Challenges and Opportunities in Applying Genomic Selection to Ruminants Owned by Smallholder Farmers
Abstract
:1. Introduction
- The very rapidly reducing costs of full genome sequencing [8];
- The momentous reductions in the time taken to sequence an entire genome, down from close to 13 years for the first human genome sequence [9] to a full sequence now being achieved in a single day [10], and with potential in the near future to achieve full genome sequencing on the same day in the field rather than at laboratory sites;
- The potential to use whole-genome sequence data to discover the mutations causing variations among animals and, in turn, using that knowledge of functional mutations to improve the accuracy of breeding value predictions [15];
- Resolution of the “missing heritability” problem [16], proving that genomic selection approaches account for significant proportions of the genetic variation for economically important complex traits;
- Vastly improved computational capacity that is now allowing the cost-effective storage and processing of petabyte (1012 bytes)-scales of data [17];
- The ability to use pooled DNA samples from groups of animals to identify the average genetic merit at low cost [18], thereby enabling the development of new, cost-effective management applications based on genomic information; and
- The increased ability to capture essential individual animal performance data (phenotypes) through the use of automated or semi-automated electronic data capture methods.
2. The Need for Accurate Phenotyping and Record-Keeping
3. The Role of Reference Populations
4. Data Analyses and Estimation of Genomic Breeding Values
5. Infrastructure and Human Capacity
6. The Value of National and International Collaborations
7. The Ability of Genomic Information to Mitigate These Challenges
8. Future Opportunities
- Resource populations could be formed at relevant new regional (e.g., national or possibly even multi-national) levels within livestock species, with those populations being managed overall with input from the smallholder farmers contributing the animals, but with the responsibility for technical areas (phenotyping, genotyping, data upload, etc.) being the remit of technicians with appropriate training;
- Initial funding would be required to cover the costs of designing the populations (to ensure local relevance) and establishing them, for phenotyping the animals for the full range of economically important traits for each of the species, and for genotyping them, although only selected animals would require the use of higher-density (and thus more expensive) SNP panels;
- Designing the resource populations should be undertaken in direct collaboration with established resource populations in other countries with similar environmental systems, to ensure compatibility of the populations for the future pooling of data for genetic evaluation purposes—in LMICs, this generally means collaborations with other resource populations that operate in tropical or sub-tropical environments;
- However, due to the assumed use of genomic information, the design of the new populations does not need to specifically generate genetic linkages across the different populations, nor is there a need to restrict the design to animals of the same breed, as demonstrated by [65];
- Capture of data from the new resource populations would be achieved electronically in the field using mobile devices and subsequently uploaded to the data platform when internet coverage is available;
- Assuming that the opportunity described in Section 5 can be realized, a web interface would be built to enable the ADGG portal to capture the data from the new resource populations, thereby avoiding the need for the new populations to develop separate software and pipelines. That portal would also provide appropriate analytical models to enable multiple-country genetic evaluations within species, with data ownership continuing to be retained by the farmers who own the livestock being evaluated. It would also become the permanent repository for data collected through those new populations, beyond the life of any research projects that initially fund the data collection, thereby overcoming another of the challenges raised earlier in this manuscript;
- Many of the countries lacking existing resource populations do have trained animal geneticists with an interest in having greater involvement in data analysis. Ongoing capacity-building of those and other interested people through the AABN (also described in Section 5) would ensure that the analytical models are directly relevant to the countries or regions where they are employed;
- The animal geneticists identified to manage the new resource populations and to undertake the genetic evaluations would be encouraged to directly collaborate with researchers in other countries, to undertake cross-country genetic evaluations that generate value for farmers across all collaborating countries;
- In countries where artificial breeding centers do not exist, such centers would be established either by governments or the private sector to collect germplasm from animals proven to be genetically superior, with the germplasm (most likely semen) being made available to smallholder farmers, thereby enabling genetic improvement of their herds/flocks;
- Over time, the beneficiaries of the genetic improvement program would be expected to contribute to the ongoing costs of maintaining the genetic improvement programs, as is now occurring in Australia to sustain the earlier research-funded populations (e.g., livestock producers paying to have their own sires evaluated through the resource populations, by contributions to the costs of the resource populations or even, in some cases, establishing populations in their own herds or flocks);
- Establishing new resource populations using these guidelines would enable smallholder farmers to directly capture the benefits of genetic improvement through the use of genetically superior breeding animals but without the need to understand the complexities or overcome the major challenges of new technologies (e.g., hardware incompatibility; complexity; language barriers; lack of electricity, computers, internet access, etc.) that have proven to be major barriers to adoption in LMICs, as described by [71].
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Phenotype and Purpose | Options to Measure Key Traits for Use in Genetic Improvement Programs in Smallholder Livestock Populations |
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Product Quantity and Quality | |
Animal live weights and weight gains for the genetic evaluation of potential meat quantity in meat-producing animals and to provide assessments of animal nutrition and the effect of environmental stressors and/or endemic diseases on individuals and groups of meat and dairy animals | Except for farmers directly engaged in well-funded genetic improvement research programs, a lack of animal-handling infrastructure and access to scales generally means that records of individual animal weights and weight gains are not feasible in smallholder herds/flocks. This is particularly true for meat animals. Future infrastructure development may enable remote walk-over weighing or similar options for measurements, although [22] concluded that current walk-over weighing systems did not justify the investment needed in individual animal electronic identification. An alternative approach is for farmers to measure the animal’s circumference and length as an indicator of body weight, but this would also require access to appropriate handling facilities to enable accurate tape placement and length and height measurements. The accuracy of these assessments is reasonable in well-designed cohorts of the same breed of animals, but it varies markedly across breeds and animal size. Hence, in situations where animal breed composition is unclear, as is often the case in smallholder herds and flocks, this is not a reliable measurement for use in genetic evaluation [23]. Predictions from images based on deep learning may become available in the near future. |
Milk volume as the primary product for dairy animals and a maternal trait for meat animals | Measuring milk volume is relatively straightforward in dairy animals in smallholder herds and flocks but is not feasible in meat-producing animals, other than indirectly through the offspring’s weaning weights. |
Meat (e.g., tenderness, flavor, juiciness) and milk quality (e.g., protein concentration, fat content, etc.) attributes | Although sensor-based meat quality assessment systems exist, they are not readily available and given the meat evaluation and pricing systems, such assessments may not provide value for money, except where animals from smallholder flocks and herds are sold through commercial value chains to meet market quality specifications. In those cases, the processor or retailer purchasing the carcasses or milk should be able to provide the phenotypes required. However, quality-based value chains are currently scarce in most LMICs. |
Live animal fat depth, eye muscle area, etc., in meat animals | Subjective assessments of fat coverage or body condition scores are feasible but the value of doing so for genetic improvement programs in the absence of other measures, such as weights, is not high. |
Efficiency of feed utilization, particularly while animals are grazing on pasture | While this phenotype represents the best opportunity to improve the amount of feed required by an animal to maintain its body weight, it is still not feasible to record even in sophisticated breeding programs in high-income countries, except when animals are fed grain-based diets in pens or assessments are made on monoculture crops/pastures. Those measures are unable to account for animals’ diet selections and browsing behaviors, both of which impact feed utilization at pasture. |
Female reproduction (most measures of male reproduction are not generally feasible in smallholder herds and flocks) | |
Age at first estrus | Smallholder farmers can be trained to recognize the signs of the first estrus in female animals that are closely monitored and the use of digital cow calendar reminder systems enable subsequent signs to be easily and accurately timed and recognized. However, as the signs vary across animals, the records are unlikely to be sufficiently accurate for genetic improvement programs. Another option could be to use solar-powered sensor networks to remotely capture livestock data, such as estrus and pregnancy status, using animal ear- or neck tags (https://www.allflex.global/product/heatimepro/, accessed 15 November 2021), but to justify the expense, this type of data would need to be captured in relatively large herds or flocks. |
Date of calving/lambing, age of first calving/lambing and inter-calving/lambing period | In closely managed smallholder herds and flocks, recording the date of calving/lambing of individual dams is feasible, though not currently routinely practiced except through research programs. Knowledge of this date enables the calculation of the age of first calving/lambing and inter-calving/lambing periods. |
Pregnancy rate | If smallholder farmers have cost-effective access to veterinary services, pregnancy testing may be a feasible option for some. Use of estrus detection ear tags (see “Age at first estrus” section above) may also provide a useful indicator of pregnancy status if the tags are also used during the breeding season by indicating which females do not return to estrus. |
Weaning rate and offspring mortality rate | If smallholder farmers practice calf/lamb weaning and the calves/lambs are routinely individually identified to their dams, it may be feasible for annual weaning rates (and, hence, annual offspring mortality rates) to be calculated if the number of females mated in the previous breeding season is also recorded. |
Adaptive traits | |
Resistance to ecto- and endo-parasites | As summarized by [24], “resistance” in this context refers to both the ability of the individual host to resist infection or control the parasite lifecycle (resistance) and also where an individual host may be infected by a parasite but suffer little or no harm (tolerance). These terms are used interchangeably here. An in-depth discussion of the ability of cattle to resist a wide range of individual parasites is given in [24], but measuring an individual animal’s resistance to any of those parasites in advanced and low- to medium-income economies is not generally feasible due to both the intermittent nature of parasite infestations and the difficulty of measurement. |
Resistance to endemic diseases transmitted by parasites | Measurement of disease resistance under pastoral conditions is generally very difficult, even in advanced economies. The simple presence/absence of disease can be subjectively assessed by a skilled recorder when the animals are observed during routine handling procedures. However, infrequent observation of animals means that incidence of disease generally goes unrecorded, except where animal deaths occur and a diagnosis giving the cause of death is possible. |
Tolerance to heat stress | Traditionally, heat stress has been recorded using repeated measurements of the rectal temperature of animals under conditions of heat stress [25,26] or through subjective coat scores of animals during the summer months [27]. Increasingly, however, tolerance to heat stress is being assessed through the use of temperature–humidity indices based on the automatic meteorological recording of ambient temperatures and humidity in local regions [28,29]. |
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Burrow, H.M.; Mrode, R.; Mwai, A.O.; Coffey, M.P.; Hayes, B.J. Challenges and Opportunities in Applying Genomic Selection to Ruminants Owned by Smallholder Farmers. Agriculture 2021, 11, 1172. https://doi.org/10.3390/agriculture11111172
Burrow HM, Mrode R, Mwai AO, Coffey MP, Hayes BJ. Challenges and Opportunities in Applying Genomic Selection to Ruminants Owned by Smallholder Farmers. Agriculture. 2021; 11(11):1172. https://doi.org/10.3390/agriculture11111172
Chicago/Turabian StyleBurrow, Heather M., Raphael Mrode, Ally Okeyo Mwai, Mike P. Coffey, and Ben J. Hayes. 2021. "Challenges and Opportunities in Applying Genomic Selection to Ruminants Owned by Smallholder Farmers" Agriculture 11, no. 11: 1172. https://doi.org/10.3390/agriculture11111172
APA StyleBurrow, H. M., Mrode, R., Mwai, A. O., Coffey, M. P., & Hayes, B. J. (2021). Challenges and Opportunities in Applying Genomic Selection to Ruminants Owned by Smallholder Farmers. Agriculture, 11(11), 1172. https://doi.org/10.3390/agriculture11111172