Genomic selection of purebreds for crossbred performance

Background

One of the main limitations of many livestock breeding programs is that selection is in pure breeds housed in high-health environments but the aim is to improve crossbred performance under field conditions. Genomic selection (GS) using high-density genotyping could be used to address this. However in crossbred populations, 1) effects of SNPs may be breed specific, and 2) linkage disequilibrium may not be restricted to markers that are tightly linked to the QTL. In this study we apply GS to select for commercial crossbred performance and compare a model with breed-specific effects of SNP alleles (BSAM) to a model where SNP effects are assumed the same across breeds (ASGM). The impact of breed relatedness (generations since separation), size of the population used for training, and marker density were evaluated. Trait phenotype was controlled by 30 QTL and had a heritability of 0.30 for crossbred individuals. A Bayesian method (Bayes-B) was used to estimate the SNP effects in the crossbred training population and the accuracy of resulting GS breeding values for commercial crossbred performance was validated in the purebred population.

Results

Results demonstrate that crossbred data can be used to evaluate purebreds for commercial crossbred performance. Accuracies based on crossbred data were generally not much lower than accuracies based on pure breed data and almost identical when the breeds crossed were closely related breeds. The accuracy of both models (ASGM and BSAM) increased with marker density and size of the training data. Accuracies of both models also tended to decrease with increasing distance between breeds. However the effect of marker density, training data size and distance between breeds differed between the two models. BSAM only performed better than AGSM when the number of markers was small (500), the number of records used for training was large (4000), and when breeds were distantly related or unrelated.

Conclusion

In conclusion, GS can be conducted in crossbred population and models that fit breed-specific effects of SNP alleles may not be necessary, especially with high marker density. This opens great opportunities for genetic improvement of purebreds for performance of their crossbred descendents in the field, without the need to track pedigrees through the system.     

Genomic selection of purebred animals for crossbred performance in the presence of dominant gene action

Background

Genomic selection is an appealing method to select purebreds for crossbred performance. In the case of crossbred records, single nucleotide polymorphism (SNP) effects can be estimated using an additive model or a breed-specific allele model. In most studies, additive gene action is assumed. However, dominance is the likely genetic basis of heterosis. Advantages of incorporating dominance in genomic selection were investigated in a two-way crossbreeding program for a trait with different magnitudes of dominance. Training was carried out only once in the simulation.

Results

When the dominance variance and heterosis were large and overdominance was present, a dominance model including both additive and dominance SNP effects gave substantially greater cumulative response to selection than the additive model. Extra response was the result of an increase in heterosis but at a cost of reduced purebred performance. When the dominance variance and heterosis were realistic but with overdominance, the advantage of the dominance model decreased but was still significant. When overdominance was absent, the dominance model was slightly favored over the additive model, but the difference in response between the models increased as the number of quantitative trait loci increased. This reveals the importance of exploiting dominance even in the absence of overdominance. When there was no dominance, response to selection for the dominance model was as high as for the additive model, indicating robustness of the dominance model. The breed-specific allele model was inferior to the dominance model in all cases and to the additive model except when the dominance variance and heterosis were large and with overdominance. However, the advantage of the dominance model over the breed-specific allele model may decrease as differences in linkage disequilibrium between the breeds increase. Retraining is expected to reduce the advantage of the dominance model over the alternatives, because in general, the advantage becomes important only after five or six generations post-training.

Conclusion

Under dominance and without retraining, genomic selection based on the dominance model is superior to the additive model and the breed-specific allele model to maximize crossbred performance through purebred selection.     

Microsatellite variation in Drosophila melanogaster and Drosophila simulans: a reciprocal test of the ascertainment bias hypothesis

Interspecific comparisons of microsatellite loci have repeatedly shown that the loci are longer and more variable in the species from which they are derived (the focal species) than are homologous loci in other (nonfocal) species. There is debate as to whether this is due to directional evolution or to an ascertainment bias during the cloning and locus selection processes. This study tests these hypotheses by performing a reciprocal study. Eighteen perfect dinucleotide microsatellite loci identified from a Drosophila simulans library screen and 18 previously identified in an identical Drosophila melanogaster library screen were used to survey natural populations of each species. No difference between focal and nonfocal species was observed for mean PCR fragment length. However, heterozygosity and number of alleles were significantly higher in the focal species than in the nonfocal species. The most common allele in the Zimbabwe population of both species was sequenced for 31 of the 36 loci. The length of the longest stretch of perfect repeat units is, on average, longer in the focal species than in the non-focal species. There is a positive correlation between the length of the longest stretch of perfect repeats and heterozygosity. The difference in heterozygosity can thus be explained by a reduction in the length of the longest stretch of perfect repeats in the nonfocal species. Furthermore, flanking-sequence length difference was noted between the two species at 58% of the loci sequenced. These data do not support the predictions of the directional-evolution hypothesis; however, consistent with the ascertainment bias hypothesis, the lower variability in nonfocal species is an artifact of the microsatellite cloning and isolation process. Our results also suggest that the magnitude of ascertainment bias for repeat unit length is a function of the microsatellite size distribution in the genomes of different species.      

Prospects for estimating nucleotide divergence with RAPDs

The technique of random amplification of polymorphic DNA (RAPD), which is simply polymerase chain reaction (PCR) amplification of genomic DNA by a single short oligonucleotide primer, produces complex patterns of anonymous polymorphic DNA fragments. The information provided by these banding patterns has proved to be of great utility for mapping and for verification of identity of bacterial strains. Here we consider whether the degree of similarity of the banding patterns can be used to estimate nucleotide diversity and nucleotide divergence. With haploid data, fragments generated by RAPD-PCR can be treated in a fashion very similar to that for restriction-fragment data. Amplification of diploid samples, on the other hand, requires consideration of the fact that presence of a band is dominant to absence of the band. After describing a method for estimating nucleotide divergence on the basis of diploid samples, we summarize the restrictions and criteria that must be met when RAPD data are used for estimating population genetic parameters.      

<Emphasis Type="Italic">α</Emphasis>-Amylase inhibitor-1 gene from <Emphasis Type="Italic">Phaseolus vulgaris</Emphasis> expressed in <Emphasis Type="Italic">Coffea arabica</Emphasis>plants inhibits α-amylases from the coffee berry borer pest

Background  

Coffee is an important crop and is crucial to the economy of many developing countries, generating around US70 billion per year. There are 115 species in the < i > Coffea < /i > genus, but only two, < i > C. arabica < /i > and < i > C. canephora < /i > , are commercially cultivated. Coffee plants are attacked by many pathogens and insect-pests, which affect not only the production of coffee but also its grain quality, reducing the commercial value of the product. The main insect-pest, the coffee berry borer ( < i > Hypotheneumus hampei < /i > ), is responsible for worldwide annual losses of around US70 billion per year. There are 115 species in the Coffea genus, but only two, C. arabica and C. canephora, are commercially cultivated. Coffee plants are attacked by many pathogens and insect-pests, which affect not only the production of coffee but also its grain quality, reducing the commercial value of the product. The main insect-pest, the coffee berry borer (Hypotheneumus hampei), is responsible for worldwide annual losses of around US500 million. The coffee berry borer exclusively damages the coffee berries, and it is mainly controlled by organochlorine insecticides that are both toxic and carcinogenic. Unfortunately, natural resistance in the genus Coffea to H. hampei has not been documented. To overcome these problems, biotechnological strategies can be used to introduce an α-amylase inhibitor gene (α-AI1), which confers resistance against the coffee berry borer insect-pest, into C. arabica plants.     

Can Nep1-like proteins form oligomers?

Nep1-like proteins (NLPs) are a novel family of microbial elicitors of plant necrosis that induce a hypersensitive-like response in dicot plants. The spatial structure and role of these proteins are yet unknown. In a paper published in BMC Plant Biology (2008; 8:50) we have proposed that the core region of Nep1-like proteins (NLPs) belong to the Cupin superfamily. Based on what is known about the Cupin superfamily, in this addendum to the paper we discuss how NLPs could form oligomers.Key words: quaternary structure, necrosis and ethylene inducing proteins, NLPs, MpNEP1, MpNEP2, NPP1, Moniliophthora perniciosa, Phytophthora parasiticaCupins may be organized as monomers, dimers, hexamers and octamers of β-barrel domains.¹ To the best of our knowledge trimers have not been detected yet. The interaction of two monomers building up a dimeric structure is basically performed by three types of interactions: hydrophobic interactions between β-strands in different subunits, salt bridges and hydrogen bonds between β-strands. In cupin dimers, the hydrophobic interactions occur between two βI strands in different subunits (Fig. 1A and B). This strand represents the central axis of rotation of the dimer as one residue in βI interacts with the corresponding residue in the other subunit (Fig. 1B). Therefore, all residues in βI must be hydrophobic, as one residue interacts with the other subunit and the next one in the sequence interacts with the interior of the protein. Charged residues in βI would disrupt such interactions. Most cupin dimers have strong hydrophobic residues such as tryptophan (W), phenylalanine (F) and methionine (M) pointing towards the own subunit (↓), while small hydrophobic residues such as leucine (L), isoleucine (I), and valine (V) point to the other subunit (↑). A particular case is leucine that interacts with other subunits, for instance, βI = liaW (positions 217–220 in Fig. 1B) and βI = LVsw of type I and II NLP consensuses, respectively. Therefore, the pattern of hydropathicity suggests that the side chain orientation is βI = l217 ↑ i218 ↓ a219 ↑ W220 ↓ d221 ↑. However we observe that just after βI there is a charged residue (aspartate D221) which would point outwards disrupting the dimer or at least making it less stable. It is interesting to observe that the requirement for a negatively charged residue at this last position is very high: 96% of all type I NLPs contains an aspartate (D) or glutamate (E) indicating an important role for it, maybe in avoiding dimerization of the NLPs. A second interesting hypothesis is as follows: several cupins are oxygenases, decarboxylases, etc. and use a negatively charged residue, such as aspartate or glutamate as proton donor.¹ Now, if the alternate pattern of side chains of the residues is βI = l217 ↓ i218 ↑ a219 ↓ W220 ↑ d221 ↓, instead of the previous one, then the aspartate or glutamate residue would point to the hydrophobic pocket and would be positioned to interact with the metal ion, as in cupins with enzymatic activity. However, there are no experimental evidences that the NLPs have enzymatic activity.Open in a separate windowFigure 1(A) Three-dimensional structure prediction for type I NLP consensus, (B) Interface between two βI strands in type I NLP consensus. From the left to the right: EF-coil with the conserved residue H162, βC and βH strands (superposed) with the conserved histidines H133 and H135 in βC, H193 and leucine L195 in βH, W220 in βI and W118 in βB. The strands in the right subunit follow the same pattern but rotated.The second type of interaction is salt bridges between charged residues in different subunits. Analyzing all interacting side chains in the 1VJ2 protein (dimer), we verify that the charged side chains of N35 and E57 (numbers in original structure) are only 2.72 Å apart. In the NLPs, this corresponds to N108^36% (Q108^60%) at the border of βB and E138^98%. The negatively charged residue D125 helps to correct the orientation of the subunits in relation to each other avoiding any disorientation. The high conservation level of these residues suggests that NLPs are dimeric structures. However, as we will see next, only hydrophobic and charged interactions are not enough to build a dimer.Garcia et al. (2007)² have used small angle X-ray scattering (SAXS) to show that, in solution, at low concentrations (<2 mg/ml) the two copies of the NLPs of Moniliophthora perniciosa, MpNEP1 and MpNEP2, exist as dimers and monomers, respectively. The same technique showed that at higher concentrations, >5 mg/ml, both proteins exist as dimers, as is the case for PpNPP1.² They also reported, based on electrophoresis analysis, that PpNPP1 and MpNEP1 exist as oligomers and MpNEP2 as monomers.² However, experiments with the PpNPP1 in size exclusion chromatography using myoglobin as size standard suggest that PpNPP1 is a monomer.³ Figure 2 compares MpNEP1, MpNEP2 and PpNPP1, where the most relevant differences in sequence are marked with asterisks (*) and are possibly related to the differences in oligomeric properties between MpNEP1 and PpNPP1 with MpNEP2. These positions are methionine M27 and leucine L35, which occur only in MpNEP2, glycine G250, which occurs only in MpNEP2 and NEP1 (Fusarium oxysporum) and lysine K31, which occurs only MpNEP2, {"type":"entrez-protein","attrs":{"text":"BAB04114","term_id":"10173008"}}BAB04114 (Bacillus halodurans) and {"type":"entrez-protein","attrs":{"text":"AAU23136","term_id":"52003194"}}AAU23136 (Bacillus licheniformis). The other residues are aspartate D28, which occurs 9 times and alanine A37 which occurs 7 times of all investigated NLPs. Thus, the sequence mdHDkiakl at the start of the NLPs seems to explain the monomeric state of MpNEP2, although at higher concentrations they form dimers. Besides the weak hydrophobic interactions, dimeric cupins and bicupins (two β barrels in the same sequence building up a dimeric-like 4d-structure) are stable structures (see Fig. 1 in ref. ⁴). By aggregating the first β-strand in the start domain of one β-barrel to the ABIDG β-sheet of the other β-barrel, composing a big ABIDGY β-sheet (Y is the first β-strand). For instance, using the bicupin 1L3J (oxalate decarboxylase) as template, the low confidence level β-strand at position 26–33 (v in H29D30 avv) in type I NLPs corresponds to the first β-strand. Since this proceeds from both barrels they can build a stable structure (see Fig. 1 in ref. ⁴). The quaternary structure is related to the presence of interaction residues in the BID β-sheet of the cupin structure. These are present in the NLPs and would enable them to form dimers.Open in a separate windowFigure 2Alignment of type I NLP consensus, PpNPP1, MpNEP1 and MpNEP2. Solid line boxes are β-strands, double line boxes are α-helices. The sequence positions marked with asterisks (*) are possibly related to the differences in oligomeric properties between MpNEP1 and PpNPP1 with MpNEP2.