ORCID Profile
0000-0003-0818-5065
Current Organisation
AgResearch Ltd
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Publisher: Elsevier BV
Date: 06-2013
Abstract: Cancer burden measured in disability adjusted life years (DALYs) captures survival and disability impacts of incident cancers. In this paper, we estimate the prospective burden of disease arising from 27 cancer sites diagnosed in 2006, by sex and ethnicity and determine how its distribution differs from that for incidence rates alone. Using a prospective approach, Markov and cancer disease models were used to estimate DALYs with inputs of population counts, incidence and excess mortality rates, disability weights, and background mortality. DALYs were discounted at 3.5% per year. The age standardised Māori:non-Māori incidence rate ratios were 1.00 for males and 1.19 for females, whereas for DALYs they were greater at 1.42 for males and 1.68 for females. The total burden of cancer for 2006 incident cases (i.e. not age standardised) was estimated to be approximately 127,000 DALYs. Breast (27%), lung (14%) and colorectal (13%) cancers for females and lung (16%), colorectal (14%), and prostate (16%) cancers for males were the top contributors. By ethnicity, Māori experienced a substantially higher burden from lung cancer (around 25% for both sexes). Due to Māori both having higher rates of cancers with a worse survival (e.g. lung cancer), and tending to have worse survival for each cancer site, ethnic disparities in the age-standardised DALY burden were greater than those for incidence (rate ratios of 1.52 and 1.07 respectively, sexes pooled).
Publisher: American Dairy Science Association
Date: 09-2023
Publisher: Hart Publishing
Publisher: Springer Science and Business Media LLC
Date: 19-02-2019
Publisher: Cold Spring Harbor Laboratory
Date: 16-04-2020
DOI: 10.1101/2020.04.16.044685
Abstract: Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where s le sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on s les sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fisher's exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized s ling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random s les of genes in the cattle genome (p=0.01). Randomly s led SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly s led SNPs within random cattle genes (p=0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p=0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle.
Publisher: Informa UK Limited
Date: 09-10-2019
Publisher: Springer Science and Business Media LLC
Date: 26-08-2020
DOI: 10.1186/S12711-020-00569-Z
Abstract: Temperament traits are of high importance across species. In humans, temperament or personality traits correlate with psychological traits and psychiatric disorders. In cattle, they impact animal welfare, product quality and human safety, and are therefore of direct commercial importance. We hypothesized that genetic factors that contribute to variation in temperament among in iduals within a species will be shared between humans and cattle. Using imputed whole-genome sequence data from 9223 beef cattle from three cohorts, a series of genome-wide association studies was undertaken on cattle flight time, a temperament phenotype measured as the time taken for an animal to cover a short-fixed distance after release from an enclosure. We also investigated the association of cattle temperament with polymorphisms in bovine orthologs of risk genes for neuroticism, schizophrenia, autism spectrum disorders (ASD), and developmental delay disorders in humans. Variants with the strongest associations were located in the bovine orthologous region that is involved in several behavioural and cognitive disorders in humans. These variants were also partially validated in independent cattle cohorts. Genes in these regions ( BARHL2 , NDN , SNRPN , MAGEL2 , A BCA12 , KIFAP3 , TOPAZ1 , FZD3 , UBE3A , and GABRA5 ) were enriched for the GO term neuron migration and were differentially expressed in brain and pituitary tissues in humans. Moreover, variants within 100 kb of ASD susceptibility genes were associated with cattle temperament and explained 6.5% of the total additive genetic variance in the largest cattle cohort. The ASD genes with the most significant associations were GABRB3 and CUL3 . Using the same 100 kb window, a weak association was found with polymorphisms in schizophrenia risk genes and no association with polymorphisms in neuroticism and developmental delay disorders risk genes. Our analysis showed that genes identified in a meta-analysis of cattle temperament contribute to neuron development functions and are differentially expressed in human brain tissues. Furthermore, some ASD susceptibility genes are associated with cattle temperament. These findings provide evidence that genetic control of temperament might be shared between humans and cattle and highlight the potential for future analyses to leverage results between species.
Publisher: Springer Science and Business Media LLC
Date: 06-10-2023
Publisher: Springer Science and Business Media LLC
Date: 27-05-2020
DOI: 10.1186/S12711-020-00547-5
Abstract: In tropically-adapted beef heifers, application of genomic prediction for age at puberty has been limited due to low prediction accuracies. Our aim was to investigate novel methods of pre-selecting whole-genome sequence (WGS) variants and alternative analysis methodologies including genomic best linear unbiased prediction (GBLUP) with multiple genomic relationship matrices (MGRM) and Bayesian (BayesR) analyses, to determine if prediction accuracy for age at puberty can be improved. Genotypes and phenotypes were obtained from two research herds. In total, 868 Brahman and 960 Tropical Composite heifers were recorded in the first population and 3695 Brahman, Santa Gertrudis and Droughtmaster heifers were recorded in the second population. Genotypes were imputed to 23 million whole-genome sequence variants. Eight strategies were used to pre-select variants from genome-wide association study (GWAS) results using conditional or joint (COJO) analyses. Pre-selected variants were included in three models, GBLUP with a single genomic relationship matrix (SGRM), GBLUP MGRM and BayesR. Five-way cross-validation was used to test the effect of marker panel density (6 K, 50 K and 800 K), analysis model, and inclusion of pre-selected WGS variants on prediction accuracy. In all tested scenarios, prediction accuracies for age at puberty were highest in BayesR analyses. The addition of pre-selected WGS variants had little effect on the accuracy of prediction when BayesR was used. The inclusion of WGS variants that were pre-selected using a meta-analysis with COJO analyses by chromosome, fitted in a MGRM model, had the highest prediction accuracies in the GBLUP analyses, regardless of marker density. When the low-density (6 K) panel was used, the prediction accuracy of GBLUP was equal (0.42) to that with the high-density panel when only six additional sequence variants (identified using meta-analysis COJO by chromosome) were included. While BayesR consistently outperforms other methods in terms of prediction accuracies, reasonable improvements in accuracy can be achieved when using GBLUP and low-density panels with the inclusion of a relatively small number of highly relevant WGS variants.
Publisher: Springer Science and Business Media LLC
Date: 22-07-2023
DOI: 10.1007/S00180-023-01387-9
Abstract: Existing methods can perform likelihood-based clustering on a multivariate data matrix of ordinal data, using finite mixtures to cluster the rows (observations) of the matrix. These models can incorporate the main effects of in idual rows and columns, as well as cluster effects, to model the matrix of responses. However, many real-world applications also include available covariates, which provide insights into the main characteristics of the clusters and determine clustering structures based on both the in iduals’ similar patterns of responses and the effects of the covariates on the in iduals' responses. In our research we have extended the mixture-based models to include covariates and test what effect this has on the resulting clustering structures. We focus on clustering the rows of the data matrix, using the proportional odds cumulative logit model for ordinal data. We fit the models using the Expectation-Maximization algorithm and assess performance using a simulation study. We also illustrate an application of the models to the well-known arthritis clinical trial data set.
Publisher: Springer Science and Business Media LLC
Date: 25-05-2022
DOI: 10.1038/S41598-022-12778-1
Abstract: Copy number variants (CNVs) are a type of genetic polymorphism which contribute to phenotypic variation in several species, including livestock. In this study, we used genomic data of 192 animals from 3 Iranian sheep breeds including 96 Baluchi sheep and 47 Lori-Bakhtiari sheep as fat-tailed breeds and 47 Zel sheep as thin-tailed sheep breed genotyped with Illumina OvineSNP50K Beadchip arrays. Also, for association test, 70 s les of Valle del Belice sheep were added to the association test as thin-tailed sheep breed. PennCNV and CNVRuler software were, respectively, used to study the copy number variation and genomic association analyses. We detected 573 and 242 CNVs in the fat and thin tailed breeds, respectively. In terms of CNV regions (CNVRs), these represented 328 and 187 CNVRs that were within or overlapping with 790 known Ovine genes. The CNVRs covered approximately 73.85 Mb of the sheep genome with average length 146.88 kb, and corresponded to 2.6% of the autosomal genome sequence. Five CNVRs were randomly chosen for validation, of which 4 were experimentally confirmed using Real time qPCR. Functional enrichment analysis showed that genes harbouring CNVs in thin-tailed sheep were involved in the adaptive immune response, regulation of reactive oxygen species biosynthetic process and response to starvation. In fat-tailed breeds these genes were involved in cellular protein modification process, regulation of heart rate, intestinal absorption, olfactory receptor activity and ATP binding. Association test identified one copy gained CNVR on chromosomes 6 harbouring two protein-coding genes HGFAC and LRPAP1. Our findings provide information about genomic structural changes and their association to the interested traits including fat deposition and environmental compatibility in sheep.
Publisher: Springer Science and Business Media LLC
Date: 15-05-2018
Publisher: Springer Science and Business Media LLC
Date: 20-06-2019
Publisher: Wiley
Date: 30-09-2019
DOI: 10.1002/MPR.1801
Publisher: Wiley
Date: 25-04-2012
DOI: 10.1002/IJC.27531
Abstract: Relative survival and excess mortality approaches are commonly used to estimate and compare net survival from cancer. These approaches are based on the assumption that the underlying (non-cancer) mortality rate of cancer patients is the same as that of the general population. This assumption is likely to be violated particularly in the context of smoking-related cancers. The magnitude of this bias has not been estimated. The objective of this article is to estimate the bias in relative survival ratios (RSRs) and excess mortality rate ratios (EMRRs) from using total population compared to correct subpopulation specific life-tables. Analyses were conducted on 1996-2001 linked census-cancer data (including smoking status) for people with lung and bladder cancer, using sex-specific (standard practice), sex- and ethnic-specific, sex- and smoking-specific and sex-, ethnic- and smoking-specific life-tables. Five-year RSRs using sex-specific life-tables, compared to fully stratified life-tables, were underestimated by 10-25% for current smoking and Maori populations. For ex le, the current smoker male bladder cancer RSR was 0.700 for sex-specific life-tables, compared to 0.838 for fully stratified life-tables. Similarly, EMRRs comparing current to never smokers and Maori to non-Maori were overestimated using sex-specific life-tables only: modestly only for lung cancer, but markedly for bladder cancer. For ex le, the EMRR comparing current to never smokers with bladder cancer in a fully adjusted regression model was 1.475 when using sex-specific life-tables only, but reduced to 1.098 when using fully stratified life-tables. Substantial bias can occur when estimating relative cancer survival across subpopulations if non-matching life-tables are used.
Publisher: Springer Science and Business Media LLC
Date: 25-06-2022
DOI: 10.1007/S10681-022-03048-2
Abstract: Chickpea (Cicer arietinum L.) is one of the most important grain legumes in the world, but its current and future production is threatened due to the increased incidence of drought and heat stress. To address this challenge, an integrated crop improvement strategy encompassing breeding, genomics, physiology and agronomy is required. Here, we review the physiological traits known to confer drought and heat adaptation in chickpea and identify areas of drought and heat adaptation research that may be prioritised in the future. Furthermore, we underscore approaches to efficiently phenotype chickpea adaptation traits and highlight the significant challenges and importance of understanding the nexus between canopy and root development. Finally, we present the opportunity to adopt multi-trait genomic prediction approaches to efficiently utilise key physiological traits, that can be assayed using high-throughput phenotyping platforms, to accelerate genetic gain in drought and heat prone environments.
Publisher: Research Square Platform LLC
Date: 28-10-2022
DOI: 10.21203/RS.3.RS-2165063/V1
Abstract: Many of the world’s agriculturally important plant and animal populations consist of hybrids of subspecies. Cattle in tropical and sub-tropical regions for ex le, originate from two genetically distinct subspecies, Bos indicus and Bos taurus. Methods to derive the underlying genetic architecture for these two subspecies are essential to develop accurate genomic predictions in these hybrid populations. We propose a novel method to achieve this. First, we use haplotypes to assign single nucleotide polymorphism (SNP) alleles to ancestral subspecies-of-origin in a multi-breed and multi-subspecies population. Then we use a BayesR framework to allow SNP alleles originating from the different subspecies to have different effects (unequal variances). Applying this method in a composite population of B. indicus and B. taurus hybrids, our results show that there are underlying genomic differences between the two subspecies, and these effects are not identified in multi-breed genomic evaluations that do not account for subspecies-of-origin effects. The method slightly improved the accuracy of genomic prediction. More significantly, by allocating SNP alleles to ancestral subspecies-of-origin, we were able to identify four SNP with high posterior probabilities of inclusion that have not been previously associated with cattle fertility and were very close to genes associated with fertility in other species. These results show that haplotypes can be used to trace subspecies-of-origin through the genome of this hybrid population and, in conjunction with our novel Bayesian analysis, subspecies SNP allele allocation can be used to increase the accuracy of quantitative trait loci (QTL) association mapping in genetically erse populations.
Publisher: Springer Science and Business Media LLC
Date: 12-03-2021
DOI: 10.1186/S12711-021-00622-5
Abstract: A cost-effective strategy to explore the complete DNA sequence in animals for genetic evaluation purposes is to sequence key ancestors of a population, followed by imputation mechanisms to infer marker genotypes that were not originally reported in a target population of animals genotyped with single nucleotide polymorphism (SNP) panels. The feasibility of this process relies on the accuracy of the genotype imputation in that population, particularly for potential causal mutations which may be at low frequency and either within genes or regulatory regions. The objective of the present study was to investigate the imputation accuracy to the sequence level in a Nellore beef cattle population, including that for variants in annotation classes which are more likely to be functional. Information of 151 key sequenced Nellore sires were used to assess the imputation accuracy from bovine HD BeadChip SNP (~ 777 k) to whole-genome sequence. The choice of the sires aimed at optimizing the imputation accuracy of a genotypic database, comprised of about 10,000 genotyped Nellore animals. Genotype imputation was performed using two computational approaches: FImpute3 and Minimac4 (after using Eagle for phasing). The accuracy of the imputation was evaluated using a fivefold cross-validation scheme and measured by the squared correlation between observed and imputed genotypes, calculated by in idual and by SNP. SNPs were classified into a range of annotations, and the accuracy of imputation within each annotation classification was also evaluated. High average imputation accuracies per animal were achieved using both FImpute3 (0.94) and Minimac4 (0.95). On average, common variants (minor allele frequency (MAF) 0.03) were more accurately imputed by Minimac4 and low-frequency variants (MAF ≤ 0.03) were more accurately imputed by FImpute3. The inherent Minimac4 Rsq imputation quality statistic appears to be a good indicator of the empirical Minimac4 imputation accuracy. Both software provided high average SNP-wise imputation accuracy for all classes of biological annotations. Our results indicate that imputation to whole-genome sequence is feasible in Nellore beef cattle since high imputation accuracies per in idual are expected. SNP-wise imputation accuracy is software-dependent, especially for rare variants. The accuracy of imputation appears to be relatively independent of annotation classification.
Publisher: Public Library of Science (PLoS)
Date: 14-09-2020
Publisher: CSIRO Publishing
Date: 07-07-2021
DOI: 10.1071/AN21057
Abstract: Context Beef cattle breeds in Australia can broadly be broken up into two subspecies, namely, Bos indicus and Bos taurus. Due to the time since ergence between the subspecies, it is likely that mutations affecting quantitative traits have developed independently in each. Aims We hypothesise that this will affect the prediction accuracy of genomic selection of admixed and composite populations that include both ancestral subspecies. Our study investigates methods to quantify population stratification in a multibreed population of tropically adapted heifers, with the aim of improving prediction accuracy of genomic selection for reproductive maturity score. Methods We used genotypes and reproductive maturity phenotypes from 3695 tropically adapted heifers from three purebred populations, namely, Brahman, Santa Gertrudis and Droughtmaster. Two of these breeds, Santa Gertrudis and Droughtmaster, are stabilised composites of varying B. indicus × B. taurus ancestry, and the third breed, Brahman, has predominately B. indicus ancestry. Genotypes were imputed to three marker-panel densities and population stratification was accounted for in genomic relationship matrices by using breed-specific allele frequencies when calculating the genomic relationships among animals. Prediction accuracy and bias were determined using a five-fold cross validation of randomly selected multibreed cohorts. Key Results Our results showed that the use of breed-adjusted genomic relationship matrices did not improve either prediction accuracy or bias for a lowly heritable trait such as reproductive maturity score. However, using breed-adjusted genomic relationship matrices allowed the capture of a higher proportion of additive genetic effects when estimating variance components. Conclusions These findings suggest that, despite seeing no improvement in prediction accuracy, it may still be beneficial to use breed-adjusted genomic relationship matrices in multibreed populations to improve the estimation of variance components. Implications As such, genomic evaluations using breed-adjusted genomic relationship matrices may be beneficial in multibreed populations.
No related grants have been discovered for Roy Costilla.