Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Multiple QTL underlie milk phenotypes at the CSF2RB locus.(BioMed Central Ltd, 2019-01-24) Lopdell TJ; Tiplady K; Couldrey C; Johnson TJJ; Keehan M; Davis SR; Harris BL; Spelman RJ; Snell RG; Littlejohn MDBackground Over many years, artificial selection has substantially improved milk production by cows. However, the genes that underlie milk production quantitative trait loci (QTL) remain relatively poorly characterised. Here, we investigate a previously reported QTL located at the CSF2RB locus on chromosome 5, for several milk production phenotypes, to better understand its underlying genetic and molecular causes. Results Using a population of 29,350 taurine dairy cows, we conducted association analyses for milk yield and composition traits, and identified highly significant QTL for milk yield, milk fat concentration, and milk protein concentration. Strikingly, protein concentration and milk yield appear to show co-located yet genetically distinct QTL. To attempt to understand the molecular mechanisms that might be mediating these effects, gene expression data were used to investigate eQTL for 11 genes in the broader interval. This analysis highlighted genetic impacts on CSF2RB and NCF4 expression that share similar association signatures to those observed for lactation QTL, strongly implicating one or both of these genes as responsible for these effects. Using the same gene expression dataset representing 357 lactating cows, we also identified 38 novel RNA editing sites in the 3′ UTR of CSF2RB transcripts. The extent to which two of these sites were edited also appears to be genetically co-regulated with lactation QTL, highlighting a further layer of regulatory complexity that involves the CSF2RB gene. Conclusions This locus presents a diversity of molecular and lactation QTL, likely representing multiple overlapping effects that, at a minimum, highlight the CSF2RB gene as having a causal role in these processes.Item Non-additive QTL mapping of lactation traits in 124,000 cattle reveals novel recessive loci(BioMed Central Ltd, 2022-12) Reynolds EGM; Lopdell T; Wang Y; Tiplady KM; Harland CS; Johnson TJJ; Neeley C; Carnie K; Sherlock RG; Couldrey C; Davis SR; Harris BL; Spelman RJ; Garrick DJ; Littlejohn MDBACKGROUND: Deleterious recessive conditions have been primarily studied in the context of Mendelian diseases. Recently, several deleterious recessive mutations with large effects were discovered via non-additive genome-wide association studies (GWAS) of quantitative growth and developmental traits in cattle, which showed that quantitative traits can be used as proxies of genetic disorders when such traits are indicative of whole-animal health status. We reasoned that lactation traits in cattle might also reflect genetic disorders, given the increased energy demands of lactation and the substantial stresses imposed on the animal. In this study, we screened more than 124,000 cows for recessive effects based on lactation traits. RESULTS: We discovered five novel quantitative trait loci (QTL) that are associated with large recessive impacts on three milk yield traits, with these loci presenting missense variants in the DOCK8, IL4R, KIAA0556, and SLC25A4 genes or premature stop variants in the ITGAL, LRCH4, and RBM34 genes, as candidate causal mutations. For two milk composition traits, we identified several previously reported additive QTL that display small dominance effects. By contrasting results from milk yield and milk composition phenotypes, we note differing genetic architectures. Compared to milk composition phenotypes, milk yield phenotypes had lower heritabilities and were associated with fewer additive QTL but had a higher non-additive genetic variance and were associated with a higher proportion of loci exhibiting dominance. CONCLUSIONS: We identified large-effect recessive QTL which are segregating at surprisingly high frequencies in cattle. We speculate that the differences in genetic architecture between milk yield and milk composition phenotypes derive from underlying dissimilarities in the cellular and molecular representation of these traits, with yield phenotypes acting as a better proxy of underlying biological disorders through presentation of a larger number of major recessive impacts.Item Allele-specific binding variants causing ChIP-seq peak height of histone modification are not enriched in expression QTL annotations.(BioMed Central Ltd, 2024-06-27) Ghoreishifar M; Chamberlain AJ; Xiang R; Prowse-Wilkins CP; Lopdell TJ; Littlejohn MD; Pryce JE; Goddard MEBACKGROUND: Genome sequence variants affecting complex traits (quantitative trait loci, QTL) are enriched in functional regions of the genome, such as those marked by certain histone modifications. These variants are believed to influence gene expression. However, due to the linkage disequilibrium among nearby variants, pinpointing the precise location of QTL is challenging. We aimed to identify allele-specific binding (ASB) QTL (asbQTL) that cause variation in the level of histone modification, as measured by the height of peaks assayed by ChIP-seq (chromatin immunoprecipitation sequencing). We identified DNA sequences that predict the difference between alleles in ChIP-seq peak height in H3K4me3 and H3K27ac histone modifications in the mammary glands of cows. RESULTS: We used a gapped k-mer support vector machine, a novel best linear unbiased prediction model, and a multiple linear regression model that combines the other two approaches to predict variant impacts on peak height. For each method, a subset of 1000 sites with the highest magnitude of predicted ASB was considered as candidate asbQTL. The accuracy of this prediction was measured by the proportion where the predicted direction matched the observed direction. Prediction accuracy ranged between 0.59 and 0.74, suggesting that these 1000 sites are enriched for asbQTL. Using independent data, we investigated functional enrichment in the candidate asbQTL set and three control groups, including non-causal ASB sites, non-ASB variants under a peak, and SNPs (single nucleotide polymorphisms) not under a peak. For H3K4me3, a higher proportion of the candidate asbQTL were confirmed as ASB when compared to the non-causal ASB sites (P < 0.01). However, these candidate asbQTL did not enrich for the other annotations, including expression QTL (eQTL), allele-specific expression QTL (aseQTL) and sites conserved across mammals (P > 0.05). CONCLUSIONS: We identified putatively causal sites for asbQTL using the DNA sequence surrounding these sites. Our results suggest that many sites influencing histone modifications may not directly affect gene expression. However, it is important to acknowledge that distinguishing between putative causal ASB sites and other non-causal ASB sites in high linkage disequilibrium with the causal sites regarding their impact on gene expression may be challenging due to limitations in statistical power.Item Investigating the genetic components of tuber bruising in a breeding population of tetraploid potatoes(BioMed Central Ltd, 2023-05-05) Angelin-Bonnet O; Thomson S; Vignes M; Biggs PJ; Monaghan K; Bloomer R; Wright K; Baldwin SBACKGROUND: Tuber bruising in tetraploid potatoes (Solanum tuberosum) is a trait of economic importance, as it affects tubers' fitness for sale. Understanding the genetic components affecting tuber bruising is a key step in developing potato lines with increased resistance to bruising. As the tetraploid setting renders genetic analyses more complex, there is still much to learn about this complex phenotype. Here, we used capture sequencing data on a panel of half-sibling populations from a breeding programme to perform a genome-wide association analysis (GWAS) for tuber bruising. In addition, we collected transcriptomic data to enrich the GWAS results. However, there is currently no satisfactory method to represent both GWAS and transcriptomics analysis results in a single visualisation and to compare them with existing knowledge about the biological system under study. RESULTS: When investigating population structure, we found that the STRUCTURE algorithm yielded greater insights than discriminant analysis of principal components (DAPC). Importantly, we found that markers with the highest (though non-significant) association scores were consistent with previous findings on tuber bruising. In addition, new genomic regions were found to be associated with tuber bruising. The GWAS results were backed by the transcriptomics differential expression analysis. The differential expression notably highlighted for the first time the role of two genes involved in cellular strength and mechanical force sensing in tuber resistance to bruising. We proposed a new visualisation, the HIDECAN plot, to integrate the results from the genomics and transcriptomics analyses, along with previous knowledge about genomic regions and candidate genes associated with the trait. CONCLUSION: This study offers a unique genome-wide exploration of the genetic components of tuber bruising. The role of genetic components affecting cellular strength and resistance to physical force, as well as mechanosensing mechanisms, was highlighted for the first time in the context of tuber bruising. We showcase the usefulness of genomic data from breeding programmes in identifying genomic regions whose association with the trait of interest merit further investigation. We demonstrate how confidence in these discoveries and their biological relevance can be increased by integrating results from transcriptomics analyses. The newly proposed visualisation provides a clear framework to summarise of both genomics and transcriptomics analyses, and places them in the context of previous knowledge on the trait of interest.Item Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes.(Springer Nature, 2024-04-26) Chen Z; Guo X; Tao R; Huyghe JR; Law PJ; Fernandez-Rozadilla C; Ping J; Jia G; Long J; Li C; Shen Q; Xie Y; Timofeeva MN; Thomas M; Schmit SL; Díez-Obrero V; Devall M; Moratalla-Navarro F; Fernandez-Tajes J; Palles C; Sherwood K; Briggs SEW; Svinti V; Donnelly K; Farrington SM; Blackmur J; Vaughan-Shaw PG; Shu X-O; Lu Y; Broderick P; Studd J; Harrison TA; Conti DV; Schumacher FR; Melas M; Rennert G; Obón-Santacana M; Martín-Sánchez V; Oh JH; Kim J; Jee SH; Jung KJ; Kweon S-S; Shin M-H; Shin A; Ahn Y-O; Kim D-H; Oze I; Wen W; Matsuo K; Matsuda K; Tanikawa C; Ren Z; Gao Y-T; Jia W-H; Hopper JL; Jenkins MA; Win AK; Pai RK; Figueiredo JC; Haile RW; Gallinger S; Woods MO; Newcomb PA; Duggan D; Cheadle JP; Kaplan R; Kerr R; Kerr D; Kirac I; Böhm J; Mecklin J-P; Jousilahti P; Knekt P; Aaltonen LA; Rissanen H; Pukkala E; Eriksson JG; Cajuso T; Hänninen U; Kondelin J; Palin K; Tanskanen T; Renkonen-Sinisalo L; Männistö S; Albanes D; Weinstein SJ; Ruiz-Narvaez E; Palmer JR; Buchanan DD; Platz EA; Visvanathan K; Ulrich CM; Siegel E; Brezina S; Gsur A; Campbell PT; Chang-Claude J; Hoffmeister M; Brenner H; Slattery ML; Potter JD; Tsilidis KK; Schulze MB; Gunter MJ; Murphy N; Castells A; Castellví-Bel S; Moreira L; Arndt V; Shcherbina A; Bishop DT; Giles GG; Southey MC; Idos GE; McDonnell KJ; Abu-Ful Z; Greenson JK; Shulman K; Lejbkowicz F; Offit K; Su Y-R; Steinfelder R; Keku TO; van Guelpen B; Hudson TJ; Hampel H; Pearlman R; Berndt SI; Hayes RB; Martinez ME; Thomas SS; Pharoah PDP; Larsson SC; Yen Y; Lenz H-J; White E; Li L; Doheny KF; Pugh E; Shelford T; Chan AT; Cruz-Correa M; Lindblom A; Hunter DJ; Joshi AD; Schafmayer C; Scacheri PC; Kundaje A; Schoen RE; Hampe J; Stadler ZK; Vodicka P; Vodickova L; Vymetalkova V; Edlund CK; Gauderman WJ; Shibata D; Toland A; Markowitz S; Kim A; Chanock SJ; van Duijnhoven F; Feskens EJM; Sakoda LC; Gago-Dominguez M; Wolk A; Pardini B; FitzGerald LM; Lee SC; Ogino S; Bien SA; Kooperberg C; Li CI; Lin Y; Prentice R; Qu C; Bézieau S; Yamaji T; Sawada N; Iwasaki M; Le Marchand L; Wu AH; Qu C; McNeil CE; Coetzee G; Hayward C; Deary IJ; Harris SE; Theodoratou E; Reid S; Walker M; Ooi LY; Lau KS; Zhao H; Hsu L; Cai Q; Dunlop MG; Gruber SB; Houlston RS; Moreno V; Casey G; Peters U; Tomlinson I; Zheng WGenome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.Item Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle(BioMed Central Ltd, 2021-07-20) Tiplady KM; Lopdell TJ; Reynolds E; Sherlock RG; Keehan M; Johnson TJJ; Pryce JE; Davis SR; Spelman RJ; Harris BL; Garrick DJ; Littlejohn MDBACKGROUND: Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk. RESULTS: Separate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk. CONCLUSIONS: This study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.Item A genome-wide association study reveals novel genomic regions and positional candidate genes for fat deposition in broiler chickens(BioMed Central Ltd, 2018-05-21) Moreira GCM; Boschiero C; Cesar ASM; Reecy JM; Godoy TF; Trevisoli PA; Cantão ME; Ledur MC; Ibelli AMG; Peixoto JDO; Moura ASAMT; Garrick D; Coutinho LLBACKGROUND: Excess fat content in chickens has a negative impact on poultry production. The discovery of QTL associated with fat deposition in the carcass allows the identification of positional candidate genes (PCGs) that might regulate fat deposition and be useful for selection against excess fat content in chicken's carcass. This study aimed to estimate genomic heritability coefficients and to identify QTLs and PCGs for abdominal fat (ABF) and skin (SKIN) traits in a broiler chicken population, originated from the White Plymouth Rock and White Cornish breeds. RESULTS: ABF and SKIN are moderately heritable traits in our broiler population with estimates ranging from 0.23 to 0.33. Using a high density SNP panel (355,027 informative SNPs), we detected nine unique QTLs that were associated with these fat traits. Among these, four QTL were novel, while five have been previously reported in the literature. Thirteen PCGs were identified that might regulate fat deposition in these QTL regions: JDP2, PLCG1, HNF4A, FITM2, ADIPOR1, PTPN11, MVK, APOA1, APOA4, APOA5, ENSGALG00000000477, ENSGALG00000000483, and ENSGALG00000005043. We used sequence information from founder animals to detect 4843 SNPs in the 13 PCGs. Among those, two were classified as potentially deleterious and two as high impact SNPs. CONCLUSIONS: This study generated novel results that can contribute to a better understanding of fat deposition in chickens. The use of high density array of SNPs increases genome coverage and improves QTL resolution than would have been achieved with low density. The identified PCGs were involved in many biological processes that regulate lipid storage. The SNPs identified in the PCGs, especially those predicted as potentially deleterious and high impact, may affect fat deposition. Validation should be undertaken before using these SNPs for selection against carcass fat accumulation and to improve feed efficiency in broiler chicken production.Item Identification of Genomic Regions Associated with Concentrations of Milk Fat, Protein, Urea and Efficiency of Crude Protein Utilization in Grazing Dairy Cows(MDPI (Basel, Switzerland), 2021-03-23) Ariyarathne HBPC; Correa-Luna M; Blair HT; Garrick DJ; Lopez-Villalobos NAbstract The objective of this study was to identify genomic regions associated with milk fat percentage (FP), crude protein percentage (CPP), urea concentration (MU) and efficiency of crude protein utilization (ECPU: ratio between crude protein yield in milk and dietary crude protein intake) using grazing, mixed-breed, dairy cows in New Zealand. Phenotypes from 634 Holstein Friesian, Jersey or crossbred cows were obtained from two herds at Massey University. A subset of 490 of these cows was genotyped using Bovine Illumina 50K SNP-chips. Two genome-wise association approaches were used, a single-locus model fitted to data from 490 cows and a single-step Bayes C model fitted to data from all 634 cows. The single-locus analysis was performed with the Efficient Mixed-Model Association eXpedited model as implemented in the SVS package. Single nucleotide polymorphisms (SNPs) with genome-wide association p-values ≤ 1.11 × 10−6 were considered as putative quantitative trait loci (QTL). The Bayes C analysis was performed with the JWAS package and 1-Mb genomic windows containing SNPs that explained > 0.37% of the genetic variance were considered as putative QTL. Candidate genes within 100 kb from the identified SNPs in single-locus GWAS or the 1-Mb windows were identified using gene ontology, as implemented in the Ensembl Genome Browser. The genes detected in association with FP (MGST1, DGAT1, CEBPD, SLC52A2, GPAT4, and ACOX3) and CPP (DGAT1, CSN1S1, GOSR2, HERC6, and IGF1R) were identified as candidates. Gene ontology revealed six novel candidate genes (GMDS, E2F7, SIAH1, SLC24A4, LGMN, and ASS1) significantly associated with MU whose functions were in protein catabolism, urea cycle, ion transportation and N excretion. One novel candidate gene was identified in association with ECPU (MAP3K1) that is involved in post-transcriptional modification of proteins. The findings should be validated using a larger population of New Zealand grazing dairy cows.