Skip to content

Advertisement

BMC Genomics

  • Research article
  • Open Access

Identification of single nucleotide polymorphisms in bovine CARD15 and their associations with health and production traits in Canadian Holsteins

  • Sameer D Pant1,
  • Flavio S Schenkel1,
  • Ivan Leyva-Baca1,
  • Bhawani S Sharma1 and
  • Niel A Karrow1Email author
BMC Genomics20078:421

https://doi.org/10.1186/1471-2164-8-421

©  Pant et al; licensee BioMed Central Ltd. 2007

Received: 10 April 2007

Accepted: 15 November 2007

Published: 15 November 2007

Abstract

Background

Toll-like receptor-2 (TLR2) and Caspase Recruitment Domain 15 (CARD15) are important pattern recognition receptors that play a role in the initiation of the inflammatory and subsequent immune response. They have been previously identified as susceptibility loci for inflammatory bowel diseases in humans and are, therefore, suitable candidate genes for inflammatory disease resistance in cattle. The objective of this study was to identify single nucleotide polymorphisms (SNPs) in the bovine TLR2 and CARD15 and evaluate the association of these SNPs with health and production traits in a population of Canadian Holstein bulls.

Results

A selective DNA pool was constructed based on the estimated breeding values (EBVs) for SCS. Gene segments were amplified from this pool in PCR reactions and the amplicons sequenced to reveal polymorphisms. A total of four SNPs, including one in intron 10 (c.2886-14A>G) and three in the exon 12 (c.3020A>T, c.4500A>C and c.4950C>T) were identified in CARD15; none were identified in TLR2. Canadian Holstein bulls (n = 338) were genotyped and haplotypes were reconstructed. Two SNPs, c.3020A>T and c.4500A>C, were associated with EBVs for health and production traits. The SNP, c.3020A>T, for example, was associated with SCS EBVs (p = 0.0097) with an allele substitution effect of 0.07 score. When compared to the most frequent haplotype Hap12(AC), Hap22(TC) was associated with increased milk (p < 0.0001) and protein (p = 0.0007) yield EBVs, and hap21(TA) was significantly associated with increased SCS EBV(p = 0.0120). All significant comparison-wise associations retained significance at 8% experimental-wise level by permutation test.

Conclusion

This study indicates that SNP c.3020A>T might play a role in the host response against mastitis and further detailed studies are needed to understand its functional mechanisms.

Keywords

MastitisMilk YieldProtein YieldProduction TraitSomatic Cell Score

Background

Inflammatory diseases such as mastitis, inflammatory bowel disease, metritis and laminitis are economically important diseases for the dairy industry. Mastitis is the most common amongst them, alone accounting for losses in excess of $2 billion to the US dairy industry [1]. A number of therapeutic, prophylactic and management strategies have been proposed to minimize this complex disease. However, a widely accepted complimentary strategy is based on improving the host genetics through selective breeding. Since it is difficult to use a direct index to measure the mastitis phenotype, milk somatic cell score (SCS) is often used as an indirect index to select animals for breeding [2]. Milk SCS is a log2 score of the milk somatic cell count and has been genetically correlated with clinical mastitis (r = 0.7) [3]. It has been demonstrated in sheep that intramammary infections occur less frequently after one generation of breeding for low SCS[4]. Breeding strategies in dairy cattle are similarly based on selection for low estimated breeding values (EBVs) for SCS [5].

The etiology of mammary inflammatory disorders is diverse and may include infections caused by bacteria, parasites, viruses and fungi. In order for an effective host immune response to occur against a wide variety of pathogens, the host must possess receptors that recognize conserved molecular patterns associated with different classes of pathogens. These are referred to as pathogen associated molecular patterns (PAMPs). Receptors that recognize PAMPs are collectively referred to as pattern recognition molecules (PRMs) [6]. The PRMs are expressed widely by different cells including monocytes, granulocytes, dendritic cells and epithelial cells [7]. PAMP recognition leads to the secretion of cytokines and chemokines by the epithelial cells that recruit effector cells (neutrophils and monocytes) of the immune system to the site of infection where they contribute to the host inflammatory immune response and subsequent acquired immune response.

Phagosomal toll-like receptor-2 (TLR2) is involved in recognizing PAMPs associated with Gram-positive bacteria. Murine studies for example, have demonstrated that TLR2 is in involved in the early recruitment of neutrophils in response to intraperitoneal challenge with either Staphylococcus aureus, or peptidoglycan (PGN) from S. aureus [8]. Ruminant studies have demonstrated that TLR2 gene (designated as 'TLR2') expression occurs in dermal and gut-associated tissues [9], and is highly induced during mastitis caused by S. aureus [10]. Bovine TLR2 was recently radiation hybrid mapped to Bos taurus autosome (Bta) 17 [11].

Caspase Recruitment Domain 15 (CARD15), also known as Nucleotide Oligomerization Domain 2 (NOD2), is a cytosolic protein capable of initiating inflammation following PAMP recognition. The gene encoding bovine CARD15 (designated as 'CARD15') is located on Bta18. It was previously categorized as a member of the CATERPILLER family, but was reassigned to the phylogenetically conserved NLR (NACHT-LRR) protein family [12]. CARD15 shares a common tripartite domain structure with other members of this family. The tripartite domain consists of a carboxy (C)-terminal leucine rich repeat (LRR) domain; a central NACHT (NAIP CIITA HET-E and TP1) domain, and an amino (N) – terminal domain that is composed of two CARD domains. The LRR domain is involved primarily in the recognition of bacterial peptidoglycans (PGN), whereas the central NACHT domain facilitates self-oligomerization and has ATPase activity. The CARD domains are known to interact with CARD containing serine/threonine kinase Rip2 (known as RICK), via homophilic CARD-CARD interaction; this leads to the activation of NF-κB [13].

The major portion of the PGN recognition system in mammals is constituted by CARD15 along with NOD1 and TLR2 [14]. CARD15 is involved in intracellular recognition of muramyl dipeptide (MDP), the minimal bioactive structure of PGN, which is common to the cell wall of both Gram-positive and gram-negative bacteria. Therefore, CARD15 acts as a general sensor of bacterial infection [15, 16]. A recent study suggests that NOD2 might be involved in sensing of PGN motifs of S. aureus, after its phagocytosis [17]. There are several reports of interactions between CARD15 and other PRMs, especially TLR2 [18–20]. This demonstrates the importance of CARD15 in signalling events associated with recognition of different PAMPs. Polymorphisms, particularly single nucleotide polymorphisms (SNPs), within the genes coding for different receptor proteins may impair the ability of certain individuals to respond properly to infections [21]. In the case of TLR2 and CARD15, they have been identified as susceptibility loci for different inflammatory bowel diseases in humans [22–25]. It is possible that SNPs within these genes influence other inflammatory diseases such as mastitis in cattle. Therefore, understanding the genetic variation underlying PRMs might help livestock breeders to identify and select animals with enhanced resistance to mastitis for breeding programs. The purpose of this study was to identify SNPs in the bovine TLR2 and CARD15 and to evaluate the association of these SNPs with SCS and other production traits in a population of Canadian Holstein bulls.

Results

SNP Detection

Investigation of the coding exon, flanking introns and promoter sequences of TLR2 revealed no polymorphisms. Investigation of exonic, flanking intronic and promoter sequences of CARD15 revealed the presence of four SNPs, including two transitions: A ↔ G at position c.2886-14A>G and A ↔ T at position c.3020A>T, and two transversions: A ↔ C at position c.4500A>C and C ↔ T at position c.4950C>T (Figure 1). The nomenclature adopted for the SNPs was based on the convention described by the human genome variation society [26]. No SNPs were found in the promoter sequence from the set of animals used in this study. SNP c.2886-14A>G was the only polymorphism found in the flanking intronic sequence (14 base pairs upstream of exon 11). All the other SNPs were found in exon 12. SNP c.3020A>T was found in the coding sequence of exon 12 and is non-synonymous; allele 'A' producing leucine and allele 'T' producing glutamine in the peptide sequence. The SNPs were submitted to the National Centre for Biotechnology Information (RefSNP rs43710287, rs43710288, rs43710289, rs43710290) and were released in dbSNP build 126.
Figure 1
Figure 1

CARD15 structure and location of SNPs. CARD15 structure showing the exons coding the LRR domain of the protein and location of identified SNPs.

Genotypic and allelic frequencies

The genotypic and allelic frequencies are summarized in Table 1. The individual frequencies of the genotypes were in Hardy-Weinberg equilibrium for all the SNPs, as determined by Chi-square test. The calculated Chi-square values ranged from 0.06 to 1.76 and were all non-significant (p < 0.05). The linkage disequilibrium was evaluated for all pairs of SNPs using r2. The r2 values ranged from 0.001 to 0.367 and were all significant (p < 0.05) except for the pair consisting of SNPs c.4500A>C and SNPc.4950C>T.
Table 1

Genotypic and allelic frequencies of CARD15 SNPs.

 

Genotypes

Alleles

 

No.

Frequency (%)

No.

Frequency (%)

c.2886-14A>G

    

AA (A)

228

67.5

556

82.2

AG

100

29.5

  

GG (G)

10

3.0

120

17.8

c.3020A>T

    

AA (A)

93

27.5

365

54.0

AT

179

53.0

  

TT (T)

66

19.5

311

46.0

c.4500A>C

    

AA (A)

42

12.4

254

37.6

AC

170

50.3

  

CC (C)

126

37.3

422

62.4

c.4950C>T

    

TT (T)

250

74.0

580

85.8

TC

80

23.6

  

CC (C)

8

2.4

96

14.2

SNP association analyses

Statistical analyses revealed associations between CARD15 SNP c.3020A>T and EBVs for milk yield, protein yield, udder depth, and SCS and between SNP c.4500A>C and milk yield, fat yield and protein yield. Amongst all the possible regression models for SCS, a single SNP model including SNP c.3020A>T was found to be the best model and showed significant association of this SNP with SCS (p = 0.0097). The average allele substitution effect of this SNP for SCS was 0.068, with allele 'T' increasing SCS over allele 'A' (21% of the SD for SCS EBV). All significant associations were retained at 8% experimental-wise significance level by permutation test. A complete description of the average allele substitution effects is presented in Table 2.
Table 2

Average allele substitution effect of SNPs c.3020A>T and c.4500A>C on EBVs for milk yield (Milk), fat yield (Fat), protein yield (Prot), udder depth (UD) and somatic cell score (SCS).

 

Trait

 

Milk (Kg)

Fat (Kg)

Prot (Kg)

UD

SCS

SNP

Allele substitution effects ± SE (comparison-wise P level)

c.3020A>T

359.77 ± 107.70 (<0.0001++)

-

9.90 ± 3.20 (0.0003++)

-1.10 ± 0.41 (0.0087+)

0.07 ± 0.03 (0.0097+)

c.4500A>C

348.44 ± 117.80 (<0.0001++)

8.29 ± 2.40 (0.0010++)

11.31 ± 3.51 (<0.0001++)

-

-

Power 1

0.93

0.74

0.93

0.56

0.54

++Significant effect by permutation test at an experimental-wise significance level of 5%

+Significant effect by permutation test at an experimental-wise significance level of 8%

1 Power of the analysis considering an experimental-wise significance level of either 5% (for Milk, Fat and Prot) or 8% (for UD and SCS).

Haplotype analysis

Two CARD15 SNPs (c.3020A>T and c.4500A>C) were used for haplotype reconstruction. The estimated haplotype frequencies were 42.6%, 26.1%, 19.8% and 11.4% for Hap12(AC), Hap21(TA), Hap22(TC) and Hap11(AA), respectively. The linear effects of each of the haplotypes were estimated by treating the effect of the most frequent haplotype (Hap12) as a control and contrasting the effects of the other haplotypes (Hap21, Hap22 and Hap11) against it (Table 3). Analysis revealed statistically significant differences between Hap21 and Hap12 for SCS and fat yield, and between Hap22 and Hap12 for milk yield and protein yield at different levels of significance. All significant associations were retained at 8% experimental-wise level by permutation test.
Table 3

Linear effects of CARD15 on EBVs for milk yield (Milk), fat yield (Fat), protein yield (Prot), udder depth (UD) and somatic cell score (SCS), when comparing haplotypes Hap11, Hap22, and Hap21, against Hap12.

 

Trait

 

Milk (Kg)

Fat (Kg)

Prot (Kg)

UD

SCS

Haplotypes

Haplotype effects ± SE (P level)

Hap11(AA)

-325.2 ± 175.30

-8.23 ± 5.60

-12.68 ± 5.20

-0.91 ± 1.00

0.02 ± 0.06

 

(0.06)

(0.14)

(0.01)

(0.36)

(0.74)

Hap22(TC)

523.5 ± 129.70

6.89 ± 4.15

13.15 ± 3.85

-1.71 ± 0.74

0.05 ± 0.05

 

(<0.0001++)

(0.07)

(0.0007++)

(0.02)

(0.26)

Hap21(TA)

-57.5 ± 81.62

-7.27 ± 2.61

-3.10 ± 2.42

-0.93 ± 0.46

0.07 ± 0.03

 

(0.48)

(0.0057++)

(0.20)

(0.04)

(0.012+)

Power 1

0.91

0.69

0.90

0.49

0.38

++Significant effect by permutation test at an experimental-wise significance level of 5%

+Significant effect by permutation test at an experimental-wise significance level of 8%

1Power of the analysis considering an experimental-wise significance level of either 5% (for Milk, Fat and Prot) or 8% (for UD and SCS).

Discussion

TLR2 and CARD15 play a role in the initiation of inflammatory and immune responses to bacterial infections. A number of studies have reported that SNPs in PRMs of different species play an important role in contributing towards disease susceptibility. Therefore, the main objective of this study was to identify SNPs in the TLR2 and CARD15 and to estimate the extent of associations between these SNPs and SCS, a trait directly related to udder inflammation. In the current experiment, the DNA pool used for the purpose of SNP detection was comprised of DNA samples from 40 animals with extreme EBVs for SCS. The exons, flanking intronic sequences, and promoter region were targeted for SNP detection. Therefore it is unlikely, that all SNPs in the population were identified.

Although no SNPs were identified in TLR2, four SNPs were identified in CARD15; only one SNP was found in the coding region (c.3020A>T) of this gene. This SNP, located in exon 12, is a non-synonymous SNP coding the terminal LRR domain of CARD15 receptor. While allele 'A' at this position codes for glutamine, allele 'T' codes for leucine. The association of the T allele with increased SCS and decreased udder depth, which predisposes animals to mammary infections, indicates that changes in the composition of the LRR domain of CARD15 may contribute to disease susceptibility. The terminal LRR domains, as in CARD15, are common to different PRMs and are responsible for PAMP recognition. There are several reports of polymorphisms in the human and mouse LRR coding gene segments that contribute to differential binding to several bacterial components [15, 24, 27]. In humans, CARD15 variants have been hypothesized to alter bacterial component recognition by altering the structure of LRR domain or the adjacent region [23]. It is possible that SNP c.3020A>T may compromise protein functionality by altering the conformation of the binding site in a similar fashion. This could contribute to the development of inflammatory disorders and warrants further investigation. The association between this SNP and udder depth might be an indirect result of the genetic correlation that exists between SCS and udder depth (rg= -0.26) [28].

Strong associations were also observed between the analyzed CARD15 SNPs and production traits. Such associations may be a result of linkage between these SNPs and other genes on the same chromosome having a significant effect on these production traits. Significant QTLs have been found on Bta18 for all the traits used in the analysis. While QTLs for SCS and udder composite index exist close to the location of this gene (about 230 kbs apart)[29, 30] on the chromosome, QTLs for milk yield, protein yield and fat yield were situated farther away (>1,500 kbs apart) [30–32].

Haplotype reconstruction revealed all four possible CARD15 haplotypes to be segregating in the sampled bulls. The second most frequent haplotype (Hap21) differed significantly from the most common haplotype (Hap12) with respect to its effect on SCS EBVs. In agreement with the results of the allele substitution analyses, the linear effect of Hap21 carrying the allele 'T' at SNP position c.3020A>T was significantly different from Hap12. However, we did not see any difference between the linear effects of Hap22 and Hap12 for SCS. Strong associations were observed between Hap22 and milk and protein yields (p < 0.0001 and p = 0.0007 respectively). Since, Hap22 carries alleles 'T' and 'C', both of which are associated with increased milk and protein yields, these results were in agreement with the allele substitution analysis. It is interesting to note that the most common haplotype, Hap12, carries alleles 'A' and 'C' at positions c.3020A>T and c.4500A>C respectively, and is beneficial not only for reducing SCS but also for increasing production. Thus selection for Hap12 in the population seems promising.

Conclusion

In conclusion, four SNPs in CARD15 in a sample of Canadian Holstein bulls were discovered. Statistical analyses revealed that SNP c.3020A>T was associated with EBVs for SCS and udder depth and milk and protein yields, while SNP c.4500A>C was only associated with milk, fat and protein yields. The most common haplotypes for these two SNPs in the population differed significantly for their effect on SCS. Moreover, the most common haplotype carried alleles at both loci that are favourable for reducing SCS and increasing production EBVs. This implies that these two SNP, together with other gene polymorphisms, may be potentially used for genetic selection for mastitis resistance and production. The findings of this study indicate that SNP c.3020A>T is a candidate for further detailed studies on its functional mechanisms.

Methods

Resource population

The resource population consisted of 2166 Holstein bulls selected on the basis of extreme EBV for either protein yield or SCS. A total of 338 semen samples of these bulls were selected within families on the basis of extreme EBVs for either protein yield or SCS for this study. The EBV's for SCS, udder depth, milk yield, protein yield and fat yield of these bulls were obtained from a national genetic evaluation database generated in August 2006 by the Canadian Dairy Network [33], Guelph, Ontario, Canada, and used in the association studies. Table 4 provides descriptive statistics of the EBVs of the bulls. The majority of the selected population consisted of half sib families from 20 sires, the size of which ranged from 2 to 30 bulls. Semen samples were kindly provided by Semex Alliance (Guelph, Ontario, Canada).
Table 4

Descriptive statistics of the EBV of the Holstein bulls sampled for the association study.

 

EBV

 

Milk yield (kg)

Fat yield (kg)

Protein yield (kg)

Udder depth

SCS1 (score)

N2

337

337

337

335

335

Mean

115.59

4.83

5.65

0.46

3.01

SD

935.95

29.63

27.75

5.24

0.33

Max

2173

101

63

12

4.21

Min

-2358

-81

-76

-12

2.34

1 SCS = Somatic cell score [log(2) scores]

2 Number of records. One bull had missing EBV for Milk, Fat and Protein yield; and three bulls had missing EBV for Udder depth and SCS.

DNA extraction

A slightly modified standard phenol chloroform procedure was used to extract DNA from semen samples [34]. An Eppendorf Biophotometer (Berlin, Germany) was used to assess the DNA concentration and quality on the basis of absorbance of UV light at 260 (A260) and 280 nm (A280).

Construction of DNA Pools for SNP Detection

Twenty bulls with high and twenty bulls with low EBVs for SCS were selected from the previously selected 338 animals, to create a DNA pool containing equal amounts of DNA from each bull. The descriptive statistics of the EBVs of the bulls used for DNA pooling are given in Table 5. Individual DNA samples were quantified and diluted using the PicoGreen dsDNA quantification procedure (Molecular Probes; Invitrogen, Carlsbad, CA) on a Victor 3 flourescent plate reader (Perkin Elmer, Wellesley, MA), until the concentration of all 40 DNA samples was 8 ± 1 ng/μl. Equal volumes of DNA from each of the 40 samples were aliquoted to a common tube to construct the DNA pool. This pool was used in PCR reactions to amplify the CARD15 exons and its flanking introns and a 2 kb promoter region, and the TLR2 coding exon, flanking introns and a 2 kb promoter region. The primers used for SNP discovery in TLR2 and CARD15 are shown in Table 6 and Table 7 respectively. Each PCR amplicon was sequenced in forward and reverse directions for SNP discovery. Polymorphisms were detected by scrutinizing the forward and reverse electropherograms generated from the sequencer.
Table 5

Descriptive statistics of the EBV of the Holstein bulls used for DNA pooling.

 

EBV

 

Milk yield (kg)

Fat yield (kg)

Protein yield (kg)

Udder depth

SCS1 (score)

N2

40

40

40

39

39

Mean

19.95

-1.4

2.15

0.18

3.04

SD

853.33

31.48

25.00

5.50

0.46

Max

1714

56

47

12

3.88

Min

-2246

-67

-60

-9

2.34

1SCS= Somatic cell score [log(2) scores]

2 Number of records. One bull had missing EBV for Udder depth and SCS.

Table 6

PCR primers and conditions for identification of SNPs in TLR2.

Exon

Primer name

Sequence, 5' → 3'

Tann, °C

Amplicon size, bp

TLR2 PROMOTER

    

Part 1

Forward

CCCTTGAATGTATTTGTCACTTCC

60

664

 

Reverse

TCCATATTTTTGAGAATCTGACTGA

  

Part 2

Forward

TGCAAGGATTACAGCAGATTTTC

59

579

 

Reverse

AAAATCATGTATTTACCCACAACG

  

Part 3

Forward

CGTACAAAGATGTGCCAGAGG

59

586

 

Reverse

CCAGTGTTCTTGCCTGGAGA

  

Part 4

Forward

GGGGCCTTTAATGAGACCTG

60

445

 

Reverse

GCCCTTCCTAAATTGAGATCAGA

  

Part 5

Forward

GTGCCTTTGCAATGAGGACT

60

496

 

Reverse

GCCTGAAGAGCAGATGAGGA

  

TLR2 GENE

    

Part 1

Forward

GCCATGATGTCAAACACAGTCA

58

613

 

Reverse

AGCACTGATCTCAAGCTCCTCA

  

Part 2

Forward

CAAAACACTTGGGGAAACATCT

59

611

 

Reverse

TTTTAACTCTGCCTGTGAGTGGA

  

Part 3

Forward

ACTGTACCCATGATGGAATTGG

61

643

 

Reverse

ACGGGTAAGAAGGAGGCATCT

  

Part 4

Forward

CACAGTTTAACCCAGTGCCTTC

59

633

 

Reverse

CATGAGGTTCTCCACCCAGTAG

  

Part 5

Forward

CGGACTGTGGTACATGAAGATG

60

643

 

Reverse

TCAGCATCAGTTCTTCCAATGA

  
Table 7

PCR primers and conditions for identification of CARD15 single-nucleotide polymorphisms.

Exon

Primer name

Sequence, 5' → 3'

Tann, °C

Amplicon size, bp

CARD15 PROMOTER

    

Part 1

Forward

CCCCACTTCTATCCTCTGGACT

58

640

 

Reverse

AGCAGAGGAAATCTCTCAGCAG

  

Part 2

Forward

GACTTGGCCATGCCTACTCTAA

59

601

 

Reverse

GAAACTGAGGCAGCCAGGTAG

  

Part 3

Forward

TAGACACAGAGTGCTGGCAAGA

58

625

 

Reverse

CCCGCAAAGATCATTAGGTTTA

  

Part 4

Forward

TCTTTCCTATCTGCAGCTGTCC

59

632

 

Reverse

AAGTCCCATCTCTGTCCTAGCC

  

CARD15 GENE

    

1

Forward

TGTAGACAGACGCTGGAGTTCCTCT

62

322

 

Reverse

ATGCACTGACTCCCATCTACTGCTC

  

2

Forward

GAGAAGCCCTGCCCTGACCT

63

599

 

Reverse

ACTATGACCCACATCTCCCCACAG

  

3

Forward

TGCATCTTACCATGCAGATGTTTTTC

62

392

 

Reverse

GAGACGCTGAGTTTTACGGAGGTTT

  

4 (part1)

Forward

CTTGTTAGTGGAGAGCCAGGAC

58

546

 

Reverse

AACAGCAGTGTTCGAAGAGCA

  

4 (part2)

Forward

TTCTCTTCGTCTTCCCATTTAGC

59

506

 

Reverse

GGACACCATCCAGGAGAAGAC

  

4 (part3)

Forward

CATCGAACTGTACCTGAGGAAGC

60

594

 

Reverse

AGGAACAATTTGGGCAGCAC

  

4 (part4)

Forward

TCAGACATCTCTTCCAAGATCACA

59

680

 

Reverse

ACTCCTGGCTCCCAGCATAA

  

56

Forward

GGGTTAAGCAAAAGTCTCTGTGG

59

595

 

Reverse

AATGAGACACTGTCCTTGTTTTCAG

  

7

Forward

CAGGTCTTGGGAGCAGTAAGG

59

375

 

Reverse

ACATCCTGAGCTTCCTGTTTATTG

  

8

Forward

CTCACTTGCTGGGACCTGAGT

59

285

 

Reverse

TCCCTCCTCACACTGGCTTC

  

9

Forward

AACGATTAGTCTGAAATGGAGCAG

58

300

 

Reverse

TACACACACATCAGCTTCCACAG

  

10

Forward

CCTTACACTTTGCTTGACCTGTTT

58

472

 

Reverse

ACCCCGAAAGATTGTTTTCTAGG

  

11

Forward

GAATTCATTGGGAATCTCAGACAG

59

355

 

Reverse

CAGGACTAGAGGTCTGAGCCATAA

  

12(part1)

Forward

ACAGGTTTACAAAGCAGCATCTTC

59

578

 

Reverse

ACCACTCAACCTGATGGATGAC

  

12(part2)

Forward

GGCTGGTCTCAAGTCAAGCTG

60

619

 

Reverse

GCACAAACAAAACCAAACCATC

  

12(part3)

Forward

GAACAACTCTGTATCCAAATGCAAC

59

641

 

Reverse

ACGCTGGGCTAGACTTCTCTAAAC

  

12(part4)

Forward

TCTCCTTGAAGGGAGGAGACAT

59

509

 

Reverse

CACAACGCTGTAAATCAACCATAC

  

12(part5)

Forward

GGCCTTGGTGAAATAATTCTTAGC

58

588

 

Reverse

CAGCAACATGGATGGATCTAGAAA

  

SNP Genotyping

The tetra-primer Amplification Refractory Mutation System (ARMS)-PCR procedure, as described by Ye et al. [35], was used to genotype all CARD15 SNPs. This method of genotyping is simple and economical, involving a single PCR resaction followed by gel electrophoresis. Primers were designed using the online primer design facility made accessible by Ye et al. [36]. The primer sets used for genotyping each identified SNP are shown in Table 8. The PCR reactions were performed in a total volume of 10 μl, containing 10 pmol of each of the inner primers, 1 pmol of each of the outer primers, 200 μM deoxyribonucleotide triphosphates, 2.5 mM MgCl2, 1× PCR buffer, and 0.5 units of AmpliTaq Gold DNA polymerase (Applied Biosystems, Foster City, CA). To increase the specificity of the reaction, a touchdown profile was followed. For touchdown reactions the annealing temperature was 4°C higher for the first cycle, decreasing by 1°C per cycle until the annealing temperature indicated in Table 8 was reached, then continuing at that temperature in the annealing step of the remaining cycles. The PCR profile was as follows: 95°C for 8 min, 34 cycles of 30 s at 94°C, 34 cycles of 30s at annealing temperature (including initial touchdown cycles), and 30s of extension at 72°C, ending with 5 min at 72°C. The annealing temperatures are shown in Table 8. A T-GRADIENT thermocycler (Biometra, Montreal Biotech Inc. Kirkland, Canada) was used to carry out the reactions. A 8 μl aliquot of the PCR products was mixed with 2 μl of loading buffer and subjected to horizontal agarose gel (2.5%) electrophoresis. The gels were stained with ethidium bromide for visualization and the genotypes were determined as shown in Figure 2.
Table 8

The tetra-primer Amplification Refractory Mutation System-PCR primers and conditions for genotyping CARD15 SNPs.

Position

Primer Name

Sequence, 5' → 3'

Tann, °C

Amplicon size, bp

c.2886-14A>G

Forward inner primer

Reverse inner primer

Forward outer primer

Reverse outer primer

CAAGCATCCTCAAAGTTCACCATTA TG

GTGGTTGTTAGACAGCCTAGAGAGGAAT AT

CCTGGCACTAAAATGCACGTATTTTTAT

AGTTATACGTTGGAAAACTGAGGCTCAG

60°C

405bp(outer)

300 bp (allele 'A')

150 bp (allele 'G')

c.3020A>T

Forward inner primer

Reverse inner primer

Forward outer primer

Reverse outer primer

GAGGAAATTGAGAAACTCAGCCAG CA

AAACATCAGAGCAAGAGTCTGGTATG CA

CTGACAGAGGGTAAAACCTGCACAGACG

CTGTGCCAGAACAAAGGTGACCTATTG

59°C

310bp (outer)

197 bp (allele 'A')

166 bp (allele 'T')

c.4500A>C

Forward inner primer

Reverse inner primer

Forward outer primer

Reverse outer primer

AGAGACGCAAGCAGGCCCCTGGGCCG CA

CCCTGGAGACACTTGGAGAGAATGGG GG

AGCAGTGTTTAGAAATAGCCTCGCAAT

AGAGAACCCACACACATGCCCTTACTG

64°C

400bp (outer)

250 bp (allele 'A')

150 bp (allele 'C')

c.4950C>T

Forward inner primer

Reverse inner primer

Forward outer primer

Reverse outer primer

TGTATCCATTCAATATATAATACATTG GC

GGCTAGCAAATGTATTATGAGGTG AA

TTCATTTGTTTGAATTTATTTTATTGAGG

GAATGTAACACAAAGGAACATATCTACAA

59°C

341bp (outer)

226 bp (allele 'T')

169 bp (allele 'C')

Figure 2
Figure 2

Agarose gel differentiating three genotypes. Agarose gel (2.5%) stained with ethidium bromide showing tetra-primer ARMS-PCR amplicons differentiating the three CARD15 genotypes for SNP c.4500A>C.

Statistical Analyses

Average allele substitution effects

Preliminary analyses revealed only two CARD15 SNPs (c.3020A>T and c.4500A>C) to be associated with any of the different traits analyzed; only these two SNPs were used for the detailed analysis. Data was analyzed using PROC REG (SAS Institute, Inc., Cary, NC), by the following model:
y j = μ + ∑ i = 1 2 β i G i + e j MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xI8qiVKYPFjYdHaVhbbf9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaGaemyEaK3aaSbaaSqaaiabdQgaQbqabaGccqGH9aqpiiGacqWF8oqBcqGHRaWkdaaeWbqaaiab=j7aInaaBaaaleaacqWGPbqAaeqaaOGaem4raC0aaSbaaSqaaiabdMgaPbqabaGccqGHRaWkcqWGLbqzdaWgaaWcbaGaemOAaOgabeaaaeaacqWGPbqAcqGH9aqpcqaIXaqmaeaacqaIYaGma0GaeyyeIuoaaaa@42E1@
where: y j = Trait EBV for the jth animal; μ = Overall mean, β i = regression coefficient (allele substitution effect) for the ith SNP, e j = random error and G i = the genotype of the ith SNP recoded as in Zeng et al [37]:
G i = 1  for a homozygote  ( eg . , AA ) 0  for a Heterozygote  ( eg . , AT ) − 1  for a homozygote  ( eg . , TT ) MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xI8qiVKYPFjYdHaVhbbf9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=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@9272@

The recoded genotypes are listed in Table 1. Mallow's criterion was used to select the final regression model. Type I experimental errors were controlled by implementing permutation test [38]. The traits of interest were milk yield, fat yield, protein yield, udder depth, and somatic cell score. For each trait, the power of the analysis was calculated using a software developed and described by Dunlap et al. [39], which takes into account the significance level used, number of independent variables, sample size, and the coefficient of determination of the multiple regression model.

Genotype Frequencies

The genotypic frequencies were tested for deviations from proportions of Hardy Weinberg Equilibrium. This was performed applying a Chi-square test. The pair-wise level of linkage disequilibrium was measured using the squared correlation of the alleles at two loci (r2) [40] and was tested for significance using a Chi-square test [41]:
x d f = 1 2 = ( n − 3 ) z 2 , MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xI8qiVKYPFjYdHaVhbbf9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaGaemiEaG3aa0baaSqaaiabdsgaKjabdAgaMjabg2da9iabigdaXaqaaiabikdaYaaakiabg2da9iabcIcaOiabd6gaUjabgkHiTiabiodaZiabcMcaPiabdQha6naaCaaaleqabaGaeGOmaidaaOGaeiilaWcaaa@3CE3@
where, n is the number of bulls genotyped and
z = 0.5 ln ( 1 + r 1 − r ) . MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xI8qiVKYPFjYdHaVhbbf9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaGaemOEaONaeyypa0JaeGimaaJaeiOla4IaeGynauJagiiBaWMaeiOBa4MaeiikaGscfa4aaSaaaeaacqaIXaqmcqGHRaWkcqWGYbGCaeaacqaIXaqmcqGHsislcqWGYbGCaaGccqGGPaqkcqGGUaGlaaa@3DFB@

Haplotype Construction and Analyses

The haplotype probabilities were reconstructed using the program 'HAPROB' developed by Boettcher et al. [42]. This program is based on an algorithm that uses a two-step Monte Carlo-based approach to estimate haplotype probabilities for the genotyped members of half-sib families where the parents lack genotypic information. The first step estimated the haplotype probabilities for the sires based on the offspring genotypes and population allelic frequencies while the second step estimated the offspring-haplotype probabilities based on the sire probabilities and population frequencies. These two steps were alternately iterated until the estimated population frequencies converged to stable values. The final results were a set of estimated haplotype probabilities for each animal.

Haplotype effects were estimated by regressing EBV's on haplotype probabilities, which are expressed as the expected number of copies of each haplotype. The following model was used for statistical analyses using SAS software (SAS Institute, 1999):
y j = μ + ∑ i = 1 4 β i H a p i j + e j MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xI8qiVKYPFjYdHaVhbbf9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaGaemyEaK3aaSbaaSqaaiabdQgaQbqabaGccqGH9aqpiiGacqWF8oqBcqGHRaWkdaaeWbqaaiab=j7aInaaBaaaleaacqWGPbqAaeqaaOGaemisaGKaemyyaeMaemiCaa3aaSbaaSqaaiabdMgaPjabdQgaQbqabaGccqGHRaWkcqWGLbqzdaWgaaWcbaGaemOAaOgabeaaaeaacqWGPbqAcqGH9aqpcqaIXaqmaeaacqaI0aana0GaeyyeIuoaaaa@46F8@

where y j is trait EBV of the jth animal, β i is the linear regression coefficient for the i th haplotype, Hapij is the probability of the ith haplotype for the jth bull, e j are random residual effects. Permutation test was implemented to control type I experimental-wise errors. For each trait, the power of the haplotype analysis was calculated using a software developed and described by Dunlap et al. [39].

Declarations

Acknowledgements

We would like thank Natural Science and Engineering Research Council, Dairygen, Canadian Bovine Mastitis Research Network and Ontario Ministry of Agriculture, Food and Rural Affairs for financial support. We would also like to thank Semex alliance for providing DNA samples for this study.

Authors’ Affiliations

(1)
Centre for Genetic Improvement of Livestock, Department of Animal and Poultry Science, University of Guelph, Guelph, Canada

References

  1. National Mastitis Council: Current concepts of bovine mastitis. 1996, Madison, WI , 4th editionGoogle Scholar
  2. Interbull: Description of National Genetic Evaluation Systems for dairy cattle traits as applied in different Interbull member countries. [http://www-interbull.slu.se/national_ges_info2/framesida-ges.htm]
  3. Rupp R, Boichard D: Genetic parameters for clinical mastitis, somatic cell score, production, udder type traits, and milking ease in first lactation Holsteins. Journal of dairy science. 1999, 82 (10): 2198-2204.PubMedView ArticleGoogle Scholar
  4. Rupp R, Bergonier D, Dion S, Hygonenq MC, Aurel MR, Foulon E, Foucras G: Effects of SCC-Based selection for mastitis resistance: First results from a divergent selectioin experiment in sheep. Eighth world congress on genetics applies to livestock production. 2006, Belo horizonte, MG, BrazilGoogle Scholar
  5. Mark T, Fikse WF, Emanuelson U, Philipsson J: International genetic evaluations of Holstein sires for milk somatic cell and clinical mastitis. Journal of dairy science. 2002, 85 (9): 2384-2392.PubMedView ArticleGoogle Scholar
  6. Schumann RR: Host cell-pathogen interface: molecular mechanisms and genetics. Vaccine. 2004, 22 Suppl 1: S21-4.PubMedView ArticleGoogle Scholar
  7. Leung E, Hong J, Fraser A, Krissansen GW: Splicing of NOD2 (CARD15) RNA transcripts. Molecular immunology. 2007, 44 (4): 284-294. 10.1016/j.molimm.2006.03.009.PubMedView ArticleGoogle Scholar
  8. Mullaly SC, Kubes P: The role of TLR2 in vivo following challenge with Staphylococcus aureus and prototypic ligands. J Immunol. 2006, 177 (11): 8154-8163.PubMedView ArticleGoogle Scholar
  9. Menzies M, Ingham A: Identification and expression of Toll-like receptors 1-10 in selected bovine and ovine tissues. Veterinary immunology and immunopathology. 2006, 109 (1-2): 23-30. 10.1016/j.vetimm.2005.06.014.PubMedView ArticleGoogle Scholar
  10. Goldammer T, Zerbe H, Molenaar A, Schuberth HJ, Brunner RM, Kata SR, Seyfert HM: Mastitis increases mammary mRNA abundance of beta-defensin 5, toll-like-receptor 2 (TLR2), and TLR4 but not TLR9 in cattle. Clinical and diagnostic laboratory immunology. 2004, 11 (1): 174-185. 10.1128/CDLI.11.1.174-185.2004.PubMed CentralPubMedGoogle Scholar
  11. McGuire K, Jones M, Werling D, Williams JL, Glass EJ, Jann O: Radiation hybrid mapping of all 10 characterized bovine Toll-like receptors. Animal genetics. 2006, 37 (1): 47-50. 10.1111/j.1365-2052.2005.01364.x.PubMedView ArticleGoogle Scholar
  12. Martinon F, Tschopp J: NLRs join TLRs as innate sensors of pathogens. Trends in immunology. 2005, 26 (8): 447-454. 10.1016/j.it.2005.06.004.PubMedView ArticleGoogle Scholar
  13. Ogura Y, Inohara N, Benito A, Chen FF, Yamaoka S, Nunez G: Nod2, a Nod1/Apaf-1 family member that is restricted to monocytes and activates NF-kappaB. The Journal of biological chemistry. 2001, 276 (7): 4812-4818. 10.1074/jbc.M008072200.PubMedView ArticleGoogle Scholar
  14. Girardin SE, Philpott DJ: Mini-review: the role of peptidoglycan recognition in innate immunity. European journal of immunology. 2004, 34 (7): 1777-1782. 10.1002/eji.200425095.PubMedView ArticleGoogle Scholar
  15. Girardin SE, Boneca IG, Viala J, Chamaillard M, Labigne A, Thomas G, Philpott DJ, Sansonetti PJ: Nod2 is a general sensor of peptidoglycan through muramyl dipeptide (MDP) detection. The Journal of biological chemistry. 2003, 278 (11): 8869-8872. 10.1074/jbc.C200651200.PubMedView ArticleGoogle Scholar
  16. Inohara N, Ogura Y, Fontalba A, Gutierrez O, Pons F, Crespo J, Fukase K, Inamura S, Kusumoto S, Hashimoto M, Foster SJ, Moran AP, Fernandez-Luna JL, Nunez G: Host recognition of bacterial muramyl dipeptide mediated through NOD2. Implications for Crohn's disease. The Journal of biological chemistry. 2003, 278 (8): 5509-5512. 10.1074/jbc.C200673200.PubMedView ArticleGoogle Scholar
  17. Kapetanovic R, Nahori MA, Balloy V, Fitting C, Philpott DJ, Cavaillon JM, Adib-Conquy M: Contribution of phagocytosis and intracellular sensing for cytokine production by Staphylococcus aureus-activated macrophages. Infection and immunity. 2007, 75 (2): 830-837. 10.1128/IAI.01199-06.PubMed CentralPubMedView ArticleGoogle Scholar
  18. Netea MG, Kullberg BJ, de Jong DJ, Franke B, Sprong T, Naber TH, Drenth JP, Van der Meer JW: NOD2 mediates anti-inflammatory signals induced by TLR2 ligands: implications for Crohn's disease. European journal of immunology. 2004, 34 (7): 2052-2059. 10.1002/eji.200425229.PubMedView ArticleGoogle Scholar
  19. Uehara A, Yang S, Fujimoto Y, Fukase K, Kusumoto S, Shibata K, Sugawara S, Takada H: Muramyldipeptide and diaminopimelic acid-containing desmuramylpeptides in combination with chemically synthesized Toll-like receptor agonists synergistically induced production of interleukin-8 in a NOD2- and NOD1-dependent manner, respectively, in human monocytic cells in culture. Cellular microbiology. 2005, 7 (1): 53-61. 10.1111/j.1462-5822.2004.00433.x.PubMedView ArticleGoogle Scholar
  20. van Heel DA, Ghosh S, Butler M, Hunt KA, Lundberg AM, Ahmad T, McGovern DP, Onnie C, Negoro K, Goldthorpe S, Foxwell BM, Mathew CG, Forbes A, Jewell DP, Playford RJ: Muramyl dipeptide and toll-like receptor sensitivity in NOD2-associated Crohn's disease. Lancet. 2005, 365 (9473): 1794-1796. 10.1016/S0140-6736(05)66582-8.PubMedView ArticleGoogle Scholar
  21. Schroder NW, Schumann RR: Single nucleotide polymorphisms of Toll-like receptors and susceptibility to infectious disease. The Lancet infectious diseases. 2005, 5 (3): 156-164.PubMedView ArticleGoogle Scholar
  22. Hampe J, Cuthbert A, Croucher PJ, Mirza MM, Mascheretti S, Fisher S, Frenzel H, King K, Hasselmeyer A, MacPherson AJ, Bridger S, van Deventer S, Forbes A, Nikolaus S, Lennard-Jones JE, Foelsch UR, Krawczak M, Lewis C, Schreiber S, Mathew CG: Association between insertion mutation in NOD2 gene and Crohn's disease in German and British populations. Lancet. 2001, 357 (9272): 1925-1928. 10.1016/S0140-6736(00)05063-7.PubMedView ArticleGoogle Scholar
  23. Hugot JP, Chamaillard M, Zouali H, Lesage S, Cezard JP, Belaiche J, Almer S, Tysk C, O'Morain CA, Gassull M, Binder V, Finkel Y, Cortot A, Modigliani R, Laurent-Puig P, Gower-Rousseau C, Macry J, Colombel JF, Sahbatou M, Thomas G: Association of NOD2 leucine-rich repeat variants with susceptibility to Crohn's disease. Nature. 2001, 411 (6837): 599-603. 10.1038/35079107.PubMedView ArticleGoogle Scholar
  24. Ogura Y, Bonen DK, Inohara N, Nicolae DL, Chen FF, Ramos R, Britton H, Moran T, Karaliuskas R, Duerr RH, Achkar JP, Brant SR, Bayless TM, Kirschner BS, Hanauer SB, Nunez G, Cho JH: A frameshift mutation in NOD2 associated with susceptibility to Crohn's disease. Nature. 2001, 411 (6837): 603-606. 10.1038/35079114.PubMedView ArticleGoogle Scholar
  25. Pierik M, Joossens S, Van Steen K, Van Schuerbeek N, Vlietinck R, Rutgeerts P, Vermeire S: Toll-like receptor-1, -2, and -6 polymorphisms influence disease extension in inflammatory bowel diseases. Inflammatory bowel diseases. 2006, 12 (1): 1-8. 10.1097/01.MIB.0000195389.11645.ab.PubMedView ArticleGoogle Scholar
  26. Society HGV: Nomenclature for the description of sequence variations. [http://www.genomic.unimelb.edu.au/mdi/mutnomen/]
  27. Matsushima N, Tachi N, Kuroki Y, Enkhbayar P, Osaki M, Kamiya M, Kretsinger RH: Structural analysis of leucine-rich-repeat variants in proteins associated with human diseases. Cell Mol Life Sci. 2005, 62 (23): 2771-2791. 10.1007/s00018-005-5187-z.PubMedView ArticleGoogle Scholar
  28. Boettcher PJ, Dekkers JC, Kolstad BW: Development of an udder health index for sire selection based on somatic cell score, udder conformation, and milking speed. Journal of dairy science. 1998, 81 (4): 1157-1168.PubMedView ArticleGoogle Scholar
  29. Ashwell MS, Van Tassell CP: Detection of putative loci affecting milk, health, and type traits in a US Holstein population using 70 microsatellite markers in a genome scan. Journal of dairy science. 1999, 82 (11): 2497-2502.PubMedView ArticleGoogle Scholar
  30. Ashwell MS, Heyen DW, Sonstegard TS, Van Tassell CP, Da Y, VanRaden PM, Ron M, Weller JI, Lewin HA: Detection of quantitative trait loci affecting milk production, health, and reproductive traits in Holstein cattle. Journal of dairy science. 2004, 87 (2): 468-475.PubMedView ArticleGoogle Scholar
  31. Kuhn C, Bennewitz J, Reinsch N, Xu N, Thomsen H, Looft C, Brockmann GA, Schwerin M, Weimann C, Hiendleder S, Erhardt G, Medjugorac I, Forster M, Brenig B, Reinhardt F, Reents R, Russ I, Averdunk G, Blumel J, Kalm E: Quantitative trait loci mapping of functional traits in the German Holstein cattle population. Journal of dairy science. 2003, 86 (1): 360-368.PubMedView ArticleGoogle Scholar
  32. Olsen HG, Gomez-Raya L, Vage DI, Olsaker I, Klungland H, Svendsen M, Adnoy T, Sabry A, Klemetsdal G, Schulman N, Kramer W, Thaller G, Ronningen K, Lien S: A genome scan for quantitative trait loci affecting milk production in Norwegian dairy cattle. Journal of dairy science. 2002, 85 (11): 3124-3130.PubMedView ArticleGoogle Scholar
  33. Network CD: Canadian Dairy Network. [http://www.cdn.ca/home.php]
  34. Winfrey MR, Rott MA, Wortman AT: Unraveling DNA: Molecular Biology for the Laboratory. 1997, Prentice Hall, New Jersey, USAGoogle Scholar
  35. Ye S, Dhillon S, Ke X, Collins AR, Day IN: An efficient procedure for genotyping single nucleotide polymorphisms. Nucleic acids research. 2001, 29 (17): E88-8. 10.1093/nar/29.17.e88.PubMed CentralPubMedView ArticleGoogle Scholar
  36. Ye S, Dhillon S, Ke X, Collins AR, Day IN: Tetra-primer ARMS-PCR. [http://cedar.genetics.soton.ac.uk/public_html/primer1.html]
  37. Zeng ZB, Wang T, Zou W: Modeling quantitative trait Loci and interpretation of models. Genetics. 2005, 169 (3): 1711-1725. 10.1534/genetics.104.035857.PubMed CentralPubMedView ArticleGoogle Scholar
  38. Churchill GA, Doerge RW: Empirical threshold values for quantitative trait mapping. Genetics. 1994, 138 (3): 963-971.PubMed CentralPubMedGoogle Scholar
  39. Dunlap WP, Xin X, Myers L: Computing aspects of power for multiple regression. Behav Res Methods Instrum Comput. 2004, 36 (4): 695-701.PubMedView ArticleGoogle Scholar
  40. Hill WG, Robertson A: Linkage disequilibrium in finite populations. Theoretical and Applied Genetics. 1968, 38 (6): 226-231. 10.1007/BF01245622.PubMedView ArticleGoogle Scholar
  41. Weir BS: Inferences about linkage disequilibrium. Biometrics. 1979, 35 (1): 235-254. 10.2307/2529947.PubMedView ArticleGoogle Scholar
  42. Boettcher PJ, Pagnacco G, Stella A: A Monte Carlo approach for estimation of haplotype probabilities in half-sib families. Journal of dairy science. 2004, 87 (12): 4303-4310.PubMedView ArticleGoogle Scholar

Copyright

© Pant et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement

BMC Genomics

ISSN: 1471-2164

Contact us