The feasibility of using low-density marker panels for genotype imputation and genomic prediction of crossbred dairy cattle of East Africa

Detection and evaluation of parameters influencing the identification of heterozygous-enriched regions in Holstein cattle based on SNP chip or whole-genome sequence data

BACKGROUND

A heterozygous-enriched region (HER) is a genomic region with high variability generated by factors such as balancing selection, introgression, and admixture processes. In this study, we evaluated the genomic background of HERs and the impact of different parameters (i.e., minimum number of SNPs in a HER, maximum distance between two consecutive SNPs, minimum length of a HER, maximum number of homozygous allowed in a HER) and scenarios [i.e., different SNP panel densities and whole-genome sequence (WGS)] on the detection of HERs. We also compared HERs characterized in Holstein cattle with those identified in Angus, Jersey, and Norwegian Red cattle using WGS data.

RESULTS

The parameters used for the identification of HERs significantly impact their detection. The maximum distance between two consecutive SNPs did not impact HERs detection as the same average of HERs (269.31 ± 787.00) was observed across scenarios. However, the minimum number of markers, maximum homozygous markers allowed inside a HER, and the minimum length size impacted HERs detection. For the minimum length size, the 10 Kb scenario showed the highest average number of HERs (1,364.69 ± 1,483.64). The number of HERs decreased as the minimum number of markers increased (621.31 ± 1,271.83 to 6.08 ± 21.94), and an opposite pattern was observed for the maximum homozygous markers allowed inside a HER (54.47 ± 195.51 to 494.89 ± 1,169.35). Forty-five HER islands located in 23 chromosomes with high Tajima's D values and differential among the observed and estimated heterozygosity were detected in all evaluated scenarios, indicating their ability to potentially detect regions under balancing selection. In total, 3,440 markers and 28 genes previously related to fertility (e.g., TP63, ZSCAN23, NEK5, ARHGAP44), immunity (e.g., TP63, IGC, ARHGAP44), residual feed intake (e.g., MAYO9A), stress sensitivity (e.g., SERPINA6), and milk fat percentage (e.g., NOL4) were identified. When comparing HER islands among breeds, there were substantial overlaps between Holstein with Angus (95.3%), Jersey (94.3%), and Norwegian Red cattle (97.1%), indicating conserved HER across taurine breeds.

CONCLUSIONS

The detection of HERs varied according to the parameters used, but some HERs were consistently identified across all scenarios. Heterozygous genotypes observed across generations and breeds appear to be conserved in HERs. The results presented could serve as a guide for defining HERs detection parameters and further investigating their biological roles in future studies.

Community-based breeding programs can realize sustainable genetic gain and economic benefits in tropical dairy cattle systems

Implementing an appropriate breeding program is crucial to control fluctuation in performance, enhance adaptation, and further improve the crossbred population of dairy cattle. Five alternative breeding programs (BPs) were modeled considering available breeding units in the study area, the existing crossbreeding practices, and the future prospects of dairy research and development in Ethiopia. The study targeted 143,576 crossbred cows of 54,822 smallholder households in the Arsi, West Shewa, and North Shewa zones of the Oromia Region, as well as the North Shewa zone of the Amhara Region. The alternative BPs include conventional on-station progeny testing (SPT), conventional on-farm progeny testing (FPT), conventional on-station and on-farm progeny testing (SFPT), genomic selection (GS), and genomic progeny testing (GPT). Input parameters for modeling the BPs were taken from the analysis of long-term data obtained from the Holetta Agricultural Research Center and a survey conducted in the study area. ZPLAN+ software was used to predict estimates of genetic gain (GG) and discounted profit for goal traits. The predicted genetic gains (GGs) for milk yield (MY) per year were 34.52 kg, 49.63 kg, 29.35 kg, 76.16 kg, and 77.51 kg for SPT, FPT, SFPT, GS, and GPT, respectively. The GGs of the other goal traits range from 0.69 to 1.19 days per year for age at first calving, from 1.20 to 2.35 days per year for calving interval, and from 0.06 to 0.12 days per year for herd life. Compared to conventional BPs, genomic systems (GPT and GS) enhanced the GG of MY by 53%-164%, reduced generation interval by up to 21%, and improved the accuracy of test bull selection from 0.33 to 0.43. The discounted profit of the BPs varied from 249.58 Ethiopian Birr (ETB, 1 USD = 39.55696 ETB) per year in SPT to 689.79 ETB per year in GS. Genomic selection outperforms SPT, SFPT, and FPT by 266, 227%, and 138% of discounted profit, respectively. Community-based crossbreeding accompanied by GS and gradual support with progeny testing (GPT) is recommended as the main way forward to attain better genetic progress in dairy farms in Ethiopia and similar scenarios in other tropical countries.

Detection of selective sweep in European wild sheep breeds

In wild animal populations, there is a differentiation between populations due to natural selection. The direction and pressure of natural selection in the wild sheep are different in the various geographic areas. Linkage disequilibrium studies showed that regions of the genome in whole wild sheep are under natural selection and that natural selection can affect immune or reproductive or metabolic traits. The study aimed to identify genomic regions under natural selection in wild sheep. For this purpose, the genetic information of 24 European wild sheep and 24 Sardinian wild sheep was used. The genotypes were determined using Illumina 50 K SNPChip arrays based on Oar_4.0 version of the sheep genome. After quality control steps, finally, 31,560 SNP markers were analyzed. The value of LD was calculated by calculating the r2 statistic between all pairs of locations through PLINK software. To identify signs of selection based on linkage disequilibrium methods, an extended haplotype homozygosity test of XP-EHH crossing population and iHS intrapopulation was used. The results of iHS studies showed that in European and Sardinian wild sheep, the highest iHS coefficient under natural selection was observed on 3 and 2 chromosome numbers, respectively. Also, the results of XP-EHH studies showed that the largest XP-EHH coefficients under natural selection in European wild sheep compared to Sardinian and vice versa in Sardinian wild sheep compared to European wild sheep were observed on 3 and 16 chromosome numbers, respectively. In addition, the results of gene cycle studies showed that COPB1, SEC24D, ZDHHC17, BBS4, RFX3, SLC26A8, CAMK2D, GRIA1, GRM1, GRID2, PPP2R1A, CPEB4, PLEKHA5 and KIF13A, VPS39, VPS53, DTNBP1, DYNC1I1, FAM91A genes are under natural selection in Sardinian and European wild sheeps, respectively. The direction and selection pressure of natural selection in the two breeds of wild sheep is different due to different geographic conditions.

Assessment of runs of homozygosity, heterozygosity-rich regions and genomic inbreeding estimates in a subpopulation of Guzerá (Bos indicus) dual-purpose cattle

For decades, inbreeding in cattle has been evaluated using pedigree information. Nowadays, inbreeding coefficients can be obtained using genomic information such as runs of homozygosity (ROH). The aims of this study were to quantify ROH and heterozygosity-rich regions (HRR) in a subpopulation of Guzerá dual-purpose cattle, to examine ROH and HRR islands, and to compare inbreeding coefficients obtained by ROH with alternative genomic inbreeding coefficients. A subpopulation of 1733 Guzerá animals genotyped for 50k SNPs was used to obtain the ROH and HRR segments. Inbreeding coefficients by ROH (FROH ), by genomic relationship matrix based on VanRaden's method 1 using reference allele frequency in the population (FGRM ), by genomic relationship matrix based on VanRaden's method 1 using allele frequency fixed in 0.5 (FGRM_0.5 ), and by the proportion of homozygous loci (FHOM ) were calculated. A total of 15,660 ROH were identified, and the chromosome with the highest number of ROH was BTA6. A total of 4843 HRRs were identified, and the chromosome with the highest number of HRRs was BTA23. No ROH and HRR islands were identified according to established criteria, but the regions closest to the definition of an island were examined from 64 to 67 Mb of BTA6, from 36 to 37 Mb of BTA2 and from 0.50 to 1.25 Mb of BTA23. The genes identified in ROH islands have previously been associated with dairy and beef traits, while genes identified on HRR islands have previously been associated with reproductive traits and disease resistance. FROH was equal to 0.095 ± 0.084, and its Spearman correlation with FGRM was low (0.44) and moderate-high with FHOM (0.79) and with FGRM_0.5 (0.80). The inbreeding coefficients determined by ROH were higher than other cattle breeds' and higher than pedigree-based inbreeding in the Guzerá breed obtained in previous studies. It is recommended that future studies investigate the effects of inbreeding determined by ROH on the traits under selection in the subpopulation studied.

Imputation accuracy from low- to medium-density SNP chips for US crossbred dairy cattle

This study aimed at evaluating the quality of imputation accuracy (IA) by marker (IAm) and by individual (IAi) in US crossbred dairy cattle. Holstein × Jersey crossbreds were used to evaluate IA from a low- (7K) to a medium-density (50K) SNP chip. Crossbred animals, as well as their sires (53), dams (77), and maternal grandsires (63), were all genotyped with a 78K SNP chip. Seven different scenarios of reference populations were tested, in which some scenarios used different family relationships and others added random unrelated purebred and crossbred individuals to those different family relationship scenarios. The same scenarios were tested on Holstein and Jersey purebred animals to compare these outcomes against those attained in crossbred animals. The genotype imputation was performed with findhap (version 4) software (VanRaden, 2015). There were no significant differences in IA results depending on whether the sire of imputed individuals was Holstein and the dam was Jersey, or vice versa. The IA increased significantly with the addition of related individuals in the reference population, from 86.70 ± 0.06% when only sires or dams were included in the reference population to 90.09 ± 0.06% when sire (S), dam (D), and maternal grandsire genomic data were combined in the reference population. In all scenarios including related individuals in the reference population, IAm and IAi were significantly superior in purebred Jersey and Holstein animals than in crossbreds, ranging from 90.75 ± 0.06 to 94.02 ± 0.06%, and from 90.88 ± 0.11 to 94.04 ± 0.10%, respectively. Additionally, a scenario called SPB+DLD(where PB indicates purebread and LD indicates low density), similar to the genomic evaluations performed on US crossbred dairy, was tested. In this scenario, the information from the 5 evaluated breeds (Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey) genotyped with a 50K SNP chip and genomic information from the dams genotyped with a 7K SNP chip were combined in the reference population, and the IAm and IAi were 80.87 ± 0.06% and 80.85 ± 0.08%, respectively. Adding randomly nonrelated genotyped individuals in the reference population reduced IA for both purebred and crossbred cows, except for scenario SPB+DLD, where adding crossbreds to the reference population increased IA values. Our findings demonstrate that IA for US Holstein × Jersey crossbred ranged from 85 to 90%, and emphasize the significance of designing and defining the reference population for improved IA.

Restriction site-associated DNA sequencing technologies as an alternative to low-density SNP chips for genomic selection: a simulation study in layer chickens

BACKGROUND

To reduce the cost of genomic selection, a low-density (LD) single nucleotide polymorphism (SNP) chip can be used in combination with imputation for genotyping selection candidates instead of using a high-density (HD) SNP chip. Next-generation sequencing (NGS) techniques have been increasingly used in livestock species but remain expensive for routine use for genomic selection. An alternative and cost-efficient solution is to use restriction site-associated DNA sequencing (RADseq) techniques to sequence only a fraction of the genome using restriction enzymes. From this perspective, use of RADseq techniques followed by an imputation step on HD chip as alternatives to LD chips for genomic selection was studied in a pure layer line.

RESULTS

Genome reduction and sequencing fragments were identified on reference genome using four restriction enzymes (EcoRI, TaqI, AvaII and PstI) and a double-digest RADseq (ddRADseq) method (TaqI-PstI). The SNPs contained in these fragments were detected from the 20X sequence data of the individuals in our population. Imputation accuracy on HD chip with these genotypes was assessed as the mean correlation between true and imputed genotypes. Several production traits were evaluated using single-step GBLUP methodology. The impact of imputation errors on the ranking of the selection candidates was assessed by comparing a genomic evaluation based on ancestry using true HD or imputed HD genotyping. The relative accuracy of genomic estimated breeding values (GEBVs) was investigated by considering the GEBVs estimated on offspring as a reference. With AvaII or PstI and ddRADseq with TaqI and PstI, more than 10 K SNPs were detected in common with the HD SNP chip, resulting in an imputation accuracy greater than 0.97. The impact of imputation errors on genomic evaluation of the breeders was reduced, with a Spearman correlation greater than 0.99. Finally, the relative accuracy of GEBVs was equivalent.

CONCLUSIONS

RADseq approaches can be interesting alternatives to low-density SNP chips for genomic selection. With more than 10 K SNPs in common with the SNPs of the HD SNP chip, good imputation and genomic evaluation results can be obtained. However, with real data, heterogeneity between individuals with missing data must be considered.

Detection of genomic regions that differentiate Bos indicus from Bos taurus ancestral breeds for milk yield in Indian crossbred cows

Introduction: In India, crossbred cows incorporate the high production of B. taurus dairy breeds and the environmental adaptation of local B. indicus cattle. Adaptation to different environments and selection in milk production have shaped the genetic differences between B. indicus and B. taurus cattle. The aim of this paper was to detect, for milk yield of crossbred cows, quantitative trait loci (QTL) that differentiate B. indicus from B. taurus ancestry, as well as QTL that are segregating within the ancestral breeds. Methods: A total of 123,042 test-day milk records for 4,968 crossbred cows, genotyped with real and imputed 770 K SNP, were used. Breed origins were assigned to haplotypes of crossbred cows, and from that, were assigned to SNP alleles. Results: At a false discovery rate (FDR) of 30%, a large number of genomic regions showed significant effects of B. indicus versus B. taurus origin on milk yield, with positive effects coming from both ancestors. No significant regions were detected for Holstein Friesian (HF) versus Jersey effects on milk yield. Additionally, no regions for SNP alleles segregating within indigenous, within HF, and within Jersey were detected. The most significant effects, at FDR 5%, were found in a region on BTA5 (43.98-49.44 Mbp) that differentiates B. indicus from B. taurus, with an estimated difference between homozygotes of approximately 10% of average yield, in favour of B. indicus origin. Discussion: Our results indicate that evolutionary differences between B. indicus and B. taurus cattle for milk yield, as expressed in crossbred cows, occur at many causative loci across the genome. Although subject to the usual first estimation bias, some of the loci appear to have large effects that might make them useful for genomic selection in crossbreds, if confirmed in subsequent studies.

Exploiting morphobiometric and genomic variability of African indigenous camel populations-A review

Camels (Camelus dromedarius) in Africa are adapted to arid and the semi-arid environmental conditions, and are valuable for meat, milk and fiber production. On account of the growing demand for camels in this continent, there is a need for knowledge on their phenotypic and genetic diversity. This is fundamental to sustainable herd management and utilization including the design of appropriate breeding and conservation strategies. We reviewed studies on the phenotypic and genetic characterization, breeding objectives, systems of production, productive and reproductive performances, and pathways for the sustainable rearing and use of camels in Africa. The morphological and genetic diversity, productive and reproductive abilities of African camels suggest the existence of genetic variations that can be utilized for breeds/ecotypes' genetic improvement and conservation. Possible areas of intervention include the establishment of open nucleus and community-based breeding schemes and utilization of modern reproductive technologies for the genetic improvement of milk and meat yields, sustainable management of rangelands, capacity building of the pastoralists and agro-pastoralists, institutional supports, formation of centralized conservation centres and efficient and effective marketing systems.

Progress and opportunities through use of genomics in animal production

The rearing of farmed animals is a vital component of global food production systems, but its impact on the environment, human health, animal welfare, and biodiversity is being increasingly challenged. Developments in genetic and genomic technologies have had a key role in improving the productivity of farmed animals for decades. Advances in genome sequencing, annotation, and editing offer a means not only to continue that trend, but also, when combined with advanced data collection, analytics, cloud computing, appropriate infrastructure, and regulation, to take precision livestock farming (PLF) and conservation to an advanced level. Such an approach could generate substantial additional benefits in terms of reducing use of resources, health treatments, and environmental impact, while also improving animal health and welfare.

Characterization of runs of homozygosity, heterozygosity-enriched regions, and population structure in cattle populations selected for different breeding goals

Background

A decline in the level of genetic diversity in livestock can result in reduced response to selection, greater incidence of genetic defects, and inbreeding depression. In this context, various metrics have been proposed to assess the level of genetic diversity in selected populations. Therefore, the main goals of this study were to: 1) investigate the population structure of 16 cattle populations from 15 different pure breeds or composite populations, which have been selected for different breeds goals; and, 2) identify and compare runs of homozygosity (ROH) and heterozygosity-enriched regions (HER) based on different single nucleotide polymorphism (SNP) panels and whole-genome sequence data (WGS), followed by functional genomic analyses.

Results

A total of 24,187 ROH were found across all cattle populations, with 55% classified in the 2-4 Mb size group. Fourteen homozygosity islands were found in five populations, where four ROH islands located on BTA1, BTA5, BTA16, and BTA19 overlapped between the Brahman (BRM) and Gyr (GIR) breeds. A functional analysis of the genes found in these islands revealed candidate genes known to play a role in the melanogenesis, prolactin signaling, and calcium signaling pathways. The correlations between inbreeding metrics ranged from 0.02 to 0.95, where the methods based on homozygous genotypes (F_HOM), uniting of gametes (F_UNI), and genotype additive variance (F_GRM) showed strong correlations among them. All methods yielded low to moderate correlations with the inbreeding coefficients based on runs of homozygosity (F_ROH). For the HER, 3576 runs and 26 islands, distributed across all autosomal chromosomes, were found in regions containing genes mainly related to the immune system, indicating potential balancing selection. Although the analyses with WGS did not enable detection of the same island patterns, it unraveled novel regions not captured when using SNP panel data.

Conclusions

The cattle populations that showed the largest amount of ROH and HER were Senepol (SEN) and Montana (MON), respectively. Overlapping ROH islands were identified between GIR and BRM breeds, indicating a possible historical connection between the populations. The distribution and pattern of ROH and HER are population specific, indicating that different breeds have experienced divergent selection processes or different genetic processes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08384-0.

Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data

Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction. The imputation accuracy will directly influence the results from subsequent analyses. In this simulation-based study, we investigate the accuracy of genotype imputation in relation to some factors characterizing SNP chip or low-coverage whole-genome sequencing (LCWGS) data. The factors included the imputation reference population size, the proportion of target markers /SNP density, the genetic relationship (distance) between the target population and the reference population, and the imputation method. Simulations of genotypes were based on coalescence theory accounting for the demographic history of pigs. A population of simulated founders diverged to produce four separate but related populations of descendants. The genomic data of 20,000 individuals were simulated for a 10-Mb chromosome fragment. Our results showed that the proportion of target markers or SNP density was the most critical factor affecting imputation accuracy under all imputation situations. Compared with Minimac4, Beagle5.1 reproduced higher-accuracy imputed data in most cases, more notably when imputing from the LCWGS data. Compared with SNP chip data, LCWGS provided more accurate genotype imputation. Our findings provided a relatively comprehensive insight into the accuracy of genotype imputation in a realistic population of domestic animals.

Genomic evaluation of milk yield in a smallholder crossbred dairy production system in India

BACKGROUND

India is the largest milk producer globally, with the largest proportion of cattle milk production coming from smallholder farms with an average herd size of less than two milking cows. These cows are mainly undefined multi-generation crosses between exotic dairy breeds and indigenous Indian cattle, with no performance or pedigree recording. Therefore, implementing genetic improvement based on genetic evaluation has not yet been possible. We present the first results from a large smallholder performance recording program in India, using single nucleotide polymorphism (SNP) genotypes to estimate genetic parameters for monthly test-day (TD) milk records and to obtain and validate genomic estimated breeding values (GEBV).

RESULTS

The average TD milk yield under the high, medium, and low production environments were 9.64, 6.88, and 4.61 kg, respectively. In the high production environment, the usual profile of a lactation curve was evident, whereas it was less evident in low and medium production environments. There was a clear trend of an increasing milk yield with an increasing Holstein Friesian (HF) proportion in the high production environment, but no increase above intermediate grades in the medium and low production environments. Trends for Jersey were small but yield estimates had a higher standard error than HF. Heritability estimates for TD yield across the lactation ranged from 0.193 to 0.250, with an average of 0.230. The additive genetic correlations between TD yield at different times in lactation were high, ranging from 0.846 to 0.998. The accuracy of phenotypic validation of GEBV from the method that is believed to be the least biased was 0.420, which was very similar to the accuracy obtained from the average prediction error variance of the GEBV.

CONCLUSIONS

The results indicate strong potential for genomic selection to improve milk production of smallholder crossbred cows in India. The performance of cows with different breed compositions can be determined in different Indian environments, which makes it possible to provide better advice to smallholder farmers on optimum breed composition for their environment.

Genomic prediction of crossbred dairy cattle in Tanzania: A route to productivity gains in smallholder dairy systems

Selection based on genomic predictions has become the method of choice for genetic improvement in dairy cattle. This offers huge opportunity for developing countries with little or no pedigree data, and preliminary studies have shown promising results. The African Dairy Genetic Gains (ADGG) project initiated a digital system of dairy performance data collection, accompanied by genotyping in Tanzania in 2016. Currently, ADGG has the largest body of dairy performance data generated in East Africa from a smallholder dairy system. This study examines the use of genomic best linear unbiased prediction (GBLUP) and single-step (ss)GBLUP for the estimation of genetic parameters and accuracy of genomic prediction for daily milk yield and body weight in Tanzania. The estimates of heritability for daily milk yield from GBLUP and ssGBLUP were essentially the same, at 0.12 ± 0.03. The heritability estimates for daily milk yield averaged over the whole lactation from random regression model (RRM) GBLUP or ssGBLUP were 0.22 and 0.24, respectively. The heritability of body weight from GBLUP was 0.24 ± 04 but was 0.22 ± 04 from the ssGBLUP analysis. Accuracy of genomic prediction for milk yield from a forward validation was 0.57 for GBLUP based on fixed regression model or 0.55 from an RRM. Corresponding estimates from ssGBLUP were 0.59 and 0.53, respectively. Accuracy for body weight, however, was much higher at 0.83 from GBLUP and 0.77 for ssGBLUP. The moderate to high levels of accuracy of genomic prediction (0.53-0.83) obtained for milk yield and body weight indicate that selection on the basis of genomic prediction is feasible in smallholder dairy systems and most probably the only initial possible pathway to implementing sustained genetic improvement programs in such systems.

Assessing single-nucleotide polymorphism selection methods for the development of a low-density panel optimized for imputation in South African Drakensberger beef cattle

A major obstacle in applying genomic selection (GS) to uniquely adapted local breeds in less-developed countries has been the cost of genotyping at high densities of single-nucleotide polymorphisms (SNP). Cost reduction can be achieved by imputing genotypes from lower to higher densities. Locally adapted breeds tend to be admixed and exhibit a high degree of genomic heterogeneity thus necessitating the optimization of SNP selection for downstream imputation. The aim of this study was to quantify the achievable imputation accuracy for a sample of 1,135 South African (SA) Drakensberger cattle using several custom-derived lower-density panels varying in both SNP density and how the SNP were selected. From a pool of 120,608 genotyped SNP, subsets of SNP were chosen (1) at random, (2) with even genomic dispersion, (3) by maximizing the mean minor allele frequency (MAF), (4) using a combined score of MAF and linkage disequilibrium (LD), (5) using a partitioning-around-medoids (PAM) algorithm, and finally (6) using a hierarchical LD-based clustering algorithm. Imputation accuracy to higher density improved as SNP density increased; animal-wise imputation accuracy defined as the within-animal correlation between the imputed and actual alleles ranged from 0.625 to 0.990 when 2,500 randomly selected SNP were chosen vs. a range of 0.918 to 0.999 when 50,000 randomly selected SNP were used. At a panel density of 10,000 SNP, the mean (standard deviation) animal-wise allele concordance rate was 0.976 (0.018) vs. 0.982 (0.014) when the worst (i.e., random) as opposed to the best (i.e., combination of MAF and LD) SNP selection strategy was employed. A difference of 0.071 units was observed between the mean correlation-based accuracy of imputed SNP categorized as low (0.01 < MAF ≤ 0.1) vs. high MAF (0.4 < MAF ≤ 0.5). Greater mean imputation accuracy was achieved for SNP located on autosomal extremes when these regions were populated with more SNP. The presented results suggested that genotype imputation can be a practical cost-saving strategy for indigenous breeds such as the SA Drakensberger. Based on the results, a genotyping panel consisting of ~10,000 SNP selected based on a combination of MAF and LD would suffice in achieving a <3% imputation error rate for a breed characterized by genomic admixture on the condition that these SNP are selected based on breed-specific selection criteria.

Impact of Marker Pruning Strategies Based on Different Measurements of Marker Distance on Genomic Prediction in Dairy Cattle

Simple Summary

The usefulness of genomic prediction (GP) has been widely proofed by breeding analysis in livestock, plants and aquatic populations. It is well known that ‘marker density’ is a critical factor that affects the accuracy of GP, however, how to properly measure ‘marker density’ in GP is yet to be determined. With population-level whole-genome sequence data or high-density single nucleotide polymorphism (SNP) data available, this question seems to be answered more convincingly. In this study, we investigated and discussed the impact of four ‘marker density’ measures that reflect genetic or physical distances between SNPs on the accuracy of GP in a Germany Holstein dairy cattle population. Our results showed that the degree of variation of physical distance between adjacent SNPs had significant effects on the accuracy of GP, while the genetic distance between SNPs had no relationship with the accuracy of GP. Therefore, for studies based on high-density SNP data, the default strategy of pruning SNPs based on genetic distance is detrimental to heritability estimation and genomic prediction. The results extended the communities knowledge of ‘marker density’ and provided useful suggestions for the application and research on genome prediction.

Abstract

With the availability of high-density single-nucleotide polymorphism (SNP) data and the development of genotype imputation methods, high-density panel-based genomic prediction (GP) has become possible in livestock breeding. It is generally considered that the genomic estimated breeding value (GEBV) accuracy increases with the marker density, while studies have shown that the GEBV accuracy does not increase or even decrease when high-density panels were used. Therefore, in addition to the SNP number, other measurements of ‘marker density’ seem to have impacts on the GEBV accuracy, and exploring the relationship between the GEBV accuracy and the measurements of ‘marker density’ based on high-density SNP or whole-genome sequence data is important for the field of GP. In this study, we constructed different SNP panels with certain SNP numbers (e.g., 1 k) by using the physical distance (PhyD), genetic distance (GenD) and random distance (RanD) between SNPs respectively based on the high-density SNP data of a Germany Holstein dairy cattle population. Therefore, there are three different panels at a certain SNP number level. These panels were used to construct GP models to predict fat percentage, milk yield and somatic cell score. Meanwhile, the mean (d¯) and variance (σd2) of the physical distance between SNPs and the mean (r2¯) and variance (σr22) of the genetic distance between SNPs in each panel were used as marker density-related measurements and their influence on the GEBV accuracy was investigated. At the same SNP number level, the d¯ of all panels is basically the same, but the σd2, r2¯ and σr22 are different. Therefore, we only investigated the effects of σd2, r2¯ and σr22 on the GEBV accuracy. The results showed that at a certain SNP number level, the GEBV accuracy was negatively correlated with σd2, but not with r2¯ and σr22. Compared with GenD and RanD, the σd2 of panels constructed by PhyD is smaller. The low and moderate-density panels (< 50 k) constructed by RanD or GenD have large σd2, which is not conducive to genomic prediction. The GEBV accuracy of the low and moderate-density panels constructed by PhyD is 3.8~34.8% higher than that of the low and moderate-density panels constructed by RanD and GenD. Panels with 20–30 k SNPs constructed by PhyD can achieve the same or slightly higher GEBV accuracy than that of high-density SNP panels for all three traits. In summary, the smaller the variation degree of physical distance between adjacent SNPs, the higher the GEBV accuracy. The low and moderate-density panels construct by physical distance are beneficial to genomic prediction, while pruning high-density SNP data based on genetic distance is detrimental to genomic prediction. The results provide suggestions for the development of SNP panels and the research of genome prediction based on whole-genome sequence data.

Inference of Ancestries and Heterozygosity Proportion and Genotype Imputation in West African Cattle Populations

Several studies have evaluated computational methods that infer the haplotypes from population genotype data in European cattle populations. However, little is known about how well they perform in African indigenous and crossbred populations. This study investigates: (1) global and local ancestry inference; (2) heterozygosity proportion estimation; and (3) genotype imputation in West African indigenous and crossbred cattle populations. Principal component analysis (PCA), ADMIXTURE, and LAMP-LD were used to analyse a medium-density single nucleotide polymorphism (SNP) dataset from Senegalese crossbred cattle. Reference SNP data of East and West African indigenous and crossbred cattle populations were used to investigate the accuracy of imputation from low to medium-density and from medium to high-density SNP datasets using Minimac v3. The first two principal components differentiated Bos indicus from European Bos taurus and African Bos taurus from other breeds. Irrespective of assuming two or three ancestral breeds for the Senegalese crossbreds, breed proportion estimates from ADMIXTURE and LAMP-LD showed a high correlation (r ≥ 0.981). The observed ancestral origin heterozygosity proportion in putative F1 crosses was close to the expected value of 1.0, and clearly differentiated F1 from all other crosses. The imputation accuracies (estimated as correlation) between imputed and the real data in crossbred animals ranged from 0.142 to 0.717 when imputing from low to medium-density, and from 0.478 to 0.899 for imputation from medium to high-density. The imputation accuracy was generally higher when the reference data came from the same geographical region as the target population, and when crossbred reference data was used to impute crossbred genotypes. The lowest imputation accuracies were observed for indigenous breed genotypes. This study shows that ancestral origin heterozygosity can be estimated with high accuracy and will be far superior to the use of observed individual heterozygosity for estimating heterosis in African crossbred populations. It was not possible to achieve high imputation accuracy in West African crossbred or indigenous populations based on reference data sets from East Africa, and population-specific genotyping with high-density SNP assays is required to improve imputation.

Small SNP panels for breed proportion estimation in Indian crossbred dairy cattle

Reliably identifying breed proportions in crossbred cattle in smallholder farms is a crucial step to improve mating decisions and optimizing management in these systems. High‐density genotype information is able to estimate higher‐order breed proportions accurately, but, are too expensive for mass application in smallholder systems. We used high‐density genotype information (777 k SNPs) of 623 crossbred cattle from India that had Holstein‐Friesian (HFX) and/or Jersey and indigenous breeds in their ancestry to select a smaller number of SNPs for breed proportion estimation. The accuracy of estimates obtained from panels with 100–500 SNP was compared to estimates based on all SNPs. Panels were selected for highest absolute allele frequency difference between exotic dairy versus indigenous Bos indicus, or between HFX versus Jersey breeds. A step‐wise pruning approach was developed showing that and increased physical distances between markers of 8.5 Mb improved breed proportion estimation compared to a standard 1 Mb distance. A panel of 500 SNPs optimized to estimate HFX versus Jersey versus indicine ancestry was able to estimate indicine breed proportions with r² = .991, HFX proportions with r² = .979 and Jersey proportions with r² = .949. The number of markers was a deciding factor in estimation accuracy, together with the distribution of markers across the genome.

A low-density SNP genotyping panel for the accurate prediction of cattle breeds

Genomic tools to better define breed composition in agriculturally important species have sparked scientific and commercial industry interest. Knowledge of breed composition can inform multiple scientifically important decisions of industry application including DNA marker-assisted selection, identification of signatures of selection, and inference of product provenance to improve supply chain integrity. Genomic tools are expensive but can be economized by deploying a relatively small number of highly informative single-nucleotide polymorphisms (SNP) scattered evenly across the genome. Using resources from the 1000 Bull Genomes Project we established calibration (more stringent quality criteria; N = 1,243 cattle) and validation (less stringent; N = 864) data sets representing 17 breeds derived from both taurine and indicine bovine subspecies. Fifteen successively smaller panels (from 500,000 to 50 SNP) were built from those SNP in the calibration data that increasingly satisfied 2 criteria, high differential allele frequencies across the breeds as measured by average Euclidean distance (AED) and high uniformity (even spacing) across the physical genome. Those SNP awarded the highest AED were in or near genes previously identified as important signatures of selection in cattle such as LCORL, NCAPG, KITLG, and PLAG1. For each panel, the genomic breed composition (GBC) of each animal in the validation dataset was estimated using a linear regression model. A systematic exploration of the predictive accuracy of the various sized panels was then undertaken on the validation population using 3 benchmarking approaches: (1) % error (expressed relative to the estimated GBC made from over 1 million SNP), (2) % breed misassignment (expressed relative to each individual's breed recorded), and (3) Shannon's entropy of estimated GBC across the 17 target breeds. Our analyses suggest that a panel of just 250 SNP represents an adequate balance between accuracy and cost-only modest gains in accuracy are made as one increases panel density beyond this point.

Ancestral Haplotype Mapping for GWAS and Detection of Signatures of Selection in Admixed Dairy Cattle of Kenya

Understanding the genetic structure of adaptation and productivity in challenging environments is necessary for designing breeding programs that suit such conditions. Crossbred dairy cattle in East Africa resulting from over 60 years of crossing exotic dairy breeds with indigenous cattle plus inter se matings form a highly variable admixed population. This population has been subject to natural selection in response to environmental stresses, such as harsh climate, low-quality feeds, poor management, and strong disease challenge. Here, we combine two complementary sets of analyses, genome-wide association (GWA) and signatures of selection (SoS), to identify genomic regions that contribute to variation in milk yield and/or contribute to adaptation in admixed dairy cattle of Kenya. Our GWA separates SNP effects due to ancestral origin of alleles from effects due to within-population linkage disequilibrium. The results indicate that many genomic regions contributed to the high milk production potential of modern dairy breeds with no region having an exceptional effect. For SoS, we used two haplotype-based tests to compare haplotype length variation within admixed and between admixed and East African Shorthorn Zebu cattle populations. The integrated haplotype score (iHS) analysis identified 16 candidate regions for positive selection in the admixed cattle while the between population Rsb test detected 24 divergently selected regions in the admixed cattle compared to East African Shorthorn Zebu. We compare the results from GWA and SoS in an attempt to validate the most significant SoS results. Only four candidate regions for SoS intersect with GWA regions using a low stringency test. The identified SoS candidate regions harbored genes in several enriched annotation clusters and overlapped with previously found QTLs and associations for different traits in cattle. If validated, the GWA and SoS results indicate potential for SNP-based genomic selection for genetic improvement of smallholder crossbred cattle.

Interest of using imputation for genomic evaluation in layer chicken

With the availability of the 600K Affymetrix Axiom high-density (HD) single nucleotide polymorphism (SNP) chip, genomic selection has been implemented in broiler and layer chicken. However, the cost of this SNP chip is too high to genotype all selection candidates. A solution is to develop a low-density SNP chip, at a lower price, and to impute all missing markers. But to routinely implement this solution, the impact of imputation on genomic evaluation accuracy must be studied. It is also interesting to study the consequences of the use of low-density SNP chips in genomic evaluation accuracy. In this perspective, the interest of using imputation in genomic selection was studied in a pure layer line. Two low-density SNP chip designs were compared: an equidistant methodology and a methodology based on linkage disequilibrium. Egg weight, egg shell color, egg shell strength, and albumen height were evaluated with single-step genomic best linear unbiased prediction methodology. The impact of imputation errors or the absence of imputation on the ranking of the male selection candidates was assessed with a genomic evaluation based on ancestry. Thus, genomic estimated breeding values (GEBV) obtained with imputed HD genotypes or low-density genotypes were compared with GEBV obtained with the HD SNP chip. The relative accuracy of GEBV was also investigated by considering as reference GEBV estimated on the offspring. A limited reordering of the breeders, selected on a multitrait index, was observed. Spearman correlations between GEBV on HD genotypes and GEBV on low-density genotypes (with or without imputation) were always higher than 0.94 with more than 3K SNP. For the genetically closer, top 150 individuals for a specific trait, with imputation, the reordering was reduced with correlation higher than 0.94 with more than 3K SNP. Without imputation, the correlations remained lower than 0.85 with less than 3K and 16K SNP for equidistant and linkage disequilibrium methodology, respectively. The differences in GEBV correlations between both methodologies were never significant. The conclusions were the same for all studied traits.

Development of a low-density panel for genomic selection of pigs in Russia

Genomic selection is routinely used worldwide in agricultural breeding. However, in Russia, it is still not used to its full potential partially due to high genotyping costs. The use of genotypes imputed from the low-density chips (LD-chip) provides a valuable opportunity for reducing the genotyping costs. Pork production in Russia is based on the conventional 3-tier pyramid involving 3 breeds; therefore, the best option would be the development of a single LD-chip that could be used for all of them. Here, we for the first time have analyzed genomic variability in 3 breeds of Russian pigs, namely, Landrace, Duroc, and Large White and generated the LD-chip that can be used in pig breeding with the negligible loss in genotyping quality. We have demonstrated that out of the 3 methods commonly used for LD-chip construction, the block method shows the best results. The imputation quality depends strongly on the presence of close ancestors in the reference population. We have demonstrated that for the animals with both parents genotyped using high-density panels high-quality genotypes (allelic discordance rate < 0.05) could be obtained using a 300 single nucleotide polymorphism (SNP) chip, while in the absence of genotyped ancestors at least 2,000 SNP markers are required. We have shown that imputation quality varies between chromosomes, and it is lower near the chromosome ends and drops with the increase in minor allele frequency. Imputation quality of the individual SNPs correlated well across breeds. Using the same LD-chip, we were able to obtain comparable imputation quality in all 3 breeds, so it may be suggested that a single chip could be used for all of them. Our findings also suggest that the presence of markers with extremely low imputation quality is likely to be explained by wrong mapping of the markers to the chromosomal positions.

Performance Evaluation of Highly Admixed Tanzanian Smallholder Dairy Cattle Using SNP Derived Kinship Matrix

The main purpose of this study was to understand the type of dairy cattle that can be optimally used by smallholder farmers in various production environments such that they will maximize their yields without increasing the level of inputs. Anecdotal evidence and previous research suggests that the optimal level of taurine inheritance in crossbred animals lies between 50 and 75% when considering total productivity in tropical management clusters. We set out to assess the relationship between breed composition and productivity for various smallholder production systems in Tanzania. We surveyed 654 smallholder dairy households over a 1-year period and grouped them into production clusters. Based on supplementary feeding, milk productivity and sale as well as household wealth status four clusters were described: low-feed-low-output subsistence, medium-feed-low-output subsistence, maize germ intensive semi-commercial and feed intensive commercial management clusters. About 839 crossbred cows were genotyped at approximately 150,000 single nucleotide polymorphism (SNP) loci and their breed composition determined. Percentage dairyness (proportion of genes from international dairy breeds) was estimated through admixture analysis with Holstein, Friesian, Norwegian Red, Jersey, Guernsey, N'Dama, Gir, and Zebu as references. Four breed types were defined as RED-GUE (Norwegian Red/Friesian-Guernsey; Norwegian Red/Friesian-Jersey), RED-HOL (Norwegian Red/Friesian-Holstein), RED-Zebu (Norwegian Red/Friesian-Zebu), Zebu-RED (Zebu-Norwegian Red/Friesian) based on the combination of breeds that make up the top 76% breed composition. A fixed regression model using a genomic kinship matrix was used to analyze milk yield records. The fitted model accounted for year-month-test-date, parity, age, breed type and the production clusters as fixed effects in the model in addition to random effects of animal and permanent environment effect. Results suggested that RED-Zebu breed type with dairyness between 75 and 85% is the most appropriate for a majority of smallholder management clusters. Additionally, for farmers in the feed intensive management group, animals with a Holstein genetic background with at least 75% dairy composition were the best performing. These results indicate that matching breed type to production management group is central to maximizing productivity in smallholder systems. The findings from this study can serve as a basis to inform the development of the dairy sector in Tanzania and beyond.

Livestock Genomics for Developing Countries - African Examples in Practice

African livestock breeds are numerous and diverse, and typically well adapted to the harsh environment conditions under which they perform. They have been used over centuries to provide livelihoods as well as food and nutritional security. However, African livestock systems are dynamic, with many small- and medium-scale systems transforming, to varying degrees, to become more profitable. In these systems the women and men livestock keepers are often seeking new livestock breeds or genotypes - typically those that increase household income through having enhanced productivity in comparison to traditional breeds while maintaining adaptedness. In recent years genomic approaches have started to be utilized in the identification and development of such breeds, and in this article we describe a number of examples to this end from sub-Saharan Africa. These comprise case studies on: (a) dairy cattle in Kenya and Senegal, as well as sheep in Ethiopia, where genomic approaches aided the identification of the most appropriate breed-type for the local productions systems; (b) a cross-breeding program for dairy cattle in East Africa incorporating genomic selection as well as other applications of genomics; (c) ongoing work toward creating a new cattle breed for East Africa that is both productive and resistant to trypanosomiasis; and (d) the use of African cattle as resource populations to identify genomic variants of economic or ecological significance, including a specific case where the discovery data was from a community based breeding program for small ruminants in Ethiopia. Lessons learnt from the various case studies are highlighted, and the concluding section of the paper gives recommendations for African livestock systems to increasingly capitalize on genomic technologies.

Genomic Selection and Use of Molecular Tools in Breeding Programs for Indigenous and Crossbred Cattle in Developing Countries: Current Status and Future Prospects

Genomic selection (GS) has resulted in rapid rates of genetic gains especially in dairy cattle in developed countries resulting in a higher proportion of genomically proven young bulls being used in breeding. This success has been undergirded by well-established conventional genetic evaluation systems. Here, the status of GS in terms of the structure of the reference and validation populations, response variables, genomic prediction models, validation methods, and imputation efficiency in breeding programs of developing countries, where smallholder systems predominate and the basic components for conventional breeding are mostly lacking is examined. Also, the application of genomic tools and identification of genome-wide signatures of selection is reviewed. The studies on genomic prediction in developing countries are mostly in dairy and beef cattle usually with small reference populations (500-3,000 animals) and are mostly cows. The input variables tended to be pre-corrected phenotypic records and the small reference populations has made implementation of various Bayesian methods feasible in addition to GBLUP. Multi-trait single-step has been used to incorporate genomic information from foreign bulls, thus GS in developing countries would benefit from collaborations with developed countries, as many dairy sires used are from developed countries where they may have been genotyped and phenotyped. Cross validation approaches have been implemented in most studies resulting in accuracies of 0.20-0.60. Genotyping animals with a mixture of HD and LD chips, followed by imputation to the HD have been implemented with imputation accuracies of 0.74-0.99 reported. This increases the prospects of reducing genotyping costs and hence the cost-effectiveness of GS. Next-generation sequencing and associated technologies have allowed the determination of breed composition, parent verification, genome diversity, and genome-wide selection sweeps. This information can be incorporated into breeding programs aiming to utilize GS. Cost-effective GS in beef cattle in developing countries may involve usage of reproductive technologies (AI and in-vitro fertilization) to efficiently propagate superior genetics from the genomics pipeline. For dairy cattle, sexed semen of genomically proven young bulls could substantially improve profitability thus increase prospects of small holder farmers buying-in into genomic breeding programs.