Browsing by Subject "Genotype"

Now showing 1 - 4 of 4
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    Analyse komplexer Merkmale beim Schwein mittels SNP-Chip Genotypen, Darmmikrobiota- und Genexpressionsdaten
    (2017) Maushammer, Maria; Bennewitz, Jörn
    In the present scientific research, SNP chip genotypes, gut microbiota and gene expression data were used for analysing complex traits in a Piétrain population. These data were collected from around 200 performance tested sows and were used for genetic and microbial analyses of complex trait as well as for structural and functional meat quality traits. The gut microbiome plays a major role in the immune system development, state of health and energy supply of the host. Quantitative-genetic methods were applied to analyse the interrelationship between pig gut microbiota compositions, complex traits (daily gain, feed conversion and feed intake) and pig genomes. The specific aims were to characterize the gut microbiota of the pigs, to analyse the effects of host genetics on gut microbial composition, and to investigate the role of gut microbial composition on the host’s complex traits. The pigs were genotyped with a standard 60K SNP chip. Microbial composition was characterized by 16S rRNA gene amplicon sequencing technology. Ten out of 51 investigated bacterial genera showed a significant host heritability, ranging from 0.32 to 0.57. Conducting genome wide association analysis showed associations of 22 SNPs and six bacterial genera. The potential candidate genes identified are involved in the immune system, mucosa structure and secretion of digestive juice. These results show, that parts of the gut microbiota are heritable and that the gut microbiome can be seen as quantitative trait. Microbial mixed linear models were applied to estimate the microbiota variance for each of the investigated traits. The fraction of phenotypic variance explained by the microbial variance was 0.28, 0.21, and 0.16 for daily gain, feed conversion, and feed intake, respectively. The SNP data and the microbiota data were used to predict the phenotypes of the traits using both, genomic best linear unbiased prediction (G-BLUP) and microbial best linear unbiased prediction (M-BLUP) methods. The prediction accuracies of G-BLUP were 0.35, 0.23, and 0.20 for daily gain, feed conversion, and feed intake, respectively. The corresponding prediction accuracies of M-BLUP were 0.41, 0.33, and 0.33. Thus, the gut microbiota can be seen as an explaining variable for complex traits like daily gain, feed conversion and feed intake. In addition, in combination with meat quality traits, transcript levels of muscle tissue were analysed at time of slaughtering. This study should give an insight into the biological processes involved in meat quality characteristics. The aims were to functionally characterise differentially expressed genes, to link the functional information with structural information obtained from GWAS, and to identify potential candidate genes based on these results. An important meat quality trait is the intramuscular fat content, since it affects the juiciness, the taste and the tenderness of the meat. Another important trait is drip loss which causes not only a loss of weight but also a loss of important proteins. Both traits have an impact on the consumer acceptance of fresh meat products. For each of the two traits, eight discordant sibling pairs were selected out of the Piétrain sample and were used for genome-wide gene expression analyses. Thirty five and 114 genes were identified as differentially expressed and trait correlated genes for intramuscular fat content and drip loss, respectively. On the basis of functional annotation, gene groups belonging to the energy metabolism of the mitochondria, the immune response and the metabolism of fat, were associated with intramuscular fat content. Gene groups associated with protein ubiquitination, mitochondrial metabolism, and muscle structural proteins were associated with drip loss. Furthermore, genome-wide association analyses were carried out for these traits and their results were linked to the genome-wide expression analysis by functional annotation. In this context, intramuscular fat was related to muscle contraction, transmembrane transport and nucleotide binding. Drip loss was characterized by the endomembrane system, the energy generation of cells, and phosphorus metabolic processes. Three and four potential candidate genes were identified for intramuscular fat content and drip loss, respectively.
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    Genotypic responses of upland rice to an altitudinal gradient
    (2012) Shrestha, Suchit Prasad; Asch, Folkard
    Adaptation strategies are required for crops to cope with changing climate. The impact of climate change on crop production is not straight forward to predict as extreme events comprise multiple combination of abiotic stresses and their impact differs in crop physiological growth stages. The mechanism on how new abiotic stress combinations translate into phenology and yield, and which cultivars are better adapted is yet unclear. Crop growth models are available that have been parameterized and validated for some aspects of possible climate change scenarios but in view of complex interactions crop responses to climate change are difficult to predict. On the other hand, prediction of the complex ideotype trait combinations may be interesting for breeders but physiological models are required that are well validated for the target environments. In upland rice grown under rainfed conditions without surface water accumulation methane emission is negligible and therefore greenhouse gas emission much lower compared to irrigated paddy rice systems. In addition, growing demand for rice and the increasing pressure on irrigated land leads to development of upland rice areas to supplement irrigated rice. Therefore, this study investigates genetically diverse upland rice genotypes from a wide range of origins across altitudinal gradient locations. The main objective of this study is to investigate genotypic responses of upland rice to different environments in order to calibrate crop growth models, which allow the evaluation of effects of climate change on upland rice systems. Multi-locational field (three locations: 1625, 965 and 25 m asl) trials comprising non-replicated phenological plots with five sowing dates (monthly staggered) in two consecutive years creating thirty different environments, and replicated physiological yield trials with two sowing dates (monthly staggered; early and late sowing) in two consecutive years creating twelve different environments were established in Madagascar. Ten contrasting upland rice genotypes were included in both field trials. Meteorological data were recorded on a daily basis during trial periods. Developmental stages were observed in the phenological plots; in the physiological plots yield and yield components were recorded. In addition, greenhouse trials were conducted with one upland rice genotype subjected to seven N-supply levels in a hydroponic system at the University of Hohenheim in order to understand the relationship between chlorophyll index, photochemical reflectance index and chlorophyll fluorescence parameters. Various statistical tools were applied to analyse field and greenhouse data sets. The phenological trial showed that duration to flowering was 117, 81 and 67 d in high (HA), mid (MA) and low (LA) altitudinal locations respectively. 90% of the total variance was explained by location when pooled over genotype, location, sowing dates and year. In HA, factors such as genotype, sowing date and year equally contributed to the observed variability whereas in MA year was the most determining factor and genotype had no significant contribution. Similarly, in LA sowing date was the main influencing factor and year had no significant effect. Aggregated data over locations, sowing dates and years indicated that each degree Celsius rise in mean air temperature decreased crop duration by 5 to 9 days depending upon genotype. Basic genotypic thermal constants Tbase ranged from 9.8 to 13.9 °C and Tsum from 816 to 1220 °C d within the selected genotypes. Cold tolerant genotypes were less affected by lower Tmin (14 °C) at booting to heading stage regarding spikelet sterility in HA, whereas others were highly affected at 15 °C (cold stress). Similarly, both cold sensitive and tolerant genotypes were affected by Tmax (above 30 °C) at flowering in MA and LA locations (heat stress). Grain yield and yield components were highly affected by location, year, sowing date, and genotypes and the interactions between these yield-determining factors were obvious. In HA, early sown cold tolerant genotypes had more than 5 t ha-1 grain yield and one month delay in sowing led to highly reduced yield whereas other genotypes had very poor yield on both sowing dates due to cold stress. In MA, yield difference between sowing date and genotypes was small (4.3 - 4.9 t ha-1). Grain yield in LA was vulnerable due to frequent tropical storms. Yield stability analysis showed that cold tolerant genotypes had above average stability. AMMI model for grain yield showed that environment and genotype by environment interactions were highly significant. Yield components determined during specific development stages of the genotype such as tillers per hill and percentage of filled spikelets were mainly influenced by environment, spikelets per panicle and thousand grain weight were influenced by genotype, and percentage of productive tillers was equally influenced by both genotype and environment. PCA biplots showed that all HA environments were equally influenced by all weather parameters with minimum air temperature having the strongest positive influence on genotypic performance. In all MA environments genotypic performance in all phenophases was strongly and positively influenced by rainfall, and strongly and negatively influenced by vapour pressure deficit, solar radiation and potential evapotranspiration. In the LA environments, main weather parameters influencing genotypic performance were maximum temperature and high rainfall accompanied by strong winds. The field measured SPAD values of the upper canopy leaves reflected the location specific N-remobilization and leaf senescence levels after flowering. Similarly, PRI values showed the abiotic stress responses among development stages and locations along the altitudinal gradient. These readings showed that genotypes were efficient in radiation use and N-remobilization after flowering in MA. The unsynchronized relationship between source (leaf) and sink (grain) explained the yield penalty. Emphasis on identification of morpho-physiological traits contributing to cold tolerance should be placed for further breeding. We conclude that genotypic responses of upland rice cultivars differed across altitudinal gradients. Genotypes that are well adapted in HA can easily be adapted in MA without yield decrease. But genotypes well adapted in MA may show a huge yield penalty in HA due to lower temperature during reproductive phase and consequently reduced sink formation. Frequent tropical storms and high temperature reduced yield potential in LA. Therefore, HA has a large potential for the future food security considering climate change scenarios. At present, MA is favorable for upland rice production systems, whereas LA is highly vulnerable and is expected to be even more vulnerable in future. Those results on genotype-specific responses to environmental conditions allow further improvement of crop models such as RIDEV and SAMARA (synthesis of SARRAH and EcoMeristem), which can be used to test a number of phenotypic traits x environments combinations to define ideotypes of upland rice varieties adapted to changing climate and cropping calendars. Genotypic responses of phyllochron, biomass production and crop growth rate, and radiation use efficiency across altitudinal gradients will be included to parameterize these models. In this regard, collaborations with AfricaRice, CIRAD and IRRI are ongoing.
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    Investigations on major gene by polygene and gene by environment interaction in German Holstein dairy cattle
    (2014) Streit, Melanie; Bennewitz, Jörn
    Putative interaction effects between DGAT1 K232A mutation and the polygenic terms (all genes except DGAT1) were investigated in chapter one. This was done for five milk production traits (milk yield, protein yield, fat yield, protein percentage and fat percentage) in the German Holstein dairy cattle population. Therefore, mixed models are used. The test for interaction relied on the comparison of polygenic variance components depending on the sire?s genotypes at DGAT1 K232A. Found substitution effects were highly significant for all traits. Significant interactions between DGAT1 K232A and the polygenic term were found for milk fat and protein percentage. These interactions could be used in breeding schemes. Depending on the DGAT1 K232A genotypes of the sample, in which the sire will be used, three polygenic breeding values of a sire can be calculated. Because the genotypes of the samples are often unknown and usually heterogeneous, this is not a practical approach. Rank correlations between the three polygenic EBVs were always above 0.95, which suggested very little re-ranking. GxE were studied in chapter two. For this, reaction norm random regression sire models were used in the German Holstein dairy cattle population. Around 2300 sires with a minimum of 50 daughters per sire and at minimum seven first-lactation test day observations per daughter were analyzed. As traits, corrected test day records for milk yield, protein yield, fat yield and somatic cell score (SCS) were used. As environmental descriptors, we used herd test day solutions (htds) for milk traits, milk energy yield or SCS. Second-order orthogonal polynomial regressions were applied to the sire effects. Results showed significant slope variances of the reaction norms, which caused a non-constant additive genetic variance across the environmental ranges considered, which pointed to the presence of minor GxE effects. When the environment improved, the additive genetic variance increased, meaning higher (lower) htds for milk traits (SCS). This was also influenced by pure scaling effects, because the non-genetic variance increased in an improved environment and the heritability was less influenced by the environment. For the environmental ranges considered in this study, GxE effects caused very little re-ranking of the sires. To obtain unbiased genetic parameters, it was important to model heterogeneous residual variances. A large genome-wide association analysis was conducted in chapter three to identify SNPs that affect general production (GP) and environmental sensitivity (ES) of milk traits. Around 13 million daughter records were used to calculate sire estimates for GP and ES with help of linear reaction norm models. Daughters were offspring from 2297 sires. The sires were genotyped with a 54k SNP chip. As environmental descriptor, the average milk energy yield performance of the herds at the time where the daughter observations were recorded was used. The sire estimates were used as observations in genome-wide association analyses using 1797 sires. With help of an independent validation set (500 sires of the same population), significant SNPs were confirmed. To separate GxE scaling and other GxE effects, the observations were log-transformed. GxE effects could be found with help of reaction norm models and numerous significant SNPs could be validated for GP and ES, whereas many SNPs affecting GP also affected ES. ES of milk traits is a typical quantitative trait, which is controlled by many genes with small effects and few genes with larger effect. Effects of some SNPs for ES were not removable by log-transformation of observations, indicating that these are not solely scaling effects. Positions of founded clusters were often in well-known candidate regions affecting milk traits. No SNPs, which show effects for GP and ES in opposite directions could be found. Environmental descriptor in GxE analyses is often modelled by average herd milk production levels. Another possibility could be the level of hygiene and udder health. In chapter four, the same models were used as in chapter three. A genome-wide association analysis was done using htds for SCS as an environmental descriptor. With help of this, several SNP clusters affecting intercept and slope could be detected and confirmed. Many SNPs or clusters affecting intercept and slope could be identified, but in total, the number of SNPs affecting intercept was larger. The same SNPs could be detected and validated with and without considering GxE in reaction norm models. Some SNPs affecting only slope were found. For slope, nearly the same SNPs could be found with SCS as an environmental descriptor as presented in chapter three, although both environmental descriptors were only slightly correlated.
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    Strategies for selecting high-yielding and broadly adapted maize hybrids for the target environment in Eastern and Southern Africa
    (2012) Windhausen, Sandra Vanessa; Melchinger, Albrecht E.
    Maize is a major food crop in Africa and primarily grown by small-holder farmers under rain-fed conditions with low fertilizer input. Projections of decreasing precipitation and increasing fertilizer prices accentuate the need to provide farmers with maize varieties tolerant to random abiotic stress, especially drought and N deficiency. Genetic improvement for the target environment in Eastern and Southern Africa can be achieved by: (i) direct selection of grain yield in random abiotic stress environments, (ii) indirect selection for a secondary trait or grain yield in optimal, low-N and/or managed stress environments, or (iii) index selection using information from all test environments. At present, the maize hybrid testing programs of the International Maize and Wheat Improvement Center (CIMMYT) select primarily for grain yield under managed stress and optimal environments and subdivide the target environment according to geographic and climatic differences. It is not known to what extend the current strategy contributes to selection gains. The same holds true for genomic prediction, a strategy that is not yet implemented into the CIMMYT maize breeding program but that may accelerate breeding progress and reduce cycle length by predicting genotype performance based on molecular markers. Regarding the different strategies mentioned for selecting high-yielding and broadly adapted maize hybrids, the breeder needs to decide which of them are most promising to increase genetic gains. Consequently, the objectives of my thesis were to (1) evaluate the potential of leaf and canopy spectral reflectance as novel secondary traits to predict grain yield across different environments, (2) estimate to what extent indirect selection in managed drought and low-N stress environments is predictive of grain yield in random abiotic stress environments, (3) investigate whether subdividing the target environment into climate, altitude, geographic, yield level or country subregions is likely to increase rates of genetic gain, and (4) evaluate the prospects of genomic prediction in the presence of population structure. The measurement of spectral reflectance (495 ? 1853 nm) of both leaves and canopy at anthesis and milk grain stage explained less than 40% of the genetic variation in grain yield after validation. Consequently, selection based on predicted grain yield is only suitable for pre-screening, while final yield evaluation will still be necessary. Nevertheless, the prospect of developing inexpensive and easy to handle devices that can provide, at anthesis, precise estimates of final grain yield warrants further research. Based on a retrospective analysis across 9 years, more than 600 trials and 448 maize hybrids, it was shown that maize hybrids were broadly adapted to climate, altitude, geographic and country subregions in Eastern and Southern Africa. Consequently, I recommend that the maize breeding programs of CIMMYT in the region should be consolidated. Within the consolidated breeding programs, genotypes should be selected for performance in low- and high yielding environments as the genotype-by-yield level interaction variance was high relative to the genetic variance and genetic correlations between low- and high-yielding environments were moderate. Genetic gains were maximized by index selection, considering the yield-level effect as fixed and appropriately weighting information from all trials. To allow better allocation of resources, locations with high occurrence of random abiotic stress need to be identified. Heritability in trials conducted at these locations may be increased by the use of row- and column designs and/or spatial adjustment. Furthermore, resources invested into managed drought trials should be maintained during early breeding stages but shifted to the conduct of low-N trials at later breeding stages. Investments in a larger number of low-N trials may increase selection gain, because performance under low-N and random abiotic stress was highly correlated and genotypes can be easily selected under different levels of soil N. Prospects are promising to accelerate breeding cycles by the use of genomic prediction. Based on two large data sets on the performance of eight breeding populations, it was shown that prediction accuracy resulted primarily from differences in mean performance of these populations. Genomic prediction may be implemented into the CIMMYT maize breeding program to predict the performance of lines from a diversity panel, segregating lines from the same or related crosses, and progenies from closed populations within a recurrent selection program. The breeding scenarios in which genomic prediction is most promising still need to be defined. Generally, the construction of larger training sets with strong relationship to the validation set and a detailed analysis of the population structure within the training and validation sets are required. In conclusion, combining index and genomic selection is the most promising strategy for providing high-yielding and broadly adapted maize genotypes for the target environments in Eastern and Southern Africa.