Improving the Efficiency of Genomic Selection

We investigate two approaches to increase the efficiency of phenotypic prediction from genome-wide markers, which is a key step for genomic selection (GS) in plant and animal breeding. The first approach is feature selection based on Markov blankets, which provide a theoretically-sound framework for identifying non-informative markers. Fitting GS models using only the informative markers results in simpler models, which may allow cost savings from reduced genotyping. We show that this is accompanied by no loss, and possibly a small gain, in predictive power for four GS models: partial least squares (PLS), ridge regression, LASSO and elastic net. The second approach is the choice of kinship coefficients for genomic best linear unbiased prediction (GBLUP). We compare kinships based on different combinations of centring and scaling of marker genotypes, and a newly proposed kinship measure that adjusts for linkage disequilibrium (LD). We illustrate the use of both approaches and examine their performances using three real-world data sets from plant and animal genetics. We find that elastic net with feature selection and GBLUP using LD-adjusted kinships performed similarly well, and were the best-performing methods in our study.Comment: 17 pages, 5 figure

Multiple Quantitative Trait Analysis Using Bayesian Networks

Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this paper we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP), and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.Comment: 28 pages, 1 figure, code at http://www.bnlearn.com/research/genetics1

Genome-wide linkage scan on estimated breeding values for a quantitative trait

Background: A genome-wide linkage scan was performed on Replicate 1 of the simulated data for fasting triglyceride levels. The aim of this study was to implement mixed-model methodology to estimate breeding values for each individual for this trait and to assess the merit of these breeding values in linkage analysis. These breeding values utilize all the pedigree information, and the genetic and phenotypic correlations with other measured traits across the two cohorts. A genome-wide linkage scan was run on both the new breeding value traits and the original traits.Results: Using breeding values, a maximum LOD of 7.78 was found on chromosome 5 at a position very close to a gene underlying the triglyceride levels. This effect was not detected using the original trait.Conclusion: The results imply that estimating breeding values may be a suitable method of deriving traits for use in genome-wide scans

Genome‐wide association mapping of Hagberg falling number, protein content, test weight and grain yield in UK wheat

Association mapping using crop cultivars allows identification of genetic loci of direct relevance to breeding. Here, 150 U.K. wheat (Triticum aestivum L.) cultivars genotyped with 23,288 single nucleotide polymorphisms (SNPs) were used for genome‐wide association studies (GWAS) using historical phenotypic data for grain protein content, Hagberg falling number (HFN), test weight, and grain yield. Power calculations indicated experimental design would enable detection of quantitative trait loci (QTL) explaining ≥20% of the variation (PVE) at a relatively high power of >80%, falling to 40% for detection of a SNP with an R(2) ≥ .5 with the same QTL. Genome‐wide association studies identified marker‐trait associations for all four traits. For HFN (h (2 )= .89), six QTL were identified, including a major locus on chromosome 7B explaining 49% PVE and reducing HFN by 44 s. For protein content (h (2 )= 0.86), 10 QTL were found on chromosomes 1A, 2A, 2B, 3A, 3B, and 6B, together explaining 48.9% PVE. For test weight, five QTL were identified (one on 1B and four on 3B; 26.3% PVE). Finally, 14 loci were identified for grain yield (h (2 )= 0.95) on eight chromosomes (1A, 2A, 2B, 2D, 3A, 5B, 6A, 6B; 68.1% PVE), of which five were located within 16 Mbp of genetic regions previously identified as under breeder selection in European wheat. Our study demonstrates the utility of exploiting historical crop datasets, identifying genomic targets for independent validation, and ultimately for wheat genetic improvement

Complementary Ionization Techniques for the Analysis of Scotch Whisky by High Resolution Mass Spectrometry

Fourier transform mass spectrometry (FTMS) is widely used to characterize the chemical complexity of mixtures, such as natural organic matter (NOM), petroleum, and agri-food products (including Scotch whisky). Although electrospray ionization (ESI) is by far the most widely used ionization source in these studies, other ionization techniques are available and may offer complementary information. In a recent study, we found matrix free laser desorption/ionization (LDI) to be effective for the analysis of Suwannee river fulvic acid (SRFA), and to provide complementary chemical insights. In this study, LDI along with atmospheric pressure photoionization (APPI) and atmospheric pressure chemical ionization (APCI) were compared to ESI for the analysis of Scotch whisky. High mass accuracy (54 ppb, mean) allowed for the assignment of 86% of peaks, with 3993 unique molecular formulas identified from four representative samples analyzed. All four ionization techniques, performed in negative mode, identified thousands of formulas. Many were unique to each ionization source, while 699 formulas were common to all techniques. Ions were identified in both deprotonated and radical anion forms. Our study highlights the importance of a multi-ionization source approach; we recommend that analysis of complex mixtures, especially novel ones, should not be limited solely to ESI

Alzheimer disease genetic risk factor APOE e4, and cognitive abilities in 111,739 UK Biobank participants

Background: the apolipoprotein (APOE) e4 locus is a genetic risk factor for dementia. Carriers of the e4 allele may be more vulnerable to conditions that are independent risk factors for cognitive decline, such as cardiometabolic diseases. Objective: we tested whether any association with APOE e4 status on cognitive ability was larger in older ages or in those with cardiometabolic diseases. Subjects: UK Biobank includes over 500,000 middle- and older aged adults who have undergone detailed medical and cognitive phenotypic assessment. Around 150,000 currently have genetic data. We examined 111,739 participants with complete genetic and cognitive data. Methods: baseline cognitive data relating to information processing speed, memory and reasoning were used. We tested for interactions with age and with the presence versus absence of type 2 diabetes (T2D), coronary artery disease (CAD) and hypertension. Results: in several instances, APOE e4 dosage interacted with older age and disease presence to affect cognitive scores. When adjusted for potentially confounding variables, there was no APOE e4 effect on the outcome variables. Conclusions: future research in large independent cohorts should continue to investigate this important question, which has potential implications for aetiology related to dementia and cognitive impairment

Investigating the genetic control of plant development in spring barley under speed breeding conditions

Key message: This study found that the genes, PPD-H1 and ELF3, control the acceleration of plant development under speed breeding, with important implications for optimizing the delivery of climate-resilient crops. Abstract: Speed breeding is a tool to accelerate breeding and research programmes. Despite its success and growing popularity with breeders, the genetic basis of plant development under speed breeding remains unknown. This study explored the developmental advancements of barley genotypes under different photoperiod regimes. A subset of the HEB-25 Nested Association Mapping population was evaluated for days to heading and maturity under two contrasting photoperiod conditions: (1) Speed breeding (SB) consisting of 22 h of light and 2 h of darkness, and (2) normal breeding (NB) consisting of 16 h of light and 8 h of darkness. GWAS revealed that developmental responses under both conditions were largely controlled by two loci: PPDH-1 and ELF3. Allelic variants at these genes determine whether plants display early flowering and maturity under both conditions. At key QTL regions, domesticated alleles were associated with late flowering and maturity in NB and early flowering and maturity in SB, whereas wild alleles were associated with early flowering under both conditions. We hypothesize that this is related to the dark-dependent repression of PPD-H1 by ELF3 which might be more prominent in NB conditions. Furthermore, by comparing development under two photoperiod regimes, we derived an estimate of plasticity for the two traits. Interestingly, plasticity in development was largely attributed to allelic variation at ELF3. Our results have important implications for our understanding and optimization of speed breeding protocols particularly for introgression breeding and the design of breeding programmes to support the delivery of climate-resilient crops

Association mapping of partitioning loci in barley

BACKGROUND: Association mapping, initially developed in human disease genetics, is now being applied to plant species. The model species Arabidopsis provided some of the first examples of association mapping in plants, identifying previously cloned flowering time genes, despite high population sub-structure. More recently, association genetics has been applied to barley, where breeding activity has resulted in a high degree of population sub-structure. A major genotypic division within barley is that between winter- and spring-sown varieties, which differ in their requirement for vernalization to promote subsequent flowering. To date, all attempts to validate association genetics in barley by identifying major flowering time loci that control vernalization requirement (VRN-H1 and VRN-H2) have failed. Here, we validate the use of association genetics in barley by identifying VRN-H1 and VRN-H2, despite their prominent role in determining population sub-structure. RESULTS: By taking barley as a typical inbreeding crop, and seasonal growth habit as a major partitioning phenotype, we develop an association mapping approach which successfully identifies VRN-H1 and VRN-H2, the underlying loci largely responsible for this agronomic division. We find a combination of Structured Association followed by Genomic Control to correct for population structure and inflation of the test statistic, resolved significant associations only with VRN-H1 and the VRN-H2 candidate genes, as well as two genes closely linked to VRN-H1 (HvCSFs1 and HvPHYC). CONCLUSION: We show that, after employing appropriate statistical methods to correct for population sub-structure, the genome-wide partitioning effect of allelic status at VRN-H1 and VRN-H2 does not result in the high levels of spurious association expected to occur in highly structured samples. Furthermore, we demonstrate that both VRN-H1 and the candidate VRN-H2 genes can be identified using association mapping. Discrimination between intragenic VRN-H1 markers was achieved, indicating that candidate causative polymorphisms may be discerned and prioritised within a larger set of positive associations. This proof of concept study demonstrates the feasibility of association mapping in barley, even within highly structured populations. A major advantage of this method is that it does not require large numbers of genome-wide markers, and is therefore suitable for fine mapping and candidate gene evaluation, especially in species for which large numbers of genetic markers are either unavailable or too costly

Determining phenological patterns associated with the onset of senescence in a wheat MAGIC mapping population

The appropriate timing of developmental transitions is critical for adapting many crops to their local climatic conditions. Therefore, understanding the genetic basis of different aspects of phenology could be useful in highlighting mechanisms underpinning adaptation, with implications in breeding for climate change. For bread wheat (Triticum aestivum), the transition from vegetative to reproductive growth, the start and rate of leaf senescence and the relative timing of different stages of flowering and grain filling all contribute to plant performance. In this study we screened under Smart house conditions a large, multi-founder “NIAB elite MAGIC” wheat population, to evaluate the genetic elements that influence the timing of developmental stages in European elite varieties. This panel of recombinant inbred lines was derived from eight parents that are or recently have been grown commercially in the UK and Northern Europe. We undertook a detailed temporal phenotypic analysis under Smart house conditions of the population and its parents, to try to identify known or novel Quantitative Trait Loci associated with variation in the timing of key phenological stages in senescence. This analysis resulted in the detection of QTL interactions with novel traits such the time between “half of ear emergence above flag leaf ligule” and the onset of senescence at the flag leaf as well as traits associated with plant morphology such as stem height. In addition, strong correlations between several traits and the onset of senescence of the flag leaf were identified. This work establishes the value of systematically phenotyping genetically unstructured populations to reveal the genetic architecture underlying morphological variation in commercial wheat

A large-scale pedigree resource of wheat reveals evidence for adaptation and selection by breeders

<div><p>Information on crop pedigrees can be used to help maximise genetic gain in crop breeding and allow efficient management of genetic resources. We present a pedigree resource of 2,657 wheat (<i>Triticum aestivum</i> L.) genotypes originating from 38 countries, representing more than a century of breeding and variety development. Visualisation of the pedigree enables illustration of the key developments in United Kingdom wheat breeding, highlights the wide genetic background of the UK wheat gene pool, and facilitates tracing the origin of beneficial alleles. A relatively high correlation between pedigree- and marker-based kinship coefficients was found, which validated the pedigree and enabled identification of errors in the pedigree or marker data. Using simulations with a combination of pedigree and genotype data, we found evidence for significant effects of selection by breeders. Within crosses, genotypes are often more closely related than expected by simulations to one of the parents, which indicates selection for favourable alleles during the breeding process. Selection across the pedigree was demonstrated on a subset of the pedigree in which 110 genotyped varieties released before the year 2000 were used to simulate the distribution of marker alleles of 45 genotyped varieties released after the year 2000, in the absence of selection. Allelic diversity in the 45 varieties was found to deviate significantly from the simulated distributions at a number of loci, indicating regions under selection over this period. The identification of one of these regions as coinciding with a strong yield component quantitative trait locus (QTL) highlights both the potential of the remaining loci as wheat breeding targets for further investigation, as well as the utility of this pedigree-based methodology to identify important breeding targets in other crops. Further evidence for selection was found as greater linkage disequilibrium (LD) for observed versus simulated genotypes within all chromosomes. This difference was greater at shorter genetic distances, indicating that breeder selections have conserved beneficial linkage blocks. Collectively, this work highlights the benefits of generating detailed pedigree resources for crop species. The wheat pedigree database developed here represents a valuable community resource and will be updated as new varieties are released at <a href="https://www.niab.com/pages/id/501/UK_Wheat_varieties_Pedigree" target="_blank">https://www.niab.com/pages/id/501/UK_Wheat_varieties_Pedigree</a>.</p></div