Optimum allocation of conservation funds and choice of conservation programs for a set of African cattle breeds

<p>Abstract</p> <p>Although funds for livestock conservation are limited there is little known about the optimal allocation of conservation funds. A new algorithm was used to allocate Mio US$1, 2, 3, 5 or unlimited funds, discounted over 50 years, on 23 African cattle breeds conserved with four different possible conservation programs. Additionally, Mio US$ 1 was preferably allocated to breeds with special traits. The conceptional <it>in situ </it>conservation programs strongly involve breeders and give them part of the responsibility for the conservation of the breed. Therefore, the pure <it>in situ </it>conservation was more efficient than cryoconservation or combined <it>in situ </it>and cryoconservation. The average annual discounted conservation cost for a breed can be as low as US$1000 to US$ 4400 depending on the design of the conservation program and the economic situation of the country of conservation. The choice of the breeds and the optimal conservation program and the amount of money allocated to each breed depend on many factors such as the amount of funds available, the conservation potential of each breed, the effects of the conservation program as well as its cost. With Mio US$1, 64$ 10, leaving 8% of the diversity to unpredictable happenings. The suggested algorithm proved to be useful for optimal allocation of conservation funds. It allocated the funds optimally among breeds by identifying the most suited conservation program for each breed, also accounting for differences in currency exchange rates between the different countries.</p

Optimum allocation of conservation funds and choice of conservation programs for a set of African cattle breeds

Although funds for livestock conservation are limited there is little known about the optimal allocation of conservation funds. A new algorithm was used to allocate Mio US$1, 2, 3, 5 or unlimited funds, discounted over 50 years, on 23 African cattle breeds conserved with four different possible conservation programs. Additionally, Mio US$ 1 was preferably allocated to breeds with special traits. The conceptional in situ conservation programs strongly involve breeders and give them part of the responsibility for the conservation of the breed. Therefore, the pure in situ conservation was more efficient than cryoconservation or combined in situ and cryoconservation. The average annual discounted conservation cost for a breed can be as low as US$1000 to US$ 4400 depending on the design of the conservation program and the economic situation of the country of conservation. The choice of the breeds and the optimal conservation program and the amount of money allocated to each breed depend on many factors such as the amount of funds available, the conservation potential of each breed, the effects of the conservation program as well as its cost. With Mio US$1, 64$ 10, leaving 8% of the diversity to unpredictable happenings. The suggested algorithm proved to be useful for optimal allocation of conservation funds. It allocated the funds optimally among breeds by identifying the most suited conservation program for each breed, also accounting for differences in currency exchange rates between the different countries

Linkage disequilibrium reveals different demographic history in egg laying chickens

<p>Abstract</p> <p>Background</p> <p>The availability of larger-scale SNP data sets in the chicken genome allows to achieve a higher resolution of the pattern of linkage disequilibrium (LD). In this study, 36 k and 57 k genotypes from two independent genotyping chips were used to systematically characterize genome-wide extent and structure of LD in the genome of four chicken populations. In total, we analyzed genotypes of 454 animals from two commercial and two experimental populations of white and brown layers which allows to some extent a generalization of the results.</p> <p>Results</p> <p>The number of usable SNPs in this study was 19 k to 37 k in brown layers and 8 k to 19 k in white layers. Our analyzes showed a large difference of LD between the lines of white and brown layers. A mean value of <it>r²</it>= 0.73 ± 0.36 was observed in pair-wise distances of < 25 Kb for commercial white layers, and it dropped to 0.60 ± 0.38 with distances of 75 to 120 Kb, the interval which includes the average inter-marker space in this line. In contrast, an overall mean value of <it>r²= </it>0.32 ± 0.33 was observed for SNPs less than 25 Kb apart from each other and dropped to 0.21 ± 0.26 at a distance of 100 kb in commercial brown layers. There was a remarkable similarity of the LD patterns among the two populations of white layers. The same was true for the two populations of brown layers, while the LD pattern between white and brown layers was clearly different. Inferring the population demographic history from LD data resulted in a larger effective population size in brown than white populations, reflecting less inbreeding among brown compared to white egg layers.</p> <p>Conclusions</p> <p>We report comprehensive LD map statistics for the genome of egg laying chickens with an up to 3 times higher resolution compared to the maps available so far. The results were found to be consistent between analyzes based on the parallel SNP chips and across different populations (commercial vs. experimental) within the brown and the white layers. It is concluded that the current density of usable markers in this study is sufficient for association mapping and the implementation of genomic selection in these populations to achieve a similar accuracy as in implementations of association mapping and genomic selection in mammalian farm animals.</p

Comparison of statistical procedures for estimating polygenic effects using dense genome-wide marker data

In this study we compared different statistical procedures for estimating SNP effects using the simulated data set from the XII QTL-MAS workshop. Five procedures were considered and tested in a reference population, i.e., the first four generations, from which phenotypes and genotypes were available. The procedures can be interpreted as variants of ridge regression, with different ways for defining the shrinkage parameter. Comparisons were made with respect to the correlation between genomic and conventional estimated breeding values. Moderate correlations were obtained from all methods. Two of them were used to predict genomic breeding values in the last three generations. Correlations between these and the true breeding values were also moderate. We concluded that the ridge regression procedures applied in this study did not outperform the simple use of a ratio of variances in a mixed model method, both providing moderate accuracies of predicted genomic breeding values

MoBPS - Modular Breeding Program Simulator

The R-package MoBPS provides a computationally efficient and flexible framework to simulate complex breeding programs and compare their economic and genetic impact. Simulations are performed on the base of individuals. MoBPS utilizes a highly efficient implementation with bit-wise data storage and matrix multiplications from the associated R-package miraculix allowing to handle large scale populations. Individual haplotypes are not stored but instead automatically derived based on points of recombination and mutations. The modular structure of MoBPS allows to combine rather coarse simulations, as needed to generate founder populations, with a very detailed modeling of todays’ complex breeding programs, making use of all available biotechnologies. MoBPS provides pre-implemented functions for common breeding practices such as optimum genetic contributions and single-step GBLUP but also allows the user to replace certain steps with personalized and/or self-written solutions

Schaffung einer umfassenden Datenbasis und Entwicklung züchterischer Strategien zur nachhaltigen Reduzierung des Schwanzbeißens in der Schweinezucht (Verbundvorhaben)

Das hier beschriebene Verbundvorhaben umfasst folgende Teilprojekte: FKZ 15NA023, FKZ 15NA085, FKZ 15NA086 und FKZ 15NA087. Ziel des Projektes war die Bestimmung von Prävalenzen von Schwanzbeißmerkmalen sowie deren ge-netischer Hintergrund zur Entwicklung von Zuchtstrategien gegen Schwanzbeißen. Schwanzboniturdaten von ca. 1750 unkupierten Tieren von der LSZ und 18,600 kupierten Tieren aus Bayern wurden berücksichtigt. Auch lagen Daten von ca. 26000 Tieren hinsichtlich Opfer-/Täter-Status sowie Verhaltensnoten und Leistungsdaten von BHZP vor. Die höchsten Prävalenzen wurden bei den unkupierten Schweinen beobachtet, und zwar am Ende der Aufzucht: 31% Nekrose, 41% Längenverluste (62% gegen Mastende), 9,5% DBH, 3% Blutung und 3,1% Schwellung. Auch in Bayern wurden die höchsten Prävalenzen am Ende der Aufzucht beobachtet mit 14% DBH und 1,6% Blutung. Inzidenzen von Nekrose und Längenverlusten lagen unter 1%. Bei Saug-ferkeln wurden Akren-Nekrosen an den Klauen (60%), Zitzen (12%), Kronsaum (3%) und Vulva (3%) beobachtet. Die Heritabilitätsschätzwerte für Schwanzverletzungsmerkmale und Schwanzbeißen variierten von 0 bis 0,22; die Heritabilität von Klauennekrose war 0,36. Höhere Prävalenzen von Opfern bzw. Tätern wurden bei Mutterrassen beobachtet: Bezogen auf die Anzahl Tiere lag die Prävalenz von Opfern bei DE und DL bei 5%-6%; bei PI und DU war sie <1%; die Prävalenz der beobachteten Täter war ~1% bei DE und DL und <0,1% bei PI und DU. Die Möglichkeit einer indirekten Selektion auf der Basis des Verhaltens unter Stress wurde untersucht. Die geschätzten Heritabilitäten für Handhabungsverhalten lagen zwischen 0,19 und 0,28. Die geneti-schen Korrelationen mit Leistungsmerkmalen waren niedrig. Die Zuchtplanungssimulation mit der Verhaltensnote als korreliertes Hilfsmerkmal zeigte nur bei engeren genetischen Korrelationen einen Rückgang von Schwanzbeißen

Predicting Genetic Values: A Kernel-Based Best Linear Unbiased Prediction With Genomic Data

Genomic data provide a valuable source of information for modeling covariance structures, allowing a more accurate prediction of total genetic values (GVs). We apply the kriging concept, originally developed in the geostatistical context for predictions in the low-dimensional space, to the high-dimensional space spanned by genomic single nucleotide polymorphism (SNP) vectors and study its properties in different gene-action scenarios. Two different kriging methods [“universal kriging” (UK) and “simple kriging” (SK)] are presented. As a novelty, we suggest use of the family of Matérn covariance functions to model the covariance structure of SNP vectors. A genomic best linear unbiased prediction (GBLUP) is applied as a reference method. The three approaches are compared in a whole-genome simulation study considering additive, additive-dominance, and epistatic gene-action models. Predictive performance is measured in terms of correlation between true and predicted GVs and average true GVs of the individuals ranked best by prediction. We show that UK outperforms GBLUP in the presence of dominance and epistatic effects. In a limiting case, it is shown that the genomic covariance structure proposed by VanRaden (2008) can be considered as a covariance function with corresponding quadratic variogram. We also prove theoretically that if a specific linear relationship exists between covariance matrices for two linear mixed models, the GVs resulting from BLUP are linked by a scaling factor. Finally, the relation of kriging to other models is discussed and further options for modeling the covariance structure, which might be more appropriate in the genomic context, are suggested

Integrating Gene Expression Data Into Genomic Prediction

Gene expression profiles potentially hold valuable information for the prediction of breeding values and phenotypes. In this study, the utility of transcriptome data for phenotype prediction was tested with 185 inbred lines of Drosophila melanogaster for nine traits in two sexes. We incorporated the transcriptome data into genomic prediction via two methods: GTBLUP and GRBLUP, both combining single nucleotide polymorphisms (SNPs) and transcriptome data. The genotypic data was used to construct the common additive genomic relationship, which was used in genomic best linear unbiased prediction (GBLUP) or jointly in a linear mixed model with a transcriptome-based linear kernel (GTBLUP), or with a transcriptome-based Gaussian kernel (GRBLUP). We studied the predictive ability of the models and discuss a concept of “omics-augmented broad sense heritability” for the multi-omics era. For most traits, GRBLUP and GBLUP provided similar predictive abilities, but GRBLUP explained more of the phenotypic variance. There was only one trait (olfactory perception to Ethyl Butyrate in females) in which the predictive ability of GRBLUP (0.23) was significantly higher than the predictive ability of GBLUP (0.21). Our results suggest that accounting for transcriptome data has the potential to improve genomic predictions if transcriptome data can be included on a larger scale