Polymorphism discovery and allele frequency estimation using high-throughput DNA sequencing of target-enriched pooled DNA samples

Gene selection

A total of 83 genes were selected for targeted re-sequencing based on: (1) a comprehensive literature review of the somatotrophic axis, including its transcriptional regulators, binding proteins and associated genes involved in gluconeogenesis and insulin nutrient partitioning-related pathways; and (2) the availability of the DNA sequences in the Ensembl and/or GenBank databases (Additional File 1, Table S1).

Animal Selection

Genomic DNA was available for 698 Holstein-Friesian progeny-tested artificial insemination (AI) bulls. An iterative algorithm was invoked to chose 150 bulls divided into two groups (n = 75) divergent for genetic merit (i.e. predicted transmitting ability) for calving interval while cognizant of the co-ancestry within each group. Firstly both genetic merit for calving interval and pairwise relationships among all animals were standardized to have equal variance. An index was derived for each animal using the (standardized) estimated breeding value of the animal and the (standardized) relationship of the animal to each of the other animals. The weighting on EBV and relatedness in the index was 60:40. An algorithm was subsequently invoked to generate the groups. Firstly, the animal with the lowest genetic merit for calving interval was selected and allocated to the low calving interval group. A second animal was selected based on its average index value with the first animal selected. Subsequently a third animal was selected based on its average index value with the bulls previously selected. This algorithm continued until 75 animals bulls were selected for the low calving interval group. The algorithm was again invoked to select the group of animals of high genetic merit for calving interval; the selection choice did not include any animal in the poor genetic merit group. This resulted in the sires in the high CIV group representing 46 different paternal half-sib groups and 44 different maternal grand-sires lines while the sires in the low CIV group represented 71 different paternal half sib groups and 61 different maternal grandsire lines. In total, 116 different sire lines (84 different paternal grand-sire lines) and 102 different maternal-grandsire lines were represented. The co-ancestry among the high CIV group was 3.0%, among the low CIV group was 0.24% and between the high and low CIV groups was 0.20%; the low CIV animals were on average 9 years older than the high CIV group. The median (inter-quartile range in parenthesis) number of daughters per sire and reliability were 160 (261) and 83% (23%) for the high CIV pool and 261 (738) and 79% (43%) for the low CIV pool. Mean predicted transmitting ability (standard deviation in parenthesis) for the 75 high and 75 low calving interval bulls was 5.3 days (1.6) and -5.8 days (1.4), respectively.

Sample preparation, target enrichment and sequencing

For both sample groups (n = 75), DNA was pooled using equimolar quantities (100 ng) of DNA from each individual animal. The pools were then prepared for high-throughput DNA sequencing using the Illumina Genome Analyzer II platform. For this, 5 μg of pooled genomic DNA was sheared for 30 min using NEBNext^® dsDNA Fragmentase™ (New England Biolabs UK Ltd., Hitchin, UK) according to manufacturer's instructions. Blunt-end fragment repair and A tailing was performed on the resulting fragments using NEBNext^® End Repair Module and NEBNext^® dA-Tailing Module (New England Biolabs UK Ltd., Hitchin, UK). Illumina standard paired end (PE), adapters (Illumina, Essex, UK), including a 6 bp index, were ligated to the fragments, and the indexed adapter ligated fragments were gel purified and enriched by 12 cycles of PCR using Illumina PE1 and PE2 primers and Phusion High-Fidelity DNA Polymerase (New England Biolabs UK Ltd., Hitchin, UK).

Indexed PE sequencing libraries were captured and enriched for the genes of interest using the SureSelect Target Enrichment for Illumina^® PE Sequencing (Agilent Technologies Ltd., Cork, Ireland) according to manufacturer's instructions. Bovine Hybloc (Applied Genetics Laboratories, Florida, USA) was used instead of human Cot1 DNA during the sequence capture process to prevent non-specific hybridisation to the sequence capture baits. Sequence capture baits were designed to target whole gene (exons and introns) sequences including 3 kb of both the 5' and 3' flanking UTR sequence for 22 genes central to the function of the somatotrophic axis (Additional File 1, Table S1). To maximise the number of genes included for analysis, the remaining baits were designed to target only the coding sequences and 5' and 3' flanking UTR regions and encompassed 61 additional genes (Additional File 1, Table S1). In total, approximately 1.5 Mb of DNA sequence was targeted for capture. Target captured libraries from both sample groups contained different indexes located at the 5' end of both reads, allowing them to be pooled into a single flow cell lane. 80 bp PE sequencing was conducted on an Illumina GAIIx (cluster kit 4PE and sequencing kit version 5) and indexed sequencing reads from the two groups of animals were separated bioinformatically.

Mapping and variant calling

All DNA sequence data were aligned to the B. taurus version 4.0 (Btau_4.0) reference genome using the Burrows-Wheeler Aligner (BWA) program version 0.5.9 [25] and the alignments were sorted and filtered for possible PCR and optical duplicates using Samtools version 0.1.17 [26]. The Genome Analysis Toolkit (GATK) version 1.0.5506 [27] was used for base quality score recalibration incorporating known Bovine SNPs from ENSEMBL [28] and indel realignment using standard hard filtering parameters [29]. DNA sequence polymorphisms were then identified for each of the sequenced regions using Samtools version 0.1.17 [26]. Samtools was also used with in-house scripts to calculate coverage estimates and to compare frequencies between the groups. Non-synonymous SNPs were identified using the Btau_4.0 annotation from ENSEMBL version 62 [30] using SNPdat (available upon request from the authors).

For variant calling, reads below stringent thresholds for mapping quality score (≤ 50) and base quality (≤ 20) were discarded. In addition, to call a variant a minimum of 4 reads supporting the non reference allele was required across both pools.

Accuracy of SNP detection and allele frequency estimation

To assess the accuracy of SNP detection and allele frequency estimation we compared the high throughput DNA sequence data to: (1) SNPs located within the 1.5 Mb of targeted sequences as reported in the dbSNP (v130) database; and (2), SNPs validated, as part of a previous larger study performed by this group, as segregating in these 150 Holstein Friesian (HF) cattle using capillary based Sanger re-sequencing and Sequenom^® MassARRAY genotyping technologies. This included analysis of genotypes previously reported by this group in IGF1[4], IGF2R, IGF2[31, 32], GH1[5] and GHR[7].

Transcription factor and microRNA binding site analysis

Bioinformatic analysis was performed on SNPs in the regulatory regions of selected genes to examine the effects of allele substitution on predicted transcription factor binding sites using MatInspector software package [33] and microRNA (miRNA) binding sites using MicroInspector software [34].

Comparison of SNP allele frequency estimations

A two-tailed Fisher's exact test was used to 1) compare the allele frequency estimates generated using high throughput sequencing and Sequenom^® MassARRAY genotyping technologies for the SNPs in common across both platforms, and 2) compare the allele frequencies generated using high throughput sequencing in the pools of animals divergent for genetic merit for calving interval. In both analyses adjustment for multiple testing was undertaken using the Benjamini and Hochberg [35] correction for an experiment-wise significance of P < 0.1 and < 0.01 in the first and second analyses, respectively.

High throughput DNA sequence analysis

Approximately 2.95 million reads were obtained generating, on average, 1.05 Gb of sequence data per pool. Of the approximate 1.5 Mb of sequence targeted for enrichment, 1.2 Mb was sequenced with an average of 44-fold coverage per base across both pools (Table 1). We identified a total of 5,028 variants (4,135 SNPs and 893 indels) across the 83 genes (Additional File 1, Table S2). Nineteen percent (n = 952) of variants were located within 5' and 3' UTRs, 72% (n = 3,612) were intronic and 9% (n = 464) were exonic, including 65 indels and 236 SNPs resulting in non-synonymous substitutions [NSS] (Table 2). Significant (P < 0.01) allele frequency differentials between low and high CIV groups were observed for 720 SNPs including 58 NSS (Additional File 1, Table S2). The top 20 most significant SNPs differentiating the high and low CIV groups, located at least 10 Mb apart, in exonic, intronic, and regulatory regions is displayed in Table 3. Distribution of the 4, 135 SNP allele frequency differentials between both pools showed a slightly heavy tailed normal distribution (Figures 1 and 2).

	Low calving interval pool	High calving interval pool
Total sequencing data generated (Gb)	1.57	1.38
Sequence data mapped to bovine genome (Gb)	1.23 (78%)	0.9 (67%)
Data mapped to targeted regions (Mb)	143 (12%)	132 (14%)
Data remaining after quality control (Mb)	74 (52%)	48 (34%)
Number of bases targeted (Mb)	1.54	1.54
Quantity of bases covered (Mb)	1.18 (76%)	1.20 (77%)
Average fold coverage per base	56	32

Percentages in parentheses represent proportions related to the category above each field.

	Shared between pools		Unique in Low CIV pool2		Unique to High CIV pool2
	SNP	Indel	SNP	Indel	SNP	Indel
5' and 3' UTR	547	86	93	50	118	58
Exonic1	278 (153)	29	57 (38)	17	65 (45)	19
Intronic	2226	277	310	189	441	168

¹: Numbers in parenthesis represent the number of SNPs resulting in non synonymous substitutions; ²: Variants classified as unique represent variants undetected in the corresponding pool.

Entrez gene/Ensembl ID	Chr.	Location	Allele	Frequency in Low CIV pool1	Frequency in High CIV pool1	P value 2	Type/Location3	Predicted effect4	Reference5
GH1	19	49749042	G>T	0.00	0.38	9.2 × 10 -15	Exon NSS	N/A	rs41917096
SST	21	49258728	C>T	0.00	0.34	4.8 × 10 -9	Exon NSS	N/A	novel
NR2F2	21	9510000	A>G	0.00	0.52	9.1 × 10 -8	Exon NSS	N/A	novel
HK3	7	37630361	T>G	0.00	0.30	8.2 × 10 -6	Exon NSS	N/A	novel
STAT5B	19	43679543	G>T	0.00	0.28	1.2 × 10 -5	Exon NSS	N/A	novel
IGFBP5	2	108855684	C>T	0.00	0.54	2.3 × 10 -12	5'	2 × pTFBS	novel
MAPK9	7	871347	C>T	0.47	0.09	5.3 × 10 -12	5'	1 × pTFBS	rs43495395
GCK	11	74275999	G>A	0.00	0.39	2.2 × 10 -9	5'	8 × pTFBS	novel
GHR	20	34207771	C>A	0.00	0.39	8.3 × 10 -9	5'	None	novel
HK1	28	24994609	C>T	0.00	0.18	2.4 × 10 -7	3'	1 × miRNA	novel
GHRH	13	66803046	A>G	0.65	0.25	3.1 × 10 -6	5'	None	novel
IRS4	X	35195377	C>G	0.00	0.83	4.9 × 10 -9	3'	None	novel
SLC2A1	3	110250920	T>G	0.00	0.38	5.0 × 10 -9	3'	4 × miRNA	novel
SLC5A1	17	73990477	C>T	0.68	0.18	9.7 × 10 -7	3'	None	rs41255339
ESR2	10	78593544	C>T	0.00	0.21	9.3 × 10 -7	3'	1 × miRNA	novel
IGF2R	9	100136966	C>T	0.79	0.00	5.5 × 10 -20	Intron	N/A	novel
PIK3R2	7	4996360	C>A	0.00	0.55	2.8 × 10 -15	Intron	N/A	novel
Q95M43	7	14524938	G>A	0.00	0.44	4.4 × 10 -9	Intron	N/A	novel
IGFBP3	4	78896406	A>T	0.00	0.42	8.8 × 10 -9	Intron	N/A	novel
SIRT2	18	48205429	T>A	0.00	0.53	1.6 × 10 -8	Intron	N/A	novel

¹: Frequency of second allele displayed; ²: Benjamini and Hochberg corrected P value; ³: NSS = SNPs resulting in non synonymous substitutions; ⁴: Predicted effects on transcription factor binding sites in the 5' regulatory regions using MatInspector software package [33] and on microRNA binding sites in the 3' regulatory regions using MicroInspector software [34], detailed information on predicted effects displayed in Tables 6 and 7; ⁵: SNPs classified according to dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/ accessed 9^th September 2011).

Accuracy of SNP detection and allele frequency estimation

Comparison to dbSNP

In total, 1,304 SNPs were reported in dbSNP v130 across the 1.5 Mb of targeted sequences. Of these, 598 SNPs were identified during the high throughput sequencing with 706 SNPs undetected (Additional File 1, Table S3). A large number of undetected SNP loci had less than 4× coverage (n = 396) in the present study. Assuming all SNPs reported in dbSNP were present in this population of HF sires, the false negative rate drops to 28.5% (268 SNPs) and 26% (252 SNPs) when considering bases with at least 10× and 30× coverage, respectively. The median for base coverage in undetected and detected groups was two and 95 respectively (Figure 3). Comparison of these data with SNPs reported in dbSNP (v130) also identified 3537 putative novel SNPs (Additional File 1, Table S2).

Comparison to previous data from this group

Analysis of the previous studies on these sires [4, 5, 7, 31, 32] identified 67 validated SNPs segregating in these 150 HF cattle, of which, 43 SNPs were identified in at least one of the two pools in this study (Table 4). The lowest minor allele frequency detected was 0.08 and 0.09 in the low and high CIV pools, respectively (Table 4). There was strong concordance between both methods with no significant differences (experiment-wise P > 0.1) between allele frequency estimates for any of the 43 SNPs across both low and high CIV pools (Table 4; Figure 4). The 24 undetected SNPs included 16 SNP loci with zero coverage, two SNP loci with combined coverage across both pools of less than 4× and six SNP loci with coverage depth of between 5× to 54× with variant allele frequencies ranging between 0.01 to 0.89 (Additional File 1, Table S4). The false negative rate reduced to 7.5% (5/67) and 3.0% (2/67) when considering variant loci with at least 10× and 30× coverage, respectively. Analysis of the allele frequencies for the two undetected SNPs with greater than 30× coverage revealed they were below detectable thresholds given their respective coverage at each loci (Additional File 1, Table S4).

EntrezGene ID	Chr.	SNP name1	Position	Allele sub.2	Allele frequency in low CIV pool3		P value4	Allele frequency in high CIV pool3		P value4	Reference5
					Actual	NGS		Actual	NGS
		S555G	33897071	T > C	0.19	0.15	1.00	0.12	0.10	1.00	AF140284
		H545	33897099	A > G	0.34	0.28	0.96	0.15	0.18	1.00	AF140284
		A536T	33897128	C > T	0.13	0.15	1.00	0.02	0.00	1.00	AF140284
		N528T	33897151	T > G	0.21	0.22	1.00	0.12	0.10	1.00	AM161140
		GHR76	33897252	A > G	0.13	0.14	1.00	0.02	0.00	1.00	n/a
		F279Y	33915503	A > T	0.00	0.00	1.00	0.09	0.09	1.00	AM161140
		GHR19.1	33994639	G > T	0.62	0.65	1.00	0.47	0.29	0.23	rs109702942
		GHR18.2	33995251	G > C	0.07	0.09	1.00	0.00	0.00	1.00	n/a
		AF126288:g.149	34086084	C > T	0.69	0.73	1.00	0.31	0.29	1.00	AF126288
GHR	20	GHR9.1	34101240	C > T	0.05	0.00	0.96	0.37	0.33	1.00	rs110979028
		GHR3.3	34166627	A > G	0.08	0.12	1.00	0.43	0.39	1.00	n/a
		GHR3.2	34166731	A > G	0.09	0.13	1.00	0.43	0.31	0.96	n/a
		GHR3.1	34166898	T > C	0.09	0.00	0.23	0.42	0.33	0.96	n/a
		GHR2.6	34166944	C > T	0.09	0.00	0.22	0.42	0.38	1.00	rs109825954
		GHR2.5	34166970	A > G	0.09	0.00	0.23	0.42	0.36	1.00	n/a
		GHR2.4	34166982	A > C	0.09	0.00	0.22	0.43	0.33	0.96	n/a
		GHR2.3	34167025	C > T	0.09	0.00	0.23	0.42	0.37	1.00	n/a
		GHR2.2	34167126	G > A	0.09	0.09	1.00	0.43	0.35	1.00	n/a
		GHR2.1	34167240	C > T	0.09	0.00	0.13	0.44	0.52	1.00	n/a
		GH19	49657225	G > A	0.90	0.86	1.00	0.84	0.91	1.00	rs41923481
		GH18	49657293	G > A	0.92	0.82	1.00	0.92	0.90	1.00	rs196003433
		GH17	49657371	C > G	0.92	1.00	1.00	0.89	1.00	1.00	rs41923483
		2291	49660125	T > G	0.08	0.00	0.13	0.12	0.08	1.00	n/a
		2141	49660275	G > C	0.33	0.36	1.00	0.09	0.11	1.00	rs41923484
		GH6	49660469	A > C	0.08	0.00	0.96	0.12	0.12	1.00	rs196003424
		GH38	49693278	G > A	0.33	0.21	1.00	0.59	0.60	1.00	rs41923525
GH1	19	GH37	49693285	C > G	0.29	0.15	0.96	0.54	0.58	1.00	rs41923524
		GH36	49693316	G > C	0.29	0.40	1.00	0.52	0.38	1.00	rs41923523
		GH35	49693328	G > A	0.30	0.35	1.00	0.55	0.29	0.96	rs41923522
		GH34	49693374	C > G	0.34	0.33	1.00	0.61	0.53	1.00	rs41923521
		GH32	49693442	A > G	0.67	0.58	1.00	0.39	0.37	1.00	rs196301608
		GH31	49693460	C > T	0.67	0.62	1.00	0.39	0.28	1.00	rs196003442
		GH30	49693512	A > G	0.66	0.65	1.00	0.38	0.38	1.00	rs196003441
		GH29	49693686	C > T	0.50	0.26	0.36	0.50	0.64	0.96	rs41923520
		rs29012855	71150007	C > T	0.99	1.00	1.00	0.90	0.93	1.00	rs29012855
		IGF1i3	71175747	T > C	0.17	0.24	0.95	0.01	0.00	1.00	rs109557731
IGF1	5	IGF1i2	71175753	A > G	0.49	0.46	1.00	0.25	0.15	0.70	rs109227434
		IGF1i1	71176219	A > T	0.26	0.16	1.00	0.37	0.25	1.00	rs110076130
		AF017143	71198324	G > A	0.35	0.26	0.96	0.62	0.76	0.92	AF017143
IGF2	29	IGF2_B_6646	51250879	A > G	0.40	0.40	1.00	0.65	0.47	0.83	rs42196909
		IGF2R_D_41515	100112956	A > G	0.87	0.79	1.00	0.87	0.87	1.00	rs41623543
IGF2R	9	IGF2R_D_41092	100113379	G > A	0.63	0.65	1.00	0.77	0.47	0.22	rs41623544
		IGF2R:g.86262	100134604	G > A	0.86	0.76	0.73	0.86	1.00	0.62	n/a

¹: SNP details according to previous studies [4, 5, 7, 31, 32]; ²: Reported from the forward strand; ³: Allele frequencies represents second allele; ⁴: Benjamini and Hochberg corrected P values; ⁵:Genbank accession or dbSNP reference (http://www.ncbi.nlm.nih.gov/projects/SNP/ accessed 9^th September 2011).

DNA pooling and allele frequency estimation

DNA pooling and comparison of allele frequencies between groups of individuals divergent for a particular phenotype is an attractive approach to candidate QTN identification primarily due to the current costs of target enrichment and high throughput sequencing of large numbers of individual genomes [36]. Although segregation at significantly different frequencies between pools does not necessarily infer a relationship with the trait and may be a result of genetic drift or high linkage disequilibrium with a causative variant, this approach efficiently captures the genetic variation of individuals divergent for a particular phenotype and has been successfully used to identify variants involved in complex traits in humans [23, 24]. However, the success of this approach is influenced by several factors including: (1) the degree of divergence of individuals for the true genetic merit of the trait as well as the effective number (i.e., after accounting for co ancestry) of individuals per pool; (2) equimolar pooling of DNA from each individual; (3) bias introduced during target enrichment prior to sequencing; (4) bias introduced during amplification during sequencing; (5) classification of variants during post sequencing data analysis; (6) sequencing error rate; (7) technical differences between sequencing lanes and (8) sampling bias during sequencing. Analysis of all the technical parameters individually was not within the remit of this study and has previously been discussed [22, 36–39].

In the current study, we assessed performance of the process retrospectively by comparing the allele frequency estimates with results from conventional genotyping and observed a strong concordance between both methods even at low read depths of less than 10× where reliable sequencing data can be difficult to achieve [39]. Although the relative contribution of each sample in pooled sequencing is a critical issue and cannot be guaranteed, the high concordance with actual genotypes provided strong evidence that minimal biases were introduced including during in-solution enrichment which captured approximately 80% of the target sequence and has previously been reported to yield better uniformity and specificity than equivalent array based capture approaches [40]. Potential biases due to technical variations such as mechanical differences in sequencing lane manufacture [39] were circumvented by indexing groups and pooling into a single lane. However, despite sequencing within a single flow cell lane, differences in capture efficiencies were observed between pools. The high CIV pool generated 37% more data mapping to the bovine genome compared with the low CIV pool. Although, the authors cannot explain the differences, it is noteworthy that other authors have also observed differences in capture efficiencies between pooled DNA samples. For example, Bansal et al. (2011) [36] observed up to a 26% difference in sequencing coverage between libraries captured using the same target capture system. Furthermore, Maricic et al. 2010 [41] reported up to a four-fold difference in the number of sequence reads obtained using captured mitochondrial DNA sequences from 46 human individuals using a similar bait-design sequence capture system.

Despite the cost effective advantages a pooled sample approach delivers, given a fixed quantity of sequence data, a compromise on the fold-coverage per pooled sample/group and thereby sensitivity is unavoidable. The combined average read coverage of 88× across both pools impacted the sensitivity to detect variants segregating at low frequencies in either pool. Accounting for the requirement of 4 non-reference alleles across both pools to be present to call a variant translates to the ability to detect alleles with MAF, on average, of 4.5%. To achieve detection of alleles with MAF < 4.5% a reduction in the quantity of sequence targeted for enrichment and/or number of pools per sequencing lane would be required. This is an important consideration for study designs incorporating a DNA pooling and sequencing approach for rare variant detection. However a reduced ability to identify rare variants by sequencing many individuals at a more shallow depth in larger pool sizes can be offset by the gains in power achieved by more accurate estimation of allele frequencies compared to sequencing fewer individuals at higher depth with smaller pool size, even accounting for higher than expected error rates [42].

When assessing false negative rates in relation to reference databases other factors other than sequencing depth need consideration including segregation of these variants in the target population and accuracy of variants reported in the reference database. Poor sequencing depth was the main factor in the false negative rates found when compared to the Sequenom^® dataset as the majority of undetected SNP loci, i.e. 93% had low read depths of less than 10×. Comparison to the dbSNP database however highlighted that other factors were involved with only 61% of undetected SNPs having read depths less than 10×. The high SNP false negative rate of 20.5% (loci with >10× coverage) compared to dbSNP is most probably due to a combination of a lack of segregation of these SNPs in HF cattle and inaccurate dbSNP data. In support of this a recent commentary by Day (2010) [43] on the human dbSNP database revealed that several studies have reported discontinuity with dbSNP variants and depending on the study dbSNP false positive rates ranged between 8 - 17%.

Identification of candidate causative variants

The identification of causative mutations or quantitative trait nucleotides (QTN) underlying performance traits in livestock is problematic with only a small number identified to date [44, 45]. This is mainly due to the polygenic nature of quantitative traits requiring dense genome wide marker or sequence analysis on large populations of animals with accurate phenotypic data to identify and accurately estimate small effects especially on lowly heritable traits [14]. Other factors include the long generation interval of livestock, costs involved, lack of inbred lines, the difficulty of producing 'knock-out's [45] as well as possible conservation of LD within small chromosomal regions.

The somatotrophic axis is a likely candidate for harbouring QTN due to its central role in animal post-natal growth, development, lactogenesis, and reproduction [2, 3]. It is therefore not surprising several groups have reported associations with variants in this axis and performance [6, 8, 46–51]. In addition to milk production and growth traits we have previously observed associations between calving interval and variants in GHR[7] and associations between an indirect measurement of reproductive performance (functional survival) and SNPs in both GH1 and IGF1[4, 5]. Our previous studies involved sequence analysis of specific regions, encompassing between only 2-5% of the sequence of each gene. Polymorphisms presented herein are the first genomic characterisation of this axis in cattle divergent for a performance trait, and were generated from sequencing entire genes and regulatory regions. It is therefore probable, even allowing for other possible genetic mechanisms such as copy number variation or epigenetic effects such as methylation, a subset of these variants underlies heritable variation in CIV. Although CIV is a lowly heritable trait (0.03-0.04; Berry et al. [52]) the sires used in the present study were of relatively high reliability. We identified variants (n = 301) within coding regions of 72 genes, consisting of either SNPs resulting in non-synonomous substitutions or indels, which could plausibly affect abundance or biological activity of their respective gene products. In this study, 58 of these SNPs were segregating at significantly different frequencies (P < 0.01) between the high and low CIV pools, all with at least 30× coverage, and warrant further investigation. In addition, SNPs in the regulatory regions flanking each gene were found to be present at different frequencies between pools (n = 116) and may harbour variants of biological significance. Interestingly, bioinformatic analysis of the top 10 most significant variants located in untranslated regions revealed SNPs located in the 5' UTR of IGFBP5, MAPK9 and GCK were predicted to collectively modulate 11 transcription factor binding sites (TFBS) and SNPs in the 3' UTR of HK1, SLC2A1 and ESR2 were predicted to modulate six miRNA binding sites. While in contrast significant SNPs analysed in the 5' UTR of GHR and GHRH and 3' region of SIRT2 and SLC5A1 were not predicted to have any effects on TF or miRNA binding. Perhaps not surprisingly, by far the largest proportion of all detected variants, 71% (n = 3612), were located in the intronic regions of the 22 genes targeted for complete sequencing, of which, frequencies of 524 SNPs were significantly different between groups. An example of the potential impact of intronic polymorphisms on gene function can be seen with one of the few QTNs identified in livestock, resulting in a major effect on muscle growth in pigs, is located within an intron of IGF2[53]. While it is interesting to investigate possible effects of these polymorphisms, it is important to reiterate the observation of differential frequencies between pools does not translate to an association with CIV but instead candidate causative variants are presumably captured and cannot be identified until subsequent genotyping and association analysis.

Genotyping all identified variants across a large population of cattle with detailed phenotypic information would provide the greatest chance for QTN identification. However due to a combination of (1) the quantity of variants identified and (2) the requirement for large numbers of genotyped individuals to attain sufficient power in the association analysis renders this a costly approach. Therefore careful selection of candidate polymorphisms prior to genotyping will be required. A parameter worth consideration during variant selection is the likely extent of linkage disequilibrium (LD) between variants in either pool. High LD could result in substantial numbers of variants displaying differential frequencies due to nothing more than physical proximity to the causative agent. One limitation of the current DNA pooling strategy however is the inability to estimate LD and subsequent variant selection could inadvertently omit QTN candidates from genotyping. Selecting variants per gene/chromosome rather than genome wide and using bioinformatic tools to extrapolate possible biological effects based on our current understanding of gene regulation and function could reduce the number of false positives and negatives carried through the process. LD in cattle was previously thought to span large distances [54, 55] but more recent evidence suggests the extent of LD in HF dairy cattle to be smaller in the region of 2 Mb (r² = 0.3) to 10 Mb (D' = 0.3) [56]. The current study identified 720 SNPs displaying significantly different allele frequencies between high and low CIV pools, located across 72 genes on 28 chromosomes with 50 of these genes separated by at least 10 Mb. Even considering the possibility of regions of high LD these results tentatively support previous observations of multiple independent effects between variants in genes of the somatotrophic axis and performance [57]. This is consistent with Fishers classical infinitesimal model of complex traits, where many genes are involved, each with small but additive effects [58].

This study is one of only two reporting the use of targeted enrichment for the analysis of large genomic regions in cattle, the previous study utilised high-throughput sequencing to identify the causative mutation underlying bovine arachnomelia, a congenial anomaly resulting in limb bone deformation [59]. To our knowledge, this report describes the first sequencing of targeted genomic regions using groups of individuals divergent for an economically important trait in livestock and the high concordance obtained between actual genotype frequencies and this data supports DNA pooling as a cost-effective alternative to individual animal genotyping for SNP allele frequency estimation in agreement with previous studies [36, 38, 60–63].

These results represent a preliminary screen for candidate causal polymorphisms in genes of the somatotrophic axis contributing to differences in genetic merit for CIV performance. Future work will include variant selection, aided by bioinformatic analysis, followed by genotyping on a large panel of cattle with detailed fertility information. As sequencing technology develops whole genome sequencing of large numbers individual genomes will become affordable for many study designs, but until then the detection of candidate causative variants, rare and common, via targeted re-sequencing followed by array based association studies will almost always be the most efficient design.