Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Genetics

Open Access

Strategies for genotype imputation in composite beef cattle

  • Tatiane C. S. Chud1,
  • Ricardo V. Ventura3, 4,
  • Flavio S. Schenkel4,
  • Roberto Carvalheiro2,
  • Marcos E. Buzanskas1,
  • Jaqueline O. Rosa1,
  • Maurício de Alvarenga Mudadu5,
  • Marcos Vinicius G. B. da Silva6,
  • Fabiana B. Mokry7,
  • Cintia R. Marcondes5,
  • Luciana C. A. Regitano5 and
  • Danísio P. Munari1Email author
BMC Genetics201516:99

https://doi.org/10.1186/s12863-015-0251-7

Received: 9 March 2015

Accepted: 9 July 2015

Published: 7 August 2015

Abstract

Background

Genotype imputation has been used to increase genomic information, allow more animals in genome-wide analyses, and reduce genotyping costs. In Brazilian beef cattle production, many animals are resulting from crossbreeding and such an event may alter linkage disequilibrium patterns. Thus, the challenge is to obtain accurately imputed genotypes in crossbred animals. The objective of this study was to evaluate the best fitting and most accurate imputation strategy on the MA genetic group (the progeny of a Charolais sire mated with crossbred Canchim X Zebu cows) and Canchim cattle. The data set contained 400 animals (born between 1999 and 2005) genotyped with the Illumina BovineHD panel. Imputation accuracy of genotypes from the Illumina-Bovine3K (3K), Illumina-BovineLD (6K), GeneSeek-Genomic-Profiler (GGP) BeefLD (GGP9K), GGP-IndicusLD (GGP20Ki), Illumina-BovineSNP50 (50K), GGP-IndicusHD (GGP75Ki), and GGP-BeefHD (GGP80K) to Illumina-BovineHD (HD) SNP panels were investigated. Seven scenarios for reference and target populations were tested; the animals were grouped according with birth year (S1), genetic groups (S2 and S3), genetic groups and birth year (S4 and S5), gender (S6), and gender and birth year (S7). Analyses were performed using FImpute and BEAGLE software and computation run-time was recorded. Genotype imputation accuracy was measured by concordance rate (CR) and allelic R square (R2).

Results

The highest imputation accuracy scenario consisted of a reference population with males and females and a target population with young females. Among the SNP panels in the tested scenarios, from the 50K, GGP75Ki and GGP80K were the most adequate to impute to HD in Canchim cattle. FImpute reduced computation run-time to impute genotypes from 20 to 100 times when compared to BEAGLE.

Conclusion

The genotyping panels possessing at least 50 thousands markers are suitable for genotype imputation to HD with acceptable accuracy. The FImpute algorithm demonstrated a higher efficiency of imputed markers, especially in lower density panels. These considerations may assist to increase genotypic information, reduce genotyping costs, and aid in genomic selection evaluations in crossbred animals.

Keywords

Canchim breedCrossbred cattleGenomic dataLow-density panelSingle nucleotide polymorphism

Background

The recent implementation of genomic selection in cattle breeding programs has allowed the rate of genetic progress to increase, especially in the dairy industry [1]. Selection based on genetic markers requires a large number of genotyped individuals and thousands of single nucleotide polymorphisms (SNP) scattered throughout the genome [2]. The improvement in accuracy of genomic selection in beef cattle, which often includes data from different breeds and crossbred animals, depends on conservation of linkage disequilibrium, consistency of the linkage phase between QTL (quantitative trait loci) and genetic markers across breeds, and similarity of QTL effects between breeds [3, 4].

In some dairy breeds, animals have been genotyped with 50,000 SNPs (50K). However, the 50K panel generally does not increase genomic selection accuracy in combined data from different breeds [1, 5]. According to de Roos et al. [6], more than 300,000 informative SNPs are required to detect conserved linkage disequilibrium and allow multi-breed genomic selection. High-density panels have higher coverage of SNPs in smaller genomic distances, greater linkage disequilibrium and conserved linkage disequilibrium across breeds, and are better for genomic selection and genome-wide association studies in beef cattle and crossbred animals [3, 7]. However, genotyping with high-density panels remains costly and can limit the number of animals used in genomic studies. An alternative that reduces these costs is genotype imputation [8, 9].

Genotype imputation is a method that allows for inferring the missing marker genotypes from individuals genotyped with low and medium density (LD) panels by using information from a reference population genotyped with high-density panels [10, 11]. This makes it possible to increase the genomic information and predict missing genotypes [7, 12], reduce genotyping costs and intensify genomic selection [13, 14], and combine data from different breeds [11, 15].

Imputation methods may be based on family information (using pedigree), thus using Mendelian segregation rules and linkage to predict genotypes, and/or on population-based information; wherein genotypes are predicted by means of linkage disequilibrium observed between markers in the reference population [11]. Imputation accuracy is influenced by several factors such as population structure, reference population size, the number of SNPs in the LD panel, marker frequency, relatedness between the reference and the target populations, and the imputation tools [10, 16, 17].

In Brazil, crossbreeding schemes have been used to develop composite breeds such as the Canchim, originating from alternate crosses between Charolais (Bos taurus taurus) and Zebu breeds (Bos taurus indicus) [18]. Generally, the final genetic composition of Canchim animals is 62.5 % Charolais and 37.5 % Zebu; however, different proportions of Charolais/Zebu genes may be present in Canchim animals due to the various mating schemes which have been used to expand the genetic base for this breed [19]. One such scheme produces the “MA” genetic group, which is the progeny of a Charolais sire mated with crossbred Canchim X Zebu cows. The expected proportion of genes for MA is approximately 65.6 % Charolais and 34.4 % Zebu.

In Brazilian beef cattle production, many animals result from crossbreeding between or within Bos taurus taurus and Bos taurus indicus. Therefore, genotype imputation in crossbred animals remains a challenging task; leading to the development of methodologies and imputation strategies that can maximize accuracy in the population of interest. The objective of this study was to evaluate the best fitting and most accurate imputation strategy for the MA genetic group and Canchim cattle.

Methods

Ethics statement

This study had the approval of the Embrapa Southeast Livestock Ethical Committee of Animal Use (CEUA- CPPSE), under protocol number 02/2009.

Data set and genotype

The genomic database used in this study was provided by the Brazilian Corporation of Agricultural Research (Embrapa), located in São Carlos, SP, Brazil.

Four hundred animals, born between 1999 and 2005, were genotyped with the BovineHD BeadChip (Illumina, Inc., San Diego, CA) panel, consisting of 786,799 SNPs distributed throughout the genome. There were 205 females and 195 males in the data set. Approximately half of the animals (194) were from Embrapa, originating from 17 different bulls; 186 were Canchim and 8 were MA. The remaining animals were from farms located in São Paulo (38 Canchim and 9 MA) and Goiás (60 Canchim and 97 MA animals) states, and 1 Canchim bull and 1 Charolais bull that were parents of 7 and 14 genotyped individuals, respectively.

The pedigree relationship matrix of these animals consisted of 4,095 animals and the average inbreeding was equal to 0.02, calculated by the CFC program [20]. The Canchim animals were progeny from 40 Canchim bulls and presented an average relatedness of 0.005, while the MA animals were progeny from 10 Charolais bulls, and presented an average relatedness of 0.018.

The average linkage disequilibrium between adjacent markers in the original HD panel (Additional file 1) was calculated using the SNPPLD software [21], with r2 as the linkage disequilibrium measure [22].

Data quality control

Only the autosomal chromosomes and SNPs with known positions in the UMD_3.1 bovine assembly map [23] were considered. Genotype quality control (QC) excluded SNPs with a call rate lower than 0.90, SNPs with deviations from the Hardy-Weinberg equilibrium (p < 10−6) as calculated by means of the Fisher’s Exact Test, SNPs with proportion of expected heterozygous higher than 0.85 [24], and SNPs with minor allele frequency (MAF) lower than 0.0025. For the QC of the samples, animals with a call rate lower than 0.90 were excluded from analysis. The final file contained 396 animals and 616,565 SNPs.

Low and medium density SNP panels

The low and medium density panels were created by masking SNPs originally present in the Illumina® BovineHD SNP panel by selecting the markers in common with the Illumina® Bovine3K (3K), Illumina® BovineLD (6K), GeneSeek® Genomic Profiler (GGP) Beef LD (GGP9K), GGP Indicus LD (GGP20Ki), Illumina® BovineSNP50 version 2 (50K), GGP Indicus HD (GGP75Ki), and GGP Beef HD (GGP80K) (Table 1). The number of SNPs that remained after QC for the GGP75Ki (for indicine breeds) and GGP80K (for taurine breeds) may reflect the genetic composition of Canchim, because as previously mentioned, the contribution of the Charolais breed (taurine) is higher than the Zebu.
Table 1

Number of SNPs in common between LDa panel and the HDb panel

LD Panel

Label

SNPs in original LD panel

SNPs in common after QCc

Illumina® Bovine 3K

3K

2,900

2,341

Illumina® Bovine LD

6K

6,909

6,280

GGP Beef LD

GGP9K

8,762

7,548

GGP Indicus LD

GGP20Ki

19,721

14,305

Illumina® BovineSNP50

50K

54,609

38,802

GGP Indicus HD

GGP75Ki

74,085

50,038

GGP Beef HD

GGP80K

76,992

67,143

aLD: low-density, bHD: high-density panel, cQC: quality Control

Genotype imputation

According to the possible situations, seven scenarios for reference and target populations were tested in order to identify the scenario that fit our data set and, as an extension, for composite beef cattle breeds (Table 2). Briefly, animals were grouped in scenarios considering birth year (S1), genetic groups (S2 and S3), genetic groups and birth year (S4 and S5), gender (S6), and gender and birth year (S7).
Table 2

Description of imputation scenarios and number of animals in referencea and targetb population

Scenarios

Description

Number of animals

  

Charolais

Canchim

MA

Total

S1

Animals born prior to 2005a

1

184

68

253

 

Animals born in 2005b

0

99

44

143

S2

All Canchim animalsa

0

283

0

283

 

All MA animalsb

0

0

112

112

S3

All MA animalsa

0

0

112

112

 

All Canchim animalsb

0

283

0

283

S4

All Canchim + MA animals born prior to 2005a

0

283

68

351

 

MA animals were born in 2005b

0

0

44

44

S5

All MA + Canchim animals born prior to 2005a

0

184

112

296

 

Canchim animals were born in 2005b

0

99

0

99

S6

All malesa

1

128

63

192

 

All femalesb

0

155

49

204

S7

All Males + Females born prior to 2005a

1

228

86

315

 

Females born in 2005b

0

55

26

81

aReference population; bTarget Population

The population genotype imputation was implemented using the FImpute v2.2 [25] and BEAGLE v3.3.2 software [26]. We used population-based imputation for both programs. The imputation accuracy was calculated by means of two criteria:

i. Concordance rate (CR) - The imputed markers were compared with the actual markers present in the original HD panel, and thus the proportion of genotypes that were imputed correctly or erroneously was calculated. The concordance rate represents the proportion of correctly imputed genotypes.

ii. Allelic r-squared correlation (allelic R2) - The allelic R2 is determined by the square of the correlation between the allele dosage of the most likely imputed genotype and the allele dosage of the true genotype [26].

The effect of genetic relatedness between the validation and reference animals (Table 3) on imputation accuracy was assessed by regressing the concordance rate on the maximum genomic relationship between each animal in the validation set and all the animals in the reference set [27]. The average genomic relationship (G) was calculated according to VanRaden [28]:
Table 3

Genomic relationship statistics between reference population and target population

Scenariosa

Genomic Relationship

Minimum

Mean

Maximum

S1

0.023

0.198

0.390

S2

0.010

0.050

0.220

S3

0.003

0.040

0.225

S4

0.028

0.193

0.330

S5

0.050

0.198

0.390

S6

0.090

0.210

0.409

S7

0.108

0.228

0.390

aAs described in the section “Genotype imputation” of “Methods”

$$ G=\frac{MM\mathit{\hbox{'}}}{{\displaystyle \sum 2{p}_i\left(1-{p}_i\right)}} $$
(1)

in which M is the incidence matrix of markers whose elements in the i th column are 0-2p i , 1-2p i, and 2-2p i for genotypes AA, AB and BB, respectively; M’ is the transpose of the incidence matrix; and p i is the frequency of allele B in the i th marker.

Results and discussion

Imputation accuracy

When the Flmpute software was used, the overall average imputation accuracy from LD to HD by concordance rate ranged from 60 to 98 %; and by the allelic R2 measure ranged from 0.33 to 0.96 (Table 4; Fig. 1). Using the BEAGLE software, the overall average imputation accuracy ranged from 55 to 96 % by CR and from 0.25 to 0.94 by the allelic R2 (Table 4). We found that when the CR is high, the allelic R2 value approaches this rate. The allelic R2 value is smaller than the CR because this method has no relationship to MAF [7, 29, 30].
Table 4

Imputation accuracy from low-density panel to high-density panel using FImpute and BEAGLE software

Scenariosa

LD panel

FImpute

BEAGLE

CR%b

R2c

CR%b

R2c

S1

3K

75.70

0.59

66.27

0.44

6K

87.72

0.79

80.79

0.68

GGP9K

88.64

0.81

82.19

0.70

GGP20Ki

92.43

0.87

87.50

0.71

50K

95.20

0.92

92.14

0.87

GGP75Ki

96.68

0.94

95.03

0.92

GGP80K

96.96

0.95

95.26

0.92

S2

3K

62.86

0.37

59.73

0.33

6K

76.17

0.58

72.23

0.58

GGP9K

77.54

0.61

73.78

0.55

GGP20Ki

83.61

0.71

79.75

0.65

50K

89.55

0.82

86.66

0.77

GGP75Ki

92.48

0.87

90.85

0.84

GGP80K

93.24

0.88

91.51

0.85

S3

3K

60.21

0.33

54.83

0.25

6K

71.46

0.51

63.00

0.38

GGP9K

72.93

0.54

64.15

0.40

GGP20Ki

79.19

0.65

69.91

0.49

50K

85.92

0.76

79.95

0.66

GGP75Ki

89.54

0.82

85.79

0.76

GGP80K

90.60

0.84

87.35

0.79

S4

3K

72.75

0.53

64.55

0.40

6K

85.17

0.74

79.32

0.65

GGP9K

86.12

0.76

80.85

0.67

GGP20Ki

90.60

0.84

86.55

0.77

50K

94.12

0.90

91.24

0.85

GGP75Ki

95.94

0.93

94.36

0.90

GGP80K

96.28

0.93

94.53

0.91

S5

3K

77.74

0.62

68.57

0.47

6K

89.84

0.83

83.86

0.73

GGP9K

90.67

0.84

85.23

0.75

GGP20Ki

94.15

0.94

90.23

0.84

50K

96.36

0.90

93.90

0.90

GGP75Ki

97.55

0.96

96.10

0.94

GGP80K

97.74

0.96

96.30

0.94

S6

3K

76.52

0.60

65.80

0.43

6K

88.71

0.81

80.35

0.67

GGP9K

89.56

0.82

81.71

0.70

GGP20Ki

93.13

0.88

87.33

0.80

50K

95.60

0.93

92.23

0.87

GGP75Ki

96.98

0.95

95.25

0.92

GGP80K

97.19

0.95

95.40

0.92

S7

3K

78.69

0.64

69.06

0.48

6K

89.98

0.83

84.16

0.73

GGP9K

90.76

0.85

85.42

0.76

GGP20Ki

94.06

0.90

90.20

0.84

50K

96.27

0.94

93.82

0.90

GGP75Ki

97.47

0.96

96.04

0.93

GGP80K

97.66

0.96

96.20

0.94

aAs described in the section “Genotype imputation” of “Methods,bCR = Concordance Rate, cR2: Allelic R square

Fig. 1

Genotype concordance rate using FImpute (a) and BEAGLE (b) software for all scenarios tested. S1: animals born prior to 2005 in reference population and in target population animals born in 2005; S2: Canchim animals in reference population and MA animals in target population; S3: MA animals in reference population and Canchim animals in target population; S4: all Canchim + MA animals born prior to 2005 in reference population and MA animals were born in 2005 in target population; S5: All MA + Canchim animals born prior to 2005 in reference population and Canchim animals were born in 2005 in target population; S6: all males in reference population and all females in target population; S7: All Males + Females born prior to 2005 in reference population and Females born in 2005 in target population

Ventura et al. [13], who imputed genotypes from 6K to 50K in Canadian crossbreed beef cattle, found overall average concordance rates ranging from 54 to 97 % (using FImpute) and from 54 to 96 % (using BEAGLE). Piccoli et al. [31] found results similar to ours when studying Brazilian Braford and Hereford beef cattle and imputing from various low-density panels to HD. Carvalheiro et al. [9], working with Nelore animals, found concordance rates of 97 and 99 % when using the GGP20Ki and GGP75Ki for genotype imputation to the HD panel. The imputation accuracy is lower in beef cattle populations than in dairy cattle populations due limited number of animals in the genotyped reference population, the larger number of effective ancestors, and the lower relatedness between reference and target populations [3].

The average gain in the CR from the GGP75Ki and GGP80K SNP panels to the HD panel, when compared to the 3K to HD, was 24 % for FImpute and 29 % for BEAGLE. The rate of correctly imputed genotypes increased as the number of SNP markers present in each of the LD panels increased (Fig. 1). The prediction of haplotypes and the linkage disequilibrium between markers are affected when the genotyping panel is composed by few SNPs; thus, the density of the tested LD panel is an important factor affecting imputation accuracy [10, 32]. Studies have shown that the accuracy of the predicted genomic value decreases with increasing imputation error rates [33, 34]. Furthermore imputation errors can lead to bias in predicting breeding values [35, 36].

The most suitable LD panels were the 50K, GGP80K, and GGP75Ki, because they had the highest imputation accuracy for genotype imputation in Canchim cattle. Although the GGP75Ki and GGP80K panels have been developed for Bos taurus indicus and Bos taurus taurus, respectively, and they have different markers, no differences in the average imputation accuracy were observed. As the Canchim is a composite breed, we can suggest that this result reflects the genetic background from both Taurine and Indicine breeds.

Target population individuals presenting a higher average relatedness to the reference population had higher concordance rates. We observed a curvilinear increase (p < 0.01) in the concordance rate when the relatedness between reference and target individuals increased (Fig. 2). The imputation accuracy from lower-density panels (3K, 6K, GGP9K, GGP20Ki) to HD was affected by the low relatedness between reference individuals and target individuals; while the imputation accuracy from higher-density panels (50K, GGP75Ki, GGP80K) to HD had better results. Zhang and Druet [37] and Carvalheiro et al. [9] obtained results similar to ours and found better imputation accuracy from lower-density panels to HD in related individuals.
Fig. 2

Average relationship between reference and target population. Figure 2 shows average relationship between reference and target population considering scenario S1 (animals grouped considering birth year) for genotype imputation from panels 3K (a), 6K (b), GGP9K (c), GGP20Ki (d), 50K (e), GGP 75Ki (f), and GGP80K (g) to High Density (HD) panel. Regression equation was significant (p < 0.01) for all panels

Because there is a different coverage of SNPs per chromosome, the panels showed different imputation errors per chromosome (Fig. 3). Chromosome 27 showed the lowest imputation accuracy from 50K to GGP80K panels to HD, while in the GGP75Ki panel, chromosome 13 had the lowest imputation accuracy. In the 50K and GGP80K panels, chromosome 13 had a greater number of SNPs (1330 SNPs and 2202 SNPs, respectively) compared to the GGP75Ki panel (1273 SNPs), providing further information for haplotype inferences. In addition, chromosome 13 showed lower average linkage disequilibrium between adjacent markers for the HD panel (S1 Table). Imputation differences may occur due to the difficulties in correctly imputing the initial and end regions of chromosomes [31], consequently shorter chromosomes presented less accurately imputed alleles. Pausch et al. [17], imputing from 50K to 777K SNP panel in Fleckvieh cattle, found higher and lower accuracies on BTA1 and on BTA25, respectively. Moreover, low imputation accuracies on chromosomes can be due to mapping errors [38].
Fig. 3

Genotype concordance rate by chromosome using FImpute and BEAGLE software. Considering individuals grouped by birth year (S1) from 50K SNP to HD (a), GGP 75Ki SNP to HD (b), and GGP80K SNP to (c)

Scenarios of reference and target populations for genotype imputation

In practical terms and for future applications, scenario S1 (Table 2) was suggested as the most appropriate for genomic studies in Canchim cattle, because the reference population includes older animals, while younger animals are included in the target population. The concordance rate from 50K, GGP75Ki and GGP80K to HD (Table 4) was acceptable for scenario S1 (overall average 95.2 %). Thus, young Canchim candidates could be genotyped with low-density panels (50K, GGP75Ki and, GGP80K), thereby reducing costs and enabling the breeding programs to include genotype data for genomic selection.

The imputation accuracy when using Canchim animals as the reference population and MA animals as the target population (S2) was better than the opposite situation (S3). Using a larger number of individuals in the reference population could aid in estimating more reliable haplotypes [10, 13, 34] and therefore present better imputation accuracy. Another important issue was that scenarios S2 and S3 had the lowest average relatedness (0.05 and 0.04 respectively) between reference and target populations (Table 3). Thus, in order to increase imputation accuracy, it is important to maintain the relatedness between reference and target populations [3].

Scenario S5 presented the highest CR. The possible reasons for this result were that S5 presents a balanced number of Canchim and MA animals in the reference population, a high number of animals in the reference population, both males and females were included in the reference and target population, the reference population considered varied ages, and the mean genomic relatedness was the third highest. Despite of a very similar construction, the S4 imputation accuracy was lower than scenario S5. The main difference between both scenarios was the mean genomic relatedness between reference and target population.

The gender division (scenarios S6 and S7) showed that the imputation of female genotypes could be carried out using only the males in a reference population. Scenarios S6 and S7 had the highest mean genomic relatedness between reference and target populations, which may have contributed to the imputation accuracy. Genotype imputation using females genotyped with low-density panels could be an appropriate strategy for large-scale female selection [39].

FImpute versus BEAGLE

FImpute demonstrated better imputation performance, especially for low-density panels (3K, 6K, GGP9K, and GGP20Ki) (Fig. 4). Moreover, low gains in imputation accuracy for 50K, GGP75Ki and GGP80K were observed. The FImpute algorithm reduces imputation error because it uses overlapping windows to identify long identity-by-descent segments, which facilitates the identification of haplotypes in panels with few markers. The BEAGLE software was developed for human populations and requires more complex algorithms due to population structure, as well as greater computational demand for haplotype construction. In our study, FImpute software reduced run-time from 20 to 100 times when compared to BEAGLE software. The issue of computational demand is very important due to the increasing number of animals being genotyped. Ventura et al. [13] reported run-time reductions of 13 to 52 times for genotype imputation when FImpute was compared to BEAGLE. Although imputation has great advantages, large-scale computational resources are required and imputation accuracy must be evaluated.
Fig. 4

Concordance Rate (a) and Allelic R-square (b) using FImpute and BEAGLE software. Considering the scenario S1 (individuals grouped by birth year)

Conclusion

Low-density panels possessing at least 50 thousands markers (50K, GGP80K, and GGP75Ki) are suitable for genotype imputation to HD with acceptable accuracy. Canchim and MA animals from both sexes should be considered in the reference population. The scenario with the MA and Canchim animals born prior to 2005 in the reference population and the young Canchim animals in the target population was the best fitting to our data; however, it would be more practical to genotype young males and females with low-density panels (50K, GGP75Ki, and GGP80K) as the target population, and maintain older animals in the reference population (S1). The FImpute algorithm demonstrated higher efficiency of imputed markers (best accuracy and lowest run-time), especially in lower density panels (3K, 6K, GGP9K, and GGP20Ki). These considerations may assist in increasing genotypic information, decrease the run-time of analyses, reduce genotyping costs, and aid in genomic selection evaluations in Canchim cattle.

Availability of supporting data

The genomic data used in this study is available upon request from Dr. Luciana Correia de Almeida Regitano (Embrapa Livestock Southeast - Rodovia Washington Luiz, km 234, São Carlos, São Paulo, 13560–970, Brazil, Tel: 55 16 3411–5600).

Abbreviations

3K: 

Illumina-Bovine3K SNP panel

6K: 

Illumina-BovineLD SNP panel

GGP: 

GeneSeek Genomic Profiler SNP panel

GGP9K: 

GeneSeek Genomic Profiler BeefLD SNP panel

GGP20Ki: 

GeneSeek Genomic Profiler IndicusLD SNP panel

50K: 

Illumina-BovineSNP50 SNP panel

GGP75Ki: 

GeneSeek Genomic Profiler IndicusHD SNP panel

GGP80K: 

GeneSeek Genomic Profiler BeefHD SNP panel

HD: 

Illumina-BovineHD SNP panel

S1: 

Scenario 1: animals were grouped considering birth year

S2: 

Scenario 2: animals were grouped considering genetic groups

S3: 

Scenario 3: animals were grouped considering genetic groups

S4: 

Scenario 4: animals were grouped considering genetic groups and birth year

S5: 

Scenario 5: animals were grouped considering genetic groups and birth year

S6: 

Scenario 6: animals were grouped considering gender (S6)

S7: 

Scenario 7: animals were grouped considering gender and birth year

CR: 

Concordance rate

R2

Allelic R square

SNP: 

Single nucleotide polymorphisms

QTL: 

Quantitative trait loci

LD: 

Low and medium density panels

MA: 

Progeny of a Charolais sire mated with crossbred Canchim X Zebu cows

r2

Linkage disequilibrium measure

QC: 

Quality control

MAF: 

Minor allele frequency

G: 

Average genomic relationship

Declarations

Acknowledgements

The authors would like to acknowledge the Brazilian Corporation of Agricultural Research (Embrapa) and the Canchim Breed Association (ABCCAN). The authors would also like to thank the Universidade Estadual Paulista (UNESP) - Faculdade de Ciências Agrárias e Veterinárias, and the University of Guelph for providing computational support. We also thank Dr. Mehdi Sargolzaei for assistance and providing FImpute and SNPPLD software. We would like to thank São Paulo Research Foundation (FAPESP) for the fellowship received by TCSC (2012/21891-8) and MEB (2013/19335-2). JOR is a CAPES fellow and DPM, LCAR are CNPq fellows. We would like to thank CAPES/Embrapa for the doctoral fellowship received by TCSC.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Departamento de Ciências Exatas, UNESP - Univ Estadual Paulista “Júlio de Mesquita Filho”
(2)
Departamento de Zootecnia, UNESP - Univ Estadual Paulista “Júlio de Mesquita Filho”
(3)
Beef Improvement Opportunities
(4)
University of Guelph
(5)
Embrapa Southeast Livestock - Brazilian Corporation of Agricultural Research
(6)
Embrapa Dairy Cattle - Brazilian Corporation of Agricultural Research
(7)
Department of Genetics and Evolution, Federal University of São Carlos

References

  1. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Invited review: Genomic selection in dairy cattle: progress and challenges. J Dairy Sci. 2009;92:433–43.PubMedView ArticleGoogle Scholar
  2. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819–29.PubMed CentralPubMedGoogle Scholar
  3. Hozé C, Fouilloux M-N, Venot E, Guillaume F, Dassonneville R, Fritz S, et al. High-density marker imputation accuracy in sixteen French cattle breeds. Genet Sel Evol. 2013;45:33.Google Scholar
  4. Moghaddar N, Swan AA, Van der Werf J. Comparing genomic prediction accuracy from purebred, crossbred and combined purebred and crossbred reference populations in sheep. Genet Sel Evol. 2014;46:58.PubMed CentralPubMedView ArticleGoogle Scholar
  5. Kachman SD, Spanger ML, Bennett GL, Hanford KJ, Kuehn LA, Snelling WM, et al. Comparison of molecular breeding values based on within and across-breed training in beef cattle. Genet Sel Evol. 2013;45:30.Google Scholar
  6. De Roos APW, Hayes BJ, Spelman RJ, Goddard ME. Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics. 2008;179:1503–12.PubMed CentralPubMedView ArticleGoogle Scholar
  7. Ma P, Brondum RF, Zhang Q, Lund MS, Su G. Comparison of different methods for imputing genome-wide marker genotypes in Swedish and Finnish Red Cattle. J Dairy Sci. 2013;96:4666–77.PubMedView ArticleGoogle Scholar
  8. Johnston J, Kistemaker G. Success rate of imputation using different imputation approaches. In : Canadian Dairy Network. 2011. https://www.cdn.ca/Articles/GEBAPR2011/Success%20rate%20of%20imputation%20-%20Jarmila%20Johnston.pdf. Accessed 20 Dec 2014.
  9. Carvalheiro R, Boison SA, Neves HHR, Sargolzaei M, Schenkel FS, Utsunomiya YT, et al. Accuracy of genotype imputation in Nelore cattle. Genet Sel Evol. 2014;46:69.Google Scholar
  10. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. Plos Genet. 2009; doi:10.1186/gb-2009-10-4-r42
  11. Hayes BJ, Bowman PJ, Daetwyler HD, Kijas JW, Van der Werf JHJ. Accuracy of genotype imputation in sheep breeds. Anim Genet. 2012;43:72–80.PubMedView ArticleGoogle Scholar
  12. Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat Rev Genet. 2010;11:499–511.PubMedView ArticleGoogle Scholar
  13. Ventura RV, Lu D, Schenkel FS, Wang Z, Li C, Miller SP. Impact of reference population on accuracy of imputation from 6K to 50K single nucleotide polymorphism chips in purebred and crossbreed beef cattle. J Anim Sci. 2014;92:1433–44.PubMedView ArticleGoogle Scholar
  14. Boichard D, Chung H, Dassonneville R, David X, Eggen A, Fritz S, et al. Design of a bovine low-density SNP array optimized for imputation. Plos One. 2012; doi: 10.1371/journal.pone.0034130.
  15. Larmer SG, Sargolzaei M, Schenkel FS. Extent of linkage disequilibrium, consistency of gametic phase, and imputation accuracy within and across Canadian dairy breeds. J Dairy Sci. 2014;97:3128–41.PubMedView ArticleGoogle Scholar
  16. Dassonneville R, Brondum RF, Druet T, Fritz S, Guillaume F, Guldbrandtsen B, et al. Effect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations. J Dairy Sci. 2011;94:3679–86.Google Scholar
  17. Pausch H, Aigner B, Emmerling R, Edel C, Götz K-U, Fries R. Imputation of high-density genotypes in the Fleckvieh cattle population. Genet Sel Evol. 2013;45:3.PubMed CentralPubMedView ArticleGoogle Scholar
  18. Alencar MM. Bovino - Raça Canchim: Origem E Desenvolvimento. Embrapa-DMU: Brasília; 1988.Google Scholar
  19. Andrade PC, Grossi DA, Paz CCP, Alencar MM, Regitano LCA, Munari DP. Association of an insulin-like growth factor 1 gene microsatellite with phenotypic variation and estimated breeding values of growth traits in Canchim cattle. Anim Genet. 2008;39:480–5.PubMedView ArticleGoogle Scholar
  20. Sargolzaei M, Iwaisaki H, Colleau J. CFC: A Tool for Monitoring Genetic Diversity. In: 8th World Congress of Genetics Appied to Livestock Production. 2006. p. 27–8.Google Scholar
  21. Sargolzaei M, Schenkel FS, Jansen GB, Schaeffer LR. Extent of linkage disequilibrium in Holstein cattle in North America. J Dairy Sci. 2008;91:2106–017.PubMedView ArticleGoogle Scholar
  22. Hill WG, Robertson A. Linkage disequilibrium in finite populations. Theor Appl Genet. 1968;38:226–31.PubMedView ArticleGoogle Scholar
  23. Zimin A V, Delcher AL, Florea L, Kelley DR, Schatz MC, Puiu D, Hanrahan F, Pertea G, Van Tassell CP, Sonstegard TS, Marçais G, Roberts M, Subramanian P, Yorke JA, Salzberg SL. A whole-genome assembly of the domestic cow, Bos taurus. Genome Biol. 2009; doi: 10.1186/gb-2009-10-4-r42.
  24. Turner S, Armstrong LL, Bradford Y, Carlson CS, Dana C, Crenshaw AT, et al. Quality control procedures for genome wide association studies. Curr Proc Hum Genet. 2011;68:1–24.Google Scholar
  25. Sargolzaei M, Chesnais JP, Schenkel FS. A new approach for efficient genotype imputation using information from relatives. BMC Genomics. 2014;15:478.PubMed CentralPubMedView ArticleGoogle Scholar
  26. Browning BL, Browning SR. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet. 2009;84:210–23.PubMed CentralPubMedView ArticleGoogle Scholar
  27. Daetwyler HD, Calus MPL, Pong-Wong R, de Los CG, Hickey JM. Genomic prediction in animals and plants: Simulation of data, validation, reporting, and benchmarking. Genetics. 2013;193:347–65.PubMed CentralPubMedView ArticleGoogle Scholar
  28. VanRaden PM. Efficient methods to compute genomic predictions. show [?A3B2 show $6#?] J Dairy Sci. 2008;91:4414–23.Google Scholar
  29. Hickey JM, Kinghorn BP, Tier B, Van der Werf JHJ, Cleveland MA. A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation. Genet Sel Evol. 2012;44:9.PubMed CentralPubMedView ArticleGoogle Scholar
  30. Calus MPL, Bouwman AC, Hickey JM, Veerkamp RF, Mulder HA. Evaluation of measures of correctness of genotype imputation in the context of genomic prediction: a review of livestock applications. Animal. 2014;8:1743–53.Google Scholar
  31. Piccoli ML, Braccini J, Cardoso FF, Sargolzaei M, Larmer SG, Schenkel FS. Accuracy of genome-wide imputation in Braford and Hereford beef cattle. BMC Genet. 2014;15:157.PubMed CentralPubMedView ArticleGoogle Scholar
  32. Hickey JM, Crossa J, Babu R, de los Campos G. Factors Affecting the Accuracy of Genotype Imputation in Populations from Several Maize Breeding Programs. Crop Sci. 2012;52:654–63.View ArticleGoogle Scholar
  33. Mulder HA, Calus MPL, Druet T, Schrooten C. Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle. J Dairy Sci. 2012;95:876–89.PubMedView ArticleGoogle Scholar
  34. Khatkar MS, Moser G, Hayes BJ, Raadsma HW. Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle. BMC Genomics. 2012;13:538.PubMed CentralPubMedView ArticleGoogle Scholar
  35. Ertl J, Edel C, Emmerling R, Pausch H, Fries R, Götz K-U. On the limited increase in validation reliability using high-density genotypes in genomic best linear unbiased prediction: observations from Fleckvieh cattle. J Dairy Sci. 2014;97:487–96.PubMedView ArticleGoogle Scholar
  36. Pimentel ECG, Edel C, Emmerling R, Götz K-U. Imputation errors bias genomic predictions. J Dairy Sci. 2015;98:4131–8.PubMedView ArticleGoogle Scholar
  37. Zhang Z, Druet T. Marker imputation with low-density marker panels in Dutch Holstein cattle. J Dairy Sci. 2010;93:5487–94.PubMedView ArticleGoogle Scholar
  38. Erbe M, Hayes BJ, Matukumalli LK, Goswami S, Bowman PJ, Reich CM, et al. Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. J Dairy Sci. 2012;95:4114–29.Google Scholar
  39. VanRaden PM, Null DJ, Sargolzaei M, Wiggans GR, Tooker ME, Cole JB, et al. Genomic imputation and evaluation using high-density Holstein genotypes. J Dairy Sci. 2013;96:668–78.Google Scholar

Copyright

© Chud et al. 2015

Advertisement