Genetic and environmental factors influencing neonatal resting-state functional connectivity.

Functional magnetic resonance imaging has been used to identify complex brain networks by examining the correlation of blood-oxygen-level-dependent signals between brain regions during the resting state. Many of the brain networks identified in adults are detectable at birth, but genetic and environmental influences governing connectivity within and between these networks in early infancy have yet to be explored. We investigated genetic influences on neonatal resting-state connectivity phenotypes by generating intraclass correlations and performing mixed effects modeling to estimate narrow-sense heritability on measures of within network and between-network connectivity in a large cohort of neonate twins. We also used backwards elimination regression and mixed linear modeling to identify specific demographic and medical history variables influencing within and between network connectivity in a large cohort of typically developing twins and singletons. Of the 36 connectivity phenotypes examined, only 6 showed narrow-sense heritability estimates greater than 0.10, with none being statistically significant. Demographic and obstetric history variables contributed to between- and within-network connectivity. Our results suggest that in early infancy, genetic factors minimally influence brain connectivity. However, specific demographic and medical history variables, such as gestational age at birth and maternal psychiatric history, may influence resting-state connectivity measures.

Energy balance of dairy cows predicted by mid-infrared spectra data of milk using Bayesian approaches.

Information on dry matter intake (DMI) and energy balance (EB) at the animal and herd level is important for management and breeding decisions. However, routine recording of these traits at commercial farms can be challenging and costly. Fourier-transform mid-infrared (FT-MIR) spectroscopy is a noninvasive technique applicable to a large cohort of animals that is routinely used to analyze milk components and is convenient for predicting complex phenotypes that are typically difficult and expensive to obtain on a large scale. We aimed to develop prediction models for EB and use the predicted phenotypes for genetic analysis. First, we assessed prediction equations using 4,485 phenotypic records from 167 Holstein cows from an experimental station. The phenotypes available were body weight (BW), milk yield (MY) and milk components, weekly-averaged DMI, and FT-MIR data from all milk samples available. We implemented mixed models with Bayesian approaches and assessed them through 50 randomized replicates of a 5-fold cross-validation. Second, we used the best prediction models to obtain predicted phenotypes of EB (EBp) and DMI (DMIp) on 5 commercial farms with 2,365 phenotypic records of MY, milk components and FT-MIR data, and BW from 1,441 Holstein cows. Third, we performed a GWAS and estimated heritability and genetic correlations for energy content in milk (EnM), BW, DMIp, and EBp using the genomic information available on the cows from commercial farms. The highest correlation between the predicted and observed phenotype (ry,y^) was obtained with DMI (0.88) and EB (0.86), while predicting BW was, as anticipated, more challenging (0.69). In our study, models that included FT-MIR information performed better than models without spectra information in the 3 traits analyzed, with increments in prediction correlation ranging from 5% to 10%. For the predicted phenotypes calculated by the prediction equations and data from the commercial farms the heritability ranged between 0.11 and 0.16 for EnM, DMIp and EBp, and 0.42 for BW. The genetic correlation between EnM and BW was -0.17, with DMIp was 0.40 and with EBp was -0.39. From the GWAS, we detected one significant QTL region for EnM, and 3 for BW, but none for DMIp and EBp. The results obtained in our study support previous evidence that FT-MIR information from milk samples contribute to improve the prediction equations for DMI, BW, and EB, and these predicted phenotypes may be used for herd management and contribute to the breeding strategy for improving cow performance.

Xylem systems genetics analysis reveals a key regulator of lignin biosynthesis in Populus deltoides.

Despite the growing resources and tools for high-throughput characterization and analysis of genomic information, the discovery of the genetic elements that regulate complex traits remains a challenge. Systems genetics is an emerging field that aims to understand the flow of biological information that underlies complex traits from genotype to phenotype. In this study, we used a systems genetics approach to identify and evaluate regulators of the lignin biosynthesis pathway in Populus deltoides by combining genome, transcriptome, and phenotype data from a population of 268 unrelated individuals of P. deltoides The discovery of lignin regulators began with the quantitative genetic analysis of the xylem transcriptome and resulted in the detection of 6706 and 4628 significant local- and distant-eQTL associations, respectively. Among the locally regulated genes, we identified the R2R3-MYB transcription factor MYB125 (Potri.003G114100) as a putative trans-regulator of the majority of genes in the lignin biosynthesis pathway. The expression of MYB125 in a diverse population positively correlated with lignin content. Furthermore, overexpression of MYB125 in transgenic poplar resulted in increased lignin content, as well as altered expression of genes in the lignin biosynthesis pathway. Altogether, our findings indicate that MYB125 is involved in the control of a transcriptional coexpression network of lignin biosynthesis genes during secondary cell wall formation in P. deltoides.

Transcriptome-Based Prediction of Complex Traits in Maize.

The ability to predict traits from genome-wide sequence information (i.e., genomic prediction) has improved our understanding of the genetic basis of complex traits and transformed breeding practices. Transcriptome data may also be useful for genomic prediction. However, it remains unclear how well transcript levels can predict traits, particularly when traits are scored at different development stages. Using maize (Zea mays) genetic markers and transcript levels from seedlings to predict mature plant traits, we found that transcript and genetic marker models have similar performance. When the transcripts and genetic markers with the greatest weights (i.e., the most important) in those models were used in one joint model, performance increased. Furthermore, genetic markers important for predictions were not close to or identified as regulatory variants for important transcripts. These findings demonstrate that transcript levels are useful for predicting traits and that their predictive power is not simply due to genetic variation in the transcribed genomic regions. Finally, genetic marker models identified only 1 of 14 benchmark flowering-time genes, while transcript models identified 5. These data highlight that, in addition to being useful for genomic prediction, transcriptome data can provide a link between traits and variation that cannot be readily captured at the sequence level.

Using residual regressions to quantify and map signal leakage in genomic prediction.

BACKGROUND: Most genomic prediction applications in animal breeding use genotypes with tens of thousands of single nucleotide polymorphisms (SNPs). However, modern sequencing technologies and imputation algorithms can generate ultra-high-density genotypes (including millions of SNPs) at an affordable cost. Empirical studies have not produced clear evidence that using ultra-high-density genotypes can significantly improve prediction accuracy. However, (whole-genome) prediction accuracy is not very informative about the ability of a model to capture the genetic signals from specific genomic regions. To address this problem, we propose a simple methodology that detects chromosome regions for which a specific model (e.g., single-step genomic best linear unbiased prediction (ssGBLUP)) may fail to fully capture the genetic signal present in such segments-a phenomenon that we refer to as signal leakage. We propose to detect regions with evidence of signal leakage by testing the association of residuals from a pedigree or a genomic model with SNP genotypes. We discuss how this approach can be used to map regions with signals that are poorly captured by a model and to identify strategies to fix those problems (e.g., using a different prior or increasing marker density). Finally, we explored the proposed approach to scan for signal leakage of different models (pedigree-based, ssGBLUP, and various Bayesian models) applied to growth-related phenotypes (average daily gain and backfat thickness) in pigs. RESULTS: We report widespread evidence of signal leakage for pedigree-based models. Including a percentage of animals with SNP data in ssGBLUP reduced the extent of signal leakage. However, local peaks of missed signals remained in some regions, even when all animals were genotyped. Using variable selection priors solves leakage points that are caused by excessive shrinkage of marker effects. Nevertheless, these models still miss signals in some regions due to low linkage disequilibrium between the SNPs on the array used and causal variants. Thus, we discuss how such problems could be addressed by adding sequence SNPs from those regions to the prediction model. CONCLUSIONS: Residual single-marker regression analysis is a simple approach that can be used to detect regional genomic signals that are poorly captured by a model and to indicate ways to fix such problems.

Advantages of treating sponge-gourd waste by mechanical refining on the properties of fiber-based poly(butylene adipate-co-terephthalate)/polylactide biocomposites.

This study compares the morphology, thermal, and dynamic-mechanical properties of composites based on polybutylene adipate terephthalate/polylactide biocomposites with sponge gourd waste treated code as R, and non-treated sponge gourd, coded as NR, by mechanical disc refining after milled process. Extrusion followed by compression molding was used to produce biocomposites with fiber contents of 0, 2.5, 5, 10, and 15% wt/wt for R and NR sponge gourd fibers. Scanning electron microscopy analysis reveals that NR has the morphology of a rigid tubular shape, whereas R is a thinner, twisted, and fibrillated fiber. Regardless of the type of sponge gourd fiber used, the thermal stability of the composite decreases as the sponge gourd content increases. At 25°C, the biocomposite with 10%wt/wt R fiber has the highest storage modulus value. The comparison of Tangent  peak values reveals that the presence of sponge gourd fibers reduces the energy dissipation of the biocomposites. The analysis of the loss modulus at 25°C reveals that R fiber contributes more to the reduction of energy dissipation of the biocomposites than NR. Furthermore, the Cole-Cole plot shows that R and NR fibers are dispersed and do not significantly change the homogeneity of the biopolymer systems.

Age-specific rate of severe and critical SARS-CoV-2 infections estimated with multi-country seroprevalence studies.

BACKGROUND: Knowing the age-specific rates at which individuals infected with SARS-CoV-2 develop severe and critical disease is essential for designing public policy, for infectious disease modeling, and for individual risk evaluation. METHODS: In this study, we present the first estimates of these rates using multi-country serology studies, and public data on hospital admissions and mortality from early to mid-2020. We combine these under a Bayesian framework that accounts for the high heterogeneity between data sources and their respective uncertainties. We also validate our results using an indirect method based on infection fatality rates and hospital mortality data. RESULTS: Our results show that the risk of severe and critical disease increases exponentially with age, but much less steeply than the risk of fatal illness. We also show that our results are consistent across several robustness checks. CONCLUSION: A complete evaluation of the risks of SARS-CoV-2 for health must take non-fatal disease outcomes into account, particularly in young populations where they can be 2 orders of magnitude more frequent than deaths.

Anatomy of the tibial nerve in relation to the tarsal tunnel: A cadaveric study.

BACKGROUND: Tarsal tunnel syndrome (TTS) is typically caused by an anatomical variant or mechanical compression of the tibial nerve (TN) with variable success after surgical treatment. METHOD: 40 lower-leg specimens were obtained. Dissections were appropriately conducted. Extremities were prepared under formaldehyde solution. The tibial nerve and branches were dissected for measurements and various characteristics. RESULTS: The flexor retinaculum had a denser consistency in 22.5% of the cases and the average length was 51.9 mm. The flexor retinaculum as an independent structure was absent and 77.2% of cases as an undistinguished extension of the crural fascia. The lateral plantar nerve (LPN) and abductor digiti minimi (ADM) nerve shared same origin in 80% of cases, 34.5% bifurcated proximal to the DM (Dellon-McKinnon malleolar-calcaneal line) line 31.2% distally and 34.3% at the same level. CONCLUSION: Understanding the tibial nerve anatomy will allow us to adapt our surgical technique to improve the treatment of this recurrent pathology.

Complex-Trait Prediction in the Era of Big Data.

Accurate prediction of complex traits requires using a large number of DNA variants. Advances in statistical and machine learning methodology enable the identification of complex patterns in high-dimensional settings. However, training these highly parameterized methods requires very large data sets. Until recently, such data sets were not available. But the situation is changing rapidly as very large biomedical data sets comprising individual genotype-phenotype data for hundreds of thousands of individuals become available in public and private domains. We argue that the convergence of advances in methodology and the advent of Big Genomic Data will enable unprecedented improvements in complex-trait prediction; we review theory and evidence supporting our claim and discuss challenges and opportunities that Big Data will bring to complex-trait prediction.

Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices.

Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5-17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.

Opportunities and limits of combining microbiome and genome data for complex trait prediction.

BACKGROUND: Analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: how useful can the microbiome be for complex trait prediction? Are estimates of microbiability reliable? Can the underlying biological links between the host's genome, microbiome, and phenome be recovered? METHODS: Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as inputs, and (ii) using variance-component approaches (Bayesian Reproducing Kernel Hilbert Space (RKHS) and Bayesian variable selection methods (Bayes C)) to quantify the proportion of phenotypic variance explained by the genome and the microbiome. The proposed simulation approach can mimic genetic links between the microbiome and genotype data by a permutation procedure that retains the distributional properties of the data. RESULTS: Using real genotype and rumen microbiota abundances from dairy cattle, simulation results suggest that microbiome data can significantly improve the accuracy of phenotype predictions, regardless of whether some microbiota abundances are under direct genetic control by the host or not. This improvement depends logically on the microbiome being stable over time. Overall, random-effects linear methods appear robust for variance components estimation, in spite of the typically highly leptokurtic distribution of microbiota abundances. The predictive performance of Bayes C was higher but more sensitive to the number of causative effects than RKHS. Accuracy with Bayes C depended, in part, on the number of microorganisms' taxa that influence the phenotype. CONCLUSIONS: While we conclude that, overall, genome-microbiome-links can be characterized using variance component estimates, we are less optimistic about the possibility of identifying the causative host genetic effects that affect microbiota abundances, which would require much larger sample sizes than are typically available for genome-microbiome-phenome studies. The R code to replicate the analyses is in https://github.com/miguelperezenciso/simubiome .

Anatomy of the Dorsomedial Cutaneous Nerve to Hallux and Surgical Implications According to the Severity of Hallux Valgus Deformity: A Cadaveric Study.

The dorsomedial cutaneous nerve to hallux provides sensation to the dorsomedial aspect of the first metatarsophalangeal joint and hallux. Postoperative damage to the dorsomedial cutaneous nerve to hallux have been reported with the dorsomedial approach and symptoms can be very debilitating. The present study aims to understand how the distance between this nerve and the extensor hallucis longus tendon are affected by the severity of the hallux valgus deformity, at the level of the first metatarsophalangeal joint. We performed a cadaveric study using 35 cadaveric lower extremities (N = 35). Each specimen was classified according to the hallux valgus severity through a 30 kg partial weight-bearing antero-posterior radiograph. Before dissection, the lower extremities' greater saphenous vein was injected with black latex to simplify the distinction between anatomical structures. We concluded that as the hallux valgus angle and the first intermetatarsal angle increase, the distance between the dorsomedial cutaneous nerve to hallux and the extensor hallucis longus tendon also increases, ranging from 12 mm in normal feet to 19 mm in severely deformed feet. Hallux valgus is a three-dimensional deformity that changes traditional surgical landmarks. To avoid harming this nerve, we established a danger zone ranging from 12 mm to 19 mm medial from the extensor hallucis longus tendon, at the level of the first metatarsophalangeal joint. The mid-medial approach to MTP should be preferred as it is out of the danger zone.

Complications on Percutaneous Hallux Valgus Surgery: A Systematic Review.

Lately there has been a growing interest in the use of percutaneous surgery for the correction of hallux valgus (HV). The purpose of the present study was to systematically review the published data about this topic and establish the efficacy and safety, stressing the complication rates found on this percutaneous technique. A systematic review of the literature available in PubMed was performed. The radiological and clinical outcomes were evaluated as well as complication rates. A total of 16 studies were included and 1157 procedures reported for percutaneous HV on 1246 patients. The mean angle correction of HV deformity improved postoperatively. Reported complications vary among the studies. The highest complication rate was joint stiffness in 18.47% of cases, followed by HV recurrence and shortening of M1, both in 15.2%, material intolerance in 10.1%, osteoarthritic changes in 9.1%, infection in 7.6%, and transfer metatarsalgia in 5.4%. There is a lack of randomized control trials and insufficient comparative case control studies to assess whether one technique is more effective than another or if the percutaneous surgery is recommended rather than open surgery with respect to complications.

Vascular anatomy of the first metatarsal bone and surgical implications according to the severity of hallux valgus deformity: A cadaveric study.

BACKGROUND: Vascular injury after hallux valgus surgery is a rare condition but serious complications can ensue. METHODS: We performed an anatomical study using 26 cadaveric lower extremities. We enhanced first metatarsal bone's (FMB) vascularization by injecting latex. Each specimen was classified according to the severity of hallux valgus deformity (HVD). Then we measured two distances: one between the first tarsometatarsal joint (FTMJ) to the first dorsal branch's origin, the other between the first metatarsophalangeal joint (MTP) to the dorsal plexus's origin. RESULTS: The distance between the FTMJ and the first dorsal branch to the FMB ranges from 10 mm in normal feet to 15 mm in severe deformed feet. The distance between the MTP and the dorsal plexus' origin ranges from 20 mm in normal feet to 25 mm in severe deformed feet. CONCLUSIONS: Understanding the foot's vascular anatomy has allowed us to adapt surgical landmarks to the severity of the HVD and to avoid post-operative complications.

Genomic heritability: what is it?

Whole-genome regression methods are being increasingly used for the analysis and prediction of complex traits and diseases. In human genetics, these methods are commonly used for inferences about genetic parameters, such as the amount of genetic variance among individuals or the proportion of phenotypic variance that can be explained by regression on molecular markers. This is so even though some of the assumptions commonly adopted for data analysis are at odds with important quantitative genetic concepts. In this article we develop theory that leads to a precise definition of parameters arising in high dimensional genomic regressions; we focus on the so-called genomic heritability: the proportion of variance of a trait that can be explained (in the population) by a linear regression on a set of markers. We propose a definition of this parameter that is framed within the classical quantitative genetics theory and show that the genomic heritability and the trait heritability parameters are equal only when all causal variants are typed. Further, we discuss how the genomic variance and genomic heritability, defined as quantitative genetic parameters, relate to parameters of statistical models commonly used for inferences, and indicate potential inferential problems that are assessed further using simulations. When a large proportion of the markers used in the analysis are in LE with QTL the likelihood function can be misspecified. This can induce a sizable finite-sample bias and, possibly, lack of consistency of likelihood (or Bayesian) estimates. This situation can be encountered if the individuals in the sample are distantly related and linkage disequilibrium spans over short regions. This bias does not negate the use of whole-genome regression models as predictive machines; however, our results indicate that caution is needed when using marker-based regressions for inferences about population parameters such as the genomic heritability.

Changes in milk characteristics and fatty acid profile during the estrous cycle in dairy cows.

The relationship of the estrous cycle to milk composition and milk physical properties was assessed on Holstein (n = 10,696), Brown Swiss (n = 20,501), Simmental (n = 17,837), and Alpine Grey (n = 8,595) cows reared in northeastern Italy. The first insemination after calving for each cow was chosen to be the day of estrus and insemination. Test days surrounding the insemination date (from 10 d before to 10 d after the day of the estrus) were selected and categorized in phases relative to estrus as diestrus high-progesterone, proestrus, estrus, metestrus, and diestrus increasing-progesterone phases. Milk components and physical properties were predicted on the basis of Fourier-transform infrared spectra of milk samples and were analyzed using a linear mixed model, which included the random effects of herd, the fixed classification effects of year-month, parity number, breed, estrous cycle phase, day nested within the estrous cycle phase, conception, partial regressions on linear and quadratic effects of days in milk nested within parity number, as well as the interactions between conception outcome with estrous cycle phase and breed with estrous cycle phase. Milk composition, particularly fat, protein, and lactose, showed clear differences among the estrous cycle phases. Fat increased by 0.14% from diestrus high-progesterone to estrous phase, whereas protein concomitantly decreased by 0.03%. Lactose appeared to remain relatively constant over diestrus high-progesterone, rising 1 d before the day of estrus followed by a gradual reduction over the subsequent phases. Specific fatty acids were also affected across the estrous cycle phases: C14:0 and C16:0 decreased (-0.34 and -0.48%) from proestrus to estrus with a concomitant increase in C18:0 and C18:1 cis-9 (0.40 and 0.73%). More general categories of fatty acids showed a similar behavior; that is, unsaturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, trans fatty acids, and long-chain fatty acids increased, whereas the saturated fatty acids, medium-chain fatty acids, and short-chain fatty acids decreased during the estrous phase. Finally, urea, somatic cell score, freezing point, pH, and homogenization index were also affected indicating variation associated with the hormonal and behavioral changes of cows in standing estrus. Hence, the variation in milk profiles of cows showing estrus should potentially be taken into account for precision dairy farming management.

Diagnosing pregnancy status using infrared spectra and milk composition in dairy cows.

Data on Holstein (16,890), Brown Swiss (31,441), Simmental (25,845), and Alpine Grey (12,535) cows reared in northeastern Italy were used to assess the ability of milk components (fat, protein, casein, and lactose) and Fourier transform infrared (FTIR) spectral data to diagnose pregnancy. Pregnancy status was defined as whether a pregnancy was confirmed by a subsequent calving and no other subsequent inseminations within 90 d of the breeding of specific interest. Milk samples were analyzed for components and FTIR full-spectrum data using a MilkoScan FT+ 6000 (Foss Electric, Hillerød, Denmark). The spectrum covered 1,060 wavenumbers (wn) from 5,010 to 925 cm-1. Pregnancy status was predicted using generalized linear models with fat, protein, lactose, casein, and individual FTIR spectral bands or wavelengths as predictors. We also fitted a generalized linear model as a simultaneous function of all wavelengths (1,060 wn) with a Bayesian variable selection model using the BGLR R-package (https://r-forge.r-project.org/projects/bglr/). Prediction accuracy was determined using the area under a receiver operating characteristic curve based on a 10-fold cross-validation (CV-AUC) assessment based on sensitivities and specificities of phenotypic predictions. Overall, the best prediction accuracies were obtained for the model that included the complete FTIR spectral data. We observed similar patterns across breeds with small differences in prediction accuracy. The highest CV-AUC value was obtained for Alpine Grey cows (CV-AUC = 0.645), whereas Brown Swiss and Simmental cows had similar performance (CV-AUC = 0.630 and 0.628, respectively), followed by Holsteins (CV-AUC = 0.607). For single-wavelength analyses, important peaks were detected at wn 2,973 to 2,872 cm-1 where Fat-B (C-H stretch) is usually filtered, wn 1,773 cm-1 where Fat-A (C=O stretch) is filtered, wn 1,546 cm-1 where protein is filtered, wn 1,468 cm-1 associated with urea and fat, wn 1,399 and 1,245 cm-1 associated with acetone, and wn 1,025 to 1,013 cm-1 where lactose is filtered. In conclusion, this research provides new insight into alternative strategies for pregnancy screening of dairy cows.

Genome-wide association study reveals putative regulators of bioenergy traits in Populus deltoides.

Genome-wide association studies (GWAS) have been used extensively to dissect the genetic regulation of complex traits in plants. These studies have focused largely on the analysis of common genetic variants despite the abundance of rare polymorphisms in several species, and their potential role in trait variation. Here, we conducted the first GWAS in Populus deltoides, a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits, and common and low-frequency single-nucleotide polymorphisms detected by targeted resequencing of 18 153 genes in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. Our results suggest that both common and low-frequency variants need to be considered for a comprehensive understanding of the genetic regulation of complex traits, particularly in species that carry large numbers of rare polymorphisms. These polymorphisms may be critical for the development of specialized plant feedstocks for bioenergy.

Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids.

KEY MESSAGE: A new genomic model that incorporates genotype × environment interaction gave increased prediction accuracy of untested hybrid response for traits such as percent starch content, percent dry matter content and silage yield of maize hybrids. The prediction of hybrid performance (HP) is very important in agricultural breeding programs. In plant breeding, multi-environment trials play an important role in the selection of important traits, such as stability across environments, grain yield and pest resistance. Environmental conditions modulate gene expression causing genotype × environment interaction (G × E), such that the estimated genetic correlations of the performance of individual lines across environments summarize the joint action of genes and environmental conditions. This article proposes a genomic statistical model that incorporates G × E for general and specific combining ability for predicting the performance of hybrids in environments. The proposed model can also be applied to any other hybrid species with distinct parental pools. In this study, we evaluated the predictive ability of two HP prediction models using a cross-validation approach applied in extensive maize hybrid data, comprising 2724 hybrids derived from 507 dent lines and 24 flint lines, which were evaluated for three traits in 58 environments over 12 years; analyses were performed for each year. On average, genomic models that include the interaction of general and specific combining ability with environments have greater predictive ability than genomic models without interaction with environments (ranging from 12 to 22%, depending on the trait). We concluded that including G × E in the prediction of untested maize hybrids increases the accuracy of genomic models.

Predicting genetic predisposition in humans: the promise of whole-genome markers.

Although genome-wide association studies have identified markers that are associated with various human traits and diseases, our ability to predict such phenotypes remains limited. A perhaps overlooked explanation lies in the limitations of the genetic models and statistical techniques commonly used in association studies. We propose that alternative approaches, which are largely borrowed from animal breeding, provide potential for advances. We review selected methods and discuss the challenges and opportunities ahead.