Efficiently Tracking Selection in a Multiparental Population: The Case of Earliness in Wheat

Multiparental populations are innovative tools for fine mapping large numbers of loci. Here we explored the application of a wheat Multiparent Advanced Generation Inter-Cross (MAGIC) population for QTL mapping. This population was created by 12 generations of free recombination among 60 founder lines, following modification of the mating system from strict selfing to strict outcrossing using the ms1b nuclear male sterility gene. Available parents and a subset of 380 SSD lines of the resulting MAGIC population were phenotyped for earliness and genotyped with the 9K i-Select SNP array and additional markers in candidate genes controlling heading date. We demonstrated that 12 generations of strict outcrossing rapidly and drastically reduced linkage disequilibrium to very low levels even at short map distances and also greatly reduced the population structure exhibited among the parents. We developed a Bayesian method, based on allelic frequency, to estimate the contribution of each parent in the evolved population. To detect loci under selection and estimate selective pressure, we also developed a new method comparing shifts in allelic frequency between the initial and the evolved populations due to both selection and genetic drift with expectations under drift only. This evolutionary approach allowed us to identify 26 genomic areas under selection. Using association tests between flowering time and polymorphisms, 6 of these genomic areas appeared to carry flowering time QTL, 1 of which corresponds to Ppd-D1, a major gene involved in the photoperiod sensitivity. Frequency shifts at 4 of 6 areas were consistent with earlier flowering of the evolved population relative to the initial population. The use of this new outcrossing wheat population, mixing numerous initial parental lines through multiple generations of panmixia, is discussed in terms of power to detect genes under selection and association mapping. Furthermore we provide new statistical methods for use in future analyses of multiparental populations.     

Renin inhibitors for the treatment of hypertension: design and optimization of a novel series of tertiary alcohol-bearing piperidines

The design and optimization of a novel series of renin inhibitor is described herein. Strategically, by committing the necessary resources to the development of synthetic sequences and scaffolds that were most amenable for late stage structural diversification, even as the focus of the SAR campaign moved from one end of the molecule to another, highly potent renin inhibitors could be rapidly identified and profiled.     

Patterns of diversifying selection in the phytotoxin-like scr74 gene family of Phytophthora infestans

Phytophthora infestans, the organism responsible for the Irish famine, causes late blight, a re-emerging disease of potato and tomato. Little is known about the molecular evolution of P. infestans genes. To identify candidate effector genes (virulence or avirulence genes) that may have co-evolved with the host, we mined expressed sequence tag (EST) data from infection stages of P. infestans for secreted and potentially polymorphic genes. This led to the identification of scr74, a gene that encodes a predicted 74-amino acid secreted cysteine-rich protein with similarity to the Phytophthora cactorum phytotoxin PcF. The expression of scr74 was upregulated approximately 60-fold 2 to 4 days after inoculation of tomato and was also significantly induced during early stages of colonization of potato. The scr74 gene was found to belong to a highly polymorphic gene family within P. infestans with 21 different sequences identified. Using the approximate and maximum likelihood (ML) methods, we found that diversifying selection likely caused the extensive polymorphism observed within the scr74 gene family. Pairwise comparisons of 17 scr74 sequences revealed elevated ratios of nonsynonymous to synonymous nucleotide-substitution rates, particularly in the mature region of the proteins. Using ML, all 21 polymorphic amino acid sites were identified to be under diversifying selection. Of these 21 amino acids, 19 are located in the mature protein region, suggesting that selection may have acted on the functional portions of the proteins. Further investigation of gene copy number and organization revealed that the scr74 gene family comprises at least three copies located in a region of no more than 300 kb of the P. infestans genome. We found evidence that recombination contributed to sequence divergence within at least one gene locus. These results led us to propose an evolutionary model that involves gene duplication and recombination, followed by functional divergence of scr74 genes. This study provides support for using diversifying selection as a criterion for identifying candidate effector genes from sequence databases.     

A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data

MOTIVATION: Microarray experiments are affected by numerous sources of non-biological variation that contribute systematic bias to the resulting data. In a dual-label (two-color) cDNA or long-oligonucleotide microarray, these systematic biases are often manifested as an imbalance of measured fluorescent intensities corresponding to Sample A versus those corresponding to Sample B. Systematic biases also affect between-slide comparisons. Making effective corrections for these systematic biases is a requisite for detecting the underlying biological variation between samples. Effective data normalization is therefore an essential step in the confident identification of biologically relevant differences in gene expression profiles. Several normalization methods for the correction of systemic bias have been described. While many of these methods have addressed intensity-dependent bias, few have addressed both intensity-dependent and spatiality-dependent bias. RESULTS: We present a neural network-based normalization method for correcting the intensity- and spatiality-dependent bias in cDNA microarray datasets. In this normalization method, the dependence of the log-intensity ratio (M) on the average log-intensity (A) as well as on the spatial coordinates (X,Y) of spots is approximated with a feed-forward neural network function. Resistance to outliers is provided by assigning weights to each spot based on how distant their M values is from the median over the spots whose A values are similar, as well as by using pseudospatial coordinates instead of spot row and column indices. A comparison of the robust neural network method with other published methods demonstrates its potential in reducing both intensity-dependent bias and spatial-dependent bias, which translates to more reliable identification of truly regulated genes.