An Improved Goodness of Fit Statistic for Probability Prediction Models

We consider the general case of probability prediction models having two or more outcomes and propose an adjusted χ² statistic which can be used to assess the goodness of fit of these models. We present a simulation study to show that our proposed statistic has an approximate χ² distribution under the null hypothesis. Two applications are provided to illustrate the use of the new statistic. The first application examines the fit of a logistic regression model using both the proposed statistic and the popular Hosmer-Lemeshow statistic and we compare and contrast these two methods. The second application evaluates the goodness of fit of a polychotomous regression model.     

Draft versus finished sequence data for DNA and protein diagnostic signature development

Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10⁻³–10⁻⁵ (~8× coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of ~1% (3× to 6× coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures.     

Detecting genomic clustering of risk variants from sequence data: cases versus controls

As the ability to measure dense genetic markers approaches the limit of the DNA sequence itself, taking advantage of possible clustering of genetic variants in, and around, a gene would benefit genetic association analyses, and likely provide biological insights. The greatest benefit might be realized when multiple rare variants cluster in a functional region. Several statistical tests have been developed, one of which is based on the popular Kulldorff scan statistic for spatial clustering of disease. We extended another popular spatial clustering method—Tango’s statistic—to genomic sequence data. An advantage of Tango’s method is that it is rapid to compute, and when single test statistic is computed, its distribution is well approximated by a scaled χ ² distribution, making computation of p values very rapid. We compared the Type-I error rates and power of several clustering statistics, as well as the omnibus sequence kernel association test. Although our version of Tango’s statistic, which we call “Kernel Distance” statistic, took approximately half the time to compute than the Kulldorff scan statistic, it had slightly less power than the scan statistic. Our results showed that the Ionita-Laza version of Kulldorff’s scan statistic had the greatest power over a range of clustering scenarios.     

The Effect of HLA Polymorphisms on the Recognition of Gag Epitopes in HIV-1 CRF01_AE Infection

Introduction

The design of a globally effective vaccine rests on the identification of epitopes capable of eliciting effective cytotoxic T lymphocyte (CTL) responses across multiple HIV clades in different populations. This study aims to discern the effect of HLA polymorphisms and the cross-clade reactivity or clade-specificity of epitopes in Thailand where HIV-1 CRF01_AE is circulating.

Materials and Methods

14 peptides based on consensus HIV-1 CRF01_AE amino acid sequences were designed for use in IFN-γ ELISpot assays and ⁵¹Cr release assays among 66 HIV-1 CRF01_AE-infected Thai patients. For ELISpot responders carrying HLA alleles currently unknown to restrict CRF01_AE epitopes, in silico epitope-HLA prediction was performed.

Results

29/66 (43.9%) patients recognized at least one peptide. In total 79 responses were seen against all 14 peptides. 28/79 (35.4%) of the responses were in patients with HLA alleles previously reported to restrict CRF01_AE epitopes, 24/79 (30.4%) responses were in individuals with HLA alleles previously reported to restrict epitopes of HIV clades other than CRF01_AE, and the remaining 27/79 (34.2%) responses were not associated with HLA alleles previously known to restrict HIV epitopes. In silico epitope prediction detected 19 novel, epitope-HLA combinations, and 11/19 (57.9%) were associated with HLA-C alleles. We further confirmed a novel HLA restriction of a previously identified HIV-1 Gag epitope [p24_122–130: PPIPVGDIY (PY9)] by HLA-B*40:01 with a standard ⁵¹Cr release assay.

Discussion

CTL recognition sites in HIV-1 Gag were similar among different clades but the HLA restriction differed in Thai patients. This disparity in HLA restriction along different populations illustrated the importance of clade- and population-specific HLA analysis prior to CTL vaccine design.     

Prediction Formulas for Individual Opioid Analgesic Requirements Based on Genetic Polymorphism Analyses

Background

The analgesic efficacy of opioids is well known to vary widely among individuals, and various factors related to individual differences in opioid sensitivity have been identified. However, a prediction model to calculate appropriate opioid analgesic requirements has not yet been established. The present study sought to construct prediction formulas for individual opioid analgesic requirements based on genetic polymorphisms and clinical data from patients who underwent cosmetic orthognathic surgery and validate the utility of the prediction formulas in patients who underwent major open abdominal surgery.

Methods

To construct the prediction formulas, we performed multiple linear regression analyses using data from subjects who underwent cosmetic orthognathic surgery. The dependent variable was 24-h postoperative or perioperative fentanyl use, and the independent variables were age, gender, height, weight, pain perception latencies (PPL), and genotype data of five single-nucleotide polymorphisms (SNPs). To examine the utility of the prediction formulas, we performed simple linear regression analyses using subjects who underwent major open abdominal surgery. Actual 24-h postoperative or perioperative analgesic use and the predicted values that were calculated using the multiple regression equations were incorporated as dependent and independent variables, respectively.

Results

Multiple linear regression analyses showed that the four SNPs, PPL, and weight were retained as independent predictors of 24-h postoperative fentanyl use (R² = 0.145, P = 5.66 × 10^-10) and the two SNPs and weight were retained as independent predictors of perioperative fentanyl use (R² = 0.185, P = 1.99 × 10^-15). Simple linear regression analyses showed that the predicted values were retained as an independent predictor of actual 24-h postoperative analgesic use (R² = 0.033, P = 0.030) and perioperative analgesic use (R² = 0.100, P = 1.09 × 10^-4), respectively.

Conclusions

We constructed prediction formulas, and the possible utility of these prediction formulas was found in another type of surgery.     

Identification of Allelic Heterogeneity at Type-2 Diabetes Loci and Impact on Prediction

Although over 60 single nucleotide polymorphisms (SNPs) have been identified by meta-analysis of genome-wide association studies for type-2 diabetes (T2D) among individuals of European descent, much of the genetic variation remains unexplained. There are likely many more SNPs that contribute to variation in T2D risk, some of which may lie in the regions surrounding established SNPs - a phenomenon often referred to as allelic heterogeneity. Here, we use the summary statistics from the DIAGRAM consortium meta-analysis of T2D genome-wide association studies along with linkage disequilibrium patterns inferred from a large reference sample to identify novel SNPs associated with T2D surrounding each of the previously established risk loci. We then examine the extent to which the use of these additional SNPs improves prediction of T2D risk in an independent validation dataset. Our results suggest that multiple SNPs at each of 3 loci contribute to T2D susceptibility (TCF7L2, CDKN2A/B, and KCNQ1; p<5×10⁻⁸). Using a less stringent threshold (p<5×10⁻⁴), we identify 34 additional loci with multiple associated SNPs. The addition of these SNPs slightly improves T2D prediction compared to the use of only the respective lead SNPs, when assessed using an independent validation cohort. Our findings suggest that some currently established T2D risk loci likely harbor multiple polymorphisms which contribute independently and collectively to T2D risk. This opens a promising avenue for improving prediction of T2D, and for a better understanding of the genetic architecture of T2D.     

Rare Earth Element Transfer from Soil to Navel Orange Pulp (Citrus sinensis Osbeck cv. Newhall) and the Effects on Internal Fruit Quality

The effects of soil rare earth element (REE) on navel orange quality and safety in rare earth ore areas have gained great attention. This study investigated the transfer characteristics of REE from soil to navel orange pulp (Citrus sinensis Osbeck cv. Newhall) and examined the effects of soil REE on internal fruit quality in Xinfeng County, Jiangxi province, China. Path analysis showed that soil REE, pH, cation exchange capacity (CEC), and Fe oxide (Fe_ox) significantly affected pulp REE concentrations. A Freundlich-type prediction model for pulp REE was established: log[REE_pulp] = -1.036 + 0.272 log[REE_soil] - 0.056 pH - 0.360 log[CEC] + 0.370 log[Fe_ox] (n = 114, R² = 0.60). From the prediction model, it was inferred that even when soil REE and Fe_ox were as high as 1038 mg kg^-1 and 96.4 g kg^-1, respectively, and pH and CEC were as low as 3.75 and 5.08 cmol kg^-1, respectively, pulp REE concentrations were much lower than the food limit standard. Additionally, soil REE levels were significantly correlated with selected fruit quality indicators, including titratable acidity (r = 0.52, P < 0.01), total soluble solids (r = 0.48, P < 0.01) and vitamin C (r = 0.56, P < 0.01). Generally, under routine methods of water and fertilization management, the cultivation of navel oranges in rare earth ore areas of south China with soil REE ranging from 38.6 to 546 mg kg^-1 had improved in internal fruit quality.     

Preventing nerve function impairment in leprosy: validation and updating of a prediction rule

Background

To validate and update a prediction rule for estimating the risk of leprosy-related nerve function impairment (NFI).

Methodology/Principal Findings

Prospective cohort using routinely collected data, in which we determined the discriminative ability of a previously published rule and an updated rule with a concordance statistic (c). Additional risk factors were analyzed with a Cox proportional hazards regression model. The population consisted of 1,037 leprosy patients newly diagnosed between 2002 and 2003 in the health care facilities of the Rural Health Program in Nilphamari and Rangpur districts in northwest Bangladesh. The primary outcome was the time until the start of treatment. An NFI event was defined as the decision to treat NFI with corticosteroids after diagnosis. NFI occurred in 115 patients (13%; 95% confidence interval 11%–16%). The original prediction rule had adequate discriminative ability (c = 0.79), but could be improved by substituting one predicting variable: ‘long-standing nerve function impairment at diagnosis’ by ‘anti-PGL-I antibodies’. The adjusted prediction rule was slightly better (c = 0.81) and identified more patients with NFI (80%) than the original prediction rule (72%).

Conclusions/Significance

NFI can well be predicted by using the risk variables ‘leprosy classification’ and ‘anti-PGL-I antibodies’. The use of these two variables that do not include NFI offer the possibility of predicting NFI, even before it occurs for the first time. Surveillance beyond the treatment period can be targeted to those most likely to benefit from preventing permanent disabilities.     

Simplification of a light-based model for estimating final internode length in greenhouse cucumber canopies

Background and Aims

Light quantity and quality affect internode lengths in cucumber (Cucumis sativus), whereby leaf area and the optical properties of the leaves mainly control light quality within a cucumber plant community. This modelling study aimed at providing a simple, non-destructive method to predict final internode lengths (FILs) using light quantity and leaf area data.

Methods

Several simplifications of a light quantity and quality sensitive model for estimating FILs in cucumber have been tested. The direct simplifications substitute the term for the red : far-red (R : FR) ratios, by a term for (a) the leaf area index (LAI, m² m⁻²) or (b) partial LAI, the cumulative leaf area per m² ground, where leaf area per m² ground is accumulated from the top of each plant until a number, n, of leaves per plant is reached. The indirect simplifications estimate the input R : FR ratio based on partial leaf area and plant density.

Key Results

In all models, simulated FILs were in line with the measured FILs over various canopy architectures and light conditions, but the prediction quality varied. The indirect simplification based on leaf area of ten leaves revealed the best fit with measured data. Its prediction quality was even higher than of the original model.

Conclusions

This study showed that for vertically trained cucumber plants, leaf area data can substitute local light quality data for estimating FIL data. In unstressed canopies, leaf area over the upper ten ranks seems to represent the feedback of the growing architecture on internode elongation with respect to light quality. This highlights the role of this domain of leaves as the primary source for the specific R : FR signal controlling the final length of an internode and could therefore guide future research on up-scaling local processes to the crop level.     

Assessing the spatial uncertainty in soil nitrogen mapping through stochastic simulations with categorical land use information

This study explores the capability of an extended sequential Gaussian simulation algorithm with incorporation of categorical land use information (SGS-CI) for simulating spatial variability of soil total nitrogen (TN) contents and assessing associated spatial uncertainty. 402 sampled data in soil TN contents in a county scale region and the categorical land use map data of the study area were used to perform sequential simulations for comparing the SGS-CI algorithm and the conventional SGS algorithm, and 135 validation samples were used to assess the improvement of SGS-CI over SGS in prediction accuracy and uncertainty reduction. Results showed that the validation data were more strongly correlated with the optimal prediction (i.e., E-type estimates) data of SGS-CI than with those of SGS, and the mean error and the root mean square error of the optimal prediction using SGS-CI were smaller than those using SGS. SGS-CI also performed slightly better than SGS in uncertainty modeling in terms of accuracy plots and goodness statistic G. In addition, because demands for soil total nitrogen by different crops are usually different in agricultural practice, we showed that SGS-CI could be used to assess spatial uncertainty of deficiency or abundance degrees of soil TN based on demands of different crops in different land use types. Therefore, SGS-CI may provide an effective method for improving prediction accuracy and reducing uncertainty in soil TN prediction.     

Dissection and Downstream Analysis of Zebra Finch Embryos at Early Stages of Development

The zebra finch (Taeniopygiaguttata) has become an increasingly important model organism in many areas of research including toxicology^1,2, behavior³, and memory and learning^4,5,6. As the only songbird with a sequenced genome, the zebra finch has great potential for use in developmental studies; however, the early stages of zebra finch development have not been well studied. Lack of research in zebra finch development can be attributed to the difficulty of dissecting the small egg and embryo. The following dissection method minimizes embryonic tissue damage, which allows for investigation of morphology and gene expression at all stages of embryonic development. This permits both bright field and fluorescence quality imaging of embryos, use in molecular procedures such as in situ hybridization (ISH), cell proliferation assays, and RNA extraction for quantitative assays such as quantitative real-time PCR (qtRT-PCR). This technique allows investigators to study early stages of development that were previously difficult to access.     

Multivariate Statistical Assessment of Predictors of Firefighters’ Muscular and Aerobic Work Capacity

Physical capacity has previously been deemed important for firefighters physical work capacity, and aerobic fitness, muscular strength, and muscular endurance are the most frequently investigated parameters of importance. Traditionally, bivariate and multivariate linear regression statistics have been used to study relationships between physical capacities and work capacities among firefighters. An alternative way to handle datasets consisting of numerous correlated variables is to use multivariate projection analyses, such as Orthogonal Projection to Latent Structures. The first aim of the present study was to evaluate the prediction and predictive power of field and laboratory tests, respectively, on firefighters’ physical work capacity on selected work tasks. Also, to study if valid predictions could be achieved without anthropometric data. The second aim was to externally validate selected models. The third aim was to validate selected models on firefighters’ and on civilians’. A total of 38 (26 men and 12 women) + 90 (38 men and 52 women) subjects were included in the models and the external validation, respectively. The best prediction (R²) and predictive power (Q²) of Stairs, Pulling, Demolition, Terrain, and Rescue work capacities included field tests (R² = 0.73 to 0.84, Q² = 0.68 to 0.82). The best external validation was for Stairs work capacity (R² = 0.80) and worst for Demolition work capacity (R² = 0.40). In conclusion, field and laboratory tests could equally well predict physical work capacities for firefighting work tasks, and models excluding anthropometric data were valid. The predictive power was satisfactory for all included work tasks except Demolition.     

Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor

Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Genomic Best Linear Unbiased Prediction (G-BLUP, a ridge-regression type method) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal breeding populations. However, breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP. Using theory, simulations, and real data analysis, we study the performance of G-BLUP when applied to data from related and unrelated human subjects. Under perfect linkage disequilibrium (LD) between markers and QTL, the prediction R-squared (R²) of G-BLUP reaches trait-heritability, asymptotically. However, under imperfect LD between markers and QTL, prediction R² based on G-BLUP has a much lower upper bound. We show that the minimum decrease in prediction accuracy caused by imperfect LD between markers and QTL is given by (1−b)², where b is the regression of marker-derived genomic relationships on those realized at causal loci. For pairs of related individuals, due to within-family disequilibrium, the patterns of realized genomic similarity are similar across the genome; therefore b is close to one inducing small decrease in R². However, with distantly related individuals b reaches very low values imposing a very low upper bound on prediction R². Our simulations suggest that for the analysis of data from unrelated individuals, the asymptotic upper bound on R² may be of the order of 20% of the trait heritability. We show how PA can be enhanced with use of variable selection or differential shrinkage of estimates of marker effects.     

Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy

The potential of near infrared spectroscopy in conjunction with partial least squares regression to predict Miscanthus xgiganteus and short rotation coppice willow quality indices was examined. Moisture, calorific value, ash and carbon content were predicted with a root mean square error of cross validation of 0.90% (R² = 0.99), 0.13 MJ/kg (R² = 0.99), 0.42% (R² = 0.58), and 0.57% (R² = 0.88), respectively. The moisture and calorific value prediction models had excellent accuracy while the carbon and ash models were fair and poor, respectively. The results indicate that near infrared spectroscopy has the potential to predict quality indices of dedicated energy crops, however the models must be further validated on a wider range of samples prior to implementation. The utilization of such models would assist in the optimal use of the feedstock based on its biomass properties.     

Beyond the E-Value: Stratified Statistics for Protein Domain Prediction

E-values have been the dominant statistic for protein sequence analysis for the past two decades: from identifying statistically significant local sequence alignments to evaluating matches to hidden Markov models describing protein domain families. Here we formally show that for “stratified” multiple hypothesis testing problems—that is, those in which statistical tests can be partitioned naturally—controlling the local False Discovery Rate (lFDR) per stratum, or partition, yields the most predictions across the data at any given threshold on the FDR or E-value over all strata combined. For the important problem of protein domain prediction, a key step in characterizing protein structure, function and evolution, we show that stratifying statistical tests by domain family yields excellent results. We develop the first FDR-estimating algorithms for domain prediction, and evaluate how well thresholds based on q-values, E-values and lFDRs perform in domain prediction using five complementary approaches for estimating empirical FDRs in this context. We show that stratified q-value thresholds substantially outperform E-values. Contradicting our theoretical results, q-values also outperform lFDRs; however, our tests reveal a small but coherent subset of domain families, biased towards models for specific repetitive patterns, for which weaknesses in random sequence models yield notably inaccurate statistical significance measures. Usage of lFDR thresholds outperform q-values for the remaining families, which have as-expected noise, suggesting that further improvements in domain predictions can be achieved with improved modeling of random sequences. Overall, our theoretical and empirical findings suggest that the use of stratified q-values and lFDRs could result in improvements in a host of structured multiple hypothesis testing problems arising in bioinformatics, including genome-wide association studies, orthology prediction, and motif scanning.     

A nonsense mutation in bacteriophage P1 eliminates the synthesis of a protein required for normal plasmid maintenance

A mutant of bacteriophage P1 that is defective in plasmid maintenance was isolated. P1 seg-101 carries an amber mutation in a region previously implicated in the control of plasmid maintenance. By use of a host bearing a temperature-sensitive suppressor, the dependence of P1 maintenance on the seg-101⁺ protein product was established. The rates of segregation of cured cells under various conditions suggest a role for the seg-101⁺ product in the partition of plasmids to daughter cells rather than in the replication of the plasmid. This hypothesis is supported by the observation that P1 seg-101 can drive host chromosomal DNA replication when integrated into the chromosome of a dnaA host under conditions that are nonpermissive for both the seg-101 and dnaA alleles.     

Comparing distance-decay parameters: A novel test under pairwise dependence

Distance-decay models fit parametric functions to assess the relationship between similarity and spatial or environmental distance. Despite the widespread use of distance-decay models in ecology and biogeography, no method has been previously developed and validated to assess the significance of differences between the parameters (i.e. intercept and slope) of two distance-decay models. The pairwise autocorrelation of similarity and spatial distance affects the variance of parameter estimates, precluding the use of ordinary t-tests. Here, we provide a test statistic (z_dep) for the equality of parameters between two distance-decay models. The test can be applied, independently, to any of the model parameters (i.e. intercept and/or slopes). The z_dep statistic accounts for pairwise dependence, thus avoiding biases associated to the inflation of degrees of freedom, and it is based on the estimation of parameters' variance using site-block resampling. To validate the z_dep statistic, type I and type II errors were empirically evaluated through a simulation study. We simulated six scenarios (three under the null and three under the alternative hypothesis) of distance-decay relationships using different functions: negative exponential, power-law or Gompertz function. We applied the z_dep statistic and computed the proportion of rejections of the null hypothesis for α = 0.01, 0.05 and 0.1 in each scenario. As a case-study, we also compared distance-decay parameters across several groups of Iberian vertebrates (cyprinids, frogs, lizards and snakes, bats, rodents and carnivores). In the simulation study, the z_dep statistic showed a good approximation of the nominal level (α, type I error) and a good statistical power (1 − type II error), the later increasing with sample size, as expected. In Iberian vertebrates, we found significant differences between ectotherms and endotherms, but not within these groups except between cyprinids and other ectotherms. The good performance of the z_dep statistic makes it the best option to test for differences in parameters obtained from models fitted from data with pairwise dependence, as distance-decay models. It can also be used beyond distance-decay approaches to compare parameters of any other regression models of pairwise dependent data (as genetic distances, for example).     

Antarctica on foot: the energy expended to walk,ski and man-haul

Polar exploration often involves travelling on foot and thus is physically intensive, with long-term excursions typically resulting in weight loss. Few studies have investigated the energy expended under such circumstances. Here, we present a range of prediction equations for estimating metabolic rate from heart rate or accelerometry data for specific activities including skiing and man-hauling which can be applied to either short- or long-term excursions. We also use some of these equations to estimate the energy expended undertaking various activities by a team of explorers while attempting to traverse the Antarctic continent during the austral winter of 2013 (as part of the White Mars Project during The Coldest Journey). Calibration equations based on either accelerometry data (from which overall dynamic body acceleration, ODBA, is derived) or heart rate showed good relationships with rate of oxygen consumption, particularly when person height was included. Periods of skiing and man-hauling on The Coldest Journey were estimated to be more energetically demanding (30.0 and 31.1 kJ min^?1, respectively) than walking (24.9 kJ min^?1), or other outdoor work (21.9 kJ min^?1). Estimates of energy expenditure during The Coldest Journey were similar to measures obtained in previous, comparative scenarios. We hope that future expeditions to Antarctica will use these prediction equations to further our understanding of the energy costs of exploring Antarctica and the nutritional requirements needed to guard against emaciation.     

Studies on the catalytic domains of multiple JmjC oxygenases using peptide substrates

The JmjC-domain-containing 2-oxoglutarate-dependent oxygenases catalyze protein hydroxylation and N^ε-methyllysine demethylation via hydroxylation. A subgroup of this family, the JmjC lysine demethylases (JmjC KDMs) are involved in histone modifications at multiple sites. There are conflicting reports as to the substrate selectivity of some JmjC oxygenases with respect to KDM activities. In this study, a panel of modified histone H3 peptides was tested for demethylation against 15 human JmjC-domain-containing proteins. The results largely confirmed known N^ε-methyllysine substrates. However, the purified KDM4 catalytic domains showed greater substrate promiscuity than previously reported (i.e., KDM4A was observed to catalyze demethylation at H3K27 as well as H3K9/K36). Crystallographic analyses revealed that the N^ε-methyllysine of an H3K27me3 peptide binds similarly to N^ε-methyllysines of H3K9me3/H3K36me3 with KDM4A. A subgroup of JmjC proteins known to catalyze hydroxylation did not display demethylation activity. Overall, the results reveal that the catalytic domains of the KDM4 enzymes may be less selective than previously identified. They also draw a distinction between the N^ε-methyllysine demethylation and hydroxylation activities within the JmjC subfamily. These results will be of use to those working on functional studies of the JmjC enzymes.     

Impact of genotyping strategy on the accuracy of genomic prediction in simulated populations of purebred swine

Single-step genomic BLUP (ssGBLUP) has been widely used in genomic evaluation due to relatively higher prediction accuracy and simplicity of use. The prediction accuracy from ssGBLUP depends on the amount of information available concerning both genotype and phenotype. This study investigated how information on genotype and phenotype that had been acquired from previous generations influences the prediction accuracy of ssGBLUP, and thus we sought an optimal balance about genotypic and phenotypic information to achieve a cost-effective and computationally efficient genomic evaluation. We generated two genetically correlated traits (h² = 0.35 for trait A, h² = 0.10 for trait B and genetic correlation 0.20) as well as two distinct populations mimicking purebred swine. Phenotypic and genotypic information in different numbers of previous generations and different genotyping rates for each litter were set to generate different datasets. Prediction accuracy was evaluated by correlating genomic estimated breeding values with true breeding values for genotyped animals in the last generation. The results revealed a negligible impact of previous generations that lacked genotyped animals on the prediction accuracy. Phenotypic and genotypic data, including the most recent three to four generations with a genotyping rate of 40% or 50% for each litter, could lead to asymptotic maximum prediction accuracy for genotyped animals in the last generation. Single-step genomic best linear unbiased prediction yielded an optimal balance about genotypic and phenotypic information to ensure a cost-effective and computationally efficient genomic evaluation of populations of polytocous animals such as purebred pigs.