Comparing genomic prediction accuracy from purebred,crossbred and combined purebred and crossbred reference populations in sheep

Background

The accuracy of genomic prediction depends largely on the number of animals with phenotypes and genotypes. In some industries, such as sheep and beef cattle, data are often available from a mixture of breeds, multiple strains within a breed or from crossbred animals. The objective of this study was to compare the accuracy of genomic prediction for several economically important traits in sheep when using data from purebreds, crossbreds or a combination of those in a reference population.

Methods

The reference populations were purebred Merinos, crossbreds of Border Leicester (BL), Poll Dorset (PD) or White Suffolk (WS) with Merinos and combinations of purebred and crossbred animals. Genomic breeding values (GBV) were calculated based on genomic best linear unbiased prediction (GBLUP), using a genomic relationship matrix calculated based on 48 599 Ovine SNP (single nucleotide polymorphisms) genotypes. The accuracy of GBV was assessed in a group of purebred industry sires based on the correlation coefficient between GBV and accurate estimated breeding values based on progeny records.

Results

The accuracy of GBV for Merino sires increased with a larger purebred Merino reference population, but decreased when a large purebred Merino reference population was augmented with records from crossbred animals. The GBV accuracy for BL, PD and WS breeds based on crossbred data was the same or tended to decrease when more purebred Merinos were added to the crossbred reference population. The prediction accuracy for a particular breed was close to zero when the reference population did not contain any haplotypes of the target breed, except for some low accuracies that were obtained when predicting PD from WS and vice versa.

Conclusions

This study demonstrates that crossbred animals can be used for genomic prediction of purebred animals using 50 k SNP marker density and GBLUP, but crossbred data provided lower accuracy than purebred data. Including data from distant breeds in a reference population had a neutral to slightly negative effect on the accuracy of genomic prediction. Accounting for differences in marker allele frequencies between breeds had only a small effect on the accuracy of genomic prediction from crossbred or combined crossbred and purebred reference populations.     

Imputation of high-density genotypes in the Fleckvieh cattle population

Background

Currently, genome-wide evaluation of cattle populations is based on SNP-genotyping using ~ 54 000 SNP. Increasing the number of markers might improve genomic predictions and power of genome-wide association studies. Imputation of genotypes makes it possible to extrapolate genotypes from lower to higher density arrays based on a representative reference sample for which genotypes are obtained at higher density.

Methods

Genotypes using 639 214 SNP were available for 797 bulls of the Fleckvieh cattle breed. The data set was divided into a reference and a validation population. Genotypes for all SNP except those included in the BovineSNP50 Bead chip were masked and subsequently imputed for animals of the validation population. Imputation of genotypes was performed with Beagle, findhap.f90, MaCH and Minimac. The accuracy of the imputed genotypes was assessed for four different scenarios including 50, 100, 200 and 400 animals as reference population. The reference animals were selected to account for 78.03%, 89.21%, 97.47% and > 99% of the gene pool of the genotyped population, respectively.

Results

Imputation accuracy increased as the number of animals and relatives in the reference population increased. Population-based algorithms provided highly reliable imputation of genotypes, even for scenarios with 50 and 100 reference animals only. Using MaCH and Minimac, the correlation between true and imputed genotypes was > 0.975 with 100 reference animals only. Pre-phasing the genotypes of both the reference and validation populations not only provided highly accurate imputed genotypes but was also computationally efficient. Genome-wide analysis of imputation accuracy led to the identification of many misplaced SNP.

Conclusions

Genotyping key animals at high density and subsequent population-based genotype imputation yield high imputation accuracy. Pre-phasing the genotypes of the reference and validation populations is computationally efficient and results in high imputation accuracy, even when the reference population is small.     

Maximizing crossbred performance through purebred genomic selection

Background

In livestock production, many animals are crossbred, with two distinct advantages: heterosis and breed complementarity. Genomic selection (GS) can be used to select purebred parental lines for crossbred performance (CP). Dominance being the likely genetic basis of heterosis, explicitly including dominance in the GS model may be an advantage to select purebreds for CP. Estimated breeding values for CP can be calculated from additive and dominance effects of alleles that are estimated using pure line data. The objective of this simulation study was to investigate the benefits of applying GS to select purebred animals for CP, based on purebred phenotypic and genotypic information. A second objective was to compare the use of two separate pure line reference populations to that of a single reference population that combines both pure lines. These objectives were investigated under two conditions, i.e. either a low or a high correlation of linkage disequilibrium (LD) phase between the pure lines.

Results

The results demonstrate that the gain in CP was higher when parental lines were selected for CP, rather than purebred performance, both with a low and a high correlation of LD phase. For a low correlation of LD phase between the pure lines, the use of two separate reference populations yielded a higher gain in CP than use of a single reference population that combines both pure lines. However, for a high correlation of LD phase, marker effects that were estimated using a single combined reference population increased the gain in CP.

Conclusions

Under the hypothesis that performance of crossbred animals differs from that of purebred animals due to dominance, a dominance model can be used for GS of purebred individuals for CP, without using crossbred data. Furthermore, if the correlation of LD phase between pure lines is high, accuracy of selection can be increased by combining the two pure lines into a single reference population to estimate marker effects.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0099-3) contains supplementary material, which is available to authorized users.     

Genome-wide association analyses for meat quality traits in Chinese Erhualian pigs and a Western Duroc?×?(Landrace × Yorkshire) commercial population

Background

Understanding the genetic mechanisms that underlie meat quality traits is essential to improve pork quality. To date, most quantitative trait loci (QTL) analyses have been performed on F₂ crosses between outbred pig strains and have led to the identification of numerous QTL. However, because linkage disequilibrium is high in such crosses, QTL mapping precision is unsatisfactory and only a few QTL have been found to segregate within outbred strains, which limits their use to improve animal performance. To detect QTL in outbred pig populations of Chinese and Western origins, we performed genome-wide association studies (GWAS) for meat quality traits in Chinese purebred Erhualian pigs and a Western Duroc × (Landrace × Yorkshire) (DLY) commercial population.

Methods

Three hundred and thirty six Chinese Erhualian and 610 DLY pigs were genotyped using the Illumina PorcineSNP60K Beadchip and evaluated for 20 meat quality traits. After quality control, 35 985 and 56 216 single nucleotide polymorphisms (SNPs) were available for the Chinese Erhualian and DLY datasets, respectively, and were used to perform two separate GWAS. We also performed a meta-analysis that combined P-values and effects of 29 516 SNPs that were common to Erhualian, DLY, F₂ and Sutai pig populations.

Results

We detected 28 and nine suggestive SNPs that surpassed the significance level for meat quality in Erhualian and DLY pigs, respectively. Among these SNPs, ss131261254 on pig chromosome 4 (SSC4) was the most significant (P = 7.97E-09) and was associated with drip loss in Erhualian pigs. Our results suggested that at least two QTL on SSC12 and on SSC15 may have pleiotropic effects on several related traits. All the QTL that were detected by GWAS were population-specific, including 12 novel regions. However, the meta-analysis revealed seven novel QTL for meat characteristics, which suggests the existence of common underlying variants that may differ in frequency across populations. These QTL regions contain several relevant candidate genes.

Conclusions

These findings provide valuable insights into the molecular basis of convergent evolution of meat quality traits in Chinese and Western breeds that show divergent phenotypes. They may contribute to genetic improvement of purebreds for crossbred performance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0120-x) contains supplementary material, which is available to authorized users.     

High-density marker imputation accuracy in sixteen French cattle breeds

Background

Genotyping with the medium-density Bovine SNP50 BeadChip^® (50K) is now standard in cattle. The high-density BovineHD BeadChip^®, which contains 777 609 single nucleotide polymorphisms (SNPs), was developed in 2010. Increasing marker density increases the level of linkage disequilibrium between quantitative trait loci (QTL) and SNPs and the accuracy of QTL localization and genomic selection. However, re-genotyping all animals with the high-density chip is not economically feasible. An alternative strategy is to genotype part of the animals with the high-density chip and to impute high-density genotypes for animals already genotyped with the 50K chip. Thus, it is necessary to investigate the error rate when imputing from the 50K to the high-density chip.

Methods

Five thousand one hundred and fifty three animals from 16 breeds (89 to 788 per breed) were genotyped with the high-density chip. Imputation error rates from the 50K to the high-density chip were computed for each breed with a validation set that included the 20% youngest animals. Marker genotypes were masked for animals in the validation population in order to mimic 50K genotypes. Imputation was carried out using the Beagle 3.3.0 software.

Results

Mean allele imputation error rates ranged from 0.31% to 2.41% depending on the breed. In total, 1980 SNPs had high imputation error rates in several breeds, which is probably due to genome assembly errors, and we recommend to discard these in future studies. Differences in imputation accuracy between breeds were related to the high-density-genotyped sample size and to the genetic relationship between reference and validation populations, whereas differences in effective population size and level of linkage disequilibrium showed limited effects. Accordingly, imputation accuracy was higher in breeds with large populations and in dairy breeds than in beef breeds. More than 99% of the alleles were correctly imputed if more than 300 animals were genotyped at high-density. No improvement was observed when multi-breed imputation was performed.

Conclusion

In all breeds, imputation accuracy was higher than 97%, which indicates that imputation to the high-density chip was accurate. Imputation accuracy depends mainly on the size of the reference population and the relationship between reference and target populations.     

Error rate for imputation from the Illumina BovineSNP50 chip to the Illumina BovineHD chip

Background

Imputation of genotypes from low-density to higher density chips is a cost-effective method to obtain high-density genotypes for many animals, based on genotypes of only a relatively small subset of animals (reference population) on the high-density chip. Several factors influence the accuracy of imputation and our objective was to investigate the effects of the size of the reference population used for imputation and of the imputation method used and its parameters. Imputation of genotypes was carried out from 50 000 (moderate-density) to 777 000 (high-density) SNPs (single nucleotide polymorphisms).

Methods

The effect of reference population size was studied in two datasets: one with 548 and one with 1289 Holstein animals, genotyped with the Illumina BovineHD chip (777 k SNPs). A third dataset included the 548 animals genotyped with the 777 k SNP chip and 2200 animals genotyped with the Illumina BovineSNP50 chip. In each dataset, 60 animals were chosen as validation animals, for which all high-density genotypes were masked, except for the Illumina BovineSNP50 markers. Imputation was studied in a subset of six chromosomes, using the imputation software programs Beagle and DAGPHASE.

Results

Imputation with DAGPHASE and Beagle resulted in 1.91% and 0.87% allelic imputation error rates in the dataset with 548 high-density genotypes, when scale and shift parameters were 2.0 and 0.1, and 1.0 and 0.0, respectively. When Beagle was used alone, the imputation error rate was 0.67%. If the information obtained by Beagle was subsequently used in DAGPHASE, imputation error rates were slightly higher (0.71%). When 2200 moderate-density genotypes were added and Beagle was used alone, imputation error rates were slightly lower (0.64%). The least imputation errors were obtained with Beagle in the reference set with 1289 high-density genotypes (0.41%).

Conclusions

For imputation of genotypes from the 50 k to the 777 k SNP chip, Beagle gave the lowest allelic imputation error rates. Imputation error rates decreased with increasing size of the reference population. For applications for which computing time is limiting, DAGPHASE using information from Beagle can be considered as an alternative, since it reduces computation time and increases imputation error rates only slightly.     

Accuracy of genotype imputation in Nelore cattle

Background

Genotype imputation from low-density (LD) to high-density single nucleotide polymorphism (SNP) chips is an important step before applying genomic selection, since denser chips tend to provide more reliable genomic predictions. Imputation methods rely partially on linkage disequilibrium between markers to infer unobserved genotypes. Bos indicus cattle (e.g. Nelore breed) are characterized, in general, by lower levels of linkage disequilibrium between genetic markers at short distances, compared to taurine breeds. Thus, it is important to evaluate the accuracy of imputation to better define which imputation method and chip are most appropriate for genomic applications in indicine breeds.

Methods

Accuracy of genotype imputation in Nelore cattle was evaluated using different LD chips, imputation software and sets of animals. Twelve commercial and customized LD chips with densities ranging from 7 K to 75 K were tested. Customized LD chips were virtually designed taking into account minor allele frequency, linkage disequilibrium and distance between markers. Software programs FImpute and BEAGLE were applied to impute genotypes. From 995 bulls and 1247 cows that were genotyped with the Illumina® BovineHD chip (HD), 793 sires composed the reference set, and the remaining 202 younger sires and all the cows composed two separate validation sets for which genotypes were masked except for the SNPs of the LD chip that were to be tested.

Results

Imputation accuracy increased with the SNP density of the LD chip. However, the gain in accuracy with LD chips with more than 15 K SNPs was relatively small because accuracy was already high at this density. Commercial and customized LD chips with equivalent densities presented similar results. FImpute outperformed BEAGLE for all LD chips and validation sets. Regardless of the imputation software used, accuracy tended to increase as the relatedness between imputed and reference animals increased, especially for the 7 K chip.

Conclusions

If the Illumina® BovineHD is considered as the target chip for genomic applications in the Nelore breed, cost-effectiveness can be improved by genotyping part of the animals with a chip containing around 15 K useful SNPs and imputing their high-density missing genotypes with FImpute.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0069-1) contains supplementary material, which is available to authorized users.     

Genome-wide association study using deregressed breeding values for cryptorchidism and scrotal/inguinal hernia in two pig lines

Background

Cryptorchidism and scrotal/inguinal hernia are the most frequent congenital defects in pigs. Identification of genomic regions that control these congenital defects is of great interest to breeding programs, both from an animal welfare point of view as well as for economic reasons. The aim of this genome-wide association study (GWAS) was to identify single nucleotide polymorphisms (SNPs) that are strongly associated with these congenital defects. Genotypes were available for 2570 Large White (LW) and 2272 Landrace (LR) pigs. Breeding values were estimated based on 1 359 765 purebred and crossbred male offspring, using a binary trait animal model. Estimated breeding values were deregressed (DEBV) and taken as the response variable in the GWAS.

Results

Heritability estimates were equal to 0.26 ± 0.02 for cryptorchidism and to 0.31 ± 0.01 for scrotal/inguinal hernia. Seven and 31 distinct QTL regions were associated with cryptorchidism in the LW and LR datasets, respectively. The top SNP per region explained between 0.96% and 1.10% and between 0.48% and 2.77% of the total variance of cryptorchidism incidence in the LW and LR populations, respectively. Five distinct QTL regions associated with scrotal/inguinal hernia were detected in both LW and LR datasets. The top SNP per region explained between 1.22% and 1.60% and between 1.15% and 1.46% of the total variance of scrotal/inguinal hernia incidence in the LW and LR populations, respectively. For each trait, we identified one overlapping region between the LW and LR datasets, i.e. a region on SSC8 (Sus scrofa chromosome) between 65 and 73 Mb for cryptorchidism and a region on SSC13 between 34 and 37 Mb for scrotal/inguinal hernia.

Conclusions

The use of DEBV in combination with a binary trait model was a powerful approach to detect regions associated with difficult traits such as cryptorchidism and scrotal/inguinal hernia that have a low incidence and for which affected animals are generally not available for genotyping. Several novel QTL regions were detected for cryptorchidism and scrotal/inguinal hernia, and for several previously known QTL regions, the confidence interval was narrowed down.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0096-6) contains supplementary material, which is available to authorized users.     

Prioritizing animals for dense genotyping in order to impute missing genotypes of sparsely genotyped animals

Background

Genotyping accounts for a substantial part of the cost of genomic selection (GS). Using both dense and sparse SNP chips, together with imputation of missing genotypes, can reduce these costs. The aim of this study was to identify the set of candidates that are most important for dense genotyping, when they are used to impute the genotypes of sparsely genotyped animals. In a real pig pedigree, the 2500 most recently born pigs of the last generation, i.e. the target animals, were used for sparse genotyping. Their missing genotypes were imputed using either Beagle or LDMIP from T densely genotyped candidates chosen from the whole pedigree. A new optimization method was derived to identify the best animals for dense genotyping, which minimized the conditional genetic variance of the target animals, using either the pedigree-based relationship matrix (MCA), or a genotypic relationship matrix based on sparse marker genotypes (MCG). These, and five other methods for selecting the T animals were compared, using T = 100 or 200 animals, SNP genotypes were obtained assuming Ne =100 or 200, and MAF thresholds set to D = 0.01, 0.05 or 0.10. The performances of the methods were compared using the following criteria: call rate of true genotypes, accuracy of genotype prediction, and accuracy of genomic evaluations using the imputed genotypes.

Results

For all criteria, MCA and MCG performed better than other selection methods, significantly so for all methods other than selection of sires with the largest numbers of offspring. Methods that choose animals that have the closest average relationship or contribution to the target population gave the lowest accuracy of imputation, in some cases worse than random selection, and should be avoided in practice.

Conclusion

Minimization of the conditional variance of the genotypes in target animals provided an effective optimization procedure for prioritizing animals for genotyping or sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/1297-9686-46-46) contains supplementary material, which is available to authorized users.     

Global gene expression in endometrium of high and low fertility heifers during the mid-luteal phase of the estrous cycle

Background

In both beef and dairy cattle, the majority of early embryo loss occurs within the first 14 days following insemination. During this time-period, embryos are completely dependent on their maternal uterine environment for development, growth and ultimately survival, therefore an optimum uterine environment is critical to their survival. The objective of this study was to investigate whether differences in endometrial gene expression during the mid-luteal phase of the estrous cycle exist between crossbred beef heifers ranked as either high (HF) or low fertility (LF) (following four rounds of artificial insemination (AI)) using the Affymetrix® 23 K Bovine Gene Chip.

Results

Conception rates for each of the four rounds of AI were within a normal range: 70–73.3%. Microarray analysis of endometrial tissue collected on day 7 of the estrous cycle detected 419 differentially expressed genes (DEG) between HF (n = 6) and LF (n = 6) animals. The main gene pathways affected were, cellular growth and proliferation, angiogenesis, lipid metabolism, cellular and tissue morphology and development, inflammation and metabolic exchange. DEG included, FST, SLC45A2, MMP19, FADS1 and GALNT6.

Conclusions

This study highlights, some of the molecular mechanisms potentially controlling uterine endometrial function during the mid-luteal phase of the estrous cycle, which may contribute to uterine endometrial mediated impaired fertility in cattle. Differentially expressed genes are potential candidate genes for the identification of genetic variation influencing cow fertility, which may be incorporated into future breeding programmes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-234) contains supplementary material, which is available to authorized users.     

Impact of whole-genome amplification on the reliability of pre-transfer cattle embryo breeding value estimates

Background

Genome-wide profiling of single-nucleotide polymorphisms is receiving increasing attention as a method of pre-implantation genetic diagnosis in humans and of commercial genotyping of pre-transfer embryos in cattle. However, the very small quantity of genomic DNA in biopsy material from early embryos poses daunting technical challenges. A reliable whole-genome amplification (WGA) procedure would greatly facilitate the procedure.

Results

Several PCR-based and non-PCR based WGA technologies, namely multiple displacement amplification, quasi-random primed library synthesis followed by PCR, ligation-mediated PCR, and single-primer isothermal amplification were tested in combination with different DNA extractions protocols for various quantities of genomic DNA inputs. The efficiency of each method was evaluated by comparing the genotypes obtained from 15 cultured cells (representative of an embryonic biopsy) to unamplified reference gDNA. The gDNA input, gDNA extraction method and amplification technology were all found to be critical for successful genome-wide genotyping. The selected WGA platform was then tested on embryo biopsies (n = 226), comparing their results to that of biopsies collected after birth. Although WGA inevitably leads to a random loss of information and to the introduction of erroneous genotypes, following genomic imputation the resulting genetic index of both sources of DNA were highly correlated (r = 0.99, P<0.001).

Conclusion

It is possible to generate high-quality DNA in sufficient quantities for successful genome-wide genotyping starting from an early embryo biopsy. However, imputation from parental and population genotypes is a requirement for completing and correcting genotypic data. Judicious selection of the WGA platform, careful handling of the samples and genomic imputation together, make it possible to perform extremely reliable genomic evaluations for pre-transfer embryos.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-889) contains supplementary material, which is available to authorized users.     

The risk for depression in patients with ankylosing spondylitis: a population-based cohort study

Introduction

Depression is frequent in ankylosing spondylitis (AS) patients. However, epidemiological data about the potential increase in risk are lacking. This study compares the rate of doctor-diagnosed depression in a well defined cohort of AS patients to the general population seeking care.

Methods

The Skåne Healthcare Register comprises healthcare data of each resident in Region Skåne, Sweden (population 1.2 million), including ICD-10 diagnoses. Using physician coded consultation data from years 1999 to 2011, we calculated depression consultation rates for all AS patients. We obtained standardized depression-rate ratios by dividing the observed depression rate in AS patients by the expected rate based on the corresponding age- and sex-specific rates of depression in the general population seeking care. A ratio >1 equals a higher rate of depression among AS patients.

Results

The AS cohort consisted of 1738 subjects (65% men) with a mean age of 54 years. The reference population consisted of 967,012 subjects. During the 13-year observation period 10% (n = 172) of the AS cohort had a doctor-diagnosed depression compared to 6% (n = 105) to be expected. The standardized estimate of depression-rate ratio was 1.81 (95% confidence interval 1.44 to 2.24) in women men and 1.49 (1.20 to 1.89) in men.

Conclusions

The rate of doctor-diagnosed depression is increased about 80% in female and 50% in male AS patients. Future challenges are to timely identify and treat the AS patients who suffer from depression.     

Influence of the ABCG2 gout risk 141?K allele on urate metabolism during a fructose challenge

Introduction

Both genetic variation in ATP-binding cassette sub-family G member 2 (ABCG2) and intake of fructose-containing beverages are major risk factors for hyperuricemia and gout. This study aimed to test the hypothesis that the ABCG2 gout risk allele 141 K promotes the hyperuricaemic response to fructose loading.

Methods

Healthy volunteers (n = 74) provided serum and urine samples immediately before and 30, 60, 120 and 180 minutes after ingesting a 64 g fructose solution. Data were analyzed based on the presence or absence of the ABCG2 141 K gout risk allele.

Results

The 141 K risk allele was present in 23 participants (31%). Overall, serum urate (SU) concentrations during the fructose load were similar in those with and without the 141 K allele (P_SNP = 0.15). However, the 141 K allele was associated with a smaller increase in SU following fructose intake (P_SNP <0.0001). Those with the 141 K allele also had a smaller increase in serum glucose following the fructose load (P_SNP = 0.002). Higher fractional excretion of uric acid (FEUA) at baseline and throughout the fructose load was observed in those with the 141 K risk allele (P_SNP <0.0001). However, the change in FEUA in response to fructose was not different in those with and without the 141 K risk allele (P_SNP = 0.39). The 141 K allele effects on serum urate and glucose were more pronounced in Polynesian participants and in those with a body mass index ≥25 kg/m².

Conclusions

In contrast to the predicted responses for a hyperuricemia/gout risk allele, the 141 K allele is associated with smaller increases in SU and higher FEUA following a fructose load. The results suggest that ABCG2 interacts with extra-renal metabolic pathways in a complex manner to regulate SU and gout risk.

Clinical Trials Registration

The study was registered by the Australian Clinical Trials Registry (ACTRN12610001036000).     

Yearly hypertension screening in women with a history of pre-eclampsia: a cost-effectiveness analysis

Background

Women with a history of preeclampsia are at increased risk for future hypertension and cardiovascular disease (CVD); until now it is not clear whether preventive measures are needed.

Methods

A decision-analytic Markov model was constructed to evaluate healthcare costs and effects of screening and treatment (100 % compliance) for hypertension post preeclampsia based on the available literature. Cardiovascular events and CVD mortality were defined as health states. Outcomes were measured in absolute costs, events, life-years and quality-adjusted life-years (QALYs). Sensitivity and threshold analyses were performed to address uncertainty.

Results

Over a 20-year time horizon, events occurred in 7.2 % of the population after screening, and in 8.5 % of the population without screening. QALYs increased from 16.37 (no screening strategy) to 16.40 (screening strategy), an increment of 0.03 (95 % CI 0.01;0.05) QALYs. Total expected costs were € 8016 in the screening strategy, and € 9087 in the none screening strategy (expected saving of € 1071 (95 % CI − 3146;-87) per person).

Conclusion

Annual hypertension screening and treatment in women with a history of preeclampsia may save costs, for at least a similar quality of life and survival due to prevented CVD compared with standard care.

Electronic supplementary material

The online version of this article (doi:10.1007/s12471-015-0760-z) contains supplementary material, which is available to authorized users.     

A new approach for efficient genotype imputation using information from relatives

Background

Genotype imputation can help reduce genotyping costs particularly for implementation of genomic selection. In applications entailing large populations, recovering the genotypes of untyped loci using information from reference individuals that were genotyped with a higher density panel is computationally challenging. Popular imputation methods are based upon the Hidden Markov model and have computational constraints due to an intensive sampling process. A fast, deterministic approach, which makes use of both family and population information, is presented here. All individuals are related and, therefore, share haplotypes which may differ in length and frequency based on their relationships. The method starts with family imputation if pedigree information is available, and then exploits close relationships by searching for long haplotype matches in the reference group using overlapping sliding windows. The search continues as the window size is shrunk in each chromosome sweep in order to capture more distant relationships.

Results

The proposed method gave higher or similar imputation accuracy than Beagle and Impute2 in cattle data sets when all available information was used. When close relatives of target individuals were present in the reference group, the method resulted in higher accuracy compared to the other two methods even when the pedigree was not used. Rare variants were also imputed with higher accuracy. Finally, computing requirements were considerably lower than those of Beagle and Impute2. The presented method took 28 minutes to impute from 6 k to 50 k genotypes for 2,000 individuals with a reference size of 64,429 individuals.

Conclusions

The proposed method efficiently makes use of information from close and distant relatives for accurate genotype imputation. In addition to its high imputation accuracy, the method is fast, owing to its deterministic nature and, therefore, it can easily be used in large data sets where the use of other methods is impractical.     

Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost

Background

Commercial breeding programs seek to maximise the rate of genetic gain while minimizing the costs of attaining that gain. Genomic information offers great potential to increase rates of genetic gain but it is expensive to generate. Low-cost genotyping strategies combined with genotype imputation offer dramatically reduced costs. However, both the costs and accuracy of imputation of these strategies are highly sensitive to several factors. The objective of this paper was to explore the cost and imputation accuracy of several alternative genotyping strategies in pedigreed populations.

Methods

Pedigree and genotype data from a commercial pig population were used. Several alternative genotyping strategies were explored. The strategies differed in the density of genotypes used for the ancestors and the individuals to be imputed. Parents, grandparents, and other relatives that were not descendants, were genotyped at high-density, low-density, or extremely low-density, and associated costs and imputation accuracies were evaluated.

Results

Imputation accuracy and cost were influenced by the alternative genotyping strategies. Given the mating ratios and the numbers of offspring produced by males and females, an optimized low-cost genotyping strategy for a commercial pig population could involve genotyping male parents at high-density, female parents at low-density (e.g. 3000 SNP), and selection candidates at very low-density (384 SNP).

Conclusions

Among the selection candidates, 95.5 % and 93.5 % of the genotype variation contained in the high-density SNP panels were recovered using a genotyping strategy that costs respectively, $24.74 and $20.58 per candidate.     

Comparison of cancer incidence among patients with rheumatic disease: a retrospective cohort study

Introduction

Rheumatic diseases (RDs) are associated with different cancers; however, it is unclear whether particular cancers are more prevalent in certain RDs. In the present study, we examined the relative incidence of several cancers in a single homogeneous cohort of patients with different RDs.

Methods

Patients (N = 3,586) diagnosed with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), systemic sclerosis (SSc), dermatomyositis (DM) or polymyositis were included. Cancer diagnosis was based on histopathology. The 2008 Korean National Cancer Registry served as the reference for calculating standardized incidence ratios (SIRs).

Results

During the follow-up period of 31,064 person-years, 187 patients developed cancer. RA and SLE patients showed an increased risk of non-Hodgkin’s lymphoma (SIR for RA patients = 3.387, 95% confidence interval (CI) = 1.462 to 6.673; SIR for SLE patients = 7.408, 95% CI = 2.405 to 17.287). SLE patients also had a higher risk of cervical cancer (SIR = 4.282, 95% CI = 1.722 to 8.824). SSc patients showed a higher risk of lung cancer (SIR = 4.917, 95% CI = 1.977 to 10.131). Endometrial cancer was increased only in patients with DM (SIR = 30.529, 95% CI = 3.697 to 110.283). RA patients had a lower risk for gastric cancer (SIR = 0.663, 95% CI = 0.327 to 0.998). The mean time between the RD and cancer diagnoses ranged from 0.1 to 16.6 years, with the shortest time observed in patients with DM (2.0 ± 2.1 years).

Conclusions

Different RDs are associated with particular cancers. Thus, cancer surveillance tailored to specific RDs might be beneficial.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-014-0428-x) contains supplementary material, which is available to authorized users.     

Serum tryptase detected during acute coronary syndrome is significantly related to the development of major adverse cardiovascular events after 2?years

Background

One of the greatest challenges in cardiovascular medicine is to define the best tools for performing an accurate risk stratification for the recurrence of ischemic events in acute coronary syndrome (ACS) patients.

Methods

We followed 65 ACS patients enrolled in a previous pilot study for 2 years after being discharged, focusing on the occurrence of major adverse cardiovascular events (MACE).The relationship between serum tryptase levels on admission, SYNergy between percutaneous coronary intervention with the TAXUS drug-eluting stent and the cardiac surgery score (SX-score), cardiovascular complexity and MACE at 2 years follow-up were analyzed.

Results

The ACS population was divided in two groups: patients with MACE (n = 23) and patients without MACE (n = 42).The tryptase measurement at admission (T0) and at discharge (T3) and SX-score were higher in patients who experienced MACE than in those without (p = 0.0001, p < 0.0001 and p = 0.006, respectively). Conversely, we found no significant association between MACE and C-reactive protein (CRP), and between MACE and maximum level of high-sensitivity troponin (hs-Tn) values.Among all patients with MACE, 96% belonged to the group that presented with cardiovascular complexity at the beginning of ACS index admission (p < 0.0001).The predictive accuracy of serum tryptase for MACE at follow up set at the cut-off point of 4.95 ng/ml at T0 and of 5.2 ng/ml at T3. Interestingly, patients with both the above cut-off tryptase values at T0 and at T3 presented a 1320% increase in the odds of developing MACE (p < 0.0001).

Conclusion

In ACS patients, serum tryptase measured during index admission is significantly correlated to the development of MACE up to 2 years, demonstrating a possible long-term prognostic role of this biomarker.

Electronic supplementary material

The online version of this article (doi:10.1186/s12948-015-0013-0) contains supplementary material, which is available to authorized users.     

Tiotropium might improve survival in subjects with COPD at high risk of mortality

Background

Inhaled therapies reduce risk of chronic obstructive pulmonary disease (COPD) exacerbations, but their effect on mortality is less well established. We hypothesized that heterogeneity in baseline mortality risk influenced the results of drug trials assessing mortality in COPD.

Methods

The 5706 patients with COPD from the Understanding Potential Long-term Impacts on Function with Tiotropium (UPLIFT®) study that had complete clinical information for variables associated with mortality (age, forced expiratory volume in 1 s, St George’s Respiratory Questionnaire, pack-years and body mass index) were classified by cluster analysis. Baseline risk of mortality between clusters, and impact of tiotropium were evaluated during the 4-yr follow up.

Results

Four clusters were identified, including low-risk (low mortality rate) patients (n = 2339; 41%; cluster 2), and high-risk patients (n = 1022; 18%; cluster 3), who had a 2.6- and a six-fold increase in all-cause and respiratory mortality compared with cluster 2, respectively. Tiotropium reduced exacerbations in all clusters, and reduced hospitalizations in high-risk patients (p < 0.05). The beneficial effect of tiotropium on all-cause mortality in the overall population (hazard ratio, 0.87; 95% confidence interval, 0.75–1.00, p = 0.054) was explained by a 21% reduction in cluster 3 (p = 0.07), with no effect in other clusters.

Conclusions

Large variations in baseline risks of mortality existed among patients in the UPLIFT® study. Inclusion of numerous low-risk patients may have reduced the ability to show beneficial effect on mortality. Future clinical trials should consider selective inclusion of high-risk patients.     

Global epigenomic analysis indicates that Epialleles contribute to Allele-specific expression via Allele-specific histone modifications in hybrid rice

Background

For heterozygous genes, alleles on the chromatin from two different parents exhibit histone modification variations known as allele-specific histone modifications (ASHMs). The regulation of allele-specific gene expression (ASE) by ASHMs has been reported in animals. However, to date, the regulation of ASE by ASHM genes remains poorly understood in higher plants.

Results

We used chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) to investigate the global ASHM profiles of trimethylation on histone H3 lysine 27 (H3K27me3) and histone H3 lysine 36 (H3K36me3) in two rice F1 hybrids. A total of 522 to 550 allele-specific H3K27me3 genes and 428 to 494 allele-specific H3K36me3 genes were detected in GL × 93-11 and GL × TQ, accounting for 11.09% and 26.13% of the total analyzed genes, respectively. The epialleles between parents were highly related to ASHMs. Further analysis indicated that 52.48% to 70.40% of the epialleles were faithfully inherited by the F1 hybrid and contributed to 33.18% to 46.55% of the ASHM genes. Importantly, 66.67% to 82.69% of monoallelic expression genes contained the H3K36me3 modification. Further studies demonstrated a significant positive correlation of ASE with allele-specific H3K36me3 but not with H3K27me3, indicating that ASHM-H3K36me3 primarily regulates ASE in this study.

Conclusions

Our results demonstrate that epialleles from parents can be inherited by the F1 to produce ASHMs in the F1 hybrid. Our findings indicate that ASHM-H3K36me3, rather than H3K27me3, mainly regulates ASE in hybrid rice.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1454-z) contains supplementary material, which is available to authorized users.