Browsing by Author "Lehnert K"

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    A Capra hircus chromosome 19 locus linked to milk production influences mammary conformation
    (BioMed Central Ltd, 2022-02-11) Jiang A; Ankersmit-Udy A; Turner S-A; Scholtens M; Littlejohn MD; Lopez-Villalobos N; Proser CG; Snell RG; Lehnert K
    Background Economically important milk production traits including milk volume, milk fat and protein yield vary considerably across dairy goats in New Zealand. A significant portion of the variation is attributable to genetic variation. Discovery of genetic markers linked to milk production traits can be utilised to drive selection of high-performance animals. A previously reported genome wide association study across dairy goats in New Zealand identified a quantitative trait locus (QTL) located on chromosome 19. The most significantly associated single nucleotide polymorphism (SNP) marker for this locus is located at position 26,610,610 (SNP marker rs268292132). This locus is associated with multiple milk production traits including fat, protein and volume. The predicted effect of selection for the beneficial haplotype would result in an average production increase of 2.2 kg fat, 1.9 kg protein and 73.6 kg milk yield. An outstanding question was whether selection for the beneficial allele would co-select for any negative pleiotropic effects. An adverse relationship between milk production and udder health traits has been reported at this locus. Therefore, a genome wide association study was undertaken looking for loci associated with udder traits. Results The QTL and production associated marker rs268292132 was identified in this study to also be associated with several goat udder traits including udder depth (UD), fore udder attachment (FUA) and rear udder attachment (RUA). Our study replicates the negative relationship between production and udder traits with the high production allele at position 19:26,610,610 (SNP marker rs268292132) associated with an adverse change in UD, FUA and RUA. Conclusions Our study has confirmed the negative relationship between udder traits and production traits in the NZ goat population. We have found that the frequency of the high production allele is relatively high in the NZ goat population, indicating that its effect on udder conformation is not significantly detrimental on animal health. It will however be important to monitor udder conformation as the chromosome 19 locus is progressively implemented for marker assisted selection. It will also be of interest to determine if the gene underlying the production QTL has a direct effect on mammary gland morphology or whether the changes observed are a consequence of the increased milk volume.
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    A common regulatory haplotype doubles lactoferrin concentration in milk.
    (BioMed Central Ltd, 2024-03-28) Lopdell TJ; Trevarton AJ; Moody J; Prowse-Wilkins C; Knowles S; Tiplady K; Chamberlain AJ; Goddard ME; Spelman RJ; Lehnert K; Snell RG; Davis SR; Littlejohn MD
    BACKGROUND: Bovine lactoferrin (Lf) is an iron absorbing whey protein with antibacterial, antiviral, and antifungal activity. Lactoferrin is economically valuable and has an extremely variable concentration in milk, partly driven by environmental influences such as milking frequency, involution, or mastitis. A significant genetic influence has also been previously observed to regulate lactoferrin content in milk. Here, we conducted genetic mapping of lactoferrin protein concentration in conjunction with RNA-seq, ChIP-seq, and ATAC-seq data to pinpoint candidate causative variants that regulate lactoferrin concentrations in milk. RESULTS: We identified a highly-significant lactoferrin protein quantitative trait locus (pQTL), as well as a cis lactotransferrin (LTF) expression QTL (cis-eQTL) mapping to the LTF locus. Using ChIP-seq and ATAC-seq datasets representing lactating mammary tissue samples, we also report a number of regions where the openness of chromatin is under genetic influence. Several of these also show highly significant QTL with genetic signatures similar to those highlighted through pQTL and eQTL analysis. By performing correlation analysis between these QTL, we revealed an ATAC-seq peak in the putative promotor region of LTF, that highlights a set of 115 high-frequency variants that are potentially responsible for these effects. One of the 115 variants (rs110000337), which maps within the ATAC-seq peak, was predicted to alter binding sites of transcription factors known to be involved in lactation-related pathways. CONCLUSIONS: Here, we report a regulatory haplotype of 115 variants with conspicuously large impacts on milk lactoferrin concentration. These findings could enable the selection of animals for high-producing specialist herds.
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    Advantage of including Genomic Information to Predict Breeding Values for Lactation Yields of Milk, Fat, and Protein or Somatic Cell Score in a New Zealand Dairy Goat Herd
    (MDPI (Basel, Switzerland), 2021-01) Scholtens M; Lopez-Villalobos N; Lehnert K; Snell R; Garrick D; Blair HT
    Selection on genomic breeding values (GBVs) is now readily available for ranking candidates in improvement schemes. Our objective was to quantify benefits in terms of accuracy of prediction from including genomic information in the single-trait estimation of breeding values (BVs) for a New Zealand mixed breed dairy goat herd. The dataset comprised phenotypic and pedigree records of 839 does. The phenotypes comprised estimates of 305-day lactation yields of milk, fat, and protein and average somatic cell score from the 2016 production season. Only 388 of the goats were genotyped with a Caprine 50K SNP chip and 41,981 of the single nucleotide polymorphisms (SNPs) passed quality control. Pedigree-based best linear unbiased prediction (PBLUP) was used to obtain across-breed breeding values (EBVs), whereas a single-step BayesC model (ssBC) was used to estimate across-breed GBVs. The average prediction accuracies ranged from 0.20 to 0.22 for EBVs and 0.34 to 0.43 for GBVs. Accuracies of GBVs were up to 103% greater than EBVs. Breed effects were more reliably estimated in the ssBC model compared with the PBLUP model. The greatest benefit of genomic prediction was for individuals with no pedigree or phenotypic records. Including genomic information improved the prediction accuracy of BVs compared with the current pedigree-based BLUP method currently implemented in the New Zealand dairy goat population.
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    DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites.
    (29/06/2022) Zhang J; Liu B; Wang Z; Lehnert K; Gahegan M
    BACKGROUND: Addressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-transcriptional control. Identification of RBPs binding sites is a key step for the anatomy of the essential mechanism of gene regulation by controlling splicing, stability, localization and translation. Traditional methods for detecting RBPs binding sites are time-consuming and computationally-intensive. Recently, the computational method has been incorporated in researches of RBPs. Nevertheless, lots of them not only rely on the sequence data of RNA but also need additional data, for example the secondary structural data of RNA, to improve the performance of prediction, which needs the pre-work to prepare the learnable representation of structural data. RESULTS: To reduce the dependency of those pre-work, in this paper, we introduce DeepPN, a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutional network (GCN) for detecting RBPs binding sites. It includes a two-layer CNN and GCN in parallel to extract the hidden features, followed by a fully connected layer to make the prediction. DeepPN discriminates the RBP binding sites on learnable representation of RNA sequences, which only uses the sequence data without using other data, for example the secondary or tertiary structure data of RNA. DeepPN is evaluated on 24 datasets of RBPs binding sites with other state-of-the-art methods. The results show that the performance of DeepPN is comparable to the published methods. CONCLUSION: The experimental results show that DeepPN can effectively capture potential hidden features in RBPs and use these features for effective prediction of binding sites.
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    Screening for phenotypic outliers identifies an unusually low concentration of a β-lactoglobulin B protein isoform in bovine milk caused by a synonymous SNP.
    (BioMed Central Ltd, 2022-03-16) Davis SR; Ward HE; Kelly V; Palmer D; Ankersmit-Udy AE; Lopdell TJ; Berry SD; Littlejohn MD; Tiplady K; Adams LF; Carnie K; Burrett A; Thomas N; Snell RG; Spelman RJ; Lehnert K
    Background Milk samples from 10,641 dairy cattle were screened by a mass spectrometry method for extreme concentrations of the A or B isoforms of the whey protein, β-lactoglobulin (BLG), to identify causative genetic variation driving changes in BLG concentration. Results A cohort of cows, from a single sire family, was identified that produced milk containing a low concentration of the BLG B protein isoform. A genome-wide association study (GWAS) of BLG B protein isoform concentration in milk from AB heterozygous cows, detected a group of highly significant single nucleotide polymorphisms (SNPs) within or close to the BLG gene. Among these was a synonymous G/A variation at position + 78 bp in exon 1 of the BLG gene (chr11:103256256G > A). The effect of the A allele of this SNP (which we named B’) on BLG expression was evaluated in a luciferase reporter assay in transfected CHO-K1 and MCF-7 cells. In both cell types, the presence of the B’ allele in a plasmid containing the bovine BLG gene from -922 to + 898 bp (relative to the transcription initiation site) resulted in a 60% relative reduction in mRNA expression, compared to the plasmid containing the wild-type B sequence allele. Examination of a mammary RNAseq dataset (n = 391) identified 14 heterozygous carriers of the B’ allele which were homozygous for the BLG B protein isoform (BB’). The level of expression of the BLG B’ allele was 41.9 ± 1.0% of that of the wild-type BLG B allele. Milk samples from three cows, homozygous for the A allele at chr11:103,256,256 (B’B’), were analysed (HPLC) and showed BLG concentrations of 1.04, 1.26 and 1.83 g/L relative to a mean of 4.84 g/L in milk from 16 herd contemporaries of mixed (A and B) BLG genotypes. The mechanism by which B’ downregulates milk BLG concentration remains to be determined. Conclusions High-throughput screening and identification of outliers, enabled the discovery of a synonymous G > A mutation in exon 1 of the B allele of the BLG gene (B’), which reduced the milk concentration of β-lactoglobulin B protein isoform, by more than 50%. Milk from cows carrying the B’ allele is expected to have improved processing characteristics, particularly for cheese-making.