Building a Sustainable Future
As the world’s population continues to grow, the agricultural industry will need to produce more livestock, crops, poultry, and aquatic organisms to feed the population.1 However, farmers face several challenges in increasing their yield, including land and water scarcity, unpredictable climate, and evolving pathogens. To help improve food sustainability and security worldwide, they need to optimize the crops and animals they grow or rear in terms of quality, productivity, and resilience to external stressors.1 For livestock breeders, this will require that they make better decisions about which animals should breed to obtain offspring with desirable phenotypic traits, such as higher milk production, greater growth rates, better reproductive performance, reduced food intake, and less methane emission.2 However, the traditional selective breeding process is quite slow, as breeders often cannot determine if they made a good choice until the progeny reaches maturity. There is also no guarantee that a mating between high-value animals possessing many of the chosen physical characteristics will produce offspring with those same attributes. The inheritance of many traits is complex, with multiple genes contributing to the animal’s phenotype, which is also affected by environmental factors.3
The advancement of agrigenomics offers livestock breeders the opportunity to minimize their environmental footprint while meeting the demands of a growing population.
iStock: TanyaJoy
Harnessing the Power of Livestock Genomics
To improve the accuracy of breeding decisions, livestock breeders need novel approaches and have turned to animal scientists and agricultural genomics, or agrigenomics, for help. Many breeders now examine the genome to identify genetic variants, such as single nucleotide polymorphisms (SNPs), underlying complex phenotypic traits.4 Once they choose these markers, scientists can design genotyping arrays to help assign a genomic estimated breeding value (GEBV) to animals in the herd and more accurately identify individuals with desirable genetic profiles for breeding. This allows breeders to make decisions earlier in the animal’s lifetime, which improves the breeding program’s efficiency and allows them to successfully enrich animals with the preferred phenotypes in their livestock populations. Although SNP microarrays are more accurate at predicting the best animals for breeding than phenotype-based assessments, these assays only generate data limited to specific markers.5
Over the last decade, the cost of genome sequencing has decreased and high-coverage whole genome sequencing (WGS) has become an option for GEBV determination. “When it comes to agrigenomics … there is increasingly more interest to get into sequencing, but it is still in its infancy,” said Lloyd Wai Yee Low, a bioinformatics data scientist at the University of Adelaide. Unlike genotyping arrays, this method provides scientists with information-rich data, which allows them to discover rare markers and detect additional variant types, such as copy number or structural variations.6 Furthermore, WGS data can later be re-analyzed to assess the animals for newly discovered markers. But high-coverage WGS is still expensive, with elevated equipment prices, running expenses, and labor costs preventing scientists from analyzing large livestock populations and employing this technique for low-budget studies.7 “Many small projects currently cannot afford to use genomics [techniques],” said Robert Henry, a professor at the Centre for Crop Science at the University of Queensland.
To gain a more comprehensive genomic profile from genotyping arrays, animal scientists can use imputation. This statistical approach allows them to infer the genotypes of animals without analyzing every locus by first sequencing a reference population to identify important variants.6 They then use the reference population’s data, which is also known as the reference panel, to make accurate predictions about the GEBVs of animals with an unknown genotype. However, the accuracy of imputation is dependent on the reference panel size and how genetically different the reference and target populations are from each other.8 Moreover, large reference panels are unavailable for many agricultural species.
Leveraging Low-Pass WGS for Enhanced Livestock Breeding
To overcome the limitations of genotyping arrays and high-coverage WGS, scientists have started using a new sequencing technique for livestock breeding called low-pass WGS. Instead of the 30x-50x depth typically used for high-coverage WGS, low-pass WGS uses a shallow depth of coverage, which is normally between 0.1x and 1x.9 Although it sequences only a fraction of the genome, this method enables scientists to analyze numerous SNP and insertion-deletion (InDel) variants across the genome and allows more comprehensive assessment than genotyping arrays. “[With low-pass WGS], you get more SNPs, [which] means more genetic information. Potentially that will lead to better accuracy in [your] genomic predictions,” said Low. Like genotyping arrays, low-pass WGS frequently requires imputation to infer the missing genetic data. But genotype imputation is far more accurate and consistent for this sequencing approach over arrays because the technique analyzes a substantially larger number of SNPs.9 This leads to more precise GEBV rankings. “If you are able to rank [the animals] accurately, that is going to help [you keep] the best animals for the trait that you are interested in,” said Low.
This reduction in depth also corresponds to a lower cost than high-coverage WGS and enables scientists to sequence more animals. “[Low-pass WGS] is more cost-effective than high-coverage sequencing, and it provides data that is superior to that which can be obtained by other techniques that do not involve sequencing, like the use of arrays,” said Henry. However, these advantages do not come at the cost of performance, as scientists examining population genomics have shown that low-pass WGS of numerous samples is more accurate than higher-depth sequencing of few samples.10 Animal scientists have now used this method in commercial breeding programs to assess the genomes of many different livestock species.6 Ultimately, this accurate, efficient, inexpensive, and high-throughput technique serves as a compromise between low-cost microarrays and data-rich high-coverage WGS, while providing a more accessible genotyping method to those in the agricultural field.
As a part of MGI’s AgriHigh Low-Pass Whole Genome Sequencing package, animal scientists will receive a pig reference panel to accelerate marker discovery.
iStock: kadmy
Unveiling MGI Tech’s Low-Pass WGS Workflow Solution
Recently, MGI Tech released a new flexible and scalable low-pass WGS workflow solution for agricultural genotyping, which they called the AgriHigh Low-Pass WGS package.11 This comprehensive bundle provides tools for all stages of the workflow, including sample preparation, library preparation, sequencing, and data analysis. Additionally, this system simplifies library preparation to a three-step process, taking only half the time of typical protocols and using fewer consumables. “I think it is a very exciting development to see this [MGI workflow solution] packaged and made available to the community,” said Henry.
MGI constructed different versions of the package for medium- and high-throughput workflows to ensure they are providing a solution for both small- and large-scale sequencing projects.11 The sequencers in these bundles employ DNBSEQ™ technology, which quickly acquires accurate genomic data, making these instruments ideal to help livestock breeders make rapid breeding decisions. For instance, scientists used MGI’s DNBSEQ-T7 sequencer to analyze 1,536 pig samples at 1.5x depth in 22-24 hours.11 Yearly, these workflows can assess between 25,000 to 384,000 samples depending on the depth and the package version the scientist chooses. “The MGI technology gives us very cost-effective, low-price data, but [of] high quality. So, we can get the quality of the data that we need at a good price,” said Henry.
Besides its many other features, the bundle also includes a pig reference panel, which enables scientists to start genotyping pigs right away. This is particularly important to Chinese researchers and breeders given that China is the leading country in both pork consumption and pig farming globally. Prior to the release of this package, animal scientists from China Agricultural University also assessed the precision DNBSEQ™ technology-enabled low-pass WGS for genotyping 2,869 Duroc pigs.8 They determined that the genomic data obtained from WGS at 0.73x coverage was 99.7 percent and 91.9 percent consistent with the results from an SNP array and WGS at 15x coverage, respectively, highlighting the technique’s accuracy. Using low-pass WGS, the scientists also discovered 11.3 million SNPs and associated 14 genomic regions with seven economically important traits, such as teat number and back fat thickness.8
Shaping the Future of Agriculture
Moving forward, it is essential for humans to minimize their environmental footprint. However, this goal is often at odds with the increasing global population and the accompanying need for more food production.12 The exploration of agrigenomics presents a promising avenue for radically enhancing the agricultural industry’s sustainability from small-scale and resource-limited farms to commercial operations. Accurate and affordable methods, such as low-pass WGS, facilitate the identification of important genetic variants associated with key phenotypic traits. This will allow livestock breeders to make rapid breeding decisions and obtain offspring with desirable characteristics. Besides improving production yields, it will be critical to generate livestock breeds that are more resistant to the increasing effects of climate change, such as animals that can tolerate higher temperatures or those that are naturally resistant to pathogens, to ensure global food security in the future.12 “The best thing we can do to improve the environment is to improve the efficiency of agriculture, [which will] ensure we can produce our food on the lowest possible footprint, allowing space for biodiversity conservation,” said Henry. “And genomics is the key tool that enables that sustainable future.”
References
1. Godfray HCJ, et al. Food security: The challenge of feeding 9 billion people. Science. 2010;327(5967):812-818.
2. Gutierrez-Reinoso MA, et al. Genomic analysis, progress and future perspectives in dairy cattle selection: A review. Animals. 2021;11(3):599.
3. Georges M. Recent progress in livestock genomics and potential impact on breeding programs. Theriogenology. 2001;55(1):15-21.
4. Meuwissen T, et al. Genomic selection: A paradigm shift in animal breeding. Anim Front. 2016;6(1):6-14.
5. Pavan S, et al. Recommendations for choosing the genotyping method and best practices for quality control in crop genome-wide association studies. Front Genet. 2020;11.
6. Jones HE, Wilson PB. Progress and opportunities through use of genomics in animal production. Trends Genet. 2022;38(12):1228-1252.
7. Ros-Freixedes R. The contribution of whole-genome sequence data to genome-wide association studies in livestock: Outcomes and perspectives. Livest Sci. 2024;281:105430.
8. Yang R, et al. Accelerated deciphering of the genetic architecture of agricultural economic traits in pigs using a low-coverage whole-genome sequencing strategy. GigaScience. 2021;10(7):giab048.
9. Li JH, et al. Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays. Genome Res. 2021;31(4):529-537.
10. Lou RN, et al. A beginner’s guide to low-coverage whole genome sequencing for population genomics. Mol Ecol. 2021;30(23):5966-5993.
11. MGI Tech. AgriHigh low-pass WGS package. MGI Brochure. 2024.
12. Martin GB. Perspective: Science and the future of livestock industries. Front Vet Sci. 2024;11.
