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. 2012 Nov 1;6(5):461–469. doi: 10.4161/pri.21866

The identification of candidate genes and SNP markers for classical bovine spongiform encephalopathy susceptibility

Jennifer M Thomson 1,2, Victoria Bowles 1, Jung-Woo Choi 1, Urmila Basu 1, Yan Meng 1, Paul Stothard 1, Stephen Moore 3,*
PMCID: PMC3510855  PMID: 22918267

Abstract

Classical bovine spongiform encephalopathy is a transmissible prion disease that is fatal to cattle and is a human health risk due to its association with a strain of Creutzfeldt-Jakob disease (vCJD). Mutations to the coding region of the prion gene (PRNP) have been associated with susceptibility to transmissible spongiform encephalopathies in mammals including bovines and humans. Additional loci such as the retinoic acid receptor beta (RARB) and stathmin like 2 (STMN2) have also been associated with disease risk. The objective of this study was to refine previously identified regions associated with BSE susceptibility and to identify positional candidate genes and genetic variation that may be involved with the progression of classical BSE. The samples included 739 samples of either BSE infected animals (522 animals) or non-infected controls (207 animals). These were tested using a custom SNP array designed to narrow previously identified regions of importance in bovine genome. Thirty one single nucleotide polymorphisms were identified at p < 0.05 and a minor allele frequency greater than 5%. The chromosomal regions identified and the positional and functional candidate genes and regulatory elements identified within these regions warrant further research.

Keywords: BSE, Susceptibility, SNP, Candidate Genes

Introduction

Classical Bovine Spongiform Encephalopathy (BSE) is the variant of transmissible spongiform encephalopathies (TSEs) found in Bos taurus or bovines. The TSEs are progressive degenerative neurological disorders caused by the conversion of endogenous prion protein (PRPc) to an abnormally folded form (PrPRes)1,2 and are always fatal.3 TSEs are unique in that they may be genetic, sporadic or transmitted but, in all cases the disease state is associated with accumulation of the abnormal PrPRes in the brain and central nervous system (CNS) causing degeneration.4 Classical BSE is a form of TSE acquired by the consumption of meat and/or bone meal contaminated with the (PrPRes) or infectious prion agent5 and was first observed in the United Kingdom (UK) in 1986.6 A large number of infected animals entered the food chain in the preclinical state and were consumed by the human population in the UK and elsewhere.7 This consumption has been linked to a variant form of Creutzfeldt-Jakob disease (vCJD)8,9 with approximately 200 reported cases in Europe , Asia, and North America (data from National Creutzfeldt-Jakob Disease Surveillance Unit (NCJDSU), Western General Hospital Edinburgh, Scotland: Worldwide vCJD statistics 2010).

Prion disease pathogenesis occurs when endogenous PrPRes auto catalyzes the conversion of PrPc into the PrPRes form. Exposure to the PrPRes promotes or facilitates this conversion through a mechanism that is not yet understood.10 PrPRes is highly resistant to breakdown by the proteases and other proteolytic machinery of the cell.11,12 This resistance results in the accumulation of the non-degraded form of the protein which results in the neural degeneration and spongiform appearance of infected CNS tissues.4 In classical BSE, the pathogenesis of the disease is the result of the transference of the infectious prion from the digestive tract into the peripheral nervous system and then to the brain.5 Studies of the pathogenesis have concluded that the route of transmission is from the ileal Peyer’s patches and the tonsils through the parasympathetic and sympathetic nerve fibers of the autonomous nervous system.5 It has been hypothesized that a number of cellular proteins and the genes may be involved in this progression.13,14 Structural variation in prion protein locus, the gene for the PrP protein, is strongly associated with the risk of all categories of prion disease in humans including vCJD.15 In addition, specific PRNP alleles have been associated with TSE susceptibility in sheep.16,17 In classical BSE, variation in the promoter region of the PRNP locus has also been shown to be associated with risk of disease incidence.18,19 In addition to the PRNP locus, single nucleotide polymorphisms (SNPs) associated with other genes such as the retinoic acid receptor beta (RARB) and stathmin-like 2 proteins (STMN2) have been shown to be related to human susceptibility to prion disease.15,20 Genome wide association studies have also identified several other genomic regions associated with BSE incidence13,14,21,22 but little is known about how these regions and the genes within them that may affect the pathogenesis of classical BSE. Additionally, genome wide association studies in humans have identified other loci of modest overall effect as research targets.23 The objective of this study was to refine previously identified regions associated with BSE susceptibility and to identify positional candidate genes and genetic variation that may be involved with the progression of classical BSE.

Results and Discussion

Candidate gene identification

Fifty seven regions on BTAU 4.024 associated with BSE incidence and susceptibility were identified utilizing data generated previously within our lab and throughout published literature.13,14,21,22 These regions are shown in Figure 1. A window of 1MB was created around each previously identified marker (500,000 bases to either side) in order to capture all of the genes closely linked to identified associations. Known genes within each 1MB pair window in BTAU 4.0 were then obtained using BIOMART.25 All previously reported candidate genes for prion disease susceptibility,21,22 were included as well as newly identified candidate genes based on potential functional role in BSE. Candidate genes were identified using NCBI Genbank,26 Gene Ontology27 and KEGG Pathway28 databases and were included if the gene was known to be expressed in nervous tissue, was involved in proteolysis, protein folding or unfolding or protein transport.

Figure 1.

Figure 1.

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Regions of BTAU 4.0 previously identified as associated to incidence of classical BSE in cattle.

Marker identification for candidate genes and genome regions

Transcript sequences from liver, hypothalamus, muscle, adipose, duodenum, kidney, lung blood, cortex and Peyer’s patch were evaluated to identify SNPs in each of the 89 candidate genes mapped to the 57 regions of the bovine genome thought to be related to BSE susceptibility. Whole-genome sequences from a Holstein bull and a Black Angus bull29were also used to identify structural variation within candidate genes. Non-synonymous mutations were selected when available as were SNPs predicted to introduce a stop codon or splice variation.

Overall structural variation was identified in 87 of 89 candidate genes. Additional SNPs were added to increase the resolution within the 57 one MB pair windows of BTAU 4.0.

The origin of all SNPs in the constructed panel is shown in Additional File 1. Whenever possible, markers were selected that were identified in more than one dataset.

Genotyping

A custom panel of 384 SNP markers was designed to evaluate 87 candidate genes identified in underlying QTLs for BSE incidence. The total number of SNP markers included in the analysis after quality control measures was 240. This panel was used to genotype 739 samples of either BSE infected animals (522 animals) or non-infected controls (207 animals). The animal samples came from a population of half-sibs and a set of unrelated case and control samples. The samples were combined for this study in a modified sib-TDT test using the DFAM procedure in the PLINK software30 that allows for addition of unrelated individuals. The combined population was examined for stratification and none was observed.

A total of 31 markers were associated with incidence of BSE in this population at a significance level of p > 0.05 and a minor allele frequency greater than 5% (Table 1). Some of these SNPs are located close to one another and are potentially within LD in the population.

Table 1. Significance of associated marker loci using the DFAM Procedure in PLINK for a sib-TDT with unrelated individuals.

Chromosome Position Reference Allele Alternate Allele Chi Square P-value Minor Allele Frequency
1
 29642496
 A
 C
 6.386
 0.0115
 0.1555
1
 89510456
 G
 A
 10.56
 0.001154
 0.4095
2
 14990009
 A
 G
 5.877
 0.01534
 0.1843
2
 15112384
 G
 A
 4.316
 0.03776
 0.1385
2
 108616181
 A
 G
 3.948
 0.04692
 0.1953
3
 32165127
 A
 G
 5.328
 0.02099
 0.1926
6
 98267764
 G
 A
 4.033
 0.04463
 0.2006
6
 98467430
 G
 A
 4.634
 0.03134
 0.09362
7
 24913972
 G
 A
 3.922
 0.04766
 0.1796
9
 63199430
 A
 G
 5.111
 0.02378
 0.4367
10
 4459856
 G
 A
 4.17
 0.04115
 0.1166
10
 21273459
 A
 G
 7.068
 0.007846
 0.1389
10
 21653055
 T
 A
 9.434
 0.00213
 0.3812
10
 38243823
 G
 A
 4.889
 0.02703
 0.2597
10
 38355911
 A
 G
 5.93
 0.01488
 0.1598
10
 38725566
 A
 G
 5.49
 0.01913
 0.4004
10
 47743503
 C
 G
 6.311
 0.012
 0.3567
10
 48274239
 A
 G
 5.917
 0.015
 0.4836
12
 80176597
 G
 A
 5.801
 0.01602
 0.308
13
 46715840
 C
 A
 5.136
 0.02343
 0.4889
14
 43784117
 A
 G
 5.381
 0.02036
 0.3536
14
 44086806
 G
 A
 4.206
 0.04028
 0.3295
15
 35224440
 A
 G
 8.253
 0.004069
 0.4907
17
 40657770
 A
 G
 6.493
 0.01083
 0.1669
17
 41455808
 A
 G
 8.599
 0.003363
 0.1543
20
 39198878
 A
 G
 8.721
 0.003146
 0.465
20
 39329256
 G
 A
 5.716
 0.01682
 0.3464
21
 13354965
 G
 A
 8.603
 0.003357
 0.2177
21
 24945877
 G
 C
 5.042
 0.02475
 0.3529
22
 42219360
 G
 A
 7.385
 0.006578
 0.1735
28 26571763 A C 27.32 1.73E-07 0.07955
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Bold indicates clusters of significantly associated markers

Classical BSE as a phenotype

Previous studies21,22 have suggested that it is difficult to test for the genetic association for the clinical presentation of BSE. The explanation provided is that this could be attributed to the fact that while animals that developed BSE are clearly susceptible to disease and the control samples may represent animals that were either not exposed to enough infectious material to develop disease or are in fact resistant to infection. This phenotypic variation may reduce the power to detect disease association. Also as noted,21,22 the use of stringent multiple testing corrections such as Bonferroni to this type of a phenotype may be overly prone to type II errors and thus discard real, though subtle associations in favor of reducing or eliminating type I or false positive results. In contrast, this study was designed to refine the previously identified results13,14,21,22by evaluating a reduced number of markers in a larger pooled population of animals. However, with the total of 240 markers included in the analysis and the criteria of p < 0.05 means that approximately 12 of the identified 31 markers may be significant by chance.

Regions previously identified for BSE susceptibility

This work utilized genomic regions previously identified or suggested to be important to classical BSE susceptibility. However, this work refined previous associations and identified novel candidate genes (Table 2). As shown in Table 2, the position of the associated markers in this study are close but not identical to those shown previously. The resolution of this work was also higher as the aim was to have markers every 100,000 base pairs in the target areas. The increased resolution allowed further refinement of candidate genes and the identification of regulatory regions that may impact disease susceptibility.

Table 2. Previously identified and currently refined genomic regions related to BSE susceptibility.

Chromosome Position (Mb) P-value Observed in outside population Reference Location Mb
1
 29.6
 0.012
 Yes
 14, 21
 29.1
1
 89.5
 0.001
 Yes
 14, 21, 22
 89.7
2
 14.9
 0.015
 No
  
  
2
 15.1
 0.038
 21
 15.3
2
 108.6
 0.047
 No
 21, 22
 108.6
3
 32.1
 0.021
 No
 22
 32.4
6
 98.2
 0.045
 No
 21
 98.8
6
 98.4
 0.031
7
 24.9
 0.048
 No
 21
 24.6
9
 63.1
 0.024
 No
 21
 62.9
10
 4.4
 0.041
 No
 21
 4.4
10
 21.2
 0.008
 Yes
 13, 21, 22
 29.2
10
 21.6
 0.002
10
 38.2
 0.027
 No
 21
 38.7
10
 38.3
 0.015
10
 38.7
 0.019
10
 47.7
 0.012
  
 21, 13
 40.0 and 48.0
10
 48.2
 0.015
 Yes
12
 80.1
 0.016
 No
 21
 80.1
13
 46.7
 0.023
 No
 21
 47.2
14
 43.7
 0.02
 Yes
 15, 21, 22
 44
14
 44.1
 0.04
15
 35.2
 0.004
 No
 21, 22
 35.7
17
 40.6
 0.011
 Yes
 14, 21, 22
 41.0 and 44.2
17
 41.4
 0.003
20
 39.2
 0.003
 Yes
 21, 13
 38.8
20
 39.3
 0.017
21
 13.3
 0.003
 No
 21, 22
 13.2 and 33.2
21
 24.9
 0.025
 No
 21
 24.8
22
 42.2
 0.007
 No
 21
 42.4
28 26.6 0.0000002 No 21 26.9
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New candidate genes

Several pathogenesis studies have concluded that the route of transmission of classical BSE is through consumption of the misfolded PRNP which is then transported into ileal Peyer’s patches and the tonsils and then through the parasympathetic and sympathetic nerve fibers of the autonomous nervous system.5 The misfolded PRNP then leads to the misfolding of native PRNP, which resists degradation and accumulates in the CNS resulting in neurodegeneration.5 It has been hypothesized that a number of cellular proteins and genes play an important role in the progression of the disease.13,14 Several proteins (Table 3) are known to participate in transport of the prion protein across the gut epithelium, transport to dendritic cells, accumulation in lymph tissues and progression from lymph tissues to CNS tissues and specifically to the brain.

Table 3. Positional and functional candidate genes for BSE susceptibility.

Significant Marker
  
  
Chromosome Position Candidate Gene (position) Relationship to BSE
6
 98267764
 ANTXR2 (6:98000385-98236768)
 Mutation results in hyalinosis, deposition of extracellular clear tissue protein lesions50
6
 98467430
7
 24913972
 SLC12A2 (7:24685293-24776754)
 Functions to maintain ion balance, related to schizophrenia51
9
 63199430
 MDN1-1 (9:63073546-63213136)
 Unfolded protein binding31
10
 21273459
 NEDD8 (10:2121578-21224714)
 Conjugates scaffold proteins to allow proteosome mediated protein degradation pathway,31,32 amyloid precursor protein binding,52 accumulates in inclusions during neurodegenerative disease,52 increased in Alzheimer's disease.35
PSME2 (10:21283913-21287173)
 Proteosome activation,53 downregulation associated with neurodegenerative disease.54
PSME1(10:21292117-21294778)
 Proteosome activation,53 downregulation associated with neurodegenerative disease.54
CHMP4A (10:21227527-21230632)
 Chaperone protein associated with transmembrane transport in neurons.55
10
 38243823
 UBR1 (10:38136589-38243823)
 Recognition component of ubiquitin protein degradation pathway, mutations associated with Johanson-blizzard syndrome56 and bipolar disorder.57
10
 38355911
10
 38725566
20
 39198878
 GDNF (20:38861046-38881932)
 Suppresses neurodegeneration36 in Parkinson's,37 mutation related to Down's Syndrome and Schizophrenia,38 variant active in Alzheimer's Disease.58
20
 39329256
21
 24945877
 MORF4L1 (21: 24920985-24938434)
 NEDD8 pathway, histone acetylation, regulation of splice variants.59
22
 42219360
 FHIT (22:40968128-42518027)
 Mutations and splice variants related to incidence of Alzheimer disease,60 Multiple sclerosis,61 and Parkinson's disease.62
28 26571763 PCBD1 (28:26334706) Strong Association to Alzheimer's disease.63
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An example of a functional candidate gene that may be linked to the pathogenesis of classical BSE is NEDD8 (Neural precursor cell expressed, developmentally down-regulated 8), which is located at approximately 21.2CM on BTAU 10. It is close to a marker (10: 21273459) that was significant at p <0.005 and would likely be linked to that marker. NEDD8 encodes an 81 amino acid polypeptide which is 60% identical and 80% homologous to ubiquitin.31 Amyloid precursor protein binding protein-1 (APP-BP1) binds to the carboxyl terminus of the amyloid precursor protein (APP) and serves as the activation enzyme for the ubiquitin-like protein, NEDD8.32 NEDD8 conjugation pathway has shown to be essential for proteolytic targeting by ubiquitination.33 Impairment of this process may contribute to the amyloid plaques evident in classical BSE. NEDD8 has been shown to accumulate in both glial and neuronal inclusions in several neurodegenerative conditions such as Parkinson disease, Alcoholic liver disease and Astrocytoma.34 Co-location of APP-BP1 and NEDD8 is shown in lipid rafts in the hippocampus of Alzheimer’s patients but not in other less affected regions of the brain.35 Further investigation is needed to validate a relationship between NEDD8 and to determine the nature of the potential relationship with classical BSE susceptibility.

Another important positional and functional candidate gene identified in this study is GDNF (glial cell derived neurotrophic factor). GDNF has been shown to mitigate neuronal degeneration in a range of neurodegenerative conditions and was one of the first investigated targets for gene therapy in Parkinson disease.36,37 GDNF is thought to promote survival and differentiation of developing neurons and to protect mature neurons.36 Mutations in or impairments of GDNF action have been shown contribute to the symptoms of Down syndrome and Schizophrenia.38 The action of GDNF could play a role in innate resistance to neurodegeneration in classical BSE. As with NEDD8, future research is needed to establish a relationship and to determine the potential role of GDNF as a target for BSE resistance.

Candidate markers near regulatory regions or CpG Islands

Several markers that were significantly associated with classical BSE susceptibility are positioned close to annotated CpG islands in the bovine genome. Vertebrate CpG islands are short DNA sequences that are significantly richer in GC bases as compared to the rest of the genome and are predominantly non-methylated.39 Most, if not all, CpG islands are sites of transcription initiation though many are not currently annotated promoters39. Shared DNA sequence features adapt CpG islands for promoter function by destabilizing nucleosomes and attracting proteins that create a transcriptionally preferable chromatin state.39 The significantly associated SNP markers in close proximity to CpG islands identified in this study are shown in Table 4.

Table 4. Associated loci in a regulatory region and nearby candidate genes.

Significant Marker
  
  
  
Chromosome Position CPG Island Position Nearby Genes Function
14
 44086806
 14:44086435-44087067
 MED30 (14:44067815-44086895)
 Thyroid hormone binding,64 RNA polymerase cofactor activity, regulation of transcription.65
EXT1 (14:43445834-43759710)
 Heparan sulfate proteoglycan biosynthetic process and disease process,66,67 recognition and binding of prion protein.68
10
 21273459
 10:21283889-21284116
 NEDD8 (10:2121578-21224714)
 Conjugates scaffold proteins to allow proteosome mediated protein degradation pathway,31,32 amyloid precursor protein binding,52 accumulates in inclusions during neurodegenerative disease,52 increased in Alzheimer's disease.35
10:21282710-21283083
 PSME2 (10:21283913-21287173)
 Proteosome activation,53 downregulation associated with neurodegenerative disease.54
10:21289609-21290219
 PSME1(10:21292117-21294778)
 Proteosome activation,53 downregulation associated with neurodegenerative disease.54
10:21294367-21294916
 CHMP4A (10:21227527-21230632)
 Chaperone protein associated with transmembrane transport in neurons.55
10
 38243823
 10:38122791-38123802
 UBR1 (10:38136589-38243823)
 Recognition component of ubiquitin protein degradation pathway, mutations associated with Johanson-blizzard syndrome56 and bipolar disorder.57
10
 38355911
 10:38287535-38287874
20
 39329256
 20:39285739-39286473
 GDNF (20:38861046-38881932)
 Suppresses neurodegeneration36 in Parkinson's,37 mutation related to Down's Syndrome and Schizophrenia,38 variant active in Alzheimer's Disease.58
21 24945877 21:24920950-24922062 MORF4L1 (21: 24920985-24938434) NEDD8 pathway, histone acetylation, regulation of splice variants.59
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One example of a candidate gene that could be relevant to classical BSE pathogenesis and is in close proximity to both a significant associated marker and CpG island is exostosin 1 (EXT1). As noted previously,22 EXT1 is an endoplasmic reticulum transmembrane glycoprotein responsible for the synthesis and display of cell surface heparin sulphate glycosaminoglycans (GAGs).40 The N terminus of PRNP contains a GAG-binding motif and that may play a functional role in the uptake and transport of aberrant PRNP in gastrointestinal and neural cells in classical BSE transmission and infection.21,41 This relationship needs to be evaluated in the bovine and the link to classical BSE disease needs to be shown in further research.

In conclusion, this research has identified the most exhaustive list of candidate genes for classical BSE susceptibility to date and has supported an association of 31 markers shown to be related to classical BSE susceptibility in a large dataset of 729 case and control animals. It has improved the resolution and power of previous analyses and provided strong functional and positional candidate genes which may lead to better understanding of this disease in its natural host and provide candidate gene and regulatory targets for further research.

Materials and Methods

Animal information

This study used two sets of animals including both case (affected) and control (unaffected) samples from two populations with different population structures. The first data set is family based and includes 225 BSE affected and 193 BSE unaffected half-sibs Holsteins from 30 sire families. These were paternal half-sibs with different dams from the UK which were collected during the BSE outbreak in the mid-1990s. The second population included 297 BSE affected and 14 BSE unaffected animals. The control animals are contemporaries of the cases and were collected from the same farms. In these two populations, BSE status was determined both by examination by qualified veterinarians and then post-mortem by histology (Veterinary Laboratories Agency, New Haw, Surrey, UK). The control animals are assumed to have been exposed to the same environment, did not exhibit any signs of disease and are assumed to have been disease free.

Genomic DNA was isolated from the family based population using a phenol chloroform extraction as described by Hernandez-Sanchez et al.13 Genomic DNA from the case-control population was isolated using a high salt phenol/chloroform extraction as described by Sherman et al.42

mRNA-seq library construction and sequencing using Illumina platform

RNA was prepared for liver (7 animals), hypothalamus (11 animals), muscle and duodenum (12 animals), kidney and lung (14 animals) and adipose (10 animals) tissues. Total RNA was extracted using TRIzol reagent (Invitrogen) from the frozen tissues. The quality and quantity of RNA was determined using a Nanodrop (Nanodrop technologies) and Agilent Bioanalyzer (Agilent).

The RNAs from each tissue were pooled and cDNA libraries were constructed from each tissue pool according to the protocol (Illumina). The first step in the process of Illumina library construction involved purifying the poly-A containing mRNA molecules from total RNA using poly-T oligo-attached magnetic beads. Poly (A) RNA was fragmented followed by random hexamer reverse transcription and second strand synthesis. These cDNA fragments were end repaired prior to ligation of the Illumina GEX PE adapters according to the standard library generation protocol (Illumina). A ~85 bp size range was excised from the library on 6% agarose gel and the resultant library was subjected to 15 cycles of PCR followed by quantification using Nanodrop (Nanodrop Technologies).

Sequencing was performed on Genome Analyzer II (Illumina) which uses a massively parallel sequencing-by-synthesis four-dye approach, to generate billions of bases of high-quality DNA sequence per run. Cluster generation and sequencing was performed on a PE 75nt X 75nt flow cell generating ~1.7-6.0 GB of sequence.

Maq (version 0.7.1) was used to map reads and to generate SNP lists.43 First, the reads were converted from the Illumina export format to the Sanger FASTQ format using the “fq_all2std.pl export2std” command. The “match” command was used to align the paired-end reads to bovine transcripts from Ensembl (release 57).44 The resulting alignments were then merged using the “mapmerge” command and consensus sequences were generated using the “assemble” command. The “cns2snp” command was used to build a list of potential SNP sites from the consensus sequences. A second shorter list of SNPs was also generated using the “maq.pl SNPfilter” command. Default settings were used in all cases, except that the -N option was used with the “assemble” command to specify the number of haplotypes present in each sample. The shorter, filtered SNP list was annotated using NGS-SNP45 and release 57 of Ensembl,44 which includes SNPs from dbSNP build 130.46

Total RNA-Seq- library construction and sequencing using SOLiD platform

For each Cortex (8), Blood (10), Peyer’s Patch (6) libraries samples were pooled into 10ul before depleting the ribosomal RNA using the RiboMinus Eukaryote kit (Invitrogen) and concentrated with a RiboMinus Concentration Module. Five hundred nanograms (as determined by Agilent RNA Nano Chip on the Agilent Bioanalyzer) were carried through library prep using the whole transcriptome RNA-Seq Kit (Applied Biosystems). Libraries were quantified by QRTPCR using the Taqman kit (Applied Biosystems). Fragment sizes were checked using the DNA 1000 chip (Agilent) with the Agilent Bioanalyzer. Beads for Solid 4 were prepared using an EZBead system (Applied Biosystems). Sequencing was conducted on the SOLiD 4 system (ABI). Reads (50bp) were aligned to bovine transcripts from Ensembl (release 57)44 and SNPs were identified by GEOSPIZA.

Whole genome sequencing of Angus and Holstein bull

Genomic DNA from a Black Angus bull and a Holstein bull was sequenced using the Applied Biosystems SOLiD 3 sequencer (Life Technologies Corporation), using a combination of fragment and mate-paired libraries. The libraries were prepared using the reagents and protocols provided by Applied Biosystems, and as reported in previous studies.29

Sequence reads were mapped to the Btau4.0 bovine genome assembly using the Bioscope 1.0 software suite (Life Technologies Corporation). A list of putative SNPs was generated for each animal from the mapped reads, using the diBayes SNP Detection module (with the “med-coverage” stringency setting) included with Bioscope. The lists were subjected to additional filtering, to remove SNPs with particularly high read depth (higher than 95% of the other SNPs from the same animal), and to remove SNPs that could not be unambiguously placed on bovine genome assembly UMD3.147 using the megablast algorithm48 in BLAST+,49 100 bp of flanking sequence, and an e-value threshold of 1e-35. SNPs were annotated using NGS-SNP45and release 57 of Ensembl,44 which includes SNPs from dbSNP build 130.46

Genotyping and data quality control

The total of 729 samples were genotyped with a custom Illumina Golden Gate Vera Code Assay containing 384 SNPs following the manufacturers recommended protocol (Illumina Inc.). Genotypes were called using the BeadStudio software (Illumina Inc.) and processed through the automated genotype calling. Genotypes were then subjected to data quality control parameters. Seventeen SNPs were removed for being monomorphic. Overall, a genotyping success rate of 96% was achieved. Forty seven SNPs from the original set of 384 SNPs were excluded on the basis of assay failure or poor genotype clustering. The remaining 320 SNPs were analyzed through the summary statistics of the PLINK program.30 Additionally, 80 SNPs were removed due to a minor allele frequency (MAF) less than 0.01. In total 240 SNPs were included in the analysis.

Statistical analysis

PLINK software v1.07 was used to perform the statistical analysis.30 The genotypic data from both populations was combined and was analyzed using the DFAM procedure in PLINK. The DFAM procedure in PLINK implements the sib-TDT and also allows for unrelated individuals to be included (via a clustered-analysis using the Cochran-Mantel-Haesnzel). This data was combined instead of being analyzed separately in the two populations due to the unbalanced nature of the second case-control population. This was the most extensive candidate gene list tested for BSE including more number of animals than previous work.

Supplementary Material

Additional material
prio-6-461-s01.pdf (106KB, pdf)

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Acknowledgements

Jennifer Thomson constructed the custom marker panel, performed analysis, and wrote the manuscript; Victoria Bowles and Yan Meng participated in sequencing library construction; Urmila Basu contributed to the library construction, sequencing and manuscript writing; Victoria Bowles, Jung-Woo Choi contributed to SNP identification and marker panel construction, Paul Stothard and Stephen Moore were involved in experimental design, and manuscript writing.

Glossary

Abbreviations:

BSE

bovine spongiform encephalopathy

PrP

prion protein

TSE

transmissible spongiform encephalopathy

vCJD

variant Creutzfeldt-Jakob disease

SNP

single nucleotide polymorphism

CNS

central nervous system

Supplemental Material

Supplemental materials may be found here:

http://www.landesbioscience.com/journals/prion/article/21866/

Footnotes

References

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