Fast-Forward Genetics

When it comes to genomics and genetic evaluation, the sum is more powerful than the parts, at least for now, apparently. It's called whole-gnome selection (WGS). We've had EPDs and estimates of genetic merit based on animal phenotypes, relatives and pedigrees, explains Jerry Taylor, professor and Wurdack Chair in Animal Genomics at the University of Missouri (MU). Whole-gnome selection seeks to establish

When it comes to genomics and genetic evaluation, the sum is more powerful than the parts, at least for now, apparently.

It's called whole-gnome selection (WGS).

“We've had EPDs and estimates of genetic merit based on animal phenotypes, relatives and pedigrees,” explains Jerry Taylor, professor and Wurdack Chair in Animal Genomics at the University of Missouri (MU). “Whole-gnome selection seeks to establish genetic merit, too, but through identifying genomic markers that have a beneficial or detrimental effect on the phenotypic performance of specific traits.”

There's a mountain of molecular science and statistics rolled into that statement.

Suffice it to say the most reliable genetic predictions for additive traits currently are Expected Progeny Differences (EPDs). These require collecting an animal's phenotype (weights, measures, etc.) and that of its relatives and progeny. All this is underpinned by knowing how related the animal is to others characterized in the same population.

Further, understand that up until now, the quest for genetic prediction with DNA has revolved around trying to identify variation in specific genes affecting one or more traits, and then developing tests to identify and permit selection for or against variants for a relatively small number of proven genes.

WGS goes the opposite direction, seeking to identify a group of SNPs (single nucleotide polymorphisms) spanning the gnome, which accurately predict total genetic merit without necessarily knowing the effects of variation at individual genes. In other words, you don't have to know exactly where all the causal genes that impact a trait are, or even what they are.

Consequently, WGS would make genetic predictions (EPDs) for additive traits, with moderate accuracy, possible the day an animal is born. It would also immediately enable at least moderately accurate predictions for traits that require phenotypes that are expensive and hard to collect — such as feed efficiency, tenderness and the like.

WGS broadens the view

“Before [with the QTL approach], DNA mapping was like being on a highway where you could only see as far as the eye allowed,” says Tim Smith, the U.S. Meat Animal Research Center (USMARC) research chemist who developed an essential piece of the WGS technology. “With this whole-gnome process, it's like being up in the sky in a helicopter over that same highway… Instead of taking one marker at a time and looking for relationships, we can do a huge number in parallel.”

Or as Mark Allan, USMARC research geneticist, explains, if you try to estimate the weight of individual calves in a pen of feeders 150 days down the road, chances are you'll be wrong most of the time. Estimate the average weight, though, and you can get awfully close.

“With WGS, by averaging across all SNPs, we do a lot better job of predicting the genetic merit for a particular trait, rather than relying on one or two genes to tell us,” he says.

Smith and Allan are part of the consortium of scientists proving the concept works. The consortium includes USMARC, MU, USDA Agricultural Research Service at Beltsville, MD, the University of Alberta and Illumina, a life sciences company that develops tools for genetic research.

Rather than representing a new direction in genomics, Taylor explains WGS is really a byproduct of the ongoing goal to identify all the causal genes.

“We didn't have a good feel early on for how many genes we would need to identify and develop tests for individually. We're coming to recognize there are a lot of genes that underlie a single trait,” Taylor says.

“The ultimate goal is still identifying all the genes that impact traits,” Taylor explains. “But, with the high-density assay and whole-gnome selection, we have technology that can be used to the industry's benefit today.”

Building a new machine

Though WGS was conceived and studied via computer simulation back in 2001, it was only recently possible to attempt with cattle.

“A year ago, we didn't have enough markers evenly spread out along the chromosome that we could build a cost-effective genotyping assay,” Taylor says. “While a whole lot of SNPs (about 2.1 million) became available from the Bovine Genome Sequence project, there were still gaps that the aforementioned consortium had to fill before they could build the assay.”

The consortium developed an assay utilizing Illumina's iSelect custom genotyping panel, which accounts for 51,386 SNPs — identified as informative through other studies — in a single assay. All are contained in a cassette about the length of your index finger.

“This group designed an assay that allows probing 51,386 SNPs that are real and informative — in one assay with one sample in three days at a cost less than hundreds of dollars,” Taylor says.

Establishing WGS prediction

USMARC scientists began using the new assay to genotype approximately 3,600 animals from its cattle populations. Chief among these are 2,600 animals from Cycle VII of their Germplasm Evaluation project. This population represents a meticulously designed cross of F1 sires and dams that is optimal for finding chunks of chromosome segregating Economically Relevant Traits (ERTs). Turns out, this visionary design is ideal for WGS and determining relationships between SNPs and ERTs.

Plus, Allan points out, “The advantage we have at USMARC is that the populations we work with are pedigreed and have been phenotyped extensively for most traits relevant to beef production and beef quality.”

“It's also extremely important from a genetic standpoint that the population sampled emulate the current cattle industry as closely as possible,” explains Mark Thallman, USMARC research geneticist. Thus, for no cost to breeders or associations, USMARC will also use the new assay to genotype 2,000 of the most heavily used bulls in the industry spanning 16 breeds. These will primarily be bulls with high-accuracy EPDs for important traits and with semen available so they can still be utilized by producers. Thallman adds, “This project is a collaboration with 16 breed associations that are responsible for identifying the bulls and providing semen from these bulls as a DNA source.”

“We're excited to participate in this project,” says Craig Huffhines, American Hereford Association executive vice president. “We think it's important to collaborate with other breed organizations and USDA in an effort that might enhance both the credibility of the research and the understanding of the results. In addition, whatever is discovered through this process will be available in the public domain rather than getting tied up in proprietary intellectual property rights that might slow the progress of its use.”

Bill Bowman, American Angus Association (AAA) vice president of information and data programs, says, “We view whole-gnome selection as a way to enhance our current selection tools, to achieve more accuracy on younger animals, and to characterize genetics for traits where it's extremely difficult to measure the phenotype.”

Bowman adds that AAA believes this is a necessary step to make genomics a fully functional tool for the industry.

He cautions, though, “We have to have some patience and let the research play out. There's a difference between being an early adopter and using information for marketing purposes, vs. getting to the point where tools like this one can be used with a lot of confidence in genetic prediction.”

Besides uncovering the relationship between SNPs and ERTs in these bulls, Thallman explains, “Our hope is that by making a large but manageable investment in genotyping these 2,000 bulls for breeders, we can jumpstart use of the technology and that providers of gene tests will carry it forward.”

Other animals that USMARC will genotype initially include animals from its twinning population, a previous genomic resource population, as well as those involved in their ongoing Germplasm Evaluation project. MU has undertaken the genotyping of more than 12,000 animals. According to Illumina, more than 40,000 animals worldwide have been, or are being, genotyped with this technology.

Odds are hopeful

Given the massive promise of genomics tools and the measly progress in terms of application thus far, all these researchers are optimistic about WGS, but cautiously so.

“We've got to be careful not to promise more than we can deliver with this whole-gnome technology,” Thallman says. “But I'm encouraged in genomics that we're at the point of developing applications for the science.”

“No one knows yet if this will work. We all believe it will. I believe the only question is how many SNPs will be required to make it work,” Taylor says. If 51,386 prove too few, there's plenty of room to expand. He explains that in human genetic study of complex diseases, there are assays genotyping more than 1 million SNPs at a time.

Without delving too deeply into the science, research suggests an informative marker needs to be identified at least every 100,000 base pairs in the gnome. The assay designed by the consortium does so every 60,000 base pairs on average.

Moreover, if this demonstration is successful, it will take additional time to build the necessary infrastructure that would enable the information to be included in current genetic evaluations.

For instance, Smith explains, “In 10 years, we had collected about 1 million genotypes at USMARC. In the first week of using the new assay, we had 7 million. Now we have more than 100 million.” Just figuring out the most efficient way to aggregate, slice and dice the data will be a weighty chore.

“There isn't a lot of value to this assay until we have the infrastructure to make it work with current genetic evaluation,” says Tom Jenkins, USMARC research animal scientist. “You can have all the data in the world, but there's nothing you can do with it until an application is developed to use it.”

The plan calls for using these genotypes initially to develop EPDs for elusive traits like feed efficiency, and to subsequently develop the infrastructure for incorporating the data into EPDs currently provided by breed associations.

According to Thallman, “WGS is not limited to traits for which EPDs don't currently exist. A primary benefit of genotyping 2,000 influential industry sires is that by using their existing EPDs we can leverage the industry's prior investment of millions of records of field data already collected to validate and refine associations between genomic regions and the traits for which EPDs already exist. Though this application is perhaps less glamorous than EPDs for new traits, the potential practical benefits include substantially higher accuracy of the EPDs on yearling bulls at the time they're sold.”

The icing on the cake to this notion comes with knowing you won't need much new learning to exploit the technology. WGS integrated into existing genetic evaluation will be invisible, as it increases the accuracy of the EPDs producers are accustomed to using. Furthermore, Thallman explains, “Selection indices that work for existing EPDs will work just as well with future (higher-accuracy) EPDs enhanced by WGS. However, there will be opportunities to improve selection indices once EPDs for feed efficiency and other difficult-to-measure traits are available.”

It will never be a silver bullet. “It still will be important for breeders to collect phenotypes on the traits that are routinely collected now,” Thallman says.

The possibilities are tantalizing, though. Before weaning, screen the top picks to find the duds; use young bulls harder, because there's more accuracy in the genetic prediction; rank bulls before buying them…

As times goes on, other tools may emerge, too. For instance, Thallman explains, “Down the road, we would expect that as we determine which SNPs have the strongest relationships to ERTs, lower-cost products would be developed using only a few hundred SNPs.”

Keep in mind, none of this means currently available DNA diagnostic tests will necessarily be supplanted.

“The whole-gnome approach simultaneously tests for every gene in the gnome that influences a trait, realizing that it's an indirect test that's not an exact predictor of the causal effect,” Taylor explains. “If scientists can demonstrate this works, then I believe commercial companies will have to figure out how they sell products based upon it, and what kinds of products.”

Since WGS is aimed at genetic evaluation, Taylor believes sorting through competing genomic products based upon it will be fairly simple. “You're selling an EPD — the ability with this to create or enhance one. Tell me the accuracy and tell me what it costs, and then I can make a decision.”

As infrastructure for genetic evaluation (with WGS) is developed, Thallman explains the aim is to make it possible for multiple sources to be included along with traditional pedigree and performance records.

“The Bovine Genome Sequence was the $50 million tool we needed to be in a position to access all these other tools,” Taylor explains. “You now must consider cattle in the same scientific arena as humans, because all the genomic tools developed for human science can now be applied to cattle.”


Visible and/or measurable expression of genes and the environment, e.g. coat color, weaning weight, etc.


Deoxyribonucleic acid — a nucleic acid containing the genetic instructions for development and functioning. It's comprised of pairs of nucleotides (see base pairs).

DNA marker

DNA that lies so close to the gene on a chromosome that the gene and that piece of DNA are inherited together. It can be a segment of DNA or the gene itself.


Quantitative trait locus — a segment of DNA closely linked to the genes affecting a particular trait. Quantitative traits are those driven by a number of genes — such as weaning weight or milking ability — vs. qualitative traits such as hide color or polledness, which are determined by one or a few genes.


Single nucleotide polymorphism — A DNA sequence in which the base of a single nucleotide has been replaced by another.

Base pairs

Nucleotides contain the bases: Adenine (A), Guanine (G), Cytosine (C) or Thymine (T).

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