A framework for gene mapping in wheat demonstrated using the Yr7 yellow rust resistance gene

We used three approaches to map the yellow rust resistance gene Yr7 and identify associated SNPs in wheat. First, we used a traditional QTL mapping approach using a double haploid (DH) population and mapped Yr7 to a low-recombination region of chromosome 2B. To fine map the QTL, we then used an association mapping panel. Both populations were SNP array genotyped allowing alignment of QTL and genome-wide association scans based on common segregating SNPs. Analysis of the association panel spanning the QTL interval, narrowed the interval down to a single haplotype block. Finally, we used mapping-by-sequencing of resistant and susceptible DH bulks to identify a candidate gene in the interval showing high homology to a previously suggested Yr7 candidate and to populate the Yr7 interval with a higher density of polymorphisms. We highlight the power of combining mapping-by-sequencing, delivering a complete list of gene-based segregating polymorphisms in the interval with the high recombination, low LD precision of the association mapping panel. Our mapping-by-sequencing methodology is applicable to any trait and our results validate the approach in wheat, where with a near complete reference genome sequence, we are able to define a small interval containing the causative gene

Genomic prediction and quantitative trait locus discovery in a cassava training population constructed from multiple breeding stages

Open Access Article; Published online: 11 Dec 2019Assembly of a training population (TP) is an important component of effective genomic selection‐based breeding programs. In this study, we examined the power of diverse germplasm assembled from two cassava (Manihot esculenta Crantz) breeding programs in Tanzania at different breeding stages to predict traits and discover quantitative trait loci (QTL). This is the first genomic selection and genome‐wide association study (GWAS) on Tanzanian cassava data. We detected QTL associated with cassava mosaic disease (CMD) resistance on chromosomes 12 and 16; QTL conferring resistance to cassava brown streak disease (CBSD) on chromosomes 9 and 11; and QTL on chromosomes 2, 3, 8, and 10 associated with resistance to CBSD for root necrosis. We detected a QTL on chromosome 4 and two QTL on chromosome 12 conferring dual resistance to CMD and CBSD. The use of clones in the same stage to construct TPs provided higher trait prediction accuracy than TPs with a mixture of clones from multiple breeding stages. Moreover, clones in the early breeding stage provided more reliable trait prediction accuracy and are better candidates for constructing a TP. Although larger TP sizes have been associated with improved accuracy, in this study, adding clones from Kibaha to those from Ukiriguru and vice versa did not improve the prediction accuracy of either population. Including the Ugandan TP in either population did not improve trait prediction accuracy. This study applied genomic prediction to understand the implications of constructing TP from clones at different breeding stages pooled from different locations on trait accuracy

Explaining additional genetic variation in complex traits

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of complex traits, discovering >6000 variants associated with >500 quantitative traits and common complex diseases in humans. The associations identified so far represent only a fraction of those that influence phenotype, because there are likely to be many variants across the entire frequency spectrum, each of which influences multiple traits, with only a small average contribution to the phenotypic variance. This presents a considerable challenge to further dissection of the remaining unexplained genetic variance within populations, which limits our ability to predict disease risk, identify new drug targets, improve and maintain food sources, and understand natural diversity. This challenge will be met within the current framework through larger sample size, better phenotyping, including recording of nongenetic risk factors, focused study designs, and an integration of multiple sources of phenotypic and genetic information. The current evidence supports the application of quantitative genetic approaches, and we argue that one should retain simpler theories until simplicity can be traded for greater explanatory power

Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology.

Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; p ≤ 5 × 10-8) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 93 novel loci, significantly expanding our understanding of the genetic etiologies of glaucoma and VCDR. Pathway analyses support the biological significance of the novel hits to VCDR: select loci near genes involved in neuronal and synaptic biology or harboring variants are known to cause severe Mendelian ophthalmic disease. Finally, the ML-based GWAS results significantly improve polygenic prediction of VCDR and primary open-angle glaucoma in the independent EPIC-Norfolk cohort

Genomic analyses of behavior traits in laying hen lines divergently selected for feather pecking

Feather pecking is a longstanding problem in commercial layer flocks. It often causes injured birds and even cannibalism. In the past, hens were beak trimmed to reduce feather pecking. Nevertheless, this procedure is already prohibited in some EU countries. Hence, a solution to this problem is urgently needed. The experimental populations analyzed in this thesis were formed by hens based on a White Leghorn layer strain which were divergently selected for high and low feather pecking since 1995. The first experimental population of this thesis was an F2 cross of about 900 hens which was established of the 10th generation of the pure selection lines. The second population consisted of about 500 hens of the 15th generation of these two lines. The aim of this thesis was to gain further knowledge of the genetic background of feather pecking and its relation to additional behavior traits and the gut microbiome. In chapter one, a novel model to detect extreme feather pecking hens was developed. Therefore, a mixture of two negative binomial distributions was fitted to feather pecking data of the F2 cross. With the estimated parameters, the trait posterior probability of a hen to belong to the extreme feather pecking subgroup (pEFP) was calculated. The fear tests tonic immobility and emerge box were conducted at juvenile and adult age of the hens to relate fearfulness to pEFP. After dichotomization, all traits were analyzed in a multivariate threshold model and subsequent genomewide association studies (GWAS) were performed. The fit revealed that extreme feather peckers made up a proportion of about one third of the hens. The new trait pEFP has a medium heritability of 0.35 and is positively correlated with the fear traits. Breeding for this new trait could be an option to reduce the proportion of extreme feather peckers. An index of fear related traits might serve as a proxy to breed indirectly against pEFP. In chapter two, the model to detect extreme feather pecking hens was applied to the pure selection lines. After calculation of the trait pEFP, GWAS with a subsequent post GWAS analysis were performed. Additionally, to find genomic regions influencing feather pecking, selection signatures were mapped by applying the intra-population iHS and the inter-population FST approach. Mapping of selection signatures revealed no clear regions under selection. GWAS revealed a region on chromosome one, where the existence of a quantitative trait locus (QTL) influencing feather pecking is likely. The candidate genes found in this region are a part of the GABAergic system. Despite the polygenic nature of feather pecking, selection on these candidate genes may reduce the extreme occurrence of it. In chapter three, the relation between agonistic behavior and feather pecking was analyzed. Therefore, the active parts of the traits (delivery of feather pecking, aggressive pecking or threatening) as well as the passive parts (reception of the traits) were considered. These groups of traits were additionally summarized by means of an index formation which led to the two additional traits Activity and Passivity, because all these behaviors are undesired in their excessive manifestations. Moreover, Indices were built by subtracting the passive traits from the respective active traits to obtain the feather pecking index, the aggression index and the threat index. Phenotypic correlations were estimated between all traits which were followed by heritability estimations and GWAS. Feather pecking is significantly positively correlated with the agonistic traits in both lines. The active traits and the feather pecking index show medium heritabilities. Hence, selection on high feather pecking leads to an increase of agonistic behavior whereas the correlation probably depends on the phase of establishing the social hierarchy and might disappear, after a stable ranking is established. GWAS revealed that the heritable traits in this study seem to be typical quantitative traits. Chapter four provides the analyses of the gut microbial composition of the two feather pecking lines, followed by the estimation of microbiabilities for feather pecking and the two agonistic behavior traits, to study the influence of the gut microbiome on behavior. Microbiota samples from digesta and mucosa were taken from ileum and caecum. The microbial communities were determined by using 16S RNA gene sequencing techniques. Although both lines differ significantly in some fractions of their gut microbial composition, the microbial animal effects were mostly negligibly small. Thus, the calculated microbiabilities were close to zero and not significant in both lines and for all traits investigated. Hence, trait variations were not affected by the gut microbial composition in both feather pecking lines. The thesis ends with a general discussion where additional results of a meta-analysis of pEFP and breeding strategies against feather pecking are considered.Federpicken ist ein lange bestehendes Problem in kommerziellen Legehennenherden. Es führt häufig zu Verletzungen und sogar Kannibalismus. In der Vergangenheit wurden Schnäbel gekürzt, um das Federpicken zu reduzieren. Da dieses Verfahren in einigen EU-Ländern bereits verboten ist, ist eine Lösung dringend erforderlich. Die analysierten Versuchspopulationen bildeten Hennen, die auf einer Weißen Leghorn Legerasse basierten und seit 1995 divergent für hohes und niedriges Federpicken selektiert wurden. Die erste Versuchspopulation war eine F2-Kreuzung von etwa 900 Hennen, die aus der 10. Generation der reinen Selektionslinien gebildet wurde. Die zweite Population bestand aus etwa 500 Hennen der 15. Generation dieser beiden Linien. Das Ziel war es, weitere Erkenntnisse über den genetischen Hintergrund des Federpickens und dessen Beziehung zu weiteren Verhaltensmerkmalen sowie dem Darmmikrobiom zu gewinnen. Im ersten Kapitel wurde ein neuartiges Modell zum Nachweis extremen Federpickens ausgearbeitet. Dazu wurde eine Mischung aus zwei negativen Binomialverteilungen an die Federpickdaten der F2-Kreuzung angepasst. Mit den geschätzten Parametern wurde das Merkmal die a posteriori Wahrscheinlichkeit einer Henne, zur Untergruppe der extremen Federpicker zu gehören (pEFP), berechnet. Die Furchttests tonische Immobilität und Emerge Box wurden in juvenilem und adultem Alter durchgeführt, um die Furcht mit pEFP in Beziehung zu setzen. Nach der Dichotomisierung wurden alle Merkmale in einem multivariaten Schwellenwertmodell analysiert und anschließend genomweite Assoziationsstudien (GWAS) durchgeführt. Extreme Federpicker machten etwa ein Drittel der Hennen aus. Das neue Merkmal pEFP hat eine mittlere Heritabilität von 0,35 und ist positiv mit den Furchtmerkmalen korreliert. Die Züchtung dieses neuen Merkmals könnte den Anteil extremer Federpicker reduzieren. Ein Index der furchtbezogenen Merkmale könnte als Hilfsmerkmal dienen, um indirekt gegen pEFP zu züchten. Im zweiten Kapitel wurde das Modell zum Nachweis extremer Federpicker auf die zwei reinen Selektionslinien angewandt. Nach der Berechnung des Merkmals pEFP wurden GWAS mit einer anschließenden post GWAS Analyse durchgeführt. Um zusätzlich genomische Regionen zu detektieren die das Federpicken beeinflussen, wurden Selektionssignaturen mittels intra- (iHS) und inter-Populations-Ansatz (FST) kartiert. Diese Kartierung ergab keine eindeutigen Regionen, an denen Selektion stattgefunden hat. Die GWAS zeigte eine Region auf Chromosom eins, in der die Existenz eines quantitative trait locus, welcher Federpicken beeinflusst, wahrscheinlich ist. Die gefundenen Kandidatengene sind ein Teil des GABA-Systems. Trotz der polygenen Natur des Merkmals Federpicken könnte die Selektion auf diese Kandidatengene das extreme Auftreten des Federpickens reduzieren. In Kapitel drei wurde die Beziehung zwischen agonistischem Verhalten und Federpicken analysiert. Dabei wurden die aktiven (Ausübung des Federpickens, aggressiven Pickens oder Drohens) und die passiven (Empfang der Merkmale) Anteile der Merkmale betrachtet. Diese Merkmalsgruppen wurden in einer Indexbildung zusammengefasst, die zu den beiden Merkmalen Aktivität und Passivität führte, da all diese Verhaltensweisen in ihrer exzessiven Ausprägung unerwünscht sind. Es wurden Indizes gebildet, indem die passiven von den jeweiligen aktiven Merkmalen subtrahiert wurden, um den Federpick-, Aggressions- und Bedrohungsindex zu erhalten. Zwischen allen Merkmalen wurden phänotypische Korrelationen und Heritabilitäten geschätzt und GWAS angewandt. Federpicken ist signifikant positiv mit den agonistischen Merkmalen in beiden Linien korreliert. Die aktiven Merkmale und der Federpick-Index zeigen mittlere Heritabilitäten. Daher führt die Selektion auf hohes Federpicken zu einer Zunahme des agonistischen Verhaltens, wobei die Korrelation wahrscheinlich von der Phase der Etablierung der sozialen Hierarchie abhängt. Die GWAS ergab, dass es sich um typische quantitative Merkmale zu handeln scheint. Kapitel vier enthält die Analysen der Darmmikrobiota der beiden Linien, sowie die Schätzung der Microbiabilities für Federpicken und agonistischer Verhaltensmerkmale, um den Einfluss des Darmmikrobioms auf das Verhalten zu untersuchen. Mikrobiotaproben aus der Digesta und Mucosa wurden aus Ileum und Caecum entnommen. Die Mikrobengemeinschaften wurden mit Hilfe von 16S-RNA Gen-Sequenzierungstechniken bestimmt. Obwohl sich beide Linien in einigen Fraktionen ihrer mikrobiellen Zusammensetzung im Darm signifikant unterscheiden, waren die mikrobiellen Tiereffekte meist vernachlässigbar gering. Somit waren die berechneten Microbiabilities nahe Null und nicht signifikant. Dies bedeutet, dass die Merkmalsvariation nicht durch die Zusammensetzung des Darmmikrobioms beeinflusst wurde. Die Dissertation endet mit einer allgemeinen Diskussion, in der zusätzliche Ergebnisse einer Meta-Analyse sowie Zuchtstrategien gegen Federpicken berücksichtigt werden

Time series canopy phenotyping enables the identification of genetic variants controlling dynamic phenotypes in soybean

Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points. Yet, most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single-time-point data. Here, we used time-series phenotypic data collected with an unmanned aircraft system for a large panel of soybean (Glycine max (L.) Merr.) varieties to identify previously uncharacterized loci. Specifically, we focused on the dissection of canopy coverage (CC) variation from this rich data set. We also inferred the speed of canopy closure, an additional dimension of CC, from the time-series data, as it may represent an important trait for weed control. Genome-wide association studies (GWASs) identified 35 loci exhibiting dynamic associations with CC across developmental stages. The time-series data enabled the identification of 10 known flowering time and plant height quantitative trait loci (QTLs) detected in previous studies of adult plants and the identification of novel QTLs influencing CC. These novel QTLs were disproportionately likely to act earlier in development, which may explain why they were missed in previous single-time-point studies. Moreover, this time-series data set contributed to the high accuracy of the GWASs, which we evaluated by permutation tests, as evidenced by the repeated identification of loci across multiple time points. Two novel loci showed evidence of adaptive selection during domestication, with different genotypes/haplotypes favored in different geographic regions. In summary, the time-series data, with soybean CC as an example, improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves

New Approaches to Use Genomics, Field Traits, and High-throughput Phenotyping for Gene Discovery in Maize (\u3ci\u3eZea mays\u3c/i\u3e)

Maize is one of major crop species over the world. With lots of genetic resources and genomic tools, maize also serves as a model species to understand genetic diversity, facilitate the development of trait extraction algorithms and map candidate functional genes. Since the first version of widely used B73 reference genome was released, independent research groups in the maize community propagated seeds themselves for further research purposes. However, unexpected or occasional contamination may happen during this process. The first study in this thesis used public RNA-seq data of B73 from 27 research groups across three countries for calling single nucleotide polymorphisms (SNP). Those SNPs were applied for investigating the distance of 27 maize B73 samples from the reference genome and three major clades were defined for determining their original sources. On the other side, maize is a plant with clear plant architecture. The second study was to employ the high-throughput plant phenotyping to dissect plant phenotypes using computer vision methods. A total of 32 maize inbreds distributed from the Genomes to Fields project were captured images in daily by 4 types of cameras (RGB, Hyperspectral, Fluorescence and Thermal-IR) for approximate 1 month. Differences between computer vision measurements and manual measurements about the plant fresh biomass were evaluated. Broad-sense heritability was estimated for extracted measurements from images. The expanded types of plant phenotype from the perspective of imaging provided a broader range of opportunities for connecting phenotypic variants with genetic variants. The third study utilized the phenome-wide variants in maize Goodman-Buckler 282 association panel to scan and associate with genetic variants of annotated genes along the maize genome. Genes detected by the proposed model, Genome-Phenome Wide Association Study (GPWAS), are significantly different from conventional GWAS detected genes. GPWAS genes tend to be more functionally conserved and more similar as classical maize mutants with known functions. Results from these researches assist to answer question about the genetic purity of same maize genotype. Methods developed in this thesis can also provide the valuable reference for trait discoveries from images and candidate functional gene identification using a broad set of phenotypes. Adviser: James C. Schnabl