is based mostly on daughter pregnancy rate (DPR), which measures the rate at which cows become pregnant after an expected voluntary waiting period of 60 days.
DPR has low heritability and can be greatly influenced by non-genetic factors such as the voluntary waiting period of the farm, the efficiency of herd personnel and facilities to get cows pregnant, and the reproductive program implemented such as timed A.I., which can hasten insemination and pregnancy.
The team of scientists led by Pinedo expects to identify SNPs that are associated with uterine health, resumption of postpartum ovulation, establishment and maintenance of pregnancy in cows, providing a wider understanding of the genetic structure of fertility traits.
These new resources would then be incorporated into existing selection programs for implementation at the farm level. The researchers anticipate that the information from this project would assist A.I. studs and dairy producers to make rational and cost-effective decisions for genetic selection to reduce the risk of uterine diseases and to improve fertility.
The project encompasses three important NIFA components: research, extension and education. The NIFA funds research that provides technological innovations to enhance agriculture in the U.S. by making it more sustainable and economically viable.
By incorporating extension and education in this project, the team of researchers expects to transfer technology to the primary end users, the dairy producers, as well as to those in the allied dairy industry, and to educate future generations of animal scientists, veterinarians and academic leaders about the development of genomic technologies for genetic selection of dairy cattle for improved fertility.
Team members for this project are Pablo J. Pinedo and Christopher Seabury (Texas A&M University); José E.P. Santos, William W. Thatcher, and Klibs N. Galvão (University of Florida); Rodrigo C. Bicalho and Robert O. Gilbert (Cornell University); Gustavo M. Schuenemann (The Ohio State University); Guilherme J. M. Rosa (University of Wisconsin); Sandra Rodriguez-Zas (University of Illinois); and Ricardo C. Chebel and John Fetrow (University of Minnesota). PD