Genome-based Prediction Research Articles

Vitamin B1 ecophysiology of marine picoeukaryotic algae: Strain‐specific differences and a new role for bacteria in vitamin cycling

We confirmed multiple picoeukaryotic algae, Ostreococcus, Micromonas, and Pelagomonas spp., as thiamine (vitamin B1) auxotrophs in laboratory experiments with axenic cultures. Examined strains have half saturation growth constants (Ks) for B1 between 1.26 and 6.22 pmol B1 L−1, which is higher than reported seawater concentrations. Minimum B1 cell quotas for Ostreococcus and Micromonas spp. are high (2.20 × 10−8–4.46 × 10−8 pmol B1 cell−1) relative to other B1 auxotrophic phytoplankton, potentially making them B1 rich prey for zooplankton and significant B1 reservoirs in oligotrophic marine habitats. Ostreococcus and Micromonas genomes are nonuniformly missing portions of the B1 biosynthesis pathway. Given their gene repertoires, Ostreococcus lucimarinus CCE9901 and Ostreococcus tauri OTH95 are expected to salvage B1 from externally provided 4-methyl-5-thiazoleethanol (HET) and 4-amino-5-hydroxymethyl-2-methylpyrimidine (HMP). However, in culture, neither could use HET plus HMP instead of B1, highlighting current limitations of genome-based prediction of B1 salvaging by picoeukaryotic algae. HMP and phosphorylated B1 use varied amongst tested strains and notably all Prasinophytes tested could not use HMP. B1-limited O. lucimarinus CCE9901 could not grow on added thiamine diphosphate (TDP), a phosophorylated B1 form. However, in co-culture with Pseudoalteromonas sp. TW7, a bacterium known to exhibit phosphatase activity, O. lucimarinus CCE9901 exhibited increased growth following TDP additions. This demonstrates that bacteria influence vitamin B1 availability beyond de novo synthesis and consumption; they can also serve as conduits that chemically alter, but not completely degrade or retain B1 analogs (e.g., TDP), and make them accessible to a broader range of microbes.

Abstract A49: Characterization of genomic alterations in pediatric osteosarcoma with differential chemosensitivities: Construction of genome-based prediction system using preoperative biopsy samples

Abstract Purpose: Sensitivity of preoperative chemotherapy, evaluated by a histological analysis using resected tumor tissue, is the most important prognostic factor for osteosarcoma (OS). Methotrexate, doxorubicin, cisplatin (MAP regimen), and ifosfamide (IFO) are recognized as key drugs for OS, although it is still debatable whether addition of IFO for all OS patient is beneficial or not. The aim of this study is to identify possible genomic markers for the prediction of chemosensitivity of preoperative chemotherapy for pediatric OS patients using CGH and NGS technologies. Methods: Pre-therapeutic biopsy tumor samples of 30 pediatric conventional OS patients treated in our institute who had homogeneous clinicopathological and therapeutic background were analyzed. The patients were divided into three groups according to the histological response to chemotherapy (A: MAP-sensitive, B: IFO-sensitive and C: resistant to both). Twenty-two tumor samples were used as a learning set for searching candidate markers. Additionally, 8 tumor samples were used for independent analysis as a validation set. Genomic DNAs were prepared from frozen biopsy samples and used for CGH analysis with Agilent 44k DNA microarray. Target-sequencing by using the Ion AmpliSeq Comprehensive Cancer Panel containing 409 cancer-related genes were also conducted for 10 samples. Results: Survival analysis of this cohort assured that chemosensitivity was significantly correlated to the patient outcome. Differential aberration analyses of the array CGH data resulted in selection of 8 putative genomic markers for classifying into A, B and C groups with differential chemosensitivity. Scoring system to predict chemosensitivity of each individual was developed with these genomic markers and subsequent validation analysis indicated that the scoring system was remarkably consistent to the patient properties. Target re-sequencing of 409 cancer-related genes by using Ion Proton sequencer is currently ongoing to identify additional genomic changes in the tumors of highly chemoresistant group. Conclusion: Our results indicated that copy number changes in OS biopsy samples could be good predictors of response to preoperative chemotherapy. These signatures as well as global mutation information may be useful for the future genomics-based personalized chemotherapy for OS patients. Citation Format: Shintaro Iwata, Tsukasa Yonemoto, Hagime Kageyama, Sana Yokoi, Hiroki Nagase, Akira Nakagawara, Takeshi Ishii, Miki Ohira. Characterization of genomic alterations in pediatric osteosarcoma with differential chemosensitivities: Construction of genome-based prediction system using preoperative biopsy samples. [abstract]. In: Proceedings of the AACR Special Conference on Pediatric Cancer at the Crossroads: Translating Discovery into Improved Outcomes; Nov 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;74(20 Suppl):Abstract nr A49.

Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction.

The efficiency of marker-assisted prediction of phenotypes has been studied intensively for different types of plant breeding populations. However, one remaining question is how to incorporate and counterbalance information from biparental and multiparental populations into model training for genome-wide prediction. To address this question, we evaluated testcross performance of 1652 doubled-haploid maize (Zea mays L.) lines that were genotyped with 56,110 single nucleotide polymorphism markers and phenotyped for five agronomic traits in four to six European environments. The lines are arranged in two diverse half-sib panels representing two major European heterotic germplasm pools. The data set contains 10 related biparental dent families and 11 related biparental flint families generated from crosses of maize lines important for European maize breeding. With this new data set we analyzed genome-based best linear unbiased prediction in different validation schemes and compositions of estimation and test sets. Further, we theoretically and empirically investigated marker linkage phases across multiparental populations. In general, predictive abilities similar to or higher than those within biparental families could be achieved by combining several half-sib families in the estimation set. For the majority of families, 375 half-sib lines in the estimation set were sufficient to reach the same predictive performance of biomass yield as an estimation set of 50 full-sib lines. In contrast, prediction across heterotic pools was not possible for most cases. Our findings are important for experimental design in genome-based prediction as they provide guidelines for the genetic structure and required sample size of data sets used for model training.

Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and years.

The calibration data for genomic prediction should represent the full genetic spectrum of a breeding program. Data heterogeneity is minimized by connecting data sources through highly related test units. One of the major challenges of genome-enabled prediction in plant breeding lies in the optimum design of the population employed in model training. With highly interconnected breeding cycles staggered in time the choice of data for model training is not straightforward. We used cross-validation and independent validation to assess the performance of genome-based prediction within and across genetic groups, testers, locations, and years. The study comprised data for 1,073 and 857 doubled haploid lines evaluated as testcrosses in 2years. Testcrosses were phenotyped for grain dry matter yield and content and genotyped with 56,110 single nucleotide polymorphism markers. Predictive abilities strongly depended on the relatedness of the doubled haploid lines from the estimation set with those on which prediction accuracy was assessed. For scenarios with strong population heterogeneity it was advantageous to perform predictions within a priori defined genetic groups until higher connectivity through related test units was achieved. Differences between group means had a strong effect on predictive abilities obtained with both cross-validation and independent validation. Predictive abilities across subsequent cycles of selection and years were only slightly reduced compared to predictive abilities obtained with cross-validation within the same year. We conclude that the optimum data set for model training in genome-enabled prediction should represent the full genetic and environmental spectrum of the respective breeding program. Data heterogeneity can be reduced by experimental designs that maximize the connectivity between data sources by common or highly related test units.

Genome-Wide Prediction of Traits with Different Genetic Architecture Through Efficient Variable Selection

In genome-based prediction there is considerable uncertainty about the statistical model and method required to maximize prediction accuracy. For traits influenced by a small number of quantitative trait loci (QTL), predictions are expected to benefit from methods performing variable selection [e.g., BayesB or the least absolute shrinkage and selection operator (LASSO)] compared to methods distributing effects across the genome [ridge regression best linear unbiased prediction (RR-BLUP)]. We investigate the assumptions underlying successful variable selection by combining computer simulations with large-scale experimental data sets from rice (Oryza sativa L.), wheat (Triticum aestivum L.), and Arabidopsis thaliana (L.). We demonstrate that variable selection can be successful when the number of phenotyped individuals is much larger than the number of causal mutations contributing to the trait. We show that the sample size required for efficient variable selection increases dramatically with decreasing trait heritabilities and increasing extent of linkage disequilibrium (LD). We contrast and discuss contradictory results from simulation and experimental studies with respect to superiority of variable selection methods over RR-BLUP. Our results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice, assumed to be influenced by a few major QTL. We extend our conclusions to the analysis of whole-genome sequence data and infer upper bounds for the number of causal mutations which can be identified by LASSO. Our results have major impact on the choice of statistical method needed to make credible inferences about genetic architecture and prediction accuracy of complex traits.

We screen newborns, don't we?: realizing the promise of public health genomics.

Genomics and public health have been uneasy bedfellows for some time. Most efforts to improve population health through genomic approaches have focused on the assessment of risks for common diseases, with the aim of tailoring interventions and screening.1 However, the improvement of population health through such an approach has remained elusive.2 Now, rapid progress in affordable, robust DNA sequencing offers a promising opportunity. By expanding the field’s focus from common to rare diseases, it may be possible to realize the promise of public health genomics by identifying those millions of individuals who unknowingly carry mutations that confer a dramatic predisposition to preventable diseases.

Sensitivity to prior specification in Bayesian genome-based prediction models

Different statistical models have been proposed for maximizing prediction accuracy in genome-based prediction of breeding values in plant and animal breeding. However, little is known about the sensitivity of these models with respect to prior and hyperparameter specification, because comparisons of prediction performance are mainly based on a single set of hyperparameters. In this study, we focused on Bayesian prediction methods using a standard linear regression model with marker covariates coding additive effects at a large number of marker loci. By comparing different hyperparameter settings, we investigated the sensitivity of four methods frequently used in genome-based prediction (Bayesian Ridge, Bayesian Lasso, BayesA and BayesB) to specification of the prior distribution of marker effects. We used datasets simulated according to a typical maize breeding program differing in the number of markers and the number of simulated quantitative trait loci affecting the trait. Furthermore, we used an experimental maize dataset, comprising 698 doubled haploid lines, each genotyped with 56110 single nucleotide polymorphism markers and phenotyped as testcrosses for the two quantitative traits grain dry matter yield and grain dry matter content. The predictive ability of the different models was assessed by five-fold cross-validation. The extent of Bayesian learning was quantified by calculation of the Hellinger distance between the prior and posterior densities of marker effects. Our results indicate that similar predictive abilities can be achieved with all methods, but with BayesA and BayesB hyperparameter settings had a stronger effect on prediction performance than with the other two methods. Prediction performance of BayesA and BayesB suffered substantially from a non-optimal choice of hyperparameters.

Genome-based prediction of test cross performance in two subsequent breeding cycles

Genome-based prediction of genetic values is expected to overcome shortcomings that limit the application of QTL mapping and marker-assisted selection in plant breeding. Our goal was to study the genome-based prediction of test cross performance with genetic effects that were estimated using genotypes from the preceding breeding cycle. In particular, our objectives were to employ a ridge regression approach that approximates best linear unbiased prediction of genetic effects, compare cross validation with validation using genetic material of the subsequent breeding cycle, and investigate the prospects of genome-based prediction in sugar beet breeding. We focused on the traits sugar content and standard molasses loss (ML) and used a set of 310 sugar beet lines to estimate genetic effects at 384 SNP markers. In cross validation, correlations >0.8 between observed and predicted test cross performance were observed for both traits. However, in validation with 56 lines from the next breeding cycle, a correlation of 0.8 could only be observed for sugar content, for standard ML the correlation reduced to 0.4. We found that ridge regression based on preliminary estimates of the heritability provided a very good approximation of best linear unbiased prediction and was not accompanied with a loss in prediction accuracy. We conclude that prediction accuracy assessed with cross validation within one cycle of a breeding program can not be used as an indicator for the accuracy of predicting lines of the next cycle. Prediction of lines of the next cycle seems promising for traits with high heritabilities.

Genomic Approach Towards Personalized Anticancer Drug Therapy

Stratification of patients for multidrug response is a promising strategy for cancer treatment. Genome-based prediction models have great potential for this purpose because the extent of drug sensitivity may be attributed to the heterogeneity of the underlying genetic characteristics of cancer. However, microarray data is difficult to analyze and is not reproducible. Several machine-learning algorithms have therefore been developed in a repeatable manner. Random forests algorithm, which uses an ensemble approach based on classification and regression trees, appears to be superior for predicting multidrug sensitivity. This is because ensemble methods are more effective when there are much more predictors than samples. Here, we review recent advances in the development of classification algorithms using microarray technology for prediction of anticancer sensitivity, discuss the availability of ensemble methods for prediction models, and present data regarding the identification of potential responders to FOLFOX therapy using random forests algorithm.

Genome-based prediction of testcross values in maize

This is the first large-scale experimental study on genome-based prediction of testcross values in an advanced cycle breeding population of maize. The study comprised testcross progenies of 1,380 doubled haploid lines of maize derived from 36 crosses and phenotyped for grain yield and grain dry matter content in seven locations. The lines were genotyped with 1,152 single nucleotide polymorphism markers. Pedigree data were available for three generations. We used best linear unbiased prediction and stratified cross-validation to evaluate the performance of prediction models differing in the modeling of relatedness between inbred lines and in the calculation of genome-based coefficients of similarity. The choice of similarity coefficient did not affect prediction accuracies. Models including genomic information yielded significantly higher prediction accuracies than the model based on pedigree information alone. Average prediction accuracies based on genomic data were high even for a complex trait like grain yield (0.72-0.74) when the cross-validation scheme allowed for a high degree of relatedness between the estimation and the test set. When predictions were performed across distantly related families, prediction accuracies decreased significantly (0.47-0.48). Prediction accuracies decreased with decreasing sample size but were still high when the population size was halved (0.67-0.69). The results from this study are encouraging with respect to genome-based prediction of the genetic value of untested lines in advanced cycle breeding populations and the implementation of genomic selection in the breeding process.

Progress in pathogenic secretome research

Secretome includes all the gene products secreted by a cell. The development of proteomicbased approaches and genome-based computational prediction technology have gradually revealed secretome plays an important role in pathogen infection recently. Secretome facilitates pathogen to proliferate and invade the host by inhibiting the host's immune system, resisting acidic PH conditions, snatching nutrition and so on.Identification of pathogenic secretome helps discovering new drug targets and biomarkers, and supporting the diagnosis and treatment. Key words: Secreted protein; Secretome; Pathogen; Proteomics

Genome-Based Prediction of Breast Cancer Risk in the General Population: A Modeling Study Based on Meta-Analyses of Genetic Associations

Genome-wide association studies identified novel breast cancer susceptibility variants that could be used to predict breast cancer in asymptomatic women. This review and modeling study aimed to investigate the current and potential predictive performance of genetic risk models. Genotypes and disease status were simulated for a population of 10,000 women. Genetic risk models were constructed from polymorphisms from meta-analysis including, in separate scenarios, all polymorphisms or statistically significant polymorphisms only. We additionally investigated the magnitude of the odds ratios (OR) for 1 to 100 hypothetical polymorphisms that would be needed to achieve similar discriminative accuracy as available prediction models [modeled range of area under the receiver operating characteristic curve (AUC) 0.70-0.80]. Of the 96 polymorphisms that had been investigated in meta-analyses, 41 showed significant associations. AUC was 0.68 for the genetic risk model based on all 96 polymorphisms and 0.67 for the 41 significant polymorphisms. Addition of 50 additional variants, each with risk allele frequencies of 0.30, requires per-allele ORs of 1.2 to increase this AUC to 0.70, 1.3 to increase AUC to 0.75, and 1.5 to increase AUC to 0.80. To achieve AUC of 0.80, even 100 additional variants would need per-allele ORs of 1.3 to 1.7, depending on risk allele frequencies. The predictive ability of genetic risk models in breast cancer has the potential to become comparable to that of current breast cancer risk models. Risk prediction based on low susceptibility variants becomes a realistic tool in prevention of nonfamilial breast cancer.

Assessment of Improved Prediction Beyond Traditional Risk Factors

The moderate predictive performance of cardiovascular disease risk models necessitates more studies that investigate the incremental value of novel biomarkers. In recent years, many new biomarkers have been evaluated for their ability to improve prediction of cardiovascular disease beyond traditional risk factors, including C-reactive protein, coronary artery calcium, and single-nucleotide polymorphisms in the 9p21 region.1 The interest in novel biomarkers is propelled, in part, by emerging discoveries from genomewide association studies of genetic variants associated with risk for many common diseases.2 Nonetheless, family history, an “old” tool in clinical practice—crucial for the diagnosis and management of genetic disorders—has not been adequately explored for its value in risk assessment and prevention of common diseases.3 Positive family history is a strong risk factor for cardiovascular diseases,4,5 reflecting the consequences of genetic and nongenetic risk factors that are shared among relatives. From a practical perspective, family history is a strong determinant of disease that is relatively easy to assess. For this reason, family history is advocated as a useful tool for identifying individuals at increased risk of disease and for tailoring preventive interventions.4,6,7 However, the challenge is to show that this knowledge has clinical utility for improving health.8 Article see p 97 In this issue of Circulation: Cardiovascular Genetics , Scheuner et al9 report the incremental value of adding family history information to the General Cardiovascular Risk Profile to improve identification of individuals with advanced coronary artery calcification. The incremental value of family history in disease prediction is assessed in several questions10: 1. Does family history improve prediction beyond traditional risk factors? 2. Does improved prediction change treatment decisions or health recommendations? 3. Do these changes improve health outcomes or have other benefits? 4. Do the incremental benefits outweigh the extra costs? This evaluation is a stepwise …

Genome-based prediction of common diseases: methodological considerations for future research

The translation of emerging genomic knowledge into public health and clinical care is one of the major challenges for the coming decades. At the moment, genome-based prediction of common diseases, such as type 2 diabetes, coronary heart disease and cancer, is still not informative. Our understanding of the genetic basis of multifactorial diseases is improving, but the currently identified susceptibility variants contribute only marginally to the development of disease. At the same time, an increasing number of companies are offering personalized lifestyle and health recommendations on the basis of individual genetic profiles. This discrepancy between the limited predictive value and the commercial availability of genetic profiles highlights the need for a critical appraisal of the usefulness of genome-based applications in clinical and public health care. Anticipating the discovery of a large number of genetic variants in the near future, we need to prepare a framework for the design and analysis of studies aiming to evaluate the clinical validity and utility of genetic tests. In this article, we review recent studies on the predictive value of genetic profiling from a methodological perspective and address issues around the choice of the study population, the construction of genetic profiles, the measurement of the predictive value, calibration and validation of prediction models, and assessment of clinical utility. Careful consideration of these issues will contribute to the knowledge base that is needed to identify useful genome-based applications for implementation in clinical and public health practice.

Usefulness of Genetic Polymorphisms and Conventional Risk Factors to Predict Coronary Heart Disease in Patients With Familial Hypercholesterolemia

Familial hypercholesterolemia (FH) is an autosomal dominant disorder with an associated high risk of coronary heart disease (CHD). The considerable variation in age of onset of CHD in patients with FH is believed to arise from conventional risk factors, as well as genetic variation other than in the low-density lipoprotein receptor gene. The degree to which currently known genetic variants can improve the prediction of CHD risk beyond conventional risk factors in this disorder was investigated. Fourteen genetic variants recently identified for association with CHD in a Dutch FH population were considered. Prediction models were constructed using Cox proportional hazards models, and predictive value was assessed using a concordance statistic (c statistic). A total of 1,337 patients with FH were completely genotyped for all genetic variants. Hazard ratios of the genetic variants ranged from 0.61 to 0.74 and 1.24 to 2.33. The c statistic of the CHD prediction model based on genetic variants was 0.59, denoting little discrimination. The model based on conventional risk factors had a c statistic of 0.75, denoting moderate discrimination. Adding genetic test results to this model increased the c statistic to 0.76. In conclusion, the contribution of 14 genetic variants to the prediction of CHD risk in patients with FH was limited. To improve genome-based prediction of CHD, larger numbers of genetic variants need to be identified that either on their own or in gene-gene interaction have substantial effects on CHD risk.

Genome-based prediction of common diseases: advances and prospects

Common diseases such as type 2 diabetes and coronary heart disease result from a complex interplay of genetic and environmental factors. Recent developments in genomics research have boosted progress in the discovery of susceptibility genes and fueled expectations about opportunities of genetic profiling for personalizing medicine. Personalized medicine requires a test that fairly accurately predicts disease risk, particularly when interventions are invasive, expensive or have major side effects. Recent studies on the prediction of common diseases based on multiple genetic variants alone or in addition to traditional disease risk factors showed limited predictive value so far, but all have investigated only a limited number of susceptibility variants. New gene discoveries from genome-wide association studies will certainly further improve the prediction of common diseases, but the question is whether this improvement is sufficient to enable personalized medicine. In this paper, we argue that new gene discoveries may not evidently improve the prediction of common diseases to a degree that it will change the management of individuals at increased risk. Substantial improvements may only be expected if we manage to understand the complete causal mechanisms of common diseases to a similar extent as we understand those of monogenic disorders. Genomics research will contribute to this understanding, but it is likely that the complexity of complex diseases may ultimately limit the opportunities for accurate prediction of disease in asymptomatic individuals as unraveling their complete causal pathways may be impossible.