Predicting human height by Victorian and genomic methods

Molecular diagnosis is an important indicator for response to growth hormone therapy in children with short stature

BACKGROUND

Significant differences have been observed in the efficacy of recombinant human growth hormone (rhGH) treatment for short children. The present study aimed to identify the genetic etiology of short stature and to assess the role of molecular diagnosis in predicting responses to rhGH treatment.

METHODS

A total of 407 short children were included in the present study, 226 of whom received rhGH treatment. Whole-exome sequencing (WES) was conducted on short children to identify the underlying genetic etiology. Correlations between molecular diagnosis and the efficacy of rhGH treatment were examined.

RESULTS

Pathogenic or likely pathogenic mutations were identified in 86 of the 407 patients (21.1%), including 36 (41.9%) novel variants. Among the multiple pathways affecting short stature, genes involved in fundamental cellular processes (38.7%) play a larger role, especially the RAS-MAPK pathway. In general, patients without pathogenic mutations responded better to rhGH than those with mutations. Furthermore, patients with hormone signaling pathway mutations had a better response to rhGH, while those with paracrine factor mutations had a worse response to rhGH.

CONCLUSIONS

This study highlights the utility of WES in identifying genetic etiology in children with short stature. Identifying likely causal mutations is an important factor in predicting rhGH response.

Assessing the prediction of type 2 diabetes risk using polygenic and clinical risk scores in South Asian study populations

Background

Genome-wide polygenic risk scores (PRS) have shown high specificity and sensitivity in predicting type 2 diabetes (T2D) risk in Europeans. However, the PRS-driven information and its clinical significance in non-Europeans are underrepresented. We examined the predictive efficacy and transferability of PRS models using variant information derived from genome-wide studies of Asian Indians (AIs) (PRSAI) and Europeans (PRSEU) using 13,974 AI individuals.

Methods

Weighted PRS models were constructed and analyzed on 4602 individuals from the Asian Indian Diabetes Heart Study/Sikh Diabetes Study (AIDHS/SDS) as discovery/training and test/validation datasets. The results were further replicated in 9372 South Asian individuals from UK Biobank (UKBB). We also assessed the performance of each PRS model by combining data of the clinical risk score (CRS).

Results

Both genetic models (PRSAI and PRSEU) successfully predicted the T2D risk. However, the PRSAI revealed 13.2% odds ratio (OR) 1.80 [95% confidence interval (CI) 1.63-1.97; p = 1.6 × 10-152] and 12.2% OR 1.38 (95% CI 1.30-1.46; p = 7.1 × 10-237) superior performance in AIDHS/SDS and UKBB validation sets, respectively. Comparing individuals of extreme PRS (ninth decile) with the average PRS (fifth decile), PRSAI showed about two-fold OR 20.73 (95% CI 10.27-41.83; p = 2.7 × 10-17) and 1.4-fold OR 3.19 (95% CI 2.51-4.06; p = 4.8 × 10-21) higher predictability to identify subgroups with higher genetic risk than the PRSEU. Combining PRS and CRS improved the area under the curve from 0.74 to 0.79 in PRSAI and 0.72 to 0.75 in PRSEU.

Conclusion

Our data suggest the need for extending genetic and clinical studies in varied ethnic groups to exploit the full clinical potential of PRS as a risk prediction tool in diverse study populations.

Anthropometric measurements of term singletons at 6 years of age born from fresh and frozen embryo transfer: A multicenter prospective study in Japan

Purpose

The purpose of this study is to compare anthropometric measurements between term singletons conceived via fresh embryo transfer (FreET) and frozen embryo transfer (FET) and those born via natural conception (NC) or fertility treatments milder than assisted reproductive technology (non-ART) at 6 years of age.

Methods

A total of 8149 children were enrolled, and questionnaires about anthropometric measures (weight, height, BMI) were addressed to parents, when the children were 1.5, 3, and 6 years of age. A total of 3299 term singletons were enrolled at birth: 533, 476, 916, and 1374 in the NC, non-ART, FreET, and FET groups, respectively.

Results

A total of 1635 term singletons (290, 176, 467, and 702 in the NC, non-ART, FreET, and FET groups respectively) were enrolled until 6 years of age (follow-up rate, approximately 50%). When non-ART group was used as control, the FreET children were 1.0 cm taller than the non-ART children at 6 years of age, after adjusting for confounding factors. However, no differences were observed in the anthropometric data among the non-ART, ART, and NC children at 6 years of age.

Conclusion

At 6 years of age, term singletons were taller in the FreET group than in the non-ART group, after adjusting for confounders.

Capturing additional genetic risk from family history for improved polygenic risk prediction

Family history of complex traits may reflect transmitted rare pathogenic variants, intra-familial shared exposures to environmental and lifestyle factors, as well as a common genetic predisposition. We developed a latent factor model to quantify trait heritability in excess of that captured by a common variant-based polygenic risk score, but inferable from family history. For 941 children in the Avon Longitudinal Study of Parents and Children cohort, a joint predictor combining a polygenic risk score for height and mid-parental height was able to explain ^~55% of the total variance in sex-adjusted adult height z-scores, close to the estimated heritability. Marginal yet consistent risk prediction improvements were also achieved among ^~400,000 European ancestry participants for 11 complex diseases in the UK Biobank. Our work showcases a paradigm for risk calculation, and supports incorporation of family history into polygenic risk score-based genetic risk prediction models.

Predicting Physical Appearance from DNA Data-Towards Genomic Solutions

The idea of forensic DNA intelligence is to extract from genomic data any information that can help guide the investigation. The clues to the externally visible phenotype are of particular practical importance. The high heritability of the physical phenotype suggests that genetic data can be easily predicted, but this has only become possible with less polygenic traits. The forensic community has developed DNA-based predictive tools by employing a limited number of the most important markers analysed with targeted massive parallel sequencing. The complexity of the genetics of many other appearance phenotypes requires big data coupled with sophisticated machine learning methods to develop accurate genomic predictors. A significant challenge in developing universal genomic predictive methods will be the collection of sufficiently large data sets. These should be created using whole-genome sequencing technology to enable the identification of rare DNA variants implicated in phenotype determination. It is worth noting that the correctness of the forensic sketch generated from the DNA data depends on the inclusion of an age factor. This, however, can be predicted by analysing epigenetic data. An important limitation preventing whole-genome approaches from being commonly used in forensics is the slow progress in the development and implementation of high-throughput, low DNA input sequencing technologies. The example of palaeoanthropology suggests that such methods may possibly be developed in forensics.

The limits of normal approximation for adult height

Adult height inspired the first biometrical and quantitative genetic studies and is a test-case trait for understanding heritability. The studies of height led to formulation of the classical polygenic model, that has a profound influence on the way we view and analyse complex traits. An essential part of the classical model is an assumption of additivity of effects and normality of the distribution of the residuals. However, it may be expected that the normal approximation will become insufficient in bigger studies. Here, we demonstrate that when the height of hundreds of thousands of individuals is analysed, the model complexity needs to be increased to include non-additive interactions between sex, environment and genes. Alternatively, the use of log-normal approximation allowed us to still use the additive effects model. These findings are important for future genetic and methodologic studies that make use of adult height as an exemplar trait.

Lenz microphthalmia syndrome in neurosurgical practice: a case report and review of the literature

Lenz microphthalmia syndrome (LMS) is an allelic X-linked syndrome correlated to a null mutation of B cell lymphoma (BCL-6) corepressor (BCOR) gene, which is essential in the early embryonic development. Phenotypically, this rare hereditary syndrome is characterized by microphthalmia/anophthalmia and other eye disorders; mental disability; dental, ear, and digital abnormalities; and variable malformations affecting the heart, skeleton (limbs and/or spine), and genitourinary tract. In this paper, a case of a young adult with LMS affected additionally by immuno-hematological disturbances was treated with decompressive craniectomy after domestic accidental fall. Case description and a brief review of the current literature about this rare condition are presented here.

Genetic Polymorphisms Associated with Idiopathic Short Stature and First-Year Response to Growth Hormone Treatment

BACKGROUND/AIMS

The term idiopathic short stature (ISS) describes short stature of unknown, but likely polygenic, etiology. This study aimed to identify genetic polymorphisms associated with the ISS phenotype, and with growth response to supplemental GH.

METHODS

Using a case-control analysis we compared the prevalence of "tall" versus "short" alleles at 52 polymorphic loci (17 in growth-related candidate genes, 35 identified in prior genome-wide association studies of adult height) in 94 children with ISS followed in the Genetics and Neuroendocrinology of Short Stature International Study, versus 143 controls from the Fels Longitudinal Study.

RESULTS

Four variants were nominally associated with ISS using a genotypic model, confirmed by a simultaneous confident inference approach: compared with controls children with ISS had lower odds of "tall" alleles (odds ratio, 95% CI) for GHR (0.52, 0.29-0.96); rs2234693/ESR1 (0.50, 0.25-0.98); rs967417/BMP2 (0.39, 0.17-0.93), and rs4743034/ZNF462 (0.40, 0.18-0.89). Children with ISS also had lower odds of the "tall" allele (A) at the IGFBP3 -202 promoter polymorphism (rs2855744; 0.40, 0.20-0.80) in the simultaneous confident inference analysis. A significant association with 1st-year height SD score increase during GH treatment was observed with rs11205277, located near 4 known genes: MTMR11, SV2A, HIST2H2AA3, and SF3B4; the latter, in which heterozygous mutations occur in Nager acrofacial dysostosis, appears the most relevant gene.

CONCLUSIONS

In children with ISS we identified associations with "short" alleles at a number of height-related loci. In addition, a polymorphic variant located near SF3B4 was associated with the GH treatment response in our cohort. The findings in our small study warrant further investigation.

Screening Human Embryos for Polygenic Traits Has Limited Utility

The increasing proportion of variance in human complex traits explained by polygenic scores, along with progress in preimplantation genetic diagnosis, suggests the possibility of screening embryos for traits such as height or cognitive ability. However, the expected outcomes of embryo screening are unclear, which undermines discussion of associated ethical concerns. Here, we use theory, simulations, and real data to evaluate the potential gain of embryo screening, defined as the difference in trait value between the top-scoring embryo and the average embryo. The gain increases very slowly with the number of embryos but more rapidly with the variance explained by the score. Given current technology, the average gain due to screening would be ≈2.5 cm for height and ≈2.5 IQ points for cognitive ability. These mean values are accompanied by wide prediction intervals, and indeed, in large nuclear families, the majority of children top-scoring for height are not the tallest.

RECENT TECHNIQUES BASED ON THE UTILIZATION OF DNA AND AUTOSOMAL SINGLE NUCLEOTIDE POLYMORPHISMS FOR IDENTIFYING HUMANS

The biological samples used in forensics can contain DNA which is highly fragmented as a consequence of exposure to any of the numerous degrading factors. Analysis of the sequence or size of the products of Polymerase chain reaction is at present responsible for the analysis of remains of humans in forensics. Despite the effectiveness of protocols based on PCR, there are certain limitations that are presented by the low numbers of copies of the template and the variations that are imposed by the decaying process to the template. The primary aim of this research is to explore the significance of autosomal SNPs in forensic science through the identification of humans at a crime scene. This study provides an exploration of the applicability of autosomal SNPs for the identification of humans at crime scene. This would fill the gap present in the current literature regarding the significance of autosomal SNPs in the identification of humans during crime scene investigation. It will also enable the identification of the criminals involved in several types of the crimes ranging from general theft to rape and sexual assault, murder, and robberies. It will also allow the identification of dead bodies in cases where it is difficult to identify the dead person due to unrecognizable condition of the body. This study will facilitate the improvement of the investigation of crime scene investigators. It will provide a significant way for the incorporation of recent techniques of the molecular genetics into forensics. Reduction in the workload of the crime scene investigators would also occur through the implementation of outcomes of this study into the field of forensic science. There are several studies which have demonstrated the applicability of SNPs in forensic investigations for identifying the humans at crime scene. Several effective and efficient technological systems have been developed by the researchers which are capable of performing analysis of biological samples containing degraded DNA because SNPs can be obtained from these samples. Physical characteristics of the individuals can be predicted through the analysis of SNPs. This can provide significant information about the color of eye, hair and skin of the individuals involved in crime.

Update on the predictability of tall stature from DNA markers in Europeans

Predicting adult height from DNA has important implications in forensic DNA phenotyping. In 2014, we introduced a prediction model consisting of 180 height-associated SNPs based on data from 10,361 Northwestern Europeans enriched with tall individuals (770 > 1.88 standard deviation), which yielded a mid-ranged accuracy (AUC = 0.75 for binary prediction of tall stature and R² = 0.12 for quantitative prediction of adult height). Here, we provide an update on DNA-based height predictability considering an enlarged list of subsequently-published height-associated SNPs using data from the same set of 10,361 Europeans. A prediction model based on the full set of 689 SNPs showed an improved accuracy relative to previous models for both tall stature (AUC = 0.79) and quantitative height (R² = 0.21). A feature selection analysis revealed a subset of 412 most informative SNPs while the corresponding prediction model retained most of the accuracy (AUC = 0.76 and R² = 0.19) achieved with the full model. Over all, our study empirically exemplifies that the accuracy for predicting human appearance phenotypes with very complex underlying genetic architectures, such as adult height, can be improved by increasing the number of phenotype-associated DNA variants. Our work also demonstrates that a careful sub-selection allows for a considerable reduction of the number of DNA predictors that achieve similar prediction accuracy as provided by the full set. This is forensically relevant due to restrictions in the number of SNPs simultaneously analyzable with forensically suitable DNA technologies in the current days of targeted massively parallel sequencing in forensic genetics.

Predicting adult height from DNA variants in a European-Asian admixed population

Accurate genomic profiling for adult height is of high practical relevance in forensics genetics. Adult height is a classical reference trait in the field of human complex trait genetics characterized by highly polygenic nature and relatively high heritability. A meta-analysis of genome-wide association studies by the Genetic Investigation of Anthropocentric Traits (GIANT) consortium has identified 697 DNA variants associated with adult height in Europeans; however, whether these variants will still be informative in non-Europeans is still in question. The present study investigated the predictive power of these 697 height-associated SNPs in 687 Uyghurs of European-Asian admixed origin. Among all GIANT SNPs, 11% showed nominally significant association (6.78 × 10^-4 < p < 0.05) with adult height in the Uyghur population and among the significant SNPs 77% of allele effects were in the same direction as those in Europeans reported in the GIANT study. Fitting linear and logistic models using a polygenic score consisting of all GIANT SNPs resulted in an 80-20 cross-validated mean R² of 10.08% (95% CI 3.16-18.40%) for quantitative height prediction and a mean AUC value of 0.65 (95% CI 0.57-0.72%) for qualitative "above average" prediction. Fine-tuning the SNP set using their association p values considerably improved the prediction results (number of SNPs = 62, R² = 15.59%, 95% CI 6.80-25.71%; AUC = 0.70, 95% CI 62-0.77) in the Uyghurs. Overall, our findings demonstrate substantial differences between the European and Asian populations in the genetics of adult height, emphasizing the importance of population heterogeneity underlying the genetic architecture of adult height.

A generalized model for combining dependent SNP-level summary statistics and its extensions to statistics of other levels

Here I propose a fundamentally new flexible model to reveal the association between a trait and a set of genetic variants in a genomic region/gene. This model was developed for the situation when original individual-level phenotype and genotype data are not available, but the researcher possesses the results of statistical analyses conducted on these data (namely, SNP-level summary Z score statistics and SNP-by-SNP correlations). The new model was analytically derived from the classical multiple linear regression model applied for the region-based association analysis of individual-level phenotype and genotype data by using the linear compression of data, where the SNP-by-SNP correlations are among the explanatory variables, and the summary Z score statistics are categorized as the response variables. I analytically show that the regional association analysis methods developed within the framework of the classical multiple linear regression model with additive effects of genetic variants can be reformulated in terms of the new model without the loss of information. The results obtained from the regional association analysis utilizing the classical model and those derived using the proposed model are identical when SNP-by-SNP correlations and SNP-level statistics are estimated from the same genetic data.

Predictive accuracy of combined genetic and environmental risk scores

The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores.

Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship

Genomic prediction is emerging in a wide range of fields including animal and plant breeding, risk prediction in human precision medicine and forensic. It is desirable to establish a theoretical framework for genomic prediction accuracy when the reference data consists of information sources with varying degrees of relationship to the target individuals. A reference set can contain both close and distant relatives as well as ‘unrelated’ individuals from the wider population in the genomic prediction. The various sources of information were modeled as different populations with different effective population sizes (N_e). Both the effective number of chromosome segments (M_e) and N_e are considered to be a function of the data used for prediction. We validate our theory with analyses of simulated as well as real data, and illustrate that the variation in genomic relationships with the target is a predictor of the information content of the reference set. With a similar amount of data available for each source, we show that close relatives can have a substantially larger effect on genomic prediction accuracy than lesser related individuals. We also illustrate that when prediction relies on closer relatives, there is less improvement in prediction accuracy with an increase in training data or marker panel density. We release software that can estimate the expected prediction accuracy and power when combining different reference sources with various degrees of relationship to the target, which is useful when planning genomic prediction (before or after collecting data) in animal, plant and human genetics.

Using a system of differential equations that models cattle growth to uncover the genetic basis of complex traits

The interplay between dynamic models of biological systems and genomics is based on the assumption that genetic variation of the complex trait (i.e., outcome of model behavior) arises from component traits (i.e., model parameters) in lower hierarchical levels. In order to provide a proof of concept of this statement for a cattle growth model, we ask whether model parameters map genomic regions that harbor quantitative trait loci (QTLs) already described for the complex trait. We conducted a genome-wide association study (GWAS) with a Bayesian hierarchical LASSO method in two parameters of the Davis Growth Model, a system of three ordinary differential equations describing DNA accretion, protein synthesis and degradation, and fat synthesis. Phenotypic and genotypic data were available for 893 Nellore (Bos indicus) cattle. Computed values for parameter k₁ (DNA accretion rate) ranged from 0.005 ± 0.003 and for α (constant for energy for maintenance requirement) 0.134 ± 0.024. The expected biological interpretation of the parameters is confirmed by QTLs mapped for k₁ and α. QTLs within genomic regions mapped for k₁ are expected to be correlated with the DNA pool: body size and weight. Single nucleotide polymorphisms (SNPs) which were significant for α mapped QTLs that had already been associated with residual feed intake, feed conversion ratio, average daily gain (ADG), body weight, and also dry matter intake. SNPs identified for k₁ were able to additionally explain 2.2% of the phenotypic variability of the complex ADG, even when SNPs for k₁ did not match the genomic regions associated with ADG. Although improvements are needed, our findings suggest that genomic analysis on component traits may help to uncover the genetic basis of more complex traits, particularly when lower biological hierarchies are mechanistically described by mathematical simulation models.

Automated Phenotyping Indicates Pupal Size in Drosophila Is a Highly Heritable Trait with an Apparent Polygenic Basis

The intense focus on studying human height has done more than any other genetic analysis to advance our understanding of the heritability of highly complex phenotypes. Here, we describe in detail the properties of a previously unexplored trait in Drosophila melanogaster that shares many salient properties with human height. The total length of the pupal case varies between 2.8 and 3.9 mm among natural variants, and we report that it is among the most heritable traits reported in this species. We have developed a simple semiautomatic phenotyping system with which a single operator can reliably score >5000 individuals in a day. The precision of the automated system is 0.042 mm (± 0.030 SD). All phenotyped individuals are available to be mated in subsequent generations or uniquely archived for future molecular work. We report both broad sense and narrow sense heritability estimates for two biologically distinct data sets. Narrow sense heritability (h²) ranged from 0.44 to 0.50, and broad sense heritability (H²) ranged from 0.58 to 0.61. We present results for mapping the trait in 195 recombinant inbred lines, which suggests that there are no loci with >10% effect size in this panel. We propose that pupal size genetics in Drosophila could represent a model complex trait amenable to deep genetic dissection using the automated system described.

Using information of relatives in genomic prediction to apply effective stratified medicine

Genomic prediction shows promise for personalised medicine in which diagnosis and treatment are tailored to individuals based on their genetic profiles for complex diseases. We present a theoretical framework to demonstrate that prediction accuracy can be improved by targeting more informative individuals in the data set used to generate the predictors ("discovery sample") to include those with genetically close relationships with the subjects put forward for risk prediction. Increase of prediction accuracy from closer relationships is achieved under an additive model and does not rely on any family or interaction effects. Using theory, simulations and real data analyses, we show that the predictive accuracy or the area under the receiver operating characteristic curve (AUC) increased exponentially with decreasing effective size (Ne), i.e. when individuals are closely related. For example, with the sample size of discovery set N = 3000, heritability h2 = 0.5 and population prevalence K = 0.1, AUC value approached to 0.9 and the top percentile of the estimated genetic profile scores had 23 times higher proportion of cases than the general population. This suggests that there is considerable room to increase prediction accuracy by using a design that does not exclude closer relationships.

Validating genetic markers of response to recombinant human growth hormone in children with growth hormone deficiency and Turner syndrome: the PREDICT validation study

OBJECTIVE

Single-nucleotide polymorphisms (SNPs) associated with the response to recombinant human growth hormone (r-hGH) have previously been identified in growth hormone deficiency (GHD) and Turner syndrome (TS) children in the PREDICT long-term follow-up (LTFU) study (Nbib699855). Here, we describe the PREDICT validation (VAL) study (Nbib1419249), which aimed to confirm these genetic associations.

DESIGN AND METHODS

Children with GHD (n = 293) or TS (n = 132) were recruited retrospectively from 29 sites in nine countries. All children had completed 1 year of r-hGH therapy. 48 SNPs previously identified as associated with first year growth response to r-hGH were genotyped. Regression analysis was used to assess the association between genotype and growth response using clinical/auxological variables as covariates. Further analysis was undertaken using random forest classification.

RESULTS

The children were younger, and the growth response was higher in VAL study. Direct genotype analysis did not replicate what was found in the LTFU study. However, using exploratory regression models with covariates, a consistent relationship with growth response in both VAL and LTFU was shown for four genes - SOS1 and INPPL1 in GHD and ESR1 and PTPN1 in TS. The random forest analysis demonstrated that only clinical covariates were important in the prediction of growth response in mild GHD (>4 to <10 μg/L on GH stimulation test), however, in severe GHD (≤4 μg/L) several SNPs contributed (in IGF2, GRB10, FOS, IGFBP3 and GHRHR).

CONCLUSIONS

The PREDICT validation study supports, in an independent cohort, the association of four of 48 genetic markers with growth response to r-hGH treatment in both pre-pubertal GHD and TS children after controlling for clinical/auxological covariates. However, the contribution of these SNPs in a prediction model of first-year response is not sufficient for routine clinical use.

'Omics' biomarkers associated with chronic low back pain: protocol of a retrospective longitudinal study

INTRODUCTION

Chronic low back pain (CLBP) produces considerable direct costs as well as indirect burdens for society, industry and health systems. CLBP is characterised by heterogeneity, inclusion of several pain syndromes, different underlying molecular pathologies and interaction with psychosocial factors that leads to a range of clinical manifestations. There is still much to understand in the underlying pathological processes and the non-psychosocial factors which account for differences in outcomes. Biomarkers that may be objectively used for diagnosis and personalised, targeted and cost-effective treatment are still lacking. Therefore, any data that may be obtained at the '-omics' level (glycomics, Activomics and genome-wide association studies-GWAS) may be helpful to use as dynamic biomarkers for elucidating CLBP pathogenesis and may ultimately provide prognostic information too. By means of a retrospective, observational, case-cohort, multicentre study, we aim to investigate new promising biomarkers potentially able to solve some of the issues related to CLBP.

METHODS AND ANALYSIS

The study follows a two-phase, 1:2 case-control model. A total of 12 000 individuals (4000 cases and 8000 controls) will be enrolled; clinical data will be registered, with particular attention to pain characteristics and outcomes of pain treatments. Blood samples will be collected to perform -omics studies. The primary objective is to recognise genetic variants associated with CLBP; secondary objectives are to study glycomics and Activomics profiles associated with CLBP.

ETHICS AND DISSEMINATION

The study is part of the PainOMICS project funded by European Community in the Seventh Framework Programme. The study has been approved from competent ethical bodies and copies of approvals were provided to the European Commission before starting the study. Results of the study will be reviewed by the Scientific Board and Ethical Committee of the PainOMICS Consortium. The scientific results will be disseminated through peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT02037789; Pre-results.

The association between maternal nutrition and lifestyle during pregnancy and 2-year-old offspring adiposity: analysis from the ROLO study

Aim

To examine the association between maternal nutrition and lifestyle factors and offspring adiposity, using baseline and 2-year postpartum follow-up data from a randomised control trial of low glycaemic index diet.

Subject and methods

Food diaries and lifestyle questionnaires were completed during pregnancy and infant feeding and maternal lifestyle questionnaires 2 years postpartum for 281 mother and infant pairs from the ROLO study. Maternal anthropometry was measured throughout pregnancy and infant and maternal anthropometry was measured 2 years postpartum.

Results

Maternal 2 year postpartum body mass index (BMI) was positively associated with offspring BMI-for-age z-score (B = 0.105, p = 0.015). Trimester 2 saturated fat intake was positively associated with offspring subscapular:triceps skinfold ratio (B = 0.018, p = 0.001). Trimester 1 glycaemic index was also positively associated with offspring sum of subscapular and triceps skinfolds (B = 0.009, p = 0.029).

Conclusions

Maternal BMI 2 years postpartum was positively associated with offspring BMI. Pregnancy saturated fat intake was positively and polyunsaturated fat negatively associated with offspring adiposity. While further research is necessary, pregnancy and the postpartum period may be early opportunities to combat childhood obesity.

Electronic supplementary material

The online version of this article (doi:10.1007/s10389-016-0740-9) contains supplementary material, which is available to authorized users.

Predicting quantitative traits from genome and phenome with near perfect accuracy

In spite of decades of linkage and association studies and its potential impact on human health, reliable prediction of an individual's risk for heritable disease remains difficult. Large numbers of mapped loci do not explain substantial fractions of heritable variation, leaving an open question of whether accurate complex trait predictions can be achieved in practice. Here, we use a genome sequenced population of ∼7,000 yeast strains of high but varying relatedness, and predict growth traits from family information, effects of segregating genetic variants and growth in other environments with an average coefficient of determination R(2) of 0.91. This accuracy exceeds narrow-sense heritability, approaches limits imposed by measurement repeatability and is higher than achieved with a single assay in the laboratory. Our results prove that very accurate prediction of complex traits is possible, and suggest that additional data from families rather than reference cohorts may be more useful for this purpose.

Polygenic Epidemiology

Much of the genetic basis of complex traits is present on current genotyping products, but the individual variants that affect the traits have largely not been identified. Several traditional problems in genetic epidemiology have recently been addressed by assuming a polygenic basis for disease and treating it as a single entity. Here I briefly review some of these applications, which collectively may be termed polygenic epidemiology. Methodologies in this area include polygenic scoring, linear mixed models, and linkage disequilibrium scoring. They have been used to establish a polygenic effect, estimate genetic correlation between traits, estimate how many variants affect a trait, stratify cases into subphenotypes, predict individual disease risks, and infer causal effects using Mendelian randomization. Polygenic epidemiology will continue to yield useful applications even while much of the specific variation underlying complex traits remains undiscovered.

Integrating Diverse Types of Genomic Data to Identify Genes that Underlie Adverse Pregnancy Phenotypes

Progress in understanding complex genetic diseases has been bolstered by synthetic approaches that overlay diverse data types and analyses to identify functionally important genes. Pre-term birth (PTB), a major complication of pregnancy, is a leading cause of infant mortality worldwide. A major obstacle in addressing PTB is that the mechanisms controlling parturition and birth timing remain poorly understood. Integrative approaches that overlay datasets derived from comparative genomics with function-derived ones have potential to advance our understanding of the genetics of birth timing, and thus provide insights into the genes that may contribute to PTB. We intersected data from fast evolving coding and non-coding gene regions in the human and primate lineage with data from genes expressed in the placenta, from genes that show enriched expression only in the placenta, as well as from genes that are differentially expressed in four distinct PTB clinical subtypes. A large fraction of genes that are expressed in placenta, and differentially expressed in PTB clinical subtypes (23–34%) are fast evolving, and are associated with functions that include adhesion neurodevelopmental and immune processes. Functional categories of genes that express fast evolution in coding regions differ from those linked to fast evolution in non-coding regions. Finally, there is a surprising lack of overlap between fast evolving genes that are differentially expressed in four PTB clinical subtypes. Integrative approaches, especially those that incorporate evolutionary perspectives, can be successful in identifying potential genetic contributions to complex genetic diseases, such as PTB.

PART of the WHOLE: A Case Study in Wellness-Oriented Personalized Medicine

We describe the Wellness and Health Omics Linked to the Environment (WHOLE) personalized medicine profile for a 50-year-old Caucasian male living in Atlanta, Georgia. Based on the principle that genomic medicine will be most effective when presented in the context of an individual's clinical and lifestyle data, we propose the use of a "risk radar" that summarizes health risks in eight domains. Rather than providing overwhelming lists of potentially deleterious genetic variants, we argue that profiles should be palatable, actionable, reproducible, and teachable: the PART principle. Genetic risk scores for this individual are strikingly concordant for his height, body mass index (BMI), waist hip ration (WHR), and cholesterol, and blood transcriptome data agrees with and complements his complete blood counts. Despite enjoying currently good health, his risk radar highlights metabolic disease as his major health concern.

Effect of summer daylight exposure and genetic background on growth in growth hormone-deficient children

The response to growth hormone in humans is dependent on phenotypic, genetic and environmental factors. The present study in children with growth hormone deficiency (GHD) collected worldwide characterised gene–environment interactions on growth response to recombinant human growth hormone (r-hGH). Growth responses in children are linked to latitude, and we found that a correlate of latitude, summer daylight exposure (SDE), was a key environmental factor related to growth response to r-hGH. In turn growth response was determined by an interaction between both SDE and genes known to affect growth response to r-hGH. In addition, analysis of associated networks of gene expression implicated a role for circadian clock pathways and specifically the developmental transcription factor NANOG. This work provides the first observation of gene–environment interactions in children treated with r-hGH.

The association between maternal dietary micronutrient intake and neonatal anthropometry - secondary analysis from the ROLO study

Background

Micronutrients are necessary for fetal growth. However increasingly pregnant women are nutritionally replete and little is known about the effect of maternal micronutrient intakes on fetal adiposity in mothers with increased BMI. The aim of this study was to examine the association of maternal dietary micronutrient intake with neonatal size and adiposity in a cohort at risk of macrosomia.

Methods

This was a cohort analysis of 554 infants from the ROLO study. Three day food diaries from each trimester were collected. Neonatal weight, length, circumferences and skinfold thicknesses were measured at birth. Multiple linear regression was used to identify associations between micronutrient intakes and neonatal anthropometry.

Results

Birthweight was negatively associated with maternal trimester 3 vitamin D intake and positively associated with trimester 3 vitamin B12 intake R2adj 19.8 % (F = 13.19, p <0.001). Birth length was positively associated with trimester 3 magnesium intake R2adj 12.9 % (F = 8.06, p <0.001). In terms of neonatal central adiposity; abdominal circumference was positively associated with maternal trimester 3 retinol intake and negatively associated with trimester 3 vitamin E and selenium intake R2adj 11.9 % (F = 2.93, p = 0.002), waist:length ratio was negatively associated with trimester 3 magnesium intake R2adj 20.1 % (F = 3.92, p <0.001) and subscapular:triceps skinfold ratio was negatively associated with trimester 1 selenium intake R2adj7.2 % (F = 2.00, p = 0.047).

Conclusions

Maternal micronutrient intake was associated with neonatal anthropometry even in women not at risk of malnutrition. Further research is necessary to determine optimal micronutrient intake in overweight and obese pregnant women.

Trial registration

Current Controlled Trials ISRCTN54392969.

Electronic supplementary material

The online version of this article (doi:10.1186/s12937-015-0095-z) contains supplementary material, which is available to authorized users.

Estimates of genetic variance and variance of predicted genetic merits using pedigree or genomic relationship matrices in six Brown Swiss cattle populations for different traits

The amount of variance captured in genetic estimations may depend on whether a pedigree-based or genomic relationship matrix is used. The purpose of this study was to investigate the genetic variance as well as the variance of predicted genetic merits (PGM) using pedigree-based or genomic relationship matrices in Brown Swiss cattle. We examined a range of traits in six populations amounting to 173 population-trait combinations. A main aim was to determine how using different relationship matrices affect variance estimation. We calculated ratios between different types of estimates and analysed the impact of trait heritability and population size. The genetic variances estimated by REML using a genomic relationship matrix were always smaller than the variances that were similarly estimated using a pedigree-based relationship matrix. The variances from the genomic relationship matrix became closer to estimates from a pedigree relationship matrix as heritability and population size increased. In contrast, variances of predicted genetic merits obtained using a genomic relationship matrix were mostly larger than variances of genetic merit predicted using pedigree-based relationship matrix. The ratio of the genomic to pedigree-based PGM variances decreased as heritability and population size rose. The increased variance among predicted genetic merits is important for animal breeding because this is one of the factors influencing genetic progress.

Genetic diagnosis and prognosis of Alzheimer's disease: challenges and opportunities

Alzheimer's disease (AD), the most common form of dementia in western societies, is a pathologically and clinically heterogeneous disease with a strong genetic component. The recent advances in high-throughput genome technologies allowing for the rapid analysis of millions of polymorphisms in thousands of subjects has significantly advanced our understanding of the genomic underpinnings of AD susceptibility. During the last 5 years, genome-wide association and whole-exome- and whole-genome sequencing studies have mapped more than 20 disease-associated loci, providing insights into the molecular pathways involved in AD pathogenesis and hinting at potential novel therapeutic targets. This review article summarizes the challenges and opportunities of when using genomic information for the diagnosis and prognosis of AD.

Pharmacogenomics related to growth disorders

Growth disorders resulting in short stature are caused by a wide range of underlying pathophysiological processes. To improve height many of these conditions are treated with recombinant human growth hormone (rhGH). However, substantial inter-individual variability in growth response both in the short and long-term is recognised. Over the last decade, disease-specific growth prediction models have been developed that the clinician can use to define a child's potential response to rhGH and to optimise starting and maintenance doses of rhGH. These models, however, are not able to predict all the variations in treatment response. There has, therefore, been recent interest in using genetic information to contribute to the evaluation of responses to rhGH, including high-throughput technologies for assessing DNA markers (genome) and mRNA transcripts (transcriptome) as pharmacogenomic tools. This review will focus on how these pharmacogenomic approaches are being applied to growth disorders.

RNA sequencing identifies dysregulation of the human pancreatic islet transcriptome by the saturated fatty acid palmitate

Pancreatic β-cell dysfunction and death are central in the pathogenesis of type 2 diabetes (T2D). Saturated fatty acids cause β-cell failure and contribute to diabetes development in genetically predisposed individuals. Here we used RNA sequencing to map transcripts expressed in five palmitate-treated human islet preparations, observing 1,325 modified genes. Palmitate induced fatty acid metabolism and endoplasmic reticulum (ER) stress. Functional studies identified novel mediators of adaptive ER stress signaling. Palmitate modified genes regulating ubiquitin and proteasome function, autophagy, and apoptosis. Inhibition of autophagic flux and lysosome function contributed to lipotoxicity. Palmitate inhibited transcription factors controlling β-cell phenotype, including PAX4 and GATA6. Fifty-nine T2D candidate genes were expressed in human islets, and 11 were modified by palmitate. Palmitate modified expression of 17 splicing factors and shifted alternative splicing of 3,525 transcripts. Ingenuity Pathway Analysis of modified transcripts and genes confirmed that top changed functions related to cell death. Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis of transcription factor binding sites in palmitate-modified transcripts revealed a role for PAX4, GATA, and the ER stress response regulators XBP1 and ATF6. This human islet transcriptome study identified novel mechanisms of palmitate-induced β-cell dysfunction and death. The data point to cross talk between metabolic stress and candidate genes at the β-cell level.

Chasing Mendel: five questions for personalized medicine

Ideas about personalized medicine are underpinned in part by evolutionary biology's Modern Synthesis. In this essay we link personalized medicine to the efforts of the early statistical investigators who quantified the heritability of human phenotype and then attempted to reconcile their observations with Mendelian genetics. As information about the heritability of common diseases was obtained, similar efforts were directed at understanding the genetic basis of disease phenotypes. These ideas were part of the rationale driving the Human Genome Project and subsequently the personalized medicine movement. In this context, we discuss: (1) the current state of the genotype-phenotype relationship in humans, (2) the common-disease-common-variant hypothesis, (3) the current ability of 'omic' information to inform clinical decision making, (4) emerging ideas about the therapeutic insight available from rare genetic variants, and (5) the social and behavioural barriers to the wider potential success of personalized medicine. There are significant gaps in knowledge as well as conceptual, intellectual, and philosophical limitations in each of these five areas. We then provide specific recommendations to mitigate these limitations and close by asking if it is time for the biomedical research community to 'stop chasing Mendel?'

Candidate genes for type 1 diabetes modulate pancreatic islet inflammation and β-cell apoptosis

Genome-wide association studies (GWAS) have identified more than 50 loci associated with genetic risk of type 1 diabetes (T1D). Several T1D candidate genes have been suggested or identified within these regions, but the molecular mechanisms by which they contribute to insulitis and β-cell destruction remain to be clarified. More than 60% of the T1D candidate genes are expressed in human pancreatic islets, suggesting that they contribute to T1D by regulating at least in part pathogenic mechanisms at the β-cell level. Recent studies by our group indicate that important genetically regulated pathways in β-cells include innate immunity and antiviral activity, involving RIG-like receptors (particularly MDA5) and regulators of type I IFNs (i.e. PTPN2 and USP18), and genes related to β-cell phenotype and susceptibility to pro-apoptotic stimuli (i.e. GLIS3). These observations reinforce the concept that the early pathogenesis of T1D is characterized by a dialogue between the immune system and pancreatic β-cells. This dialogue is probably influenced by polymorphisms in genes expressed at the β-cell and/or immune system level, leading to inadequate responses to environmental cues such as viral infections. Further studies are needed to clarify how these disease-associated variants affect pancreatic β-cell responses to inflammation and the subsequent triggering of autoimmune responses and progressive β-cell loss.

Interaction between polygenic risk for cigarette use and environmental exposures in the Detroit Neighborhood Health Study

Cigarette smoking is influenced both by genetic and environmental factors. Until this year, all large-scale gene identification studies on smoking were conducted in populations of European ancestry. Consequently, the genetic architecture of smoking is not well described in other populations. Further, despite a rich epidemiologic literature focused on the social determinants of smoking, few studies have examined the moderation of genetic influences (for example, gene-environment interactions) on smoking in African Americans. In the Detroit Neighborhood Health Study (DNHS), a sample of randomly selected majority African American residents of Detroit, we constructed a genetic risk score (GRS), in which we combined top (P-value <5 × 10(-7)) genetic variants from a recent meta-analysis conducted in a large sample of African Americans. Using regression (effective n=399), we first tested for association between the GRS and cigarettes per day, attempting to replicate the findings from the meta-analysis. Second, we examined interactions with three social contexts that may moderate the genetic association with smoking: traumatic events, neighborhood social cohesion and neighborhood physical disorder. Among individuals who had ever smoked cigarettes, the GRS significantly predicted the number of cigarettes smoked per day and accounted for ~3% of the overall variance in the trait. Significant interactions were observed between the GRS and number of traumatic events experienced, as well as between the GRS and average neighborhood social cohesion; the association between genetic risk and smoking was greater among individuals who had experienced an increased number of traumatic events in their lifetimes, and diminished among individuals who lived in a neighborhood characterized by greater social cohesion. This study provides support for the utility of the GRS as an alternative approach to replication of common polygenic variation, and in gene-environment interaction, for smoking behaviors. In addition, this study indicates that environmental determinants have the potential to both exacerbate (traumatic events) and diminish (neighborhood social cohesion) genetic influences on smoking behaviors.

Region-based association analysis of human quantitative traits in related individuals

Regional-based association analysis instead of individual testing of each SNP was introduced in genome-wide association studies to increase the power of gene mapping, especially for rare genetic variants. For regional association tests, the kernel machine-based regression approach was recently proposed as a more powerful alternative to collapsing-based methods. However, the vast majority of existing algorithms and software for the kernel machine-based regression are applicable only to unrelated samples. In this paper, we present a new method for the kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The method is based on the GRAMMAR+ transformation of phenotypes of related individuals, followed by use of existing kernel machine-based regression software for unrelated samples. We compared the performance of kernel-based association analysis on the material of the Genetic Analysis Workshop 17 family sample and real human data by using our transformation, the original untransformed trait, and environmental residuals. We demonstrated that only the GRAMMAR+ transformation produced type I errors close to the nominal value and that this method had the highest empirical power. The new method can be applied to analysis of related samples by using existing software for kernel-based association analysis developed for unrelated samples.

Gene × environment interaction by a longitudinal epigenome-wide association study (LEWAS) overcomes limitations of genome-wide association study (GWAS)

The goal of genome-wide association studies is to identify SNPs unique to disease. It usually involves a single sampling from subjects' lifetimes. While primary DNA sequence variation influences gene-expression levels, expression is also influenced by epigenetics, including the 'somatic epitype' (G(SE)), an epigenotype acquired postnatally. While genes are inherited, and novel polymorphisms do not routinely appear, G(SE) is fluid. Furthermore, G(SE) could respond to environmental factors (such as heavy metals) and to differences in exercise, maternal care and dietary supplements - all of which postnatally modify oxidation or methylation of DNA, leading to altered gene expression. Change in epigenetic status may be critical for the development of many diseases. We propose a 'longitudinal epigenome-wide association study', wherein G(SE) are measured at multiple time points along with subjects' histories. This Longitudinal epigenome-wide association study, based on the 'dynamic' somatic epitype over the 'static' genotype, merits further investigation.

Potential assessment of genome-wide association study and genomic selection in Japanese pear Pyrus pyrifolia

Although the potential of marker-assisted selection (MAS) in fruit tree breeding has been reported, bi-parental QTL mapping before MAS has hindered the introduction of MAS to fruit tree breeding programs. Genome-wide association studies (GWAS) are an alternative to bi-parental QTL mapping in long-lived perennials. Selection based on genomic predictions of breeding values (genomic selection: GS) is another alternative for MAS. This study examined the potential of GWAS and GS in pear breeding with 76 Japanese pear cultivars to detect significant associations of 162 markers with nine agronomic traits. We applied multilocus Bayesian models accounting for ordinal categorical phenotypes for GWAS and GS model training. Significant associations were detected at harvest time, black spot resistance and the number of spurs and two of the associations were closely linked to known loci. Genome-wide predictions for GS were accurate at the highest level (0.75) in harvest time, at medium levels (0.38-0.61) in resistance to black spot, firmness of flesh, fruit shape in longitudinal section, fruit size, acid content and number of spurs and at low levels (<0.2) in all soluble solid content and vigor of tree. Results suggest the potential of GWAS and GS for use in future breeding programs in Japanese pear.

The insulin-like growth factor 1 receptor (IGF1R) contributes to reduced size in dogs

Domestic dog breeds have undergone intense selection for a variety of morphologic features, including size. Among small-dog breeds, defined as those averaging less than ~15 in. at the withers, there remains still considerable variation in body size. Yet essentially all such dogs are fixed for the same allele at the insulin-like growth factor 1 gene, which we and others previously found to be a size locus of large effect. In this study we sought to identify additional genes that contribute to tiny size in dogs using an association scan with the single nucleotide polymorphism (SNP) dataset CanMap, in which 915 purebred dogs were genotyped at 60,968 SNP markers. Our strongest association for tiny size (defined as breed-average height not more than 10 in. at the withers) was on canine chromosome 3 (p = 1.9 × 10(-70)). Fine mapping revealed a nonsynonymous SNP at chr3:44,706,389 that changes a highly conserved arginine at amino acid 204 to histidine in the insulin-like growth factor 1 receptor (IGF1R). This mutation is predicted to prevent formation of several hydrogen bonds within the cysteine-rich domain of the receptor's ligand-binding extracellular subunit. Nine of 13 tiny dog breeds carry the mutation and many dogs are homozygous for it. This work underscores the central importance of the IGF1 pathway in controlling the tremendous size diversity of dogs.

Heritability estimates of body size in fetal life and early childhood

Background

The objective was to estimate the heritability for height and weight during fetal life and early childhood in two independent studies, one including parent and singleton offsprings and one of mono- and dizygotic twins.

Methods

This study was embedded in the Generation R Study (n = 3407, singletons) and the Netherlands Twin Register (n = 33694, twins). For the heritability estimates in Generation R, regression models as proposed by Galton were used. In the Twin Register we used genetic structural equation modelling. Parental height and weight were measured and fetal growth characteristics (femur length and estimated fetal weight) were measured by ultrasounds in 2^nd and 3^rd trimester (Generation R only). Height and weight were assessed at multiple time-points from birth to 36 months in both studies.

Results

Heritability estimates for length increased from 2^nd to 3^rd trimester from 13% to 28%. At birth, heritability estimates for length in singletons and twins were both 26% and 27%, respectively, and at 36 months, the estimates for height were 63% and 72%, respectively. Heritability estimates for fetal weight increased from 2^nd to 3^rd trimester from 17% to 27%. For birth weight, heritability estimates were 26% in singletons and 29% in twins. At 36 months, the estimate for twins was 71% and higher than for singletons (42%).

Conclusions

Heritability estimates for height and weight increase from second trimester to infancy. This increase in heritability is observed in singletons and twins. Longer follow-up studies are needed to examine how the heritability develops in later childhood and puberty.

Did a discrete event 200,000-100,000 years ago produce modern humans?

Scenarios for modern human origins are often predicated on the assumption that modern humans arose 200,000-100,000 years ago in Africa. This assumption implies that something 'special' happened at this point in time in Africa, such as the speciation that produced Homo sapiens, a severe bottleneck in human population size, or a combination of the two. The common thread is that after the divergence of the modern human and Neandertal evolutionary lineages ∼400,000 years ago, there was another discrete event near in time to the Middle-Late Pleistocene boundary that produced modern humans. Alternatively, modern human origins could have been a lengthy process that lasted from the divergence of the modern human and Neandertal evolutionary lineages to the expansion of modern humans out of Africa, and nothing out of the ordinary happened 200,000-100,000 years ago in Africa. Three pieces of biological (fossil morphology and DNA sequences) evidence are typically cited in support of discrete event models. First, living human mitochondrial DNA haplotypes coalesce ∼200,000 years ago. Second, fossil specimens that are usually classified as 'anatomically modern' seem to appear shortly afterward in the African fossil record. Third, it is argued that these anatomically modern fossils are morphologically quite different from the fossils that preceded them. Here I use theory from population and quantitative genetics to show that lengthy process models are also consistent with current biological evidence. That this class of models is a viable option has implications for how modern human origins is conceptualized.

Computational genetic discoveries that could improve perioperative medicine

PURPOSE OF REVIEW

The review examines the rationale and translational utility of computational genetic studies using murine models of biomedical traits.

RECENT FINDINGS

Computational genetic mapping studies have identified the genetic basis for biomedical trait differences in 16 different murine models, including several that are of importance to perioperative medicine.

SUMMARY

The results have generated new treatments for alleviating incisional pain and narcotic drug withdrawal symptoms, which are now in clinical trials. A recent study identified allelic differences affecting chronic pain responses in mice and humans, which may enable a new 'personalized' approach to treating chronic pain.

Using whole-genome sequence data to predict quantitative trait phenotypes in Drosophila melanogaster

Predicting organismal phenotypes from genotype data is important for plant and animal breeding, medicine, and evolutionary biology. Genomic-based phenotype prediction has been applied for single-nucleotide polymorphism (SNP) genotyping platforms, but not using complete genome sequences. Here, we report genomic prediction for starvation stress resistance and startle response in Drosophila melanogaster, using ∼2.5 million SNPs determined by sequencing the Drosophila Genetic Reference Panel population of inbred lines. We constructed a genomic relationship matrix from the SNP data and used it in a genomic best linear unbiased prediction (GBLUP) model. We assessed predictive ability as the correlation between predicted genetic values and observed phenotypes by cross-validation, and found a predictive ability of 0.239±0.008 (0.230±0.012) for starvation resistance (startle response). The predictive ability of BayesB, a Bayesian method with internal SNP selection, was not greater than GBLUP. Selection of the 5% SNPs with either the highest absolute effect or variance explained did not improve predictive ability. Predictive ability decreased only when fewer than 150,000 SNPs were used to construct the genomic relationship matrix. We hypothesize that predictive power in this population stems from the SNP–based modeling of the subtle relationship structure caused by long-range linkage disequilibrium and not from population structure or SNPs in linkage disequilibrium with causal variants. We discuss the implications of these results for genomic prediction in other organisms.

The ability to accurately predict values of complex phenotypes from genotype data will revolutionize plant and animal breeding, personalized medicine, and evolutionary biology. To date, genomic prediction has utilized high-density single-nucleotide polymorphism (SNP) genotyping arrays, but the availability of sequence data opens new frontiers for genomic prediction methods. This article is the first application of genomic phenotype prediction using whole-genome sequence data in a substantial sample of a higher eukaryote. We use ∼2.5 million SNPs with minor allele frequency greater than 2.5% derived from genomic sequences of the “Drosophila Genetic Reference Panel” to predict phenotypes for two traits, starvation resistance and startle-induced locomotor behavior. We systematically address prediction within versus across sexes, genomic best linear unbiased prediction (GBLUP) versus a Bayesian approach, and the effect of SNP density. We find that (i) genomic prediction can be efficiently implemented using sequence data via GBLUP, (ii) there is little gain in predictive ability if the number of SNPs is increased above 150,000, and (iii) neither implicit nor explicit marker selection substantially improves the predictive ability. Although the findings must be seen against the background of small sample sizes, the results illustrate both the potential of the approach and the challenges ahead.