Mixed model approaches for the identification of QTLs within a maize hybrid breeding program

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance.     

Monocytes/macrophages express chemokine receptor CCR9 in rheumatoid arthritis and CCL25 stimulates their differentiation

Introduction  

Monocytes/macrophages accumulate in the rheumatoid (RA) synovium where they play a central role in inflammation and joint destruction. Identification of molecules involved in their accumulation and differentiation is important to inform therapeutic strategies. This study investigated the expression and function of chemokine receptor CCR9 in the peripheral blood (PB) and synovium of RA, non-RA patients and healthy volunteers.     

Towards genomic selection in apple (Malus ×domestica Borkh.) breeding programmes: Prospects, challenges and strategies

The apple genome sequence and the availability of high-throughput genotyping technologies have initiated a new era where SNP markers are abundant across the whole genome. Genomic selection (GS) is a statistical approach that utilizes all available genome-wide markers simultaneously to estimate breeding values or total genetic values. For breeding programmes, GS is a promising alternative to the traditional marker-assisted selection for manipulating complex polygenic traits often controlled by many small-effect genes. Various factors, such as genetic architecture of selection traits, population size and structure, genetic evaluation systems, density of SNP markers and extent of linkage disequilibrium, have been shown to be the key drivers of the accuracy of GS. In this paper, we provide an overview of the status of these aspects in current apple-breeding programmes. Strategies for GS for fruit quality and disease resistance are discussed, and an update on an empirical genomic selection study in a New Zealand apple-breeding programme is provided, along with a foresight of expected accuracy from such selection.     

Comparison of marker-based pairwise relatedness estimators on a pedigreed plant population

Several estimators have been proposed that use molecular marker data to infer the degree of relatedness for pairs of individuals. The objective of this study was to evaluate the performance of seven estimators when applied to marker data of a set of 33 key individuals from a large complex apple pedigree. The evaluation considered different scenarios of allele frequencies and different numbers of marker loci. The method of moments estimators were Similarity, Queller-Goodknight, Lynch-Ritland and Wang. The maximum likelihood estimators were Thompson, Anderson-Weir and Jacquard. The pedigree-based coancestry coefficients were taken as the point of reference in calculating correlations and root mean square error (RMSE). The marker data comprised 86 multi-allelic SSR markers on 17 linkage groups, covering 11 Morgans. Additionally, we simulated 10 datasets conditional on the real pedigree to support the results on the real dataset. None of the estimators outperformed the others. Knowledge of allele frequencies appeared to be the most influential, i.e., the highest correlations and lowest RMSE were found when frequencies from the founder population were available. When equal allele frequencies were used, all estimators resulted in very similar, but on average lower, correlations. The use of allele frequencies estimated from the set of 33 individuals gave, on average, the poorest results. The maximum likelihood estimators and the Lynch-Ritland estimator were the most sensitive to allele frequencies. The results from the simulation study fully supported the trends in results of the real dataset. This study indicated that high correlations (up to 0.90) and small RMSE (below 0.03), may be obtained when population allelic frequencies are available. In this scenario, the performances of the various estimators were similar, but seemed to favor the maximum likelihood estimators. In the absence of reliable allele frequencies the method of moments estimators were shown to be more robust. The number of marker loci influenced the average performance of the estimators; however, the ranking was not affected. Correlations up to 0.80 were obtained when two markers per chromosome and appropriate allele frequencies were available. Adding more markers to the current dataset may lead to marginal improvements.     

Bayesian Markov Random Field Analysis for Protein Function Prediction Based on Network Data

Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S.cereviciae validation network with 1622 proteins against 90 Gene Ontology terms of different levels of abstraction. Compared to three other protein function prediction methods, our approach shows very good prediction performance. Our method can be directly applied to protein-protein interaction or coexpression networks, but also can be extended to use multiple data sources. We apply our method to physical protein interaction data from S. cerevisiae and provide novel predictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and we evaluate the predictions using the available literature.     

Evaluation of c-erbB-2 overexpression and Her-2/neu gene copy number heterogeneity in Barrett's adenocarcinoma.

Amplification of the Her-2/neu gene is accompanied by overexpression of its cell surface receptor product, c-erbB-2 protein. To investigate the degree of intratumoural heterogeneity we applied immunohistochemistry in primary Barrett's adenocarcinoma (BCA) (n = 6) and dysplasia adjacent to the carcinoma (n = 4). In addition, fluorescence in situ hybridisation (FISH) was performed in primary BCA (n = 5) and dysplastic areas (n = 4). For an objective evaluation digital image analysis and laser scanning microscopy were used. Five of six BCA showed a marked intratumoral heterogeneous staining pattern ranging from areas in which the tumour cells were negative or faintly positive to tumour areas with a strong staining of the entire membrane. Among the two dysplastic areas also a heterogeneous staining pattern was observed. FISH analysis revealed marked heterogeneity of intratumoral gene copy number changes in all BCA showing populations with different fractions of cells with polysomy, low level amplification and high level amplification. One dysplasia showed a minor population with Her-2/neu signal clusters. In conclusion, we observed marked intratumoural heterogeneity of c-erbB-2 protein overexpression and Her-2/neu gene copy number in the majority of the primary BCA analyzed. Digital image analysis and laser scanning microscopy were helpful in quantifying the variations in protein expression and DNA copy number in individual tumour cells. The observed heterogeneity could hamper the exact diagnostic determination of the c-erbB-2 status in small biopsies and possibly influence the effectiveness of a potential c-erbB-2 targeting therapy. Figures on http://www.esacp.org/acp/2000/20-1/walch.htm+ ++.     

A transmission/disequilibrium test approach to screen for quantitative trait loci in two selected lines of large white pigs

Pedigree and marker data from a multiple-generation pig selection experiment have been analysed to screen for loci affecting quantitative traits (QTL). Pigs from a base population were selected either for low backfat thickness at fixed live weight (L-line) or high live weight at fixed age (F-line). Selection was based on single-trait own performance and DNA was available on selected individuals only. Genotypes for three marker loci with known positions on chromosome 4 were available. The transmission/disequilibrium test (TDT) was originally described in human genetics to test for linkage between a genetic marker and a disease-susceptibility locus, in the presence of association. Here, we adapt the TDT to test for linkage between a marker and QTL favoured by selection, and for linkage disequilibrium between them in the base population. The a priori unknown distribution of the test statistic under the null hypothesis, no linkage, was obtained via Monte Carlo simulation. Significant TDT statistics were found for markers AFABP and SW818 in the F-line, indicating the presence of a closely linked QTL affecting growth performance. In the L-line, none of the markers studied showed significance. This study emphasizes the potential of the TDT as a quick and simple approach to screen for QTL in situations where marker genotypes are available on selected individuals. The results suggest that previously identified QTL in crosses of genetically diverse breeds may also segregate in commercial selection lines.