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

Vanille Déru, Francesco Tiezzi, Paul M Vanraden, Emmanuel A Lozada-Soto, Sajjad Toghiani, Christian Maltecca

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Vanille Déru, Francesco Tiezzi, Paul M Vanraden, Emmanuel A Lozada-Soto, Sajjad Toghiani, et al. Imputation accuracy from low- to medium-density SNP chips for US crossbred dairy cattle. Journal of Dairy Science, 2024, 107, pp.398 - 411. 10.3168/jds.2023-23250. hal-05076683

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

Vanille Déru,<fnref n="1" />* Francesco Tiezzi,<fnref n="2" /> Paul M. VanRaden,<fnref n="3" /> Emmanuel A. Lozada-Soto,<fnref n="1" /> Sajjad Toghiani,<fnref n="3" /> and Christian Maltecca<fnref n="1" />

## ABSTRACT

This study aimed at evaluating the quality of imputation accuracy (IA) by marker ($IA_m$) and by individual ($IA_i$) 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, $IA_m$ and $IA_i$ 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 $S_{PB}+D_{LD}$ (where PB indicates purebred 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 $IA_m$ and $IA_i$ 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 $S_{PB}+D_{LD}$, 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.

**Key words:** dairy crossbred, imputation accuracy, SNP chip

## INTRODUCTION

Low-density SNP chips are employed in dairy populations to genotype the female population. In contrast, medium-density chips (50K SNPs or more), which are expensive, are typically reserved for valuable individuals, mostly contributing to the male path of selection (Wiggans et al., 2012). For these low-density genotyped individuals, imputation to medium density is performed before including information in genomic prediction models (VanRaden et al., 2013a). This genotyping strategy has been highly successful, and now the dairy population in the Council on Dairy Cattle Breeding (CDCB) database contains more than 6 million genotyped individuals. High imputation accuracy is crucial for estimating the most accurate genetic values of animals, as reviewed in Calus et al. (2014). In purebred dairy cattle, several studies have shown that imputation accuracy (IA) from low- to medium- or high-density SNP chips is high (Weigel et al., 2010b; Mulder et al., 2012; VanRaden et al., 2013b), and accuracy of genomic selection is not notably affected by imputation (Weigel et al., 2010a; Wiggans et al., 2012; VanRaden et al., 2013b). However, most of these studies have been conducted in purebred dairy cattle.

<footnote n="1">¹Department of Animal Science, North Carolina State University, Raleigh, NC 27607</footnote>

<footnote n="2">²Department of Agriculture, Food, Environment and Forestry, University of Florence, Florence, 50144, Italy</footnote>

<footnote n="3">³USDA, Agricultural Research Service, Animal Genomics and Improvement Laboratory, Beltsville, MD 20705-2350</footnote>
In addition to heterosis and breed complementarity, crossbreeding is a potential approach to improve cow fertility, cow health, calf survival, and disease resistance (Heins et al., 2008; Sørensen et al., 2008). Crossbreeding in dairy cattle has increased in popularity in recent years. In the United States, almost 7% of the 3.7 million milk-recorded cattle were crossbred in 2020 (Norman et al., 2021; Wiggans et al., 2021). Currently, the number of crossbred dairy cattle with genomic information in the CDCB database is increasing, with more than 50K individuals, and it continues to grow (Figure 1). Genomic data of crossbred cows contribute to the genomic prediction of sires, and the genomic evaluation of crossbred cattle in the United States is currently performed with inclusion of the 5 evaluated breeds (Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey) genotyped with medium-density SNP chips and dams genotyped with low-density SNP chips (Wiggans et al., 2021). However, it is not known how IA of crossbred dairy cattle is different from their purebred counterparts. To our knowledge, only 2 studies have estimated IA for dairy crossbred populations, one in African dairy cattle population (Aliloo et al., 2018) and the other in a Girolando (Gyr × Holstein) population (Oliveira Júnior et al., 2017). To date, no study has yet reported results of IA in US crossbred cows.

The aims of this study were as follows: to assess IA in US crossbred dairy cattle from a low-density to a medium-density SNP chip, using different defined scenarios to evaluate the effects of reference population size and its relationship with imputed crossbred dairy population, to compare IA values obtained in crossbred and purebred dairy populations, to compare the proportions of individuals and markers based on their imputation accuracy among scenarios, and to compare IA for each chromosome under different defined scenarios.

## MATERIALS AND METHODS

No live animals were used in this study, and therefore Institutional Animal Care and Use Committee approval was not required. This study focused on crossbreds Holstein × Jersey (HO×JE, Holstein sire and Jersey dam) and Jersey × Holstein (JE×HO, Jersey sire and Holstein dam) dairy populations, since these types of crossing are the most popular ones in the US. In the CDCB database, 79% of the genotyped US crossbred dairy population were HO×JE or JE×HO (Table 1).

### Animals and Data

To investigate IA under different genomic information availability, 110 F₁ crossbreds HO×JE and JE×HO genotyped with a 78K GGP-HD chip (GeneSeek Genomic

Profiler, >77K markers; Neogen Corporation; 78K) were chosen based on available pedigree and genomic information. The breed base representations (BBR) of crossbred were 50 ± 10% Holstein and 50 ± 10% Jersey. The BBR estimated the similarity of the alleles present in the 5 purebred reference groups against those of individuals genotyped. These values were restricted to be between 0 and 100% for each genotyped animal and summed, as explained in VanRaden et al. (2020) and Wiggans et al. (2021). These animals' sires, dams, and maternal grandsires were genotyped with the same SNP chip (Supplemental Table S1, https://doi.org/10.6084/m9.fishshare.21858864; Déru, 2023). Of these 110 crossbreds, 53 were HO×JE and 57 were JE×HO.

### Genotypes

Before imputation, real genotypes were phased within each purebred and crossbred population for all animals with Beagle 5.3 (Browning et al., 2021). Then, markers of the 78K SNP chip were masked to mimic the Illumina Bovine LD v2.0 BeadChip chip (7,931 markers, Illumina Inc.; 7K SNP chip) in the validation population. Similarly, markers were masked to mimic the BovineSNP50 Genotyping BeadChip chip (54,609 markers, Illumina Inc.; 50K SNP chip) in the reference population. In total, 6,912 SNP chip overlapped with the 50K SNP chip. These 2 SNP chips were studied because they are the most commonly used low- and medium-density chips for predicting the genetic merit of dairy cattle (Schefers and Weigel, 2012).

In previous studies conducted in crossbred beef and dairy cows, the genetic connectedness between the reference and the validation population has shown an influence on IA values (Ventura et al., 2014; Chud et al., 2015; Oliveira Júnior et al., 2017; Aliloo et al., 2018). Therefore, a measure of genomic relationship between animals in reference and validation sets was calculated within each scenario to investigate the effect of connectedness between reference and imputed populations on IA values. For this purpose, the genomic relationship matrix (G) was calculated following the first method of VanRaden (VanRaden, 2008). Then, the averaged genomic value (extracted from the G matrix) between all individuals included in the reference and validation population were estimated within each scenario and called the genomic relationship coefficient (GRC).

### Imputation Scenarios

Different reference population scenarios for imputing US crossbred cattle were created and are summarized in Table 2. Seven scenarios differed based on the relatives' information between reference and validation
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Percentage of individuals and markers according to the imputation accuracy values in the 8 different scenarios presented in Table 2 (original) and with the addition, in each scenario, of 1,000 crossbred animals (+1,000CR), 1,000 purebred Holstein dairy cattle (+1,000HO) and 1,000 Jersey dairy cattle (+1,000JE) separately and combined (+1,000HO/JE). S+D = sires and dams genotyped with a 50K chip in reference population; S+D<sub>LD</sub> = sires genotyped with a 50K chip and dams genotyped with a 7K chip in reference population; D = dams genotyped with a 50K chip in reference population; S+GS = sires and maternal grandsires genotyped with a 50K chip in reference population; S+D+GS = sires, dams, and maternal grandsires genotyped with a 50K chip in reference population; S<sub>PB</sub>+D<sub>LD</sub> = purebred bulls from 5 different bulls genotyped with a 50K chip and dams genotyped with a 7K chip in the reference population.</figcaption></figure>
<table><caption>**Table 1.** Number of genotyped US crossbred dairy cattle (in bold) and those with available genomic data for the 78K marker chip in particular (in italics) depending on the sire breed in row and dam breed in column in the Council of Dairy Cattle Breeding database</caption><thead><tr><th rowspan="2">Sire breed</th><th colspan="6">Dam breed</th></tr><tr><th>Holstein</th><th>Jersey</th><th>Ayrshire</th><th>Brown Swiss</th><th>Guernsey</th><th>Crossbred</th></tr></thead><tbody><tr><td>Holstein</td><td>—</td><td><strong>19,641</strong></td><td><strong>64</strong></td><td><strong>102</strong></td><td><strong>15</strong></td><td><strong>3,750</strong></td></tr><tr><td>Jersey</td><td><strong>21,541</strong></td><td>—</td><td><strong>23</strong></td><td><strong>14</strong></td><td><strong>7</strong></td><td><strong>5,897</strong></td></tr><tr><td>Ayrshire</td><td><strong>422</strong></td><td><strong>13</strong></td><td>—</td><td><strong>1</strong></td><td><strong>0</strong></td><td><strong>7</strong></td></tr><tr><td>Brown Swiss</td><td><strong>270</strong></td><td><strong>46</strong></td><td><strong>0</strong></td><td>—</td><td><strong>1</strong></td><td><strong>28</strong></td></tr><tr><td>Guernsey</td><td><strong>57</strong></td><td><strong>15</strong></td><td><strong>0</strong></td><td><strong>4</strong></td><td>—</td><td><strong>5</strong></td></tr><tr><td>Crossbred</td><td><strong>45</strong></td><td><strong>66</strong></td><td><strong>0</strong></td><td><strong>0</strong></td><td><strong>0</strong></td><td>—</td></tr></tbody></table>

populations. The population of imputed individuals always consisted of the 110 from each crossbred type (either HO×JE or JE×HO) described above; however, the reference panel changed from one scenario to another. The genotypes indicated in each scenario were those represented in the reference population. The 7 scenarios were the following:

* **S+D:** This scenario included genomic information of the sires (S) and the dams (D) of crossbred animals genotyped with the 50K SNP chip.

* **S+D<sub>LD</sub>:** This scenario included genomic information of the sires genotyped with the 50K SNP chip and the dams of crossbred animals genotyped with the 7K SNP chip (low density, **LD**).

* **S:** This scenario included genomic information of the sires of crossbred animals genotyped with the 50K SNP chip.

* **D:** This scenario included genomic information of the dams of crossbred animals genotyped with the 50K SNP chip.

* **S+GS:** This scenario included genomic information of the sires and maternal grandsires (GS) of crossbred animals genotyped with the 50K SNP chip.

* **S+D+GS:** This scenario included genomic information of the sires, dams, and maternal grandsires of crossbred animals all genotyped with the 50K SNP chip.

* **S<sub>PB</sub>+D<sub>LD</sub>:** This scenario included genomic information of purebred (PB) bulls from 5 different breeds (53 Ayrshire, 53 Guernsey, 53 Brown Swiss, 53 Holstein, and 53 Jersey), including sires of crossbred (26 out of the 53 Jersey and 27 out of the 53 Holstein, respectively) genotyped with the 50K SNP chip and the dams of crossbred animals genotyped with the 7K SNP chip.

The objective of the S<sub>PB</sub>+D<sub>LD</sub> scenario was to be similar to the genomic evaluations currently performed by the CDCB (Wiggans et al., 2021). The purebred bulls were randomly chosen based on whether they were genotyped with a 78K SNP chip, had a BBR of 100%, and were born after 2008. Among the 53 Jersey and 53 Holstein, 26 and 27, respectively, were the sires of the crossbreds. The random selection of these animals was repeated 5 times. The mean of the 5 replicates was subsequently employed as a metric for subsequent IA calculations and shown in this scenario.

In addition, we evaluated the effects of adding purebred or crossbred genomic information in the reference population. For all aforementioned scenarios, the effects on IA of adding to the reference panel 1,000 random purebred Holsteins or Jerseys, or a combination of the 2, as well as 1,000 crossbred animals were evaluated. For purebred cattle, all randomly chosen individuals were genotyped with a 78K SNP chip, and their genomic information was masked to mimic the 50K SNP chip. They were not the parents or grandparents of the crossbreds in the validation population. Because insufficient HO×JE crossbreds were genotyped with the 78K SNP chip, the selection of crossbred animals was expanded to include any crossbred composed of 2 or more of the main US breeds (Guernsey, Ayrshire, Brown Swiss, Jersey, or Holstein). A total of 4,318 crossbred animals met these requirements. The sampling of 1,000 random individuals was repeated 5 times (with replacement). The average of the 5 replicates was subsequently employed as a metric for subsequent calculation, as shown in the results.

Finally, purebred Holstein or Jersey cattle were used in the validation population for the first 6 reference population scenarios, serving as a benchmark for IA (Supplemental Table S2, https://doi.org/10.6084/m9 .figshare.21858906.v1; Déru et al., 2023a).
<table><caption>Table 2. Imputation scenarios used in the study for 110 Holstein × Jersey (HO×JE) and Jersey × Holstein (JE×HO) crosses<fnref n="1" /></caption><thead><tr><th>Scenario (abbreviation)</th><th>No. reference</th><th>Description of reference</th><th>Description of crossbreds</th></tr></thead><tbody><tr><td>Sire + dam (S+D)</td><td>130 (53 sires + 77 dams)</td><td>Dams and sires genotyped with a 50K chip</td><td>53 HO×JE, 57 JE×HO</td></tr><tr><td>Sire + dam<sub>LD</sub> (S+D<sub>LD</sub>)</td><td>130 (53 sires + 77 dams)</td><td>Sires genotyped with a 50K chip, dams genotyped with a 7K chip</td><td>53 HO×JE, 57 JE×HO</td></tr><tr><td>Sire only (S)</td><td>53</td><td>Sires genotyped with a 50K chip</td><td>53 HO×JE, 57 JE×HO</td></tr><tr><td>Dam only (D)</td><td>77</td><td>Dams genotyped with a 50K chip</td><td>53 HO×JE, 57 JE×HO</td></tr><tr><td>Sire + maternal grandsire (S+GS)</td><td>115 (53 sires + 62 maternal grandsires)</td><td>Sires and maternal grandsires genotyped with a 50K chip</td><td>53 HO×JE, 57 JE×HO</td></tr><tr><td>Sire + dam + maternal grandsire (S+D+GS)</td><td>192 (77 dams + 62 maternal grandsires + 53 sires)</td><td>Sires, dams, and maternal grandsires genotyped with a 50K chip</td><td>53 HO×JE, 57 JE×HO</td></tr><tr><td>Sire<sub>PB</sub> + dam<sub>LD</sub> (S<sub>PB</sub>+D<sub>LD</sub>)</td><td>342 (53 Holstein bulls + 53 Jersey bulls + 53 Guernsey bulls + 53 Ayrshire bulls + 53 Brown Swiss bulls + 77 dams)</td><td>Purebred bulls included sires of crossbreds genotyped with a 50K chip and dams genotyped with a 7K chip</td><td>53 HO×JE, 57 JE×HO</td></tr></tbody></table>

## Imputation Accuracy

Genotype imputation was performed using findhap software (version 4; VanRaden, 2015). The latest version of findhap was used; this version is currently not quite ready for routine use on US chip data but performs better than version 3 for sequence data. The parameter settings were those recommended to combine 3K or 6K with 50K SNP chip (VanRaden, 2015). The maximum and minimum lengths of segments to check for haplotyping were 600 and 75 SNPs, respectively. The number of steps to get from maximum to minimum length was 3; the maximum iterations for haplotype imputation was 4; and the maximum number of different haplotypes within any segment was fixed at 1,000. The fraction of miscalled alleles was set to be 0.004.

The IA per marker (IA<sub>m</sub>) and per individual (IA<sub>i</sub>) were calculated as the averaged proportion of correctly imputed genotypes between actual and imputed genotypes per marker and per individual (i.e., concordance rate, as presented in Calus et al., 2014), with the SNPs on the 29 autosomal chromosomes considered; thus not on sex chromosomes.

## Post-Analysis Statistics

A statistical comparison was conducted to assess the differences in IA<sub>m</sub> and IA<sub>i</sub> between the different scenarios. A linear model was fitted for IA<sub>m</sub>:

$$y = X\beta + e,$$

where y is IA<sub>m</sub>; β is the vector of fixed effects: scenarios and interaction between chromosomes and scenarios; X is the incidence matrix relating observations to fixed effects; and e ~ N(0, Iσ<sub>e</sub><sup>2</sup>) is the residual random effect, with I the incidence matrix and σ<sub>e</sub><sup>2</sup> the residual variance.

A similar model for IA<sub>i</sub> was fitted, including only the effect of scenarios. Least squares means (LSM) for IA<sub>m</sub> and IA<sub>i</sub> were obtained for each scenario using the lsmeans() function of the lsmeans package in R (Lenth, 2016). The LSM were compared between scenarios with a t-test via the contrast() function of the same package. The distributions of IA<sub>m</sub> and IA<sub>i</sub> were obtained for all scenarios. Individuals and markers were classified in 3 classes according to their IA values: less than 80% (low), between 80 and 90% (moderate), and above 90% (high). To compare the proportion of individuals or markers between groups and scenarios, a 2-proportions Z-test was performed with the prop.test() function in R (R Core Team, 2016).

To observe any differences in IA<sub>m</sub> for a given chromosome between scenarios, the difference in IA<sub>m</sub> between scenarios for each chromosome were compared. The contrasts between LSM obtained between scenarios were estimated with the contrast() function in R (Lenth, 2016), which allows pairwise comparisons of LSM by testing linear contrasts among predictions. To evaluate the effects and the significance of the length of the chromosome on IA values, the Pearson correlation between the length of the chromosome and IA was calculated for each scenario with the cor.test() function in R (R Core Team, 2016). Similarly, the

<footnote n="1">¹HO = Holstein; JE = Jersey; S = sire; D = dam; GS = maternal grandsire; PB = purebred; LD = low density. HO×JE: sire is Holstein, dam is Jersey. JE×HO: dam is Jersey, sire is Holstein.</footnote>
<table><caption>**Table 3.** Least squares means of imputation accuracy per marker and per individual in the different scenarios for Holstein × Jersey (HO×JE), Jersey × Holstein (JE×HO), both combined (Both), purebred Holstein (HO), and purebred Jersey (JE), along with their standard errors</caption><thead><tr><th rowspan="2">Scenario<sup>1</sup></th><th colspan="5">LSM<sup>2</sup> (SE),<br>imputation accuracy by marker</th><th colspan="5">LSM<sup>3</sup> (SE),<br>imputation accuracy by individual</th></tr><tr><th>HO×JE</th><th>JE×HO</th><th>Both</th><th>HO</th><th>JE</th><th>HO×JE</th><th>JE×HO</th><th>Both</th><th>HO</th><th>JE</th></tr></thead><tbody><tr><td>S+D</td><td>89.70<sup>a</sup><br>(0.06)</td><td>89.66<sup>a</sup><br>(0.06)</td><td>89.68<sup>a</sup><br>(0.06)</td><td>94.02<sup>a</sup><br>(0.06)</td><td>93.94<sup>a</sup><br>(0.06)</td><td>89.65<sup>a</sup><br>(0.10)</td><td>89.59<sup>a</sup><br>(0.12)</td><td>89.62<sup>a</sup><br>(0.08)</td><td>94.04<sup>a</sup><br>(0.10)</td><td>94.01<sup>a</sup><br>(0.11)</td></tr><tr><td>S+D<sub>LD</sub></td><td>87.69<sup>b</sup><br>(0.06)</td><td>87.58<sup>a</sup><br>(0.06)</td><td>87.64<sup>b</sup><br>(0.06)</td><td>92.80<sup>b</sup><br>(0.06)</td><td>92.73<sup>b</sup><br>(0.06)</td><td>87.71<sup>b</sup><br>(0.10)</td><td>87.53<sup>b</sup><br>(0.12)</td><td>87.62<sup>b</sup><br>(0.08)</td><td>92.89<sup>b</sup><br>(0.10)</td><td>92.82<sup>b</sup><br>(0.11)</td></tr><tr><td>S</td><td>86.79<sup>c</sup><br>(0.06)</td><td>86.61<sup>b</sup><br>(0.06)</td><td>86.70<sup>c</sup><br>(0.06)</td><td>91.18<sup>c</sup><br>(0.06)</td><td>91.01<sup>c</sup><br>(0.06)</td><td>86.85<sup>c</sup><br>(0.10)</td><td>86.64<sup>c</sup><br>(0.12)</td><td>86.74<sup>c</sup><br>(0.08)</td><td>91.26<sup>c</sup><br>(0.10)</td><td>91.15<sup>c</sup><br>(0.11)</td></tr><tr><td>D</td><td>86.66<sup>c</sup><br>(0.06)</td><td>86.99<sup>c</sup><br>(0.06)</td><td>86.83<sup>c</sup><br>(0.06)</td><td>91.36<sup>c</sup><br>(0.06)</td><td>91.35<sup>d</sup><br>(0.06)</td><td>86.51<sup>c</sup><br>(0.10)</td><td>86.88<sup>c</sup><br>(0.12)</td><td>86.70<sup>c</sup><br>(0.08)</td><td>91.53<sup>c</sup><br>(0.10)</td><td>91.54<sup>c</sup><br>(0.11)</td></tr><tr><td>S+GS</td><td>88.42<sup>d</sup><br>(0.06)</td><td>88.29<sup>d</sup><br>(0.06)</td><td>88.35<sup>d</sup><br>(0.06)</td><td>91.28<sup>c</sup><br>(0.06)</td><td>90.75<sup>e</sup><br>(0.06)</td><td>88.49<sup>d</sup><br>(0.10)</td><td>88.39<sup>d</sup><br>(0.12)</td><td>88.44<sup>d</sup><br>(0.08)</td><td>91.27<sup>c</sup><br>(0.10)</td><td>90.88<sup>c</sup><br>(0.11)</td></tr><tr><td>S+D+GS</td><td>90.08<sup>e</sup><br>(0.06)</td><td>90.10<sup>e</sup><br>(0.06)</td><td>90.09<sup>e</sup><br>(0.06)</td><td>93.65<sup>d</sup><br>(0.06)</td><td>92.99<sup>f</sup><br>(0.06)</td><td>89.99<sup>a</sup><br>(0.10)</td><td>89.98<sup>a</sup><br>(0.12)</td><td>89.99<sup>e</sup><br>(0.08)</td><td>93.74<sup>a</sup><br>(0.10)</td><td>93.11<sup>b</sup><br>(0.11)</td></tr><tr><td>S<sub>PB</sub>+D<sub>LD</sub></td><td>80.91<sup>f</sup><br>(0.06)</td><td>80.84<sup>f</sup><br>(0.06)</td><td>80.87<sup>f</sup><br>(0.06)</td><td>—</td><td>—</td><td>80.91<sup>e</sup><br>(0.12)</td><td>80.79<sup>e</sup><br>(0.12)</td><td>80.85<sup>f</sup><br>(0.08)</td><td>—</td><td>—</td></tr></tbody></table>

Pearson correlation between the percentage of homozygosity per marker and the IA<sub>m</sub> obtained was also calculated.

## RESULTS

### Comparison of Imputation Accuracy by Marker and Individual

The LSM of IA<sub>m</sub> and IA<sub>i</sub> in the different scenarios for HO×JE and JE×HO separately, combined, and within purebred animals, are presented in Table 3.

In all scenarios, we found no significant difference between the imputation accuracy (IA<sub>m</sub> and IA<sub>i</sub>) between HO×JE, JE×HO, and both type of crossbred combined (*P* = 0.65). Thus, only results for the combined population are shown and those found in purebreds. In scenarios S and D, IA was 86.70 ± 0.06 and 86.83 ± 0.06 for crossbred but significantly higher in purebred animals (*P* < 0.05; >91.01 ± 0.06). No significant differences were observed in crossbreds for IA<sub>m</sub> and IA<sub>i</sub>, regardless of whether the genomic information of the sire or the dam was included in the reference population. In the S+D scenario, IA was significantly higher than in

scenarios S and D (*P* < 0.05), with values that were still higher in purebreds than in crossbreds (around +4 percentage points higher in purebreds). In the scenario S+D<sub>LD</sub>, IA values were between those of scenario S+D and scenarios S and D. In scenario S+GS, IA<sub>m</sub> and IA<sub>i</sub> were lower than in the S+D scenario for crossbreds (−1.37 percentage points for IA<sub>m</sub> and −1.18 percentage points for IA<sub>i</sub>; *P* < 0.05). The same observation was made in purebred populations, but IA values were still significantly higher than in crossbred populations for this scenario (*P* < 0.05). In the scenarios including related individuals in the reference population, the lowest IA was observed for the scenario S<sub>PB</sub>+D<sub>LD</sub> (IA<sub>m</sub> = 80.87 ± 0.06 and IA<sub>i</sub> = 80.85 ± 0.08). Finally, the highest IA<sub>m</sub> and IA<sub>i</sub> were found for the scenario that combined the genomic information of sires, dams, and maternal grandsires in the reference (scenario S+D+GS) in all crossbreds (IA<sub>m</sub> = 90.09 ± 0.06 and IA<sub>i</sub> = 89.99 ± 0.08), purebred Jerseys (IA<sub>m</sub> = 92.99 ± 0.10 and IA<sub>i</sub> = 93.11 ± 0.11), and purebred Holsteins (IA<sub>m</sub> = 93.65 ± 0.06 and IA<sub>i</sub> = 93.74 ± 0.06).

In all scenarios with the addition of related individuals (S, D, S+D, S+D<sub>LD</sub>, S+GS, and S+D+GS), IA values were significantly higher in purebred animals than in

<footnote>a-f Least squares means in the same column with different superscripts are statistically different according to a t-test (P < 0.05).</footnote>

<footnote n="1">¹S+D = sires and dams genotyped with a 50K chip in reference population; S+D<sub>LD</sub> = sires genotyped with a 50K chip and dams genotyped with a 7K chip in reference population; S = sire; D = dams genotyped with a 50K chip in reference population; S+GS = sires and maternal grandsires genotyped with a 50K chip in reference population; S+D+GS = sires, dams, and maternal grandsires genotyped with a 50K chip in reference population; S<sub>PB</sub>+D<sub>LD</sub> = purebred bulls from 5 different bulls genotyped with a 50K chip and dams genotyped with a 7K chip in the reference population.</footnote>

<footnote n="2">²From a linear mixed model including the fixed effects of the scenario, the chromosome, and the interaction between the scenario and the chromosome. All effects were significant for *P* < 0.0001.</footnote>

<footnote n="3">³From a linear mixed model including the fixed effects of the scenario, which was significant for *P* < 0.0001. The effect of the type of crossbred (HO×JE and JE×HO) was tested but not included in the model because this was not significant (*P* = 0.65).</footnote>
<table><caption>**Table 4.** Percentage increase in imputation accuracy by marker and by individual in different scenarios with 110 crossbreds (CR), purebred Holstein (HO), or purebred Jersey (JE), and with the addition of 1,000 random purebred Holstein (+1,000 HO), Jersey (+1,000 JE), crossbred (+1,000 CR), or +1,000 Holstein and +1,000 Jersey combined (+1,000 HO/JE) in the reference population</caption><thead><tr><th rowspan="2">Scenario<sup>1</sup></th><th colspan="4">Percentage increase of imputation accuracy per marker<sup>2</sup></th><th colspan="4">Percentage increase of imputation accuracy per individual<sup>2</sup></th></tr><tr><th>+1,000 HO</th><th>+1,000 JE</th><th>+1,000 CR</th><th>+1,000 HO/JE</th><th>+1,000 HO</th><th>+1,000 JE</th><th>+1,000 CR</th><th>+1,000 HO/JE</th></tr></thead><tbody><tr><td><strong>Crossbred</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>S+D</td><td>-2%*</td><td>-1%*</td><td>-2%*</td><td>-2%*</td><td>-2%*</td><td>-2%*</td><td>-2%*</td><td>-2%*</td></tr><tr><td>S+D<sub>LD</sub></td><td>-3%*</td><td>-3%*</td><td>-2%*</td><td>-2%*</td><td>-3%*</td><td>-3%*</td><td>-2%*</td><td>-2%*</td></tr><tr><td>S</td><td>-2%*</td><td>-1%*</td><td>-1%*</td><td>-2%*</td><td>2%*</td><td>-2%*</td><td>-1%*</td><td>-2%*</td></tr><tr><td>D</td><td>-1%*</td><td>-2%*</td><td>-1%*</td><td>-2%*</td><td>-1%*</td><td>-2%*</td><td>-1%*</td><td>-2%*</td></tr><tr><td>S+GS</td><td>-2%*</td><td>-3%*</td><td>-4%*</td><td>-3%*</td><td>-3%*</td><td>-3%*</td><td>-4%*</td><td>-3%*</td></tr><tr><td>S+D+GS</td><td>-2%*</td><td>-2%*</td><td>-2%*</td><td>-2%*</td><td>-2%*</td><td>-2%*</td><td>-2%*</td><td>-2%*</td></tr><tr><td>S<sub>PB</sub>+D<sub>LD</sub></td><td>-2%*</td><td>-3%*</td><td>+3%*</td><td>-1%*</td><td>-2%*</td><td>-3%*</td><td>+3%*</td><td>-2%*</td></tr><tr><td><strong>Purebred Holstein</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>S+D</td><td>-6%*</td><td>—</td><td>—</td><td>—</td><td>-6%*</td><td>—</td><td>—</td><td>—</td></tr><tr><td>S+D<sub>LD</sub></td><td>6%*</td><td>—</td><td>—</td><td>—</td><td>-8%**</td><td>—</td><td>—</td><td>—</td></tr><tr><td>S</td><td>-4%*</td><td>—</td><td>—</td><td>—</td><td>-4%*</td><td>—</td><td>—</td><td>—</td></tr><tr><td>D</td><td>-4%*</td><td>—</td><td>—</td><td>—</td><td>-4%*</td><td>—</td><td>—</td><td>—</td></tr><tr><td>S+GS</td><td>-4%*</td><td>—</td><td>—</td><td>—</td><td>-4%*</td><td>—</td><td>—</td><td>—</td></tr><tr><td>S+D+GS</td><td>-6%*</td><td>—</td><td>—</td><td>—</td><td>-6%*</td><td>—</td><td>—</td><td>—</td></tr><tr><td><strong>Purebred Jersey</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>S+D</td><td>—</td><td>-3%*</td><td>—</td><td>—</td><td>—</td><td>-3%*</td><td>—</td><td>—</td></tr><tr><td>S+D<sub>LD</sub></td><td>—</td><td>-2%*</td><td>—</td><td>—</td><td>-2%*</td><td>—</td><td>—</td><td>—</td></tr><tr><td>S</td><td>—</td><td>-1%*</td><td>—</td><td>—</td><td>—</td><td>-1%*</td><td>—</td><td>—</td></tr><tr><td>D</td><td>—</td><td>-1%*</td><td>—</td><td>—</td><td>—</td><td>-1%*</td><td>—</td><td>—</td></tr><tr><td>S+GS</td><td>—</td><td>-2%**</td><td>—</td><td>—</td><td>—</td><td>-0%</td><td>—</td><td>—</td></tr><tr><td>S+D+GS</td><td>—</td><td>-2%*</td><td>—</td><td>—</td><td>—</td><td>-2%*</td><td>—</td><td>—</td></tr></tbody></table>

crossbred animals, and adding more related individuals to the reference population increased IA values.

## Effects of Extended Reference Population With Unrelated Individuals on Imputation Accuracy

Table 4 highlights the effects of adding 1,000 unrelated individuals of crossbreds and purebreds to the reference population on IA<sub>m</sub> and IA<sub>i</sub> in the corresponding validation crossbred or purebred populations.

For scenarios S, D, S+D, S+D<sub>LD</sub>, S+GS, and S+D+GS, we detected no increase of IA<sub>m</sub> and IA<sub>i</sub> with adding 1,000 unrelated individuals to crossbreds and purebred in the reference population. A decrease of IA in all scenarios (from -1% to -6%) was observed with the addition of these individuals. However, for S<sub>PB</sub>+D<sub>LD</sub>, the inclusion of 1,000 crossbreds to the reference population increased IA<sub>m</sub> (+3%) and IA<sub>i</sub> (+3%), but the inclusion of purebreds did not increase IA values.

In original scenarios (S, D, S+D, S+D<sub>LD</sub>, S+GS, and S+D+GS) without the addition of random individuals, the GRC was between 0.316 and 0.323. With the addition of random purebreds and crossbreds in the

reference population, the GRC was lower than in original scenarios (0.287 to 0.305), except in the scenario S<sub>PB</sub>+D<sub>LD</sub>, where GRC was lower in original scenario (0.262 to 0.264) than with the addition of supplementary individuals (0.270 to 0.284; Supplemental Table S3, https://figshare.com/articles/figure/Supplemental Table_S3/21858924; Déru et al., 2023b).

Figures 2a and 2b report the values and percentage of increase of IA<sub>m</sub> and IA<sub>i</sub> when adding related individuals to the 1,000 unrelated individuals in comparison to a scenario with only these random individuals and unrelated individuals in the reference population.

When no related individuals were included in the reference population, IA were higher when +1,000 random crossbreds were added to the reference population (IA<sub>m</sub> = 80.75 and IA<sub>i</sub> = 79.14) than when +1,000 HO (IA<sub>m</sub> = 75.92 and IA<sub>i</sub> = 75.01), +1,000 JE (IA<sub>m</sub> = 77.30 and IA<sub>i</sub> = 77.77), and 1,000 HO/JE (IA<sub>m</sub> = 77.99 and IA<sub>i</sub> = 79.14) were added to the reference population.

When random individuals were added to the reference population, IA were higher when related individuals were already present in the reference population (scenarios S, D, S+D, S+D<sub>LD</sub>, S+GS, and S+D+GS).

<footnote n="1">¹S+D = sires and dams genotyped with a 50K chip in reference population; S+D<sub>LD</sub> = sires genotyped with a 50K chip and dams genotyped with a 7K chip in reference population; D = dams genotyped with a 50K chip in reference population; S+GS = sires and maternal grandsires genotyped with a 50K chip in reference population; S<sub>PB</sub>+D<sub>LD</sub> = purebred bulls from 5 different bulls genotyped with a 50K chip and dams genotyped with a 7K chip in the reference population.</footnote>

<footnote n="2">²Least squares of imputation accuracy were compared before and after the addition of supplementary dairy cattle in the reference population by a t-test.</footnote>

<footnote>*P < 0.05. **P < 0.01.</footnote>
<figure><img src="image_4.png" /><figcaption>**Figure 2.** (a) Percentage increase in imputation accuracy by marker in different scenarios compared with the scenario with no related individuals in the reference population, within scenarios with the addition of 1,000 random purebred Holstein (+1,000HO), Jersey (+1,000JE), crossbred (+1,000CR) or +1,000 Holstein and +1,000 Jersey combined (+1,000HO/JE) in the reference population. (b) Percentage increase in imputation accuracy by individual in different scenarios compared with the scenario with no related individuals in the reference population, within scenarios with +1,000HO, +1,000JE, +1,000CR, or +1,000HO/JE in the reference population. S+D = sires and dams genotyped with a 50K chip in the reference population; S+D<sub>LD</sub> = sires genotyped with a 50K chip and dams genotyped with a 7K chip in reference population; D = dams genotyped with a 50K chip in reference population; S+GS = sires and maternal grandsires genotyped with a 50K chip in reference population; S+D+GS = sires, dams, and maternal grandsires genotyped with a 50K chip in reference population; S<sub>PB</sub>+D<sub>LD</sub> = purebred bulls from 5 different bulls genotyped with a 50K chip and dams genotyped with a 7K chip in the reference population. Blue-red color represents positive values in blue, negative values in red, and values close to zero in yellow.</figcaption></figure>
compared with when no related individuals were pres-
ent, in the order of +5 to +15%. However, IA were
higher in the scenario with no relatives but 1,000 cross-
breds, compared with the S<sub>PB</sub>+D<sub>LD</sub> scenario with 1,000
additional crossed individuals (+1 to +3%).

Distribution of Individuals and Markers According
to Imputation Accuracy

Figure 1 illustrates the percentages of individuals and
markers based on estimated IA values within differ-
ent imputation scenarios; detailed values are shown in
Supplemental Tables S4 (https://doi.org/10.6084/m9
 figshare.21858930; Déru et al., 2023c) and S5 (https:
 //doi.org/10.6084/m9 figshare.21858948; Déru et al.,
 2023d).

The results between crossbred and purebred animals within the imputation scenarios compared and the percentages of individuals and markers on estimated IA values were categorized into 3 groups (IA <80%, 80% < IA < 90%, and IA >90%). Across all original imputation scenarios having relative individuals in the reference population, the percentage of higher imputed markers (IA<sub>m</sub> >90%) was significantly higher for purebreds (66–79%) than for crossbreds (44–60%). Additionally, purebred animals had a significantly higher percentage of accurate imputed individuals (>92%) than crossbred animals (0–53%). In all these imputation scenarios, the percentage of lower (IA<sub>m</sub> <80%) or medium (80% < IA<sub>m</sub> < 90%) imputed markers was higher and observed more in crossbred than in purebred animals.

Furthermore, the results of imputed crossbreds within
defined imputation scenarios were compared. As more
related individuals were added to the reference popu-
lation, the percentage of accurately imputed markers
and individuals increased. In fact, the percentages of
imputed markers greater than 90% in the S, D, S+D<sub>LD</sub>,
S+GS, S+D, and S+D+GS scenarios were 44, 44, 48,
53, 58, and 60%, respectively. Similarly, the percentages
of individuals well imputed over 90% when imputation
scenarios defined for S, D, S+D<sub>LD</sub>, S+GS, S+D, and
S+D+GS in the reference populations were 0, 0, 0, 0,
32, and 53%, respectively. However, the percentages of
markers and individuals well imputed over 90% in the
S<sub>PB</sub>+D<sub>LD</sub> scenario were low, with 20% and 0%, respec-
tively.

The addition of 1,000 unrelated genotyped cross-
breds to the reference population of scenarios (S, D,
S+D<sub>LD</sub>, S+GS, S+D, and S+D+GS) did not improve
the percentage of imputed markers and individuals,
and in some scenarios this percentage was decreased.
A similar pattern was observed when 1,000 unrelated
genotyped purebreds (Holstein or Jersey) were added
to those predetermined reference population scenarios.

In scenario S<sub>PB</sub>+D<sub>LD</sub>, an increase in IA values was al-
ready observed with adding 1,000 unrelated crossbred
animals to the reference population. In this case, the
percentage of well imputed markers over 90% increased
from 20 to 27%, and the percentage of lower imputed
markers (IA<sub>m</sub> < 80%) decreased from 6 to 0%.

Imputation Accuracy by Chromosome

Variability in IA by chromosome was observed across different imputation scenarios and was similar to overall IA<sub>m</sub> across all scenarios. The IA by chromosome rang- ing between 86.01 and 93.20%, 84.47 to 90.18%, 82.78 to 90.18%, 84.04 to 90.17%, 84.42 to 92.10%, 86.73 to 93.38%, and 78.24 to 82.78% for S+D, S+D<sub>LD</sub>, S, D, S+GS, S+D+GS, and S<sub>PB</sub>+D<sub>LD</sub> scenarios, respectively. Lower IA was found for chromosomes 27, 27, 27, 28, 29, 25, and 28, and higher IA was found for chromosomes 26, 26, 6, 19, 20, 19 and 26 in scenarios S+D, S+D<sub>LD</sub>, S, D, S+GS, S+D+GS, and S<sub>PB</sub>+D<sub>LD</sub>, respectively.

Only 5 imputation scenarios (S+D: 0.381 *P* < 0.05), S: 0.52 (*P* < 0.01), D: 0.50 (*P* < 0.01), S+D+GS: 0.52 (*P* < 0.01), S<sub>PB</sub>+D<sub>LD</sub>: 0.46 (*P* < 0.05)] revealed a statistically significant Pearson correlation between chromosome length and IA<sub>m</sub>. On the contrary, we found a meaningful correlation between marker homozygosity and IA<sub>m</sub>, with Pearson correlations greater than 0.82 in all defined imputation scenarios (*P* < 0.0001).

Differences in Chromosome Imputation Accuracy
Between Scenarios

Figure 3 depicts the comparison of LSMeans of IA
by chromosome between paired imputation scenarios
for imputed crossbred animals. Even though the aver-
aged IA_m was not significantly different between the S
and D scenarios, the estimated IA by chromosome were
significantly different for 13 out of the 29 chromosomes
between these 2 scenarios (Chromosomes 2, 4, 6, 10, 12,
15, 18, 19, 22, 23, 24, 27, 28; *P* < 0.05).

With the exception of chromosomes 6, 15, and 22, all
chromosomes showed an increase in IA from extend-
ing reference population scenario S to S+D. However,
expanding reference population scenario D to S+D
increased IA on all chromosomes except for chromo-
somes 23, 27, and 29. Additionally, it was discovered
that when the genotypes of the dams were added to the
reference population of imputation scenario S+GS, IA
was not significantly increased on chromosome 6 but
was significantly increased on chromosomes 15 and 22
(*P* > 0.05 and *P* > 0.001, respectively), which was not
the case when adding the genotypes of the dams to the
reference population scenario S. When maternal grand-
sire genotypes were added to the reference population
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Comparison between LSM of imputation accuracy by chromosome across pairwise scenarios, with their associated *P*-values (*P* < 0.05). S+D = sires and dams genotyped with a 50K chip in reference population; S+D<sub>LD</sub> = sires genotyped with a 50K chip and dams genotyped with a 7K chip in reference population; D = dams genotyped with a 50K chip in reference population; S+GS = sires and maternal grandsires genotyped with a 50K chip in reference population; S+D+GS = sires, dams, and maternal grandsires genotyped with a 50K chip in reference population; S<sub>PB</sub>+D<sub>LD</sub> = purebred bulls from 5 different bulls genotyped with a 50K chip and dams genotyped with a 7K chip in the reference population.</figcaption></figure>

of imputation scenario S and S+D, the IA of 5 common
chromosomes of 14, 17, 19, 23, and 27 was significantly
increased. Thus, the results of estimated IA by chromo-
somes showed that incorporating first- or second-order
relationships of individuals in the validation population
into the imputation scenarios did not result in the same
set of chromosomes increasing the IA.

DISCUSSION

The present study aimed to evaluate the quality of
IA<sub>m</sub> and IA<sub>i</sub> in US crossbred dairy cattle. It was car-
ried out by exploring the IA<sub>m</sub> and IA<sub>i</sub>, as well as their

distributions, from imputing 7K to 50K SNP chips, the
most common chips used by dairy breeders. The effects
of the size and relationships between the reference and
validation population were also evaluated.

Imputation Accuracy for Crossbred Versus Purebred
Dairy Population

When related individuals were included in the refer-
ence population scenarios, the estimated IA_m and IA_i
for crossbreds ranged from 86.70 to 90.09%, whereas
purebreds exceeded 90.75%. Our IA results for cross-
breds were consistent with those imputed from 20K
to 50K in a tropical crossbred dairy cattle population, ranging from 50 to 94% (Oliveira Júnior et al., 2017). However, estimated IA in our study were greater than those found in crossbred Canadian beef reported by Ventura et al. (2014) imputing 6K to 50K SNP chip, and they investigated the IA for crossbreds using a reference population of older purebreds, showing that it ranged from 54 to 74% accuracy. This difference in the magnitude of IA could be explained in part by the fact that their population was admixed, whereas the imputed crossbreds in the validation population in our study were 50% Jersey and 50% Holstein. The reference population in our study had a strong relationship (sires, dams, maternal grandsire, or combination of those) with validation population for crossbred, which was the explanation of greater estimated IA compared with the study of Ventura et al. (2014). When only purebreds were used as a reference population to impute genotyped crossbred in a validation population for African dairy cattle, the IA based on correlation between masked and imputed genotypes was reported to be less than 82% using 3 different imputation software programs (Aliloo et al., 2018). However, when related purebred breeds were added to the reference population of imputation scenarios in this study, the estimated IA were greater than those observed by Aliloo et al. (2018).

Numerous studies showed high IA for using purebred dairy in the reference population to impute the same purebred breed, which was confirmed by our findings for Jersey and Holstein cattle. For instance, the percentage of correctly imputed genotypes for a subset of purebred US Holsteins to impute the same population were 90.5% from 3K to 99.0% from 50K using findhap software, and 91.1% from 3K to 99.3% from 50K using FImpute (VanRaden et al., 2013b). In addition, a study to impute purebred US Jersey cows from 2K or 4K to 50K SNP chips revealed IA in the range of 80 to 95% (Weigel et al., 2010b). In our study, the same imputation scenario was applied to compare IA for crossbred versus purebred Jersey and Holstein. Thus, it is concluded that IA in purebred cows was significantly higher than in crossbred cows because the inclusion of 2 different purebred breeds in reference population increases the number of probable haplotypes, and long-range haplotypes in crossbreds will likely differ from those in a purebred reference population (Aliloo et al., 2018), which may explain the lower estimated IA in crossbreds compared with purebreds. A significant correlation between homozygosity and IA was also revealed and highlighted in our study. The higher IA in purebred dairy populations compared with crossbred dairy populations may also be due to homozygous regions, which were more common in purebred cows than in crossbred cows.

## Effects of Reference Population Scenarios on Crossbreds in Validation

In this study, the estimated IA in crossbred and purebred populations significantly increased as the proportion of relationship between individuals in the reference and validation population increased. Aliloo et al. (2018) reported similar findings for African crossbred dairy cows and discovered that IA increased as the genetic relationship between the reference and validation populations increased. The importance of relatedness between reference and validation populations was also confirmed by Oliveira Júnior et al. (2017) for increasing IA for tropical crossbred dairy cattle. In a multi-breed beef cattle population, Ventura et al. (2014) reported higher IA when closely related individuals, along with a representation of the breed composition of the imputed group, were included in the reference population. The recommendation to add closely related animals to impute genotypes of crossbred animals has also been made in other species, such as pigs (Xiang et al., 2015) and sheep (Ventura et al., 2016). Indeed, closely related individuals shared longer haplotype segments, which were used to infer missing markers. The imputation methodology used in our study by findhap (version 4) software (VanRaden, 2015) constructed haplotype segments that were as large as possible and iteratively moved to smaller ones if no consistent haplotypes were found in the reference population. Thus, when reference and validation populations were unrelated, they shared very short haplotypes, explaining the lower IA in these scenarios. In addition, this software considers the pedigree information to phase the haplotypes, allowing higher IA when related individuals are present in the reference population.

In this study, it is important to note that all of the data were phased ahead of time using the 78K SNP chip genotypes and Beagle 5.3 software (Browning et al., 2021). However, it is worth mentioning that real-world data may be phased using medium- or low-density chips, which could result in slightly lower imputation accuracies than those observed in this study.

Based on our results, adding 1,000 unrelated purebred animals in the reference population when related individuals are already present in the reference population did not increase IA and can even reduce these values and reduce the percentage of well-imputed markers and individuals. One reason could be that the reference population was less related to the population of crossbred animals in the validation population than in scenarios where only closely related animals are added, based on the GRC (Supplemental Table S4). According to published research, Ventura et al. (2014) showed that adding purebred animals to the reference population of
another purebred population did not improve the IA
from a 6K to a 50K SNP chip. In addition, inclusion of
purebred Holstein and Gyr in the reference population
had limited or no gain in IA for imputing crossbred
Girolando (Gyr × Holstein) dairy cattle (Oliveira
Júnior et al., 2017). In the literature, the addition of
crossbred animals to the reference population improved
the IA in Girolando (Oliveira Júnior et al., 2017), tropi-
cal crossbreds (Aliloo et al., 2018), and US crossbreds
(VanRaden et al., 2020). Based on our analysis, we ex-
pected an increase in IA for crossbreds as they shared
more common haplotypes with the imputed crossbred
population compared with purebreds. However, adding
1,000 crossbred animals to the reference population did
not result in improved IA for HO×JE or JE×HO cross-
breds in the validation population, as per our study.

The fact that IA have not improved in our study
may be due to using genotypes of crossbred animals
from different breeds in the reference population, with
few genotype data available for HO×JE and JE×HO
to impute our Holstein and Jersey crossbreds in the
validation population. Additionally, the GRC between
the validation and reference populations was lower in
the scenario with the 1,000 crossbred animals randomly
added to the reference population than it was in the
scenarios without these animals (Supplemental Table
S4). The crossbred animals in the reference population
likely had few haplotypes in common with the crossbred
animals in the validation population, and therefore no
increase in IA was observed. Another hypothesis sug-
gests that IA has already reached a maximum value
for scenarios that included parents (i.e., the closest
individuals). In fact, we also noticed results of the same
nature in purebred Jersey and Holstein, and the IA
was not improved by the inclusion of 1,000 random
individuals to the reference population.

Thus, our results showed that it is possible to achieve
greater than 85% IA in US crossbred dairy cattle when
related individuals of those crossbreds were added to
the reference population. The addition of unrelated
crossbred genotypes to the reference population had
no effect on IA improvement. However, the addition of
genotype information from relative crossbred animals
to the reference population could not be tested in our
study but could increase and improve IA, as already
demonstrated by Aliloo et al. (2018).

Effects of Chromosomes on Imputation Accuracy

The estimated IA value for each individual chro-
mosome in our study was similar to the estimated
IA for the whole genome. In 3 out of the 7 imputa-
tion scenarios, IA value increased as the chromosome
length increased, but even for large chromosomes, this

difference was minimal. Evidence suggests association
between IA and chromosome length in sheep and beef
cattle (Sun et al., 2012; Piccoli et al., 2014; Ventura et
al., 2016). Chromosomes 1 to 29 were arranged in de-
creasing order of length, and it was discovered that the
last chromosomes frequently had the lowest IA value
in different reference population scenarios. In addition,
studies on Angus, Braford, and Hereford beef cattle,
as well as sheep, had previously supported the finding
that the last chromosomes typically had lower IA than
the first chromosomes (Sun et al., 2012; Piccoli et al.,
2014; Ventura et al., 2016).

In this study, the relatedness between the reference
and validation populations did not have the same effect
on chromosomal IA, and different chromosomes showed
an increase in IA values depending on whether the ge-
nomic data from the sire, dams, or maternal grandsire
was added to the reference population. However, when-
ever related animals of validation population included
to the reference population of imputation scenarios, the
IA values of the first 5 long chromosomes increased
noticeably, and the IA values of the last shorter chro-
mosomes decreased. This may be because the shorter
the chromosomes, the lower the overall chromosome
accuracy, as misattributed distal regions comprise a
larger proportion of the overall chromosome (Sun et
al., 2012).

To conclude, the IA on each chromosome differed
according to the degree of relatedness between refer-
ence and validation population for crossbreds. Includ-
ing more related individuals into reference population
seems promising to increase IA, particularly for the
first chromosomes but not for the last.

Imputation for US Crossbred Dairy Population

The findings of this study confirmed that IA was in
the range of 85 to 90% for HO×JE and JE×HO US
crossbred dairy population when imputing from 7K to
50K SNP chip, still lower than in purebred animals. The
scenario of S+D+GS achieved the highest imputation
accuracy (~90%) for imputing commercial 50K SNP
panels. This result is promising because according to
the CDCB database, 53% of the HO×JE and JE×HO
crossbred animals had available genomic information
of the sire, dam, and maternal grandsire (Supplemen-
tal Table S1). The second most common scenario in
the CDCB database was the S+D~LD~ scenario with IA
of around 87%, and the potential addition of related
crossbred individuals of HO×JE or JE×HO to the
reference population could be beneficial to improve IA
for this scenario and should be tested. The third most
common scenario in the CDCB database is the scenario
S+D (8% JE×HO and HO×JE, Supplemental Table
S1). This case is promising because the IA was only one point lower than in the scenario S+D+GS. However, the scenario S<sub>PB</sub>+D<sub>LD</sub>, which is the most frequently used approach by CDCB for genomic evaluation of crossbreds, did not give the most promising IA results (~80%), so the strategy could eventually be rethought. Thus, adding genomic information from bulls of other breeds than Holstein or Jersey in the reference population would not be a good strategy to impute crossbred animals. In addition, in cases where no genotyped related individuals were available, the best scenario was to put crossbred individuals in the reference population rather than purebred animals.

The breeding scheme for dairy cattle is already an
advantage for the imputation of crossbred, as dairy
cattle are characterized by a small number of sires with
large family sizes and complete pedigree information
in many generations. This structure is a considerable
advantage for the imputation compared with other spe-
cies such as beef cattle (Ventura et al., 2014) or sheep
(Hayes et al., 2012), where alternative solutions have
to be found to face the problem of pedigree structure.

This study determined IA values only for F₁ HO×JE and JE×HO crossbreds. The same type of analysis can also be verified later in other types of crosses commonly found in the United States, such as Ayrshire × Holstein and Brown Swiss × Holstein. The results can also be investigated for crossbreds rather than F₁ crossbreds. Indeed, 19% of crossbreds with genomic data in the CDCB database result from of a cross between a purebred animal and an already crossed individual (Supplemental Table S1), such as backcross, second backcross, or 3-breed backcross, for example. Different approaches will likely need to be considered in this type of situation, and the makeup of the reference panel will need to be examined.

The accuracy of the imputation is crucial for the es-
timation of correct genetic values. Numerous studies on
purebred Jersey and Holstein cows revealed that the ac-
curacy of genomic selection did not appear to decrease
with imputation (Weigel et al., 2010a; VanRaden, et
al., 2011; Mulder et al., 2012). However, for crossbred
dairy populations, no study confirms that the accuracy
of genomic selection does not decline with imputation.
The study by VanRaden et al. (2020) did not observe
the quality of imputation, but highlighted that genomic
predictions weighted by BBR were slightly more ac-
curate than predictions using only the predominant
breed. Thus, one of the solutions for estimating genetic
values of crossbreds could be to consider the BBR to
estimate the genetic values, as suggested by VanRaden
et al. (2020). Further studies will need to ensure that
the accuracy of the genetic values is not affected by the

imputation of the data, even if some studies explained
that imputations with high error rates and bias from
wrongly inferred genotypes will not propagate in ac-
curacy of subsequent genomic predictions (Wu et al.,
2016).

CONCLUSIONS

This study aimed to evaluate the IA for the US cross-
bred dairy cattle population (HO×JE and JE×HO).
The results may provide information to assist future
studies involving genomic data in crossbred US dairy
cattle. The highest IA for crossbreds were shown for
the S+D+GS scenario and confirmed the importance
of adding relative individuals of crossbred animals in
the reference population for imputation strategy. Ad-
ditional research should be performed on other types of
crosses that are also found in the United States, with a
focus on ensuring that the influence of imputation on
the genetic evaluation of these crossbreds is kept to a
minimum.

ACKNOWLEDGMENTS

The authors thank the Council on Dairy Cattle
Breeding (CDCB, Bowie, MD) for providing the data
in this study. The authors have not stated any conflicts
of interest.

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ORCIDS

Vanille Déru ● https://orcid.org/0000-0003-1722-5698

Francesco Tiezzi ◉ https://orcid.org/0000-0002-4358-9236

Paul M. VanRaden ◉ https://orcid.org/0000-0002-9123-7278

Emmanuel A. Lozada-Soto ◉ https://orcid.org/0000-0001-8381-7988

Sajjad Toghiani ◉ https://orcid.org/0000-0002-5090-728X

Christian Maltecca ◉ https://orcid.org/0000-0002-9996-4680