Multibreed genomic prediction using multitrait genomic residual maximum likelihood and multitask Bayesian variable selection.

Genomic prediction is applicable to individuals of different breeds. Empirical results to date, however, show limited benefits in using information on multiple breeds in the context of genomic prediction. We investigated a multitask Bayesian model, presented previously by others, implemented in a Bayesian stochastic search variable selection (BSSVS) model. This model allowed for evidence of quantitative trait loci (QTL) to be accumulated across breeds or for both QTL that segregate across breeds and breed-specific QTL. In both cases, single nucleotide polymorphism effects were estimated with information from a single breed. Other models considered were a single-trait and multitrait genomic residual maximum likelihood (GREML) model, with breeds considered as different traits, and a single-trait BSSVS model. All single-trait models were applied to each of the 2 breeds separately and to the pooled data of both breeds. The data used included a training data set of 6,278 Holstein and 722 Jersey bulls, as well as 374 Jersey validation bulls. All animals had genotypes for 474,773 single nucleotide polymorphisms after editing and phenotypes for milk, fat, and protein yields. Using the same training data, BSSVS consistently outperformed GREML. The multitask BSSVS, however, did not outperform single-trait BSSVS, which used pooled Holstein and Jersey data for training. Thus, the rigorous assumption that the traits are the same in both breeds yielded a slightly better prediction than a model that had to estimate the correlation between the breeds from the data. Adding the Holstein data significantly increased the accuracy of the single-trait GREML and BSSVS in predicting the Jerseys for milk and protein, in line with estimated correlations between the breeds of 0.66 and 0.47 for milk and protein yields, whereas only the BSSVS model significantly improved the accuracy for fat yield with an estimated correlation between breeds of only 0.05. The relatively high genetic correlations for milk and protein yields, and the superiority of the pooling strategy, is likely the result of the observed admixture between both breeds in our data. The Bayesian model was able to detect several QTL in Holsteins, which likely enabled it to outperform GREML. The inability of the multitask Bayesian models to outperform a simple pooling strategy may be explained by the fact that the pooling strategy assumes equal effects in both breeds; furthermore, this assumption may be valid for moderate- to large-sized QTL, which are important for multibreed genomic prediction.

Future Roadmaps for Precision Medicine Applied to Diabetes: Rising to the Challenge of Heterogeneity.

Precision medicine, the concept that specific treatments can be targeted to groups of individuals with specific genetic, cellular, or molecular features, is a key aspect of modern healthcare, and its use is rapidly expanding. In diabetes, the application of precision medicine has been demonstrated in monogenic disease, where sulphonylureas are used to treat patients with neonatal diabetes due to mutations in ATP-dependent potassium (KATP) channel genes. However, diabetes is highly heterogeneous, both between and within polygenic and monogenic subtypes. Making the correct diagnosis and using the correct treatment from diagnosis can be challenging for clinicians, but it is crucial to prevent long-term morbidity and mortality. To facilitate precision medicine in diabetes, research is needed to develop a better understanding of disease heterogeneity and its impact on potential treatments for specific subtypes. Animal models have been used in diabetes research, but they are not translatable to humans in the majority of cases. Advances in molecular genetics and functional laboratory techniques and availability and sharing of large population data provide exciting opportunities for human studies. This review will map the key elements of future diabetes research in humans and its potential for clinical translation to promote precision medicine in all diabetes subtypes.

Psychiatric morbidity in children with KCNJ11 neonatal diabetes.

AIMS: Mutations in the KCNJ11 gene, which encodes the Kir6.2 subunit of the pancreatic KATP channel, cause neonatal diabetes. KCNJ11 is also expressed in the brain, and ~ 20% of those affected have neurological features, which may include features suggestive of psychiatric disorder. No previous studies have systematically characterized the psychiatric morbidity in people with KCNJ11 neonatal diabetes. We aimed to characterize the types of psychiatric disorders present in children with KCNJ11 mutations, and explore their impact on families. METHODS: The parents and teachers of 10 children with neonatal diabetes due to KCNJ11 mutations completed the Strengths and Difficulties Questionnaire and the Development and Wellbeing Assessment. Strengths and Difficulties Questionnaire scores were compared with normative data. Diagnoses from the Development and Wellbeing Assessment were compared with known clinical diagnoses. RESULTS: Strengths and Difficulties Questionnaire scores indicated high levels of psychopathology and impact. Psychiatric disorder(s) were present in all six children with the V59M or R201C mutation, and the presence of more than one psychiatric disorder was common. Only two children had received a formal clinical diagnosis, with a further one awaiting assessment, and the coexistence of more than one psychiatric disorder had been missed. Neurodevelopmental (attention deficit hyperactivity disorder and autism) and anxiety disorders predominated. CONCLUSIONS: Systematic assessment using standardized validated questionnaires reveals a range of psychiatric morbidity in children with KCNJ11 neonatal diabetes. This is under-recognized clinically and has a significant impact on affected children and their families. An integrated collaborative approach to clinical care is needed to manage the complex needs of people with KCNJ11 neonatal diabetes.

Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits.

BACKGROUND: Dense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants. RESULTS: We introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits. CONCLUSIONS: BayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species.

Genetic analyses of fertility and predictor traits in Holstein herds with low and high mean calving intervals and in Jersey herds.

Genetic parameters were estimated with the aim of identifying useful predictor traits for the genetic evaluation of fertility. For this study, data included calving interval (CI), days from calving to first service (CFS), pregnancy diagnosis, lactation length (LL), daily milk yield close to 90 d of lactation (milk yield), and survival to second lactation on Australian Holstein and Jersey cows. The effect of level of fertility, measured here as CI, on correlations among traits was investigated by dividing the Holstein herds into those that managed short CI (proxy for seasonal-calving herds) and long CI (proxy for herds that practice extended lactations). In all cases, genetic correlations of CI with CFS, pregnancy, and LL were high (>0.7). Genetic correlations between fertility and predictor traits were generally similar in the 2 Holstein herd groups and in Jerseys. However, some differences in both the direction and strength of correlations were observed. In Jerseys, the genetic correlation between CI and survival was positive, but in Holstein herds, this correlation was negative. Particularly in low mean CI herds, the correlation suggests that cows with a genetic potential for longer CI were more likely to be culled. The genetic correlation of CI with survival was intermediate in high mean CI Holstein herds. Furthermore, Jersey cows with a high genetic potential for milk yield had a higher chance of surviving than those with low genetic potential. In contrast, the genetic correlation between milk yield and survival in low mean CI Holstein herds was near zero. The high genetic correlation between CI and LL suggests that LL could be used as proxy for CI in cows that do not calve again. Although the phenotypic variance for CI in high mean CI herds was nearly twice that in Jerseys and low mean CI herds, we found no bull reranking for CI due to having daughters in low or high mean CI herds. However, the ranges in estimated breeding values (EBV) were narrower in low mean CI herds than in high mean CI herds. The genetic trend in cows and bulls showed that CI EBV were increasing by 0.3 to 0.8 d/yr in both Holstein and Jersey. Phenotypically, CI was increasing by 2 d/yr in high mean CI Holstein herds and by 1 d/yr in Jersey and low mean CI Holstein herds. However, in recent years, both phenotypic and genetic trends have stabilized. In summary, if the main trait for genetic evaluation of fertility is CI, predictor traits such as milk yield, survival, LL, and other fertility traits can be used in joint analyses to increase reliability of bull EBV. If the genetic evaluation is to be carried out simultaneously for Holstein and Jersey using the same variance-covariance matrix, survival should not be used as a predictor because its correlation with CI is different in Jersey than in Holstein. On the other hand, LL could be used instead of CI for cows that do not calve again in both breeds and herd groups.

Long-term selection strategies for complex traits using high-density genetic markers.

Selection of animals for breeding ranked on estimated breeding value maximizes genetic gain in the next generation but does not necessarily maximize long-term response. An alternative method, as practiced by plant breeders, is to build a desired genotype by selection on specific loci. Maximal long-term response in animal breeding requires selection on estimated breeding values with constraints on coancestry. In this paper, we compared long-term genetic response using either a genotype building or a genomic estimated breeding value (GEBV) strategy for the Australian Selection Index (ASI), a measure of profit. First, we used real marker effects from the Australian Dairy Herd Improvement Scheme to estimate breeding values for chromosome segments (approximately 25 cM long) for 2,650 Holstein bulls. Second, we selected 16 animals to be founders for a simulated breeding program where, between them, founders contain the best possible combination of 2 segments from 2 animals at each position in the genome. Third, we mated founder animals and their descendants over 30 generations with 2 breeding objectives: (1) to create a population with the "ideal genotype," where the best 2 segments from the founders segregate at each position, or (2) obtain the highest possible response in ASI with coancestry lower than that achieved under breeding objective 1. Results show that genotype building achieved the ideal genotype for breeding objective 1 and obtained a large gain in ASI over the current population (+A$864.99). However, selection on overall GEBV had greater short-term response and almost as much long-term gain (+A$820.42). When coancestry was lowered under breeding objective 2, selection on overall GEBV achieved a higher response in ASI than the genotype building strategy. Selection on overall GEBV seems more flexible in its selection decisions and was therefore better able to precisely control coancestry while maximizing ASI. We conclude that selection on overall GEBV while minimizing average coancestry is the more practical strategy for dairy cattle where selection is for highly polygenic traits, the reproductive rate is relatively low, and there is low tolerance of coancestry. The outcome may be different for traits controlled by few loci of relatively large effects or for different species. In contrast to other simulations, our results indicate that response to selection on overall GEBV may continue for several generations. This is because long-term genetic change in complex traits requires favorable changes to allele frequencies for many loci located throughout the genome.

Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels.

Achieving accurate genomic estimated breeding values for dairy cattle requires a very large reference population of genotyped and phenotyped individuals. Assembling such reference populations has been achieved for breeds such as Holstein, but is challenging for breeds with fewer individuals. An alternative is to use a multi-breed reference population, such that smaller breeds gain some advantage in accuracy of genomic estimated breeding values (GEBV) from information from larger breeds. However, this requires that marker-quantitative trait loci associations persist across breeds. Here, we assessed the gain in accuracy of GEBV in Jersey cattle as a result of using a combined Holstein and Jersey reference population, with either 39,745 or 624,213 single nucleotide polymorphism (SNP) markers. The surrogate used for accuracy was the correlation of GEBV with daughter trait deviations in a validation population. Two methods were used to predict breeding values, either a genomic BLUP (GBLUP_mod), or a new method, BayesR, which used a mixture of normal distributions as the prior for SNP effects, including one distribution that set SNP effects to zero. The GBLUP_mod method scaled both the genomic relationship matrix and the additive relationship matrix to a base at the time the breeds diverged, and regressed the genomic relationship matrix to account for sampling errors in estimating relationship coefficients due to a finite number of markers, before combining the 2 matrices. Although these modifications did result in less biased breeding values for Jerseys compared with an unmodified genomic relationship matrix, BayesR gave the highest accuracies of GEBV for the 3 traits investigated (milk yield, fat yield, and protein yield), with an average increase in accuracy compared with GBLUP_mod across the 3 traits of 0.05 for both Jerseys and Holsteins. The advantage was limited for either Jerseys or Holsteins in using 624,213 SNP rather than 39,745 SNP (0.01 for Holsteins and 0.03 for Jerseys, averaged across traits). Even this limited and nonsignificant advantage was only observed when BayesR was used. An alternative panel, which extracted the SNP in the transcribed part of the bovine genome from the 624,213 SNP panel (to give 58,532 SNP), performed better, with an increase in accuracy of 0.03 for Jerseys across traits. This panel captures much of the increased genomic content of the 624,213 SNP panel, with the advantage of a greatly reduced number of SNP effects to estimate. Taken together, using this panel, a combined breed reference and using BayesR rather than GBLUP_mod increased the accuracy of GEBV in Jerseys from 0.43 to 0.52, averaged across the 3 traits.

Accuracy of genomic predictions of residual feed intake and 250-day body weight in growing heifers using 625,000 single nucleotide polymorphism markers.

Feed makes up a large proportion of variable costs in dairying. For this reason, selection for traits associated with feed conversion efficiency should lead to greater profitability of dairying. Residual feed intake (RFI) is the difference between actual and predicted feed intakes and is a useful selection criterion for greater feed efficiency. However, measuring individual feed intakes on a large scale is prohibitively expensive. A panel of DNA markers explaining genetic variation in this trait would enable cost-effective genomic selection for this trait. With the aim of enabling genomic selection for RFI, we used data from almost 2,000 heifers measured for growth rate and feed intake in Australia (AU) and New Zealand (NZ) genotyped for 625,000 single nucleotide polymorphism (SNP) markers. Substantial variation in RFI and 250-d body weight (BW250) was demonstrated. Heritabilities of RFI and BW250 estimated using genomic relationships among the heifers were 0.22 and 0.28 in AU heifers and 0.38 and 0.44 in NZ heifers, respectively. Genomic breeding values for RFI and BW250 were derived using genomic BLUP and 2 bayesian methods (BayesA, BayesMulti). The accuracies of genomic breeding values for RFI were evaluated using cross-validation. When 624,930 SNP were used to derive the prediction equation, the accuracies averaged 0.37 and 0.31 for RFI in AU and NZ validation data sets, respectively, and 0.40 and 0.25 for BW250 in AU and NZ, respectively. The greatest advantage of using the full 624,930 SNP over a reduced panel of 36,673 SNP (the widely used BovineSNP50 array) was when the reference population included only animals from either the AU or the NZ experiment. Finally, the bayesian methods were also used for quantitative trait loci detection. On chromosome 14 at around 25 Mb, several SNP closest to PLAG1 (a gene believed to affect stature in humans and cattle) had an effect on BW250 in both AU and NZ populations. In addition, 8 SNP with large effects on RFI were located on chromosome 14 at around 35.7 Mb. These SNP may be associated with the gene NCOA2, which has a role in controlling energy metabolism.

Accuracy of genotype imputation in sheep breeds.

Although genomic selection offers the prospect of improving the rate of genetic gain in meat, wool and dairy sheep breeding programs, the key constraint is likely to be the cost of genotyping. Potentially, this constraint can be overcome by genotyping selection candidates for a low density (low cost) panel of SNPs with sparse genotype coverage, imputing a much higher density of SNP genotypes using a densely genotyped reference population. These imputed genotypes would then be used with a prediction equation to produce genomic estimated breeding values. In the future, it may also be desirable to impute very dense marker genotypes or even whole genome re-sequence data from moderate density SNP panels. Such a strategy could lead to an accurate prediction of genomic estimated breeding values across breeds, for example. We used genotypes from 48 640 (50K) SNPs genotyped in four sheep breeds to investigate both the accuracy of imputation of the 50K SNPs from low density SNP panels, as well as prospects for imputing very dense or whole genome re-sequence data from the 50K SNPs (by leaving out a small number of the 50K SNPs at random). Accuracy of imputation was low if the sparse panel had less than 5000 (5K) markers. Across breeds, it was clear that the accuracy of imputing from sparse marker panels to 50K was higher if the genetic diversity within a breed was lower, such that relationships among animals in that breed were higher. The accuracy of imputation from sparse genotypes to 50K genotypes was higher when the imputation was performed within breed rather than when pooling all the data, despite the fact that the pooled reference set was much larger. For Border Leicesters, Poll Dorsets and White Suffolks, 5K sparse genotypes were sufficient to impute 50K with 80% accuracy. For Merinos, the accuracy of imputing 50K from 5K was lower at 71%, despite a large number of animals with full genotypes (2215) being used as a reference. For all breeds, the relationship of individuals to the reference explained up to 64% of the variation in accuracy of imputation, demonstrating that accuracy of imputation can be increased if sires and other ancestors of the individuals to be imputed are included in the reference population. The accuracy of imputation could also be increased if pedigree information was available and was used in tracking inheritance of large chromosome segments within families. In our study, we only considered methods of imputation based on population-wide linkage disequilibrium (largely because the pedigree for some of the populations was incomplete). Finally, in the scenarios designed to mimic imputation of high density or whole genome re-sequence data from the 50K panel, the accuracy of imputation was much higher (86-96%). This is promising, suggesting that in silico genome re-sequencing is possible in sheep if a suitable pool of key ancestors is sequenced for each breed.

Validation of single nucleotide polymorphisms associated with milk production traits in dairy cattle.

Single nucleotide polymorphism (SNP) associations with milk production traits found to be significant in different screening experiments, including SNP in genes hypothesized to be in gene pathways affecting milk production, were tested in a validation population to confirm their association. In total, 423 SNP were genotyped across 411 Holstein bulls, and their association with 6 milk production traits--Australian Selection Index (indicating the profitability of an animal's milk production), protein, fat, and milk yields, and protein and fat composition--were tested using single SNP regressions. Seventy-two SNP were significantly associated with one or more of the traits; their effects were in the same direction as in the screening experiment and therefore their association was considered validated. An over-representation of SNP (43 of the 423) on chromosome 20 was observed, including a SNP in the growth hormone receptor gene previously published as having an association with protein composition and protein and milk yields. The association with protein composition was confirmed in this experiment, but not the association with protein and milk yields. A multiple SNP regression analysis for all SNP on chromosome 20 was performed for all 6 traits, which revealed that this mutation was not significantly associated with any of the milk production traits and that at least 2 other quantitative trait loci were present on chromosome 20.

Carbimazole embryopathy: implications for the choice of antithyroid drugs in pregnancy.

Maternal thyrotoxicosis, predominantly secondary to Graves' disease, affects 0.2% of all pregnancies. The Endocrine Society guidelines recommend the use of propylthiouracil as a first-line drug for thyrotoxicosis in pregnancy because of associations between carbimazole or methimazole and congenital anomalies. However, recent studies have highlighted the risk of severe liver injury with propylthiouracil. Here, we report another case with multiple congenital anomalies following in utero exposure to carbimazole and review the literature to consider the risks and benefits of available pharmacological treatments for thyrotoxicosis in pregnancy.

Validation of a single-sample urinary C-peptide creatinine ratio as a reproducible alternative to serum C-peptide in patients with Type 2 diabetes.

AIMS: Serum C-peptide can be used in Type 2 diabetes as a measure of endogenous insulin secretion, but practicalities of collection limit its routine clinical use. Urine C-peptide creatinine ratio is a non-invasive alternative that is stable for at least 3 days at room temperature in boric acid preservative. We aimed to assess the utility of urine C-peptide creatinine ratio in individuals with Type 2 diabetes as an alternative to serum C-peptide. METHODS: We assessed, in 77 individuals with Type 2 diabetes, the reproducibility of, and correlations between, fasting and postprandial urine C-peptide creatinine ratio and serum C-peptide, and the impact of renal impairment (estimated glomerular filtration rate < 60 ml min(-1) 1.73 m(-2)) on these correlations. RESULTS: Urine C-peptide creatinine ratio was at least as reproducible as serum C-peptide [fasting coefficient of variation mean (95% CI): 28 (21-35)% vs. 38 (26-59)% and 2-h post-meal 26 (18-33)% vs. 27 (20-34)%. Urine C-peptide creatinine ratio 2 h post-meal was correlated with stimulated serum C-peptide, both the 2-h value (r = 0.64, P < 0.001) and the 2-h area under the C-peptide curve (r = 0.63, P < 0.001). The association seen was similar in patients with and without moderate renal impairment (P = 0.6). CONCLUSIONS: In patients with Type 2 diabetes, a single urine C-peptide creatinine ratio is a stable, reproducible measure that is well correlated with serum C-peptide following meal stimulation, even if there is moderate renal impairment. Urine C-peptide creatinine ratio therefore has potential for use in clinical practice in the assessment of Type 2 diabetes.

Heterozygous ABCC8 mutations are a cause of MODY.

AIMS/HYPOTHESIS: The ABCC8 gene encodes the sulfonylurea receptor 1 (SUR1) subunit of the pancreatic beta cell ATP-sensitive potassium (K(ATP)) channel. Inactivating mutations cause congenital hyperinsulinism (CHI) and activating mutations cause transient neonatal diabetes (TNDM) or permanent neonatal diabetes (PNDM) that can usually be treated with sulfonylureas. Sulfonylurea sensitivity is also a feature of HNF1A and HNF4A MODY, but patients referred for genetic testing with clinical features of these types of diabetes do not always have mutations in the HNF1A/4A genes. Our aim was to establish whether mutations in the ABCC8 gene cause MODY that is responsive to sulfonylurea therapy. METHODS: We sequenced the ABCC8 gene in 85 patients with a BMI <30 kg/m², no family history of neonatal diabetes and who were deemed sensitive to sulfonylureas by the referring clinician or were sulfonylurea-treated. All had tested negative for mutations in the HNF1A and HNF4A genes. RESULTS: ABCC8 mutations were found in seven of the 85 (8%) probands. Four patients were heterozygous for previously reported mutations and four novel mutations, E100K, G214R, Q485R and N1245D, were identified. Only four probands fulfilled MODY criteria, with two diagnosed after 25 years and one patient, who had no family history of diabetes, as a result of a proven de novo mutation. CONCLUSIONS/INTERPRETATION: ABCC8 mutations can cause MODY in patients whose clinical features are similar to those with HNF1A/4A MODY. Therefore, sequencing of ABCC8 in addition to the known MODY genes should be considered if such features are present, to facilitate optimal clinical management of these patients.

Urine C-peptide creatinine ratio is an alternative to stimulated serum C-peptide measurement in late-onset, insulin-treated diabetes.

AIMS: Serum C-peptide measurement can assist clinical management of diabetes, but practicalities of collection limit widespread use. Urine C-peptide creatinine ratio may be a non-invasive practical alternative. The stability of C-peptide in urine allows outpatient or community testing. We aimed to assess how urine C-peptide creatinine ratio compared with serum C-peptide measurement during a mixed-meal tolerance test in individuals with late-onset, insulin-treated diabetes. METHODS: We correlated the gold standard of a stimulated serum C-peptide in a mixed-meal tolerance test with fasting and stimulated (mixed-meal tolerance test, standard home meal and largest home meal) urine C-peptide creatinine ratio in 51 subjects with insulin-treated diabetes (diagnosis after age 30 years, median age 66 years, median age at diagnosis 54, 42 with Type 2 diabetes, estimated glomerular filtration rate > 60 ml min(-1) 1.73 m(-2) ). RESULTS: Ninety-minute mixed-meal tolerance test serum C-peptide is correlated with mixed-meal tolerance test-stimulated urine C-peptide creatinine ratio (r = 0.82), urine C-peptide creatinine ratio after a standard breakfast at home (r = 0.73) and urine C-peptide creatinine ratio after largest home meal (r = 0.71). A stimulated (largest home meal) urine C-peptide creatinine ratio cut-off of 0.3 nmol/mmol had a 100% sensitivity and 96% specificity (area under receiver operating characteristic curve = 0.99) in identifying subjects without clinically significant endogenous insulin secretion (mixed-meal tolerance test-stimulated C-peptide < 0.2 nmol/l). In detecting a proposed serum C-peptide threshold for insulin requirement (stimulated serum C-peptide < 0.6 nmol/l), a stimulated (largest home meal) urine C-peptide creatinine ratio cut-off of 0.6 nmol/mmol had a sensitivity and specificity of 92%. CONCLUSION: In patients with insulin-treated diabetes diagnosed after age 30 years, urine C-peptide creatinine ratio is well correlated with serum C-peptide and may provide a practical alternative measure to detect insulin deficiency for use in routine clinical practice.

Short communication: Genomic selection using a multi-breed, across-country reference population.

Three breeds (Fleckvieh, Holstein, and Jersey) were included in a reference population, separately and together, to assess the accuracy of prediction of genomic breeding values in single-breed validation populations. The accuracy of genomic selection was defined as the correlation between estimated breeding values, calculated using phenotypic data, and genomic breeding values. The Holstein and Jersey populations were from Australia, whereas the Fleckvieh population (dual-purpose Simmental) was from Austria and Germany. Both a BLUP with a multi-breed genomic relationship matrix (GBLUP) and a Bayesian method (BayesA) were used to derive the prediction equations. The hypothesis tested was that having a multi-breed reference population increased the accuracy of genomic selection. Minimal advantage existed of either GBLUP or BayesA multi-breed genomic evaluations over single-breed evaluations. However, when the goal was to predict genomic breeding values for a breed with no individuals in the reference population, using 2 other breeds in the reference was generally better than only 1 breed.

A validated genome-wide association study in 2 dairy cattle breeds for milk production and fertility traits using variable length haplotypes.

Genome-wide association studies (GWAS) were used to discover genomic regions explaining variation in dairy production and fertility traits. Associations were detected with either single nucleotide polymorphism (SNP) markers or haplotypes of SNP alleles. An across-breed validation strategy was used to narrow the genomic interval containing causative mutations. There were 39,048 SNP tested in a discovery population of 780 Holstein sires and validated in 386 Holsteins and 364 Jersey sires. Previously identified mutations affecting milk production traits were confirmed. In addition, several novel regions were identified, including a putative quantitative trait loci for fertility on chromosome 18 that was detected only using haplotypes greater than 3 SNP long. It was found that the precision of quantitative trait loci mapping increased with haplotype length as did the number of validated haplotypes discovered, especially across breed. Promising candidate genes have been identified in several of the validated regions.

Genetic markers for lactation persistency in primiparous Australian dairy cows.

Good performance in extended lactations of dairy cattle may have a beneficial effect on food costs, health, and fertility. Because data for extended lactation performance is scarce, lactation persistency has been suggested as a suitable selection criterion. Persistency phenotypes were calculated in several ways: P1 was yield relative to an approximate peak, P2 was the slope after peak production, and P3 was a measure derived to be phenotypically uncorrelated to yield and calculated as a function of linear regressions on test-day deviations of days in milk. Phenotypes P1, P2, and P3 were calculated for sires as solutions estimated from a random regression model fitted to milk yield. Because total milk yield, calculated as the sum of daily sire solutions, was correlated to P1 and P2 (r=0.30 and 0.35 for P1 and P2, respectively), P1 and P2 were also adjusted for milk yield (P1adj and P2adj, respectively). To find genomic regions associated with the persistency phenotypes, we used a discovery population of 743 Holstein bulls proven before 2005 and 2 validation data sets of 357 Holstein bulls proven after 2005 and 294 Jersey sires. Two strategies were used to search for genomic regions associated with persistency: 1) persistency solutions were regressed on each single nucleotide polymorphism (SNP) in turn and 2) a genomic selection method (BayesA) was used where all SNP were fitted simultaneously. False discovery rates in the validation data were high (>66% in Holsteins and >77% in Jerseys). However, there were 2 genomic regions on chromosome 6 that validated in both breeds, including a cluster of 6 SNP at around 13.5 to 23.7 Mbp and another cluster of 5 SNP (70.4 to 75.6 Mbp). A third cluster validated in both breeds on chromosome 26 (0.33 to 1.46 Mbp). Validating SNP effects across 2 breeds is unlikely to happen by chance even when false discovery rates within each breed are high. However, marker-assisted selection on these selected SNP may not be the best way to improve this trait because the average variation explained by validated SNP was only 1 to 2%. Genomic selection could be a better alternative. Correlations between genomic breeding values predicted using all SNP simultaneously and estimated breeding values based on progeny test were twice as high as the equivalent correlations between estimated breeding values and parent average. Persistency is a good candidate for genomic selection because the trait is expressed late in lactation.

Invited review: Genomic selection in dairy cattle: progress and challenges.

A new technology called genomic selection is revolutionizing dairy cattle breeding. Genomic selection refers to selection decisions based on genomic breeding values (GEBV). The GEBV are calculated as the sum of the effects of dense genetic markers, or haplotypes of these markers, across the entire genome, thereby potentially capturing all the quantitative trait loci (QTL) that contribute to variation in a trait. The QTL effects, inferred from either haplotypes or individual single nucleotide polymorphism markers, are first estimated in a large reference population with phenotypic information. In subsequent generations, only marker information is required to calculate GEBV. The reliability of GEBV predicted in this way has already been evaluated in experiments in the United States, New Zealand, Australia, and the Netherlands. These experiments used reference populations of between 650 and 4,500 progeny-tested Holstein-Friesian bulls, genotyped for approximately 50,000 genome-wide markers. Reliabilities of GEBV for young bulls without progeny test results in the reference population were between 20 and 67%. The reliability achieved depended on the heritability of the trait evaluated, the number of bulls in the reference population, the statistical method used to estimate the single nucleotide polymorphism effects in the reference population, and the method used to calculate the reliability. A common finding in 3 countries (United States, New Zealand, and Australia) was that a straightforward BLUP method for estimating the marker effects gave reliabilities of GEBV almost as high as more complex methods. The BLUP method is attractive because the only prior information required is the additive genetic variance of the trait. All countries included a polygenic effect (parent average breeding value) in their GEBV calculation. This inclusion is recommended to capture any genetic variance not associated with the markers, and to put some selection pressure on low-frequency QTL that may not be captured by the markers. The reliabilities of GEBV achieved were significantly greater than the reliability of parental average breeding values, the current criteria for selection of bull calves to enter progeny test teams. The increase in reliability is sufficiently high that at least 2 dairy breeding companies are already marketing bull teams for commercial use based on their GEBV only, at 2 yr of age. This strategy should at least double the rate of genetic gain in the dairy industry. Many challenges with genomic selection and its implementation remain, including increasing the accuracy of GEBV, integrating genomic information into national and international genetic evaluations, and managing long-term genetic gain.