Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle

Background

The use of whole-genome sequence data can lead to higher accuracy in genome-wide association studies and genomic predictions. However, to benefit from whole-genome sequence data, a large dataset of sequenced individuals is needed. Imputation from SNP panels, such as the Illumina BovineSNP50 BeadChip and Illumina BovineHD BeadChip, to whole-genome sequence data is an attractive and less expensive approach to obtain whole-genome sequence genotypes for a large number of individuals than sequencing all individuals. Our objective was to investigate accuracy of imputation from lower density SNP panels to whole-genome sequence data in a typical dataset for cattle.

Methods

Whole-genome sequence data of chromosome 1 (1737 471 SNPs) for 114 Holstein Friesian bulls were used. Beagle software was used for imputation from the BovineSNP50 (3132 SNPs) and BovineHD (40 492 SNPs) beadchips. Accuracy was calculated as the correlation between observed and imputed genotypes and assessed by five-fold cross-validation. Three scenarios S40, S60 and S80 with respectively 40%, 60%, and 80% of the individuals as reference individuals were investigated.

Results

Mean accuracies of imputation per SNP from the BovineHD panel to sequence data and from the BovineSNP50 panel to sequence data for scenarios S40 and S80 ranged from 0.77 to 0.83 and from 0.37 to 0.46, respectively. Stepwise imputation from the BovineSNP50 to BovineHD panel and then to sequence data for scenario S40 improved accuracy per SNP to 0.65 but it varied considerably between SNPs.

Conclusions

Accuracy of imputation to whole-genome sequence data was generally high for imputation from the BovineHD beadchip, but was low from the BovineSNP50 beadchip. Stepwise imputation from the BovineSNP50 to the BovineHD beadchip and then to sequence data substantially improved accuracy of imputation. SNPs with a low minor allele frequency were more difficult to impute correctly and the reliability of imputation varied more. Linkage disequilibrium between an imputed SNP and the SNP on the lower density panel, minor allele frequency of the imputed SNP and size of the reference group affected imputation reliability.     

Right-hand-side updating for fast computing of genomic breeding values

Background

Since both the number of SNPs (single nucleotide polymorphisms) used in genomic prediction and the number of individuals used in training datasets are rapidly increasing, there is an increasing need to improve the efficiency of genomic prediction models in terms of computing time and memory (RAM) required.

Methods

In this paper, two alternative algorithms for genomic prediction are presented that replace the originally suggested residual updating algorithm, without affecting the estimates. The first alternative algorithm continues to use residual updating, but takes advantage of the characteristic that the predictor variables in the model (i.e. the SNP genotypes) take only three different values, and is therefore termed “improved residual updating”. The second alternative algorithm, here termed “right-hand-side updating” (RHS-updating), extends the idea of improved residual updating across multiple SNPs. The alternative algorithms can be implemented for a range of different genomic predictions models, including random regression BLUP (best linear unbiased prediction) and most Bayesian genomic prediction models. To test the required computing time and RAM, both alternative algorithms were implemented in a Bayesian stochastic search variable selection model.

Results

Compared to the original algorithm, the improved residual updating algorithm reduced CPU time by 35.3 to 43.3%, without changing memory requirements. The RHS-updating algorithm reduced CPU time by 74.5 to 93.0% and memory requirements by 13.1 to 66.4% compared to the original algorithm.

Conclusions

The presented RHS-updating algorithm provides an interesting alternative to reduce both computing time and memory requirements for a range of genomic prediction models.     

A comparison of principal component regression and genomic REML for genomic prediction across populations

Background

Genomic prediction faces two main statistical problems: multicollinearity and n ≪ p (many fewer observations than predictor variables). Principal component (PC) analysis is a multivariate statistical method that is often used to address these problems. The objective of this study was to compare the performance of PC regression (PCR) for genomic prediction with that of a commonly used REML model with a genomic relationship matrix (GREML) and to investigate the full potential of PCR for genomic prediction.

Methods

The PCR model used either a common or a semi-supervised approach, where PC were selected based either on their eigenvalues (i.e. proportion of variance explained by SNP (single nucleotide polymorphism) genotypes) or on their association with phenotypic variance in the reference population (i.e. the regression sum of squares contribution). Cross-validation within the reference population was used to select the optimum PCR model that minimizes mean squared error. Pre-corrected average daily milk, fat and protein yields of 1609 first lactation Holstein heifers, from Ireland, UK, the Netherlands and Sweden, which were genotyped with 50 k SNPs, were analysed. Each testing subset included animals from only one country, or from only one selection line for the UK.

Results

In general, accuracies of GREML and PCR were similar but GREML slightly outperformed PCR. Inclusion of genotyping information of validation animals into model training (semi-supervised PCR), did not result in more accurate genomic predictions. The highest achievable PCR accuracies were obtained across a wide range of numbers of PC fitted in the regression (from one to more than 1000), across test populations and traits. Using cross-validation within the reference population to derive the number of PC, yielded substantially lower accuracies than the highest achievable accuracies obtained across all possible numbers of PC.

Conclusions

On average, PCR performed only slightly less well than GREML. When the optimal number of PC was determined based on realized accuracy in the testing population, PCR showed a higher potential in terms of achievable accuracy that was not capitalized when PC selection was based on cross-validation. A standard approach for selecting the optimal set of PC in PCR remains a challenge.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0060-x) contains supplementary material, which is available to authorized users.     

Genomic prediction of breeding values using previously estimated SNP variances

Background

Genomic prediction requires estimation of variances of effects of single nucleotide polymorphisms (SNPs), which is computationally demanding, and uses these variances for prediction. We have developed models with separate estimation of SNP variances, which can be applied infrequently, and genomic prediction, which can be applied routinely.

Methods

SNP variances were estimated with Bayes Stochastic Search Variable Selection (BSSVS) and BayesC. Genome-enhanced breeding values (GEBV) were estimated with RR-BLUP (ridge regression best linear unbiased prediction), using either variances obtained from BSSVS (BLUP-SSVS) or BayesC (BLUP-C), or assuming equal variances for each SNP. Datasets used to estimate SNP variances comprised (1) all animals, (2) 50% random animals (RAN50), (3) 50% best animals (TOP50), or (4) 50% worst animals (BOT50). Traits analysed were protein yield, udder depth, somatic cell score, interval between first and last insemination, direct longevity, and longevity including information from predictors.

Results

BLUP-SSVS and BLUP-C yielded similar GEBV as the equivalent Bayesian models that simultaneously estimated SNP variances. Reliabilities of these GEBV were consistently higher than from RR-BLUP, although only significantly for direct longevity. Across scenarios that used data subsets to estimate GEBV, observed reliabilities were generally higher for TOP50 than for RAN50, and much higher than for BOT50. Reliabilities of TOP50 were higher because the training data contained more ancestors of selection candidates. Using estimated SNP variances based on random or non-random subsets of the data, while using all data to estimate GEBV, did not affect reliabilities of the BLUP models. A convergence criterion of 10⁻⁸ instead of 10⁻¹⁰ for BLUP models yielded similar GEBV, while the required number of iterations decreased by 71 to 90%. Including a separate polygenic effect consistently improved reliabilities of the GEBV, but also substantially increased the required number of iterations to reach convergence with RR-BLUP. SNP variances converged faster for BayesC than for BSSVS.

Conclusions

Combining Bayesian variable selection models to re-estimate SNP variances and BLUP models that use those SNP variances, yields GEBV that are similar to those from full Bayesian models. Moreover, these combined models yield predictions with higher reliability and less bias than the commonly used RR-BLUP model.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0052-x) contains supplementary material, which is available to authorized users.     

Approaching MALDI molecular imaging for clinical proteomic research: current state and fields of application

MALDI imaging mass spectrometry ('MALDI imaging') is an increasingly recognized technique for biomarker research. After years of method development in the scientific community, the technique is now increasingly applied in clinical research. In this article, we discuss the use of MALDI imaging in clinical proteomics and put it in context with classical proteomics techniques. We also highlight a number of upcoming challenges for personalized medicine, development of targeted therapies and diagnostic molecular pathology where MALDI imaging could help.     

Identification and confirmation of increased fibrinopeptide a serum protein levels in gastric cancer sera by magnet bead assisted MALDI-TOF mass spectrometry

Gastric cancer is the second most common malignancy and prognosis remains dismal. The reasons for the poor prognosis are the lack of sensitive serum markers for early detection and screening of high-risk individuals as well as the limited treatment options in advanced cancer stages. Using MALDI-TOF mass spectrometry after prefractionation of sera with magnet hydrophobic C8 coated beads sera from 14 patients with gastric cancer and 14 healthy controls mass spectra were generated. A peptide fragment was found to be highly elevated in cancer sera and was identified as fibrinopeptide A. To confirm proteome analysis of gastric cancer sera, we then screened a larger series of patients with gastric cancer (n = 99), high-risk individuals (n = 13) and normal controls (n = 111) for fibrinopeptide A serum levels. Interestingly, the mean logarithmic concentrations of serum fibrinopeptide A levels were significantly higher in cancer patients (mean 3.636 +/- 0.3738; p < 0.0001) and high-risk individuals (mean 3.569 +/- 0.4722; p < 0.05) compared to normal controls (mean 3.303 +/- 0.4012). In contrast, we observed no association of fibrinopeptide A levels with tumor stage, tumor location, presence of regional or distant metastasis, and Lauren type of gastric cancer. In conclusion, MALDI-TOF mass spectrometry of prefractionated gastric cancer sera allows the identification of potential biomarkers that may lead to the development of serum based tests for screening of high-risk individuals.     

2-N-Arylthiazole inhibitors of Mycobacterium tuberculosis

To develop agents for the treatment of infections caused by Mycobacterium tuberculosis, a novel phenotypic screen was undertaken that identified a series of 2-N-aryl thiazole-based inhibitors of intracellular Mycobacterium tuberculosis. Analogs were optimized to improve potency against an attenuated BSL2 H37Ra laboratory strain cultivated in human macrophage cells in vitro. The insertion of a carboxylic acid functionality resulted in compounds that retained potency and greatly improved microsomal stability. However, the strong potency trends we observed in the attenuated H37Ra strain were inconsistent with the potency observed for virulent strains in vitro and in vivo.     

Sequence organization of the human genome

The organization of three sequence classes—single copy, repetitive, and inverted repeated sequences—within the human genome has been studied by renaturation techniques, hydroxylapatite binding methods, and DNA hyperchromism. Repetitive sequence classes are distributed throughout 80% or more of the genome. Slightly more than half of the genome consists of short single copy sequences, with a length of about 2 kb interspersed with repetitive sequences. The average length of the repetitive sequences is also small and approximates the length of these sequences found in other organisms. The sequence organization of the human genome therefore resembles the sequence organization found in Xenopus and sea urchin. The inverted repeats are essentially randomly positioned with respect to both sequence class and sequence arrangement, so that all three sequence classes are found to be mutually interspersed in a portion of the genome.     

Characterization of pre-insertion loci of de novo L1 insertions

The human Long Interspersed Element-1 (LINE-1) and the Short Interspersed Element (SINE) Alu comprise 28% of the human genome. They share the same L1-encoded endonuclease for insertion, which recognizes an A+T-rich sequence. Under a simple model of insertion distribution, this nucleotide preference would lead to the prediction that the populations of both elements would be biased towards A+T-rich regions. Genomic L1 elements do show an A+T-rich bias. In contrast, Alu is biased towards G+C-rich regions when compared to the genome average. Several analyses have demonstrated that relatively recent insertions of both elements show less G+C content bias relative to older elements. We have analyzed the repetitive element and G+C composition of more than 100 pre-insertion loci derived from de novo L1 insertions in cultured human cancer cells, which should represent an evolutionarily unbiased set of insertions. An A+T-rich bias is observed in the 50 bp flanking the endonuclease target site, consistent with the known target site for the L1 endonuclease. The L1, Alu, and G+C content of 20 kb of the de novo pre-insertion loci shows a different set of biases than that observed for fixed L1s in the human genome. In contrast to the insertion sites of genomic L1s, the de novo L1 pre-insertion loci are relatively L1-poor, Alu-rich and G+C neutral. Finally, a statistically significant cluster of de novo L1 insertions was localized in the vicinity of the c-myc gene. These results suggest that the initial insertion preference of L1, while A+T-rich in the initial vicinity of the break site, can be influenced by the broader content of the flanking genomic region and have implications for understanding the dynamics of L1 and Alu distributions in the human genome.