Comparison between multiple-trait and random regression models for genetic evaluation of weight traits in Australian meat sheep

Random regression (RR) models are recommended as an alternative to multiple-trait (MT) models for better capturing the variance-covariance structure over a trajectory and hence more accurate genetic evaluation of traits that are repeatedly measured and genetically change gradually over time. However, a limited number of studies have been done to empirically compare RR over a MT model to determine how much extra benefit could be achieved from one method over another. We compared the prediction accuracy of RR and MT models for growth traits of Australian meat sheep measured from 60 to 525 d, using 102,579 weight records from 24,872 animals. Variance components and estimated breeding values (EBVs) estimated at specific ages were compared and validated with forward prediction. The accuracy of EBVs obtained from the MT model was 0.58, 0.51, 0.54, and 0.56 for weaning, postweaning, yearling, and hogget weight stages, respectively. RR model produced accuracy estimates of 0.56, 0.51, 0.54, and 0.54 for equivalent weight stages. Regression of adjusted phenotype on EBVs was very similar between the MT and the RR models (P > 0.05). Although the RR model did not significantly increase the accuracy of predicting future progeny performance, there are other benefits of the model such as no limit to the number of records per animal, estimation of EBVs for early and late growth, no need for age correction. Therefore, RR can be considered a more flexible method for the genetic evaluation of Australian sheep for early and late growth, and no need for age correction.

Including genomic information in the genetic evaluation of production and reproduction traits in South African Merino sheep

Genomic selection (GS) has become common in sheep breeding programmes in Australia, New Zealand, France and Ireland but requires validation in South Africa (SA). This study aimed to compare the predictive ability, bias and dispersion of pedigree BLUP (ABLUP) and single-step genomic BLUP (ssGBLUP) for production and reproduction traits in South African Merinos. Animals in this study originated from five research and five commercial Merino flocks and included between 54,072 and 79,100 production records for weaning weight (WW), yearling weight (YW), fibre diameter (FD), clean fleece weight (CFW) and staple length (SL). For reproduction traits, the dataset included 58,744 repeated records from 17,268 ewes for the number of lambs born (NLB), number of lambs weaned (NLW) and the total weight weaned (TWW). The single-step relationship matrix, H, was calculated using PreGS90 software combining 2811 animals with medium density (~50 K) genotypes and the pedigree of 88,600 animals. Assessment was based on single-trait analysis using ASREML V4.2 software. The accuracy of prediction was evaluated according to the 'LR-method' by a cross-validation design. Validation candidates were assigned according to Scenario I: born after a certain time point; and Scenario II: born in a particular flock. In Scenario I, the genotyping rate for the reference population between 2006 and the 2013 cut-off point approached 7% of animals with phenotypes and 20% of their sires. For reproduction traits, about 20% of ewes born between 2006 and 2011 cut-off were genotyped, along with 15% of their sires. Genotyping rates in the validation set of Scenario I were 3.7% (production) and 11.4% (reproduction) for candidates and 35% of their sires. Sires were allowed to have progeny in both the reference and validation set. In Scenario I, ssGBLUP increased the accuracy of prediction for all traits except NLB, ranging between +8% (0.62-0.67) for FD and +44% (0.36-0.52) for WW. This showed a promising gain in accuracy despite a modestly sized reference population. In the 'across flock validation' (Scenario II), overall accuracy was lower, but with greater differences between ABLUP and ssGBLUP ranging between +17% (0.12-0.14) for TWW and +117% (0.18-0.39) for WW. There was little indication of severe bias, but some traits were prone to over dispersion and the use of genomic information did not improve this. These results were the first to validate the benefit of genomic information in South African Merinos. However, because production traits are moderately heritable and easy to measure at an early age, future research should be aimed at best exploiting GS methods towards genetic prediction of sex-limited and/or lowly heritable traits such as NLW. GS methods should be combined with dedicated efforts to increase genetic links between sectors and improved phenotyping by commercial flocks.

Micro-genetic environmental sensitivity across macro-environments of chickens reared in Burkina Faso and France

BACKGROUND

Commercial poultry production systems follow a pyramidal structure with a nucleus of purebred animals under controlled conditions at the top and crossbred animals under commercial production conditions at the bottom. Genetic correlations between the same phenotypes on nucleus and production animals can therefore be influenced by differences both in purebred-crossbred genotypes and in genotype-by-environment interactions across the two environments, known as macro-genetic environmental sensitivity (GES). Within each environment, genotype-by-environment interactions can also occur due to so-called micro-GES. Micro-GES causes heritable variation in phenotypes and decreases uniformity. In this study, genetic variances of body weight (BW) and of micro-GES of BW and the impacts of purebred-crossbred differences and macro-environmental differences on micro-GES of BW were estimated. The dataset contained three subpopulations of slow-growing broiler chickens: purebred chickens (PB) reared in France, and crossbred chickens reared in France (FR) under the same conditions as PB or reared in Burkina Faso (BF) under local conditions. The crossbred chickens were offspring of the same dam line and had PB as their sire line.

RESULTS

Estimates of heritability of BW and micro-GES of BW were 0.54 (SE of 0.02) and 0.06 (0.01), 0.67 (0.03) and 0.03 (0.01), and 0.68 (0.04) and 0.02 (0.01) for the BF, FR, and PB subpopulations, respectively. Estimates of the genetic correlations for BW between the three subpopulations were moderately positive (0.37 to 0.53) and those for micro-GES were weakly to moderately positive (0.01 to 0.44).

CONCLUSIONS

The results show that the heritability of the micro-GES of BW varies with macro-environment, which indicates that responses to selection are expected to differ between macro-environments. The weak to moderate positive genetic correlations between subpopulations indicate that both macro-environmental differences and purebred-crossbred differences can cause re-ranking of sires based on their estimated breeding values for micro-GES of BW. Thus, the sire that produces the most variable progeny in one macro-environment may not be the one that produces the most variable offspring in another. Similarly, the sire that produces the most variable purebred progeny may not produce the most variable crossbred progeny. The results highlight the need for investigating micro-GES for all subpopulations included in the selection scheme, to ensure optimal genetic gain in all subpopulations.

Parameter estimation and assessment of bias in genetic evaluation of carcass traits in Hanwoo cattle using real and simulated data

Most carcass and meat quality traits are moderate to highly heritable, indicating that they can be improved through selection. Genetic evaluation for these types of traits is performed using performance data obtained from commercial and progeny testing evaluation. The performance data from commercial farms are available in large volume, however, some drawbacks have been observed. The drawback of the commercial data is mainly due to sorting of animals based on live weight prior to slaughter, and this could lead to bias in the genetic evaluation of later measured traits such as carcass traits. The current study has two components to address the drawback of the commercial data. The first component of the study aimed to estimate genetic parameters for carcass and meat quality traits in Korean Hanwoo cattle using a large sample size of industry-based carcass performance records (n = 469,002). The second component of the study aimed to describe the impact of sorting animals into different contemporary groups based on an early measured trait and then examine the effect on the genetic evaluation of subsequently measured traits. To demonstrate our objectives, we used real performance data to estimate genetic parameters and simulated data was used to assess the bias in genetic evaluation. The results of our first study showed that commercial data obtained from slaughterhouses is a potential source of carcass performance data and useful for genetic evaluation of carcass traits to improve beef cattle performance. However, we observed some harvesting effect which leads to bias in genetic evaluation of carcass traits. This is mainly due to the selection of animal based on their body weight before arrival to slaughterhouse. Overall, the non-random allocation of animals into a contemporary group leads to a biased estimated breeding value in genetic evaluation, the severity of which increases when the evaluation traits are highly correlated.

The genomic structure of isolation across breed, country and strain for important South African and Australian sheep populations

Background

South Africa and Australia shares multiple important sheep breeds. For some of these breeds, genomic breeding values are provided to breeders in Australia, but not yet in South Africa. Combining genomic resources could facilitate development for across country selection, but the influence of population structures could be important to the compatability of genomic data from varying origins. The genetic structure within and across breeds, countries and strains was evaluated in this study by population genomic parameters derived from SNP-marker data. Populations were first analysed by breed and country of origin and then by subpopulations of South African and Australian Merinos.

Results

Mean estimated relatedness according to the genomic relationship matrix varied by breed (-0.11 to 0.16) and bloodline (-0.08 to 0.06) groups and depended on co-ancestry as well as recent genetic links. Measures of divergence across bloodlines (F_ST: 0.04–0.12) were sometimes more distant than across some breeds (F_ST: 0.05–0.24), but the divergence of common breeds from their across-country equivalents was weak (F_ST: 0.01–0.04). According to mean relatedness, F_ST, PCA and Admixture, the Australian Ultrafine line was better connected to the SA Cradock Fine Wool flock than with other AUS bloodlines. Levels of linkage disequilibrium (LD) between adjacent markers was generally low, but also varied across breeds (r²: 0.14–0.22) as well as bloodlines (r²: 0.15–0.19). Patterns of LD decay was also unique to breeds, but bloodlines differed only at the absolute level. Estimates of effective population size (N_e) showed genetic diversity to be high for the majority of breeds (N_e: 128–418) but also for bloodlines (N_e: 137–369).

Conclusions

This study reinforced the genetic complexity and diversity of important sheep breeds, especially the Merino breed. The results also showed that implications of isolation can be highly variable and extended beyond breed structures. However, knowledge of useful links across these population substructures allows for a fine-tuned approach in the combination of genomic resources. Isolation across country rarely proved restricting compared to other structures considered. Consequently, research into the accuracy of across-country genomic prediction is recommended.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08020-3.

The accuracy of genomic prediction for meat quality traits in Hanwoo cattle when using genotypes from different SNP densities and preselected variants from imputed whole genome sequence

Context Genomic prediction is the use of genomic data in the estimation of genomic breeding values (GEBV) in animal breeding. In beef cattle breeding programs, genomic prediction increases the rates of genetic gain by increasing the accuracy of selection at earlier ages. Aims The objectives of the study were to examine the effect of single-nucleotide polymorphism (SNP) density and to evaluate the effect of using SNPs preselected from imputed whole-genome sequence for genomic prediction. Methods Genomic and phenotypic data from 2110 Hanwoo steers were used to predict GEBV for marbling score (MS), meat texture (MT), and meat colour (MC) traits. Three types of SNP densities including 50k, high-density (HD), and whole-genome sequence data and preselected SNPs from genome-wide association study (GWAS) were used for genomic prediction analyses. Two scenarios (independent and dependent discovery populations) were used to select top significant SNPs. The accuracy of GEBV was assessed using random cross-validation. Genomic best linear unbiased prediction (GBLUP) was used to predict the breeding values for each trait. Key results Our result showed that very similar prediction accuracies were observed across all SNP densities used in the study. The prediction accuracy among traits ranged from 0.29±0.05 for MC to 0.46±0.04 for MS. Depending on the studied traits, up to 5% of prediction accuracy improvement was obtained when the preselected SNPs from GWAS analysis were included in the prediction analysis. Conclusions High SNP density such as HD and the whole-genome sequence data yielded a similar prediction accuracy in Hanwoo beef cattle. Therefore, the 50K SNP chip panel is sufficient to capture the relationships in a breed with a small effective population size such as the Hanwoo cattle population. Preselected variants improved prediction accuracy when they were included in the genomic prediction model. Implications The estimated genomic prediction accuracies are moderately accurate in Hanwoo cattle and for searching for SNPs that are more productive could increase the accuracy of estimated breeding values for the studied traits.

THI Modulation of Genetic and Non-genetic Variance Components for Carcass Traits in Hanwoo Cattle

The phenotype of carcass traits in beef cattle are affected by random genetic and non-genetic effects, which both can be modulated by an environmental variable such as Temperature-Humidity Index (THI), a key environmental factor in cattle production. In this study, a multivariate reaction norm model (MRNM) was used to assess if the random genetic and non-genetic (i.e., residual) effects of carcass weight (CW), back fat thickness (BFT), eye muscle area (EMA), and marbling score (MS) were modulated by THI, using 9,318 Hanwoo steers (N = 8,964) and cows (N = 354) that were genotyped on the Illumina Bovine SNP50 BeadChip (50K). THI was measured based on the period of 15–45 days before slaughter. Both the correlation and the interaction between THI and random genetic and non-genetic effects were accounted for in the model. In the analyses, it was shown that the genetic effects of EMA and the non-genetic effects of CW and MS were significantly modulated by THI. No significant THI modulation of such effects was found for BFT. These results highlight the relevance of THI changes for the genetic and non-genetic variation of CW, EMA, and MS in Hanwoo beef cattle. Importantly, heritability estimates for CW, EMA, and MS from additive models without considering THI interactions were underestimated. Moreover, the significance of interaction can be biased if not properly accounting for the correlation between THI and genetic and non-genetic effects. Thus, we argue that the estimation of genetic parameters should be based on appropriate models to avoid any potential bias of estimates. Our finding should serve as a basis for future studies aiming at revealing genotype by environment interaction in estimation and genomic prediction of breeding values.

Keys to innovation in animal science: genomics, big data and collaboration

As the sophistication of genomic technologies increases and their cost continues to decrease, they are becoming routine tools in livestock breeding programs and production systems. The convergence of electronic measurement systems, cloud-based computing and fast internet enable the use of powerful decision support to help automate and manage livestock production, animal welfare and quality control. The complexity of livestock production systems, combined with the challenges of producing top-quality discretionary products for consumers, means that no single organisation has the range of expertise to support coordinated development of innovation in the relevant supply chains. Collaboration between a broad spectrum of scientists and industry partners is essential to ensure well integrated input to the design and implementation of programs to deliver improvements in efficiency, quality and profit. The need for collaboration among researchers, among research organisations and with end-users has never been more important. Collaboration brings together the skills needed to manage complex problems and enables the sharing of facilities and scarce resources within Australia and internationally. However, the most important component of effective collaboration is the early engagement of end-users to contribute to the design of programs of innovation, to ensure that investment is well targeted and that there is ownership of the problem as well as the solutions delivered through research. Although the potential benefits of effective collaboration are clear, it often takes more than logic to get individuals and organisations to work together. There needs to be a significant financial incentive combined with strong industry leadership and agreed common goals. Allocating resources to establish these foundations for effective collaboration should precede any major research and development funding initiative. The present paper argues that the new face of animal science in Australia should be structured around coordinated programs of research and development, on the basis of effective collaboration.

Detecting Genotype-Population Interaction Effects by Ancestry Principal Components

Heterogeneity in the phenotypic mean and variance across populations is often observed for complex traits. One way to understand heterogeneous phenotypes lies in uncovering heterogeneity in genetic effects. Previous studies on genetic heterogeneity across populations were typically based on discrete groups in populations stratified by different countries or cohorts, which ignored the difference of population characteristics for the individuals within each group and resulted in loss of information. Here, we introduce a novel concept of genotype-by-population (G × P) interaction where population is defined by the first and second ancestry principal components (PCs), which are less likely to be confounded with country/cohort-specific factors. We applied a reaction norm model fitting each of 70 complex traits with significant SNP-heritability and the PCs as covariates to examine G × P interactions across diverse populations including white British and other white Europeans from the UK Biobank (N = 22,229). Our results demonstrated a significant population genetic heterogeneity for behavioral traits such as age at first sexual intercourse and academic qualification. Our approach may shed light on the latent genetic architecture of complex traits that underlies the modulation of genetic effects across different populations.

Whole-Genome Approach Discovers Novel Genetic and Nongenetic Variance Components Modulated by Lifestyle for Cardiovascular Health

Background

Both genetic and nongenetic factors can predispose individuals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease prevention.

Methods and Results

We used a novel whole‐genome approach to estimate the genetic and nongenetic effects on—and hence their predispositions to—cardiovascular risk and determined whether they vary with respect to lifestyle factors such as physical activity, smoking, alcohol consumption, and dietary intake. We performed analyses on the ARIC (Atherosclerosis Risk in Communities) Study (N=6896–7180) and validated findings using the UKBB (UK Biobank, N=14 076–34 538). Lifestyle modulation was evident for many cardiovascular traits such as body mass index and resting heart rate. For example, alcohol consumption modulated both genetic and nongenetic effects on body mass index, whereas smoking modulated nongenetic effects on heart rate, pulse pressure, and white blood cell count. We also stratified individuals according to estimated genetic and nongenetic effects that are modulated by lifestyle factors and showed distinct phenotype–lifestyle relationships across the stratified groups. Finally, we showed that neglecting lifestyle modulations of cardiovascular traits would on average reduce single nucleotide polymorphism heritability estimates of these traits by a small yet significant amount, primarily owing to the overestimation of residual variance.

Conclusions

Lifestyle changes are relevant to cardiovascular disease prevention. Individual differences in the genetic and nongenetic effects that are modulated by lifestyle factors, as shown by the stratified group analyses, implies a need for personalized lifestyle interventions. In addition, single nucleotide polymorphism–based heritability of cardiovascular traits without accounting for lifestyle modulations could be underestimated.

Genetic parameters for milk yield in imported Jersey and Jersey-Friesian cows using daily milk records in Sri Lanka

OBJECTIVE

This study was conducted to estimate genetic parameters for milk yield traits using daily milk yield records from parlour data generated in an intensively managed commercial dairy farm with Jersey and Jersey-Friesian cows in Sri Lanka.

METHODS

Genetic parameters were estimated for first and second lactation predicted and realized 305-day milk yield using univariate animal models. Genetic parameters were also estimated for total milk yield for each 30-day intervals of the first lactation using univariate animal models and for daily milk yield using random regression models fitting second-order Legendre polynomials and assuming heterogeneous residual variances. Breeding values for predicted 305-day milk yield were estimated using an animal model.

RESULTS

For the first lactation, the heritability of predicted 305-day milk yield in Jersey cows (0.08±0.03) was higher than that of Jersey-Friesian cows (0.02±0.01). The second lactation heritability estimates were similar to that of first lactation. The repeatability of the daily milk records was 0.28±0.01 and the heritability ranged from 0.002±0.05 to 0.19±0.02 depending on day of milk. Pearson product-moment correlations between the bull estimated breeding values (EBVs) in Australia and bull EBVs in Sri Lanka for 305-day milk yield were 0.39 in Jersey cows and -0.35 in Jersey-Friesian cows.

CONCLUSION

The heritabilities estimated for milk yield in Jersey and Jersey-Friesian cows in Sri Lanka were low, and were associated with low additive genetic variances for the traits. Sire differences in Australia were not expressed in the tropical low-country of Sri Lanka. Therefore, genetic progress achieved by importing genetic material from Australia can be expected to be slow. This emphasizes the need for a within-country evaluation of bulls to produce locally adapted dairy cows.

Weighted Genomic Best Linear Unbiased Prediction for Carcass Traits in Hanwoo Cattle

The genomic best linear unbiased prediction (GBLUP) method has been widely used in routine genomic evaluation as it assumes a common variance for all single nucleotide polymorphism (SNP). However, this is unlikely in the case of traits influenced by major SNP. Hence, the present study aimed to improve the accuracy of GBLUP by using the weighted GBLUP (WGBLUP), which gives more weight to important markers for various carcass traits of Hanwoo cattle, such as backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Linear and different nonlinearA SNP weighting procedures under WGBLUP were evaluated and compared with unweighted GBLUP and traditional pedigree-based methods (PBLUP). WGBLUP methods were assessed over ten iterations. Phenotypic data from 10,215 animals from different commercial herds that were slaughtered at approximately 30-month-old of age were used. All these animals were genotyped using customized Hanwoo 50K SNP chip and were divided into a training and a validation population by birth date on 1 November 2015. Genomic prediction accuracies obtained in the nonlinearA weighting methods were higher than those of the linear weighting for all traits. Moreover, unlike with linear methods, no sudden drops in the accuracy were noted after the peak was reached in nonlinearA methods. The average accuracies using PBLUP were 0.37, 0.49, 0.40, and 0.37, and 0.62, 0.74, 0.67, and 0.65 using GBLUP for BFT, CWT, EMA, and MS, respectively. Moreover, these accuracies of genomic prediction were further increased to 4.84% and 2.70% for BFT and CWT, respectively by using the nonlinearA method under the WGBLUP model. For EMA and MS, WGBLUP was as accurate as GBLUP. Our results indicate that the WGBLUP using a nonlinearA weighting method provides improved predictions for CWT and BFT, suggesting that the ability of WGBLUP over the other models by weighting selected SNPs appears to be trait-dependent.

Genome-Wide Association Study of Meat Quality Traits in Hanwoo Beef Cattle Using Imputed Whole-Genome Sequence Data

The discovery of single nucleotide polymorphisms (SNP) and the subsequent genotyping of large numbers of animals have enabled large-scale analyses to begin to understand the biological processes that underpin variation in animal populations. In beef cattle, genome-wide association studies using genotype arrays have revealed many quantitative trait loci (QTL) for various production traits such as growth, efficiency and meat quality. Most studies regarding meat quality have focused on marbling, which is a key trait associated with meat eating quality. However, other important traits like meat color, texture and fat color have not commonly been studied. Developments in genome sequencing technologies provide new opportunities to identify regions associated with these traits more precisely. The objective of this study was to estimate variance components and identify significant variants underpinning variation in meat quality traits using imputed whole genome sequence data. Phenotypic and genomic data from 2,110 Hanwoo cattle were used. The estimated heritabilities for the studied traits were 0.01, 0.16, 0.31, and 0.49 for fat color, meat color, meat texture and marbling score, respectively. Marbling score and meat texture were highly correlated. The genome-wide association study revealed 107 significant SNPs located on 14 selected chromosomes (one QTL region per selected chromosome). Four QTL regions were identified on BTA2, 12, 16, and 24 for marbling score and two QTL regions were found for meat texture trait on BTA12 and 29. Similarly, three QTL regions were identified for meat color on BTA2, 14 and 24 and five QTL regions for fat color on BTA7, 10, 12, 16, and 21. Candidate genes were identified for all traits, and their potential influence on the given trait was discussed. The significant SNP will be an important inclusion into commercial genotyping arrays to select new breeding animals more accurately.

Variation in Latent Classes of Adult Attention-Deficit Hyperactivity Disorder by Sex and Environmental Adversity

OBJECTIVE

The findings of genetic, imaging and neuropsychological studies of attention-deficit hyperactivity disorder (ADHD) are mixed. To understand why this might be the case we use both dimensional and categorical symptom measurement to provide alternate and detailed perspectives of symptom expression.

METHOD

Interviewers collected ADHD, conduct problems (CP) and sociodemographic data from 3793 twins and their siblings aged 22 to 49 (M = 32.6). We estimate linear weighting of symptoms across ADHD and CP items. Latent class analyses and regression describe associations between measured variables, environmental risk factors and subsequent disadvantage. Additionally, the clinical relevance of each class was estimated.

RESULTS

Five classes were found for women and men; few symptoms, hyperactive-impulsive, CP, inattentive, combined symptoms with CP. Women within the inattentive class reported more symptoms and reduced emotional health when compared to men and to women within other latent classes. Women and men with combined ADHD symptoms reported comorbid conduct problems but those with either inattention or hyperactivity-impulsivity only did not.

CONCLUSION

The dual perspective of dimensional and categorical measurement of ADHD provides important detail about symptom variation across sex and with environmental covariates.

Genetics Selection Evolution reviewer acknowledgement 2014

The Genetics Selection Evolution editorial team would sincerely like to thank all of our reviewers who contributed to peer review for the journal in 2014.

Genomic best linear unbiased prediction (gBLUP) for the estimation of genomic breeding values

Genomic best linear unbiased prediction (gBLUP) is a method that utilizes genomic relationships to estimate the genetic merit of an individual. For this purpose, a genomic relationship matrix is used, estimated from DNA marker information. The matrix defines the covariance between individuals based on observed similarity at the genomic level, rather than on expected similarity based on pedigree, so that more accurate predictions of merit can be made. gBLUP has been used for the prediction of merit in livestock breeding, may also have some applications to the prediction of disease risk, and is also useful in the estimation of variance components and genomic heritabilities.

Attention deficit hyperactivity disorder in Australian adults: prevalence, persistence, conduct problems and disadvantage

Background

The Prevalence and persistence of ADHD have not been described in young Australian adults and few studies have examined how conduct problems (CP) are associated with ADHD for this age group. We estimate lifetime and adult prevalence and persistence rates for three categories of ADHD for 3795 Australian adults, and indicate how career, health and childhood risk factors differ for people with ADHD symptoms and ADHD symptoms plus CP.

Methodology

Trained interviewers collected participant experience of ADHD, CP, education, employment, childhood experience, relationship and health variables. Three diagnostic definitions of ADHD used were (i) full DSM-IV criteria; (ii) excluding the age 7 onset criterion (no age criterion); (iii) participant experienced difficulties due to ADHD symptoms (problem symptoms).

Results

Prevalence rates in adulthood were 1.1%, 2.3% and 2.7% for each categorization respectively. Persistence of ADHD from childhood averaged across gender was 55.3% for full criteria, 50.3% with no age criterion and 40.2% for problem symptoms. ADHD symptoms were associated with parental conflict, poor health, being sexually assaulted during childhood, lower education, income loss and higher unemployment. The lifetime prevalence of conduct problems for adults with ADHD was 57.8% and 6.9% for adults without ADHD. The greatest disadvantage was experienced by participants with ADHD plus CP.

Conclusion

The persistence of ADHD into adulthood was greatest for participants meeting full diagnostic criteria and inattention was associated with the greatest loss of income and disadvantage. The disadvantage associated with conduct problems differed in severity and was relevant for a high proportion of adults with ADHD. Women but not men with ADHD reported more childhood adversity, possibly indicating varied etiology and treatment needs. The impact and treatment needs of adults with ADHD and CP and the report of sexual assault during childhood by women and men with ADHD also deserve further study.

Use of a bovine genome array to identify new biological pathways for beef marbling in Hanwoo (Korean Cattle)

BACKGROUND

Marbling (intramuscular fat) is a valuable trait that impacts on meat quality and an important factor determining price of beef in the Korean beef market. Animals that are destined for this high marbling market are fed a high concentrate ration for approximately 30 months in the Korean finishing farms. However, this feeding strategy leads to inefficiencies and excessive fat production. This study aimed to identify candidate genes and pathways associated with intramuscular fat deposition on highly divergent marbling phenotypes in adult Hanwoo cattle.

RESULTS

Bovine genome array analysis was conducted to detect differentially expressed genes (DEGs) in m. longissimus with divergent marbling phenotype (marbling score 2 to 7). Three data-processing methods (MAS5.0, GCRMA and RMA) were used to test for differential expression (DE). Statistical analysis identified 21 significant transcripts from at least two data-processing methods (P < 0.01). All 21 differentially expressed genes were validated by real-time PCR. Results showed a high concordance in the gene expression fold change between the microarrays and the real time PCR data. Gene Ontology (GO) and pathway analysis demonstrated that some genes (ADAMTS4, CYP51A and SQLE) over expressed in high marbled animals are involved in a protein catabolic process and a cholesterol biosynthesis process. In addition, pathway analysis also revealed that ADAMTS4 is activated by three regulators (IL-17A, TNFα and TGFβ1). QRT-PCR was used to investigate gene expression of these regulators in muscle with divergent intramuscular fat contents. The results demonstrate that ADAMTS4 and TGFβ1 are associated with increasing marbling fat. An ADAMTS4/TGFβ1 pathway seems to be associated with the phenotypic differences between high and low marbled groups.

CONCLUSIONS

Marbling differences are possibly a function of complex signaling pathway interactions between muscle and fat. These results suggest that ADAMTS4, which is involved in connective tissue degradation, could play a role in an important biological pathway for building up marbling in cattle. Moreover, ADAMTS4 and TGFβ1could potentially be used as an early biological marker for marbling fat content in the early stages of growth.

Evidence for multiple alleles effecting muscling and fatness at the ovine GDF8 locus

Background

The current investigation surveyed genetic polymorphism at the ovine GDF8 locus and determined its contribution to variation in muscling and fatness in sheep.

Results

Re-sequencing 2988 bp from a panel of 15 sires revealed a total of six SNP, none of which were located within exons of the gene. One of the identified SNP, g+6723G>A, is known to increase muscularity within the Belgian Texel. A genetic survey of 326 animals revealed that the mutation is near fixation within Australian Texels and present in additional breeds including White Suffolk, Poll Dorset and Lincoln. Using a resource population comprising 15 sires and 1191 half-sib progeny with genotypic data, the effect of this and other SNP was tested against a set of 50 traits describing growth, muscling, fatness, yield, meat and eating quality. The loss of function allele (g+6723A) showed significant effects on slaughter measurements of muscling and fatness. No effect was detected on objectively assessed meat quality however evidence was found for an association between g+6723G>A, decreased intramuscular fat and reduced eating quality. Haplotype analysis using flanking microsatellites was performed to search for evidence of currently unidentified mutations which might affect production traits. Four haplotypes were identified that do not carry g+6723A but which showed significant associations with muscling and fatness.

Conclusion

The finding that g+6723G>A is present within Australian sheep facilitated an independent evaluation into its phenotypic consequence. Testing was conducted using a separate genetic background and animals raised in different environments to the Belgian Texel in which it was first identified. The observation that the direction and size of effects for g+6723A is approximately consistent represented a robust validation of the effects of the mutation. Based on observed allele frequencies within breeds, selection for g+6723A will have the largest impact within the White Suffolk. GDF8 may harbour additional mutations which serve to influence economically important traits in sheep.