Browsing by Subject "Hologenomic selection"

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Analysis of phosphorus utilization using the host genome and microbiota variability in Japanese quail
(2021) Vollmar, Solveig Deniece; Bennewitz, Jörn
Phosphorus (P) is an essential element for growth and performance of avian species. It is predominantly bound as phytic acids and salts (phytate) in plant seeds. Phytases and other phosphatases can harness P by cleaving P groups. Nonruminants have low endogenous phytase activity in the gastrointestinal tract, and thus, the requirement of this element is not met from exclusive plant-based diets. Therefore, mineral P or phytase enzymes are supplemented in poultry feed. Due to the finite quantities of high quality mineral P worldwide, it is of great economic interest. P supplementation is increasingly causing environmental problems. Past studies investigated the P utilization (PU) of different poultry species. They revealed a high phenotypic variation in PU among individuals. Moderate heritabilities indicates that breeding for this trait is in principle possible. The overall aim of this thesis was to gain a deeper understanding of the variability of P utilization in relation to host genetics, ileal microbiota composition and their interaction in the model species Japanese quail. The objective of chapter two was to verify whether variation in PU in quail is a heritable trait conditioned by a few quantitative trait loci (QTL) with detectable effects. For this purpose, individuals were genome-wide genotyped with a 4k SNP chip, and a linkage map was generated. Based on this map, QTL linkage analysis was performed using multimarker regression analysis in a line-crossing model to map QTL for PU. We identified a few QTL regions with significant effects. Among them was a QTL peak at Coturnix japonica chromosome (CJA) 3 for PU. Several genes were found in the region surrounding this peak, which requires further functional gene analysis. Based on these results, we hypothesized that these traits are polygenically determined due to several small QTL effects, which we could not detect significantly. The overlap of the QTL regions indicated linkage of the traits and confirmed their genetic correlations. With the aim of predicting microbiota-related host traits, chapter three examined the composition of the ileum microbiota and differential abundance analysis (DAA). Based on this study, it was shown that a sex-specific influence on microbiota composition exists. The digesta samples of all animals were dominated by five genera, which contributed to more than 70% of the total ileum microbial community. In examining the microbiota composition of each of the 50 animals with the highest and lowest PU, DAA revealed genera significantly associated with PU. In chapter four, we characterized the influence of performance-related gut microbiota to unravel the microbial architecture of the traits evaluated. The aim of this study was to determine whether the variation in PU is partly driven by the microbial community in the ileum. We used microbial mixed linear models to estimate microbiabilities (m^2). This determines the fraction of phenotypic variance that can be explained by the gut microbiota. The estimation of m^2 was 0.15 for PU and was highly significant. It was also highly significant for feed intake, body weight gain and feed per gain. This model was bivariately extended and showed a high microbial correlation of the traits. Based on both results, the ileum microbiota composition plays a substantial role in PU as well as in performance traits, and there is a considerable animal microbiota correlation, showing that the microbiota affects multiple traits. The microbial drivers of this microbial fraction were identified by applying microbiome-wide association studies (MWAS). By back-solving the microbial linear mixed model, we approximated the effect of single OTUs on the phenotypic traits from the microbial model solutions. An MWAS at the genus level uncovered several traits associated with bacterial genera. Subsequently, we assessed whether the microbial community in the ileum is a heritable host trait that can be used for breeding individuals with improved PU. In chapter five we applied QTL analysis using specific genera to examine whether they are linked with genomic SNP markers. These QTL analyses revealed a link between some microbiota species and host genomic regions of chromosomes and SNP markers. By estimating significant heritabilities for some genera, we were able to provide evidence for the hypothesis that the microbial community and microbial features are at least partially related to host genetics. We predicted the animal microbial effects on PU and correlated performance traits by applying microbial best linear unbiased predictions (M-BLUP). In addition, genomic best linear unbiased predictions (G-BLUP) were used to predict the SNP effect for the predicted animal microbial effect. A combination of those two may help to predict genomic breeding values of the microbiota effects for future hologenomic breeding programs.
Genomic and microbial analyses of quantitative traits in poultry
(2023) Haas, Valentin Peter; Bennewitz, Jörn
Feed and nutrient efficiency will become increasingly important in poultry production in the coming years. In addition to feed efficiency, particular attention is paid to phosphorus (P) in nonruminants. Especially growing animals have a high demand of P but through the low usability of plant-based P sources for nonruminants, mineral P is added to their feeds. Due to worldwide limited mineral P sources, the high environmental impact of P in excretions and high supplementation costs, a better utilization of P from feed components is required. Animals’ P utilization (PU) is known to be influenced by the host genetics and by gastrointestinal microbiota. The overall aim of this thesis was to investigate the relationships between host genetics, gastrointestinal microbiota composition and quantitative traits with the focus on PU and related traits in F2 cross Japanese quail (Coturnix japonica). Japanese quail represent a model species for agriculturally important poultry species. In Chapter one, a genetic linkage map for 4k genome-wide distributed SNPs in the study design was constructed and quantitative trait loci (QTL) linkage mapping for performance as well as bone ash traits using a multi-marker regression approach was conducted. Several genome-wide significant QTL were mapped, and subsequent single marker association analyses were performed to find trait associated marker within the significant QTL regions. The analyses revealed a polygenic nature of the traits with few significant QTL and many undetectable QTL. Some overlapping QTL regions for different traits were found, which agreed with the genetic correlations between the traits. Potential candidate genes within the discovered QTL regions were identified and discussed. Chapter two provided a new perspective on utilization and efficiency traits by incorporating gastrointestinal microbiota and investigated the links between host genetics, gastrointestinal microbiota and quantitative traits. We demonstrated the host genetic influences on parts of the microbial colonization localized in the ileum by estimating heritabilities and mapping QTL regions. From 59 bacterial genera, 24 showed a significant heritability and six genome-wide significant QTL were found. Structural equation models (SEM) were applied to determine causal relationships between the heritable part of the microbiota and efficiency traits. Furthermore, accuracies of different microbial and genomic trait predictions were compared and a hologenomic selection approach was investigated based on the host genome and the heritable part of the ileum microbiota composition. This chapter confirmed the indirect influence of host genetics via the microbiota composition on the quantitative traits. Chapter three further extended the approaches to identify causalities from chapter two. Bayesian learning algorithms were used to discover causal networks. In this approach, microbial diversity was considered as an additional quantitative trait and analyzed jointly with the efficiency traits in order to model and identify their directional relationships. The detected directional relationships were confirmed using SEM and extended to SEM association analyses to separate total SNP effects on a trait into direct or indirect SNP effects mediated by upstream traits. This chapter showed that up to one half of the total SNP effects on a trait are composed of indirect SNP effects via mediating traits. A method for detecting causal relationships between microbial and efficiency traits was established, allowing separation of direct and indirect SNP effects. Chapter four includes an invited review on the major genetic-statistical studies involving the gut microbiota information of nonruminants. The review discussed the analyses conducted in chapter one to three and places the analyses published in these chapters in the context of other statistical approaches. Chapter four completed the microbial genetic approaches published to date and discussed the potential use of microbial information in poultry and pig breeding. The general discussion includes further results not presented in any of the chapters and discusses the general findings across the chapters.
Genomische und mikrobielle Analysen von Effizienzmerkmalen beim Schwein
(2022) Weishaar, Ramona Ribanna; Bennewitz, Jörn
Most traits in animal breeding, including efficiency traits in pigs, are influenced by many genes with small effect and have moderate heritabilities between 0.1 and 0.5, which enables efficient selection. These so-called quantitative traits are influenced by genetic factors and environmental factors. The use of next-generation sequencing methods, such as 16S rRNA sequencing to analyse the gut microbiome of livestock, allows identification and analysis of the gut microbiota. It has been shown that the composition of the microbiota in the gastrointestinal tract is heritable and has an influence on efficiency traits. Thus, the animal genome influences the phenotype not only directly by altering metabolic pathways, but also indirectly by changing the composition of the microbiota. This increases the interest in implementing gut microbiota into existing breeding strategies as an explanatory variable. The potential of an efficient utilization and absorption of nutrients varies between individuals. Differences in nutrient absorption depend on feed intake, digestion of dietary components in the stomach and intestine, and intake of digested nutrients from the gastrointestinal tract into blood and lymphatic vessels. Undigested nitrogen is excreted as urea and can be detected by blood urea nitrogen (BUN). The BUN is correlated with efficiency traits and there exist differences between pig breeds. Thus, therefore the BUN would be conceivable as an easier recordable trait for nitrogen utilisation efficiency in pig breeding. In the first chapter of this study, an existing data set of the Department for Animal Genetics and Breeding of the University of Hohenheim was used. This is a data set with 207 phenotyped and genotyped Piétrain sows. The relationship between gut microbial composition, efficiency traits and the porcine genome is investigated using quantitative genetic methods. The heritabilities of the traits FVW, RFI, TZ, and FI ranged from 0.11 to 0.47. The microbiabilities of the traits were significant and ranged from 0.16 to 0.45. In a further step, the previously generated microbial animal effects were used as observation vector for a genomic mixed model. Subsequently, heritabilities for the microbial animal effect were estimated, ranging from 0.20 to 0.61. The similarity of the heritabilities and microbiabilities suggests that the traits are influenced to a similar extent by both genetics and gut microbiota and that the microbial animal effect is determined by the host. These results are underlined by the identification of genera and phyla with significant effects on efficiency traits. The microbial architecture of the traits demonstrated a poly-microbial nature, there are many OTUs with small effects involved in the variation of the observed traits. Genomic Best Linear Unbiased Predictions (G-BLUP) and Microbial Best Linear Unbiased Predictions (M-BLUP) were performed to predict complex traits. The accuracies of M-BLUP and G-BLUP were all in a similar range between 0.14-0.41. This shows that gut microbiota could be used to predict performance traits or be included as a variable in the existing models of breeding value estimation to realize an increase in accuracies. The second part of the paper analysed a dataset from a research project called "ProtiPig". The data set included 475 sows and castrates of crossbreds of German Landrace x Piétrain and was analysed for protein utilization efficiency and nitrogen(N)-utilization efficiency. N-utilization efficiency is a trait that is difficult to record. Because conventional metabolic cage methods are a very complex procedure and difficult to integrate in the standard recording, it was tested whether the BUN is suitable as a proxy trait. Moderate to medium heritabilities could be estimated for all traits and ranged from 0.13 to 0.49. The genome-wide association studies showed that the traits were polygenic. For the BUN, SNPs could be detected that were above the genome-wide significance level. Significant genetic and phenotypic correlations were found between some traits. In particular, the heritabilities of BUNs and the significant genetic correlation between BUN and N-utilization efficiency indicate an opportunity to use the BUN to select for improved N-utilization efficiency. Before the research results generated here can be implemented in breeding practice, further questions must be clarified. In addition, a larger number of animals is needed to validate the results. The results presented here demonstrate the potential of microbial-assisted breeding value estimation and the use of BUN to identify selection candidates for breeding for increased efficiency.