Micronutrients, especially iron (Fe) and zinc (Zn), are deficient in the diets of people in underdeveloped countries. Biofortification of food crops is the best approach for alleviating the micronutrient deficiencies. Identification of germplasm with high grain Fe and Zn and understanding the genetic basis of their accumulation are the prerequisites for manipulation of these micronutrients. Some wild relatives of wheat were found to have higher grain Fe and Zn concentrations compared with the cultivated bread wheat germplasm. One accession of Triticum boeoticum (pau5088) that had relatively higher grain Fe and Zn was crossed with Triticum monococcum (pau14087), and a recombinant inbred line (RIL) population generated from this cross was grown at 2 locations over 2 years. The grains of the RIL population were evaluated for Fe and Zn concentration using atomic absorption spectrophotometer. The grain Fe and Zn concentrations in the RIL population ranged from 17.8 to 69.7 and 19.9 to 64.2 mg/kg, respectively. A linkage map available for the population was used for mapping quantitative trait loci (QTL) for grain Fe and Zn accumulation. The QTL analysis led to identification of 2 QTL for grain Fe on chromosomes 2A and 7A and 1 QTL for grain Zn on chromosome 7A. The grain Fe QTL were mapped in marker interval Xwmc382-Xbarc124 and Xgwm473-Xbarc29, respectively, each explaining 12.6% and 11.7% of the total phenotypic variation and were designated as QFe.pau-2A and QFe.pau-7A. The QTL for grain Zn, which mapped in marker interval Xcfd31-Xcfa2049, was designated as QZn.pau-7A and explained 18.8% of the total phenotypic variation.

Glucosinolates play important roles in plant defense against herbivores and microbes, as well as in human nutrition. Some glucosinolate-derived isothiocyanate and nitrile compounds have been clinically proven for their anticarcinogenic activity. To better understand glucosinolate biosynthesis in Brassica rapa, we conducted a comparative genomics study with Arabidopsis thaliana and identified total 56 putative biosynthetic and regulator genes. This established a high colinearity in the glucosinolate biosynthesis pathway between Arabidopsis and B. rapa. Glucosinolate genes in B. rapa share 72–94% nucleotide sequence identity with the Arabidopsis orthologs and exist in different copy numbers. The exon/intron split pattern of B. rapa is almost identical to that of Arabidopsis, although inversion, insertion, deletion and intron size variations commonly occur. Four genes appear to be nonfunctional as a result of the presence of a frame shift mutation and retrotransposon insertion. At least 12 paralogs of desulfoglucosinolate sulfotransferase were found in B. rapa, whereas only three were found in Arabidopsis. The expression of those paralogs was not tissue-specific but varied greatly depending on B. rapa tissue types. Expression was also developmentally regulated in some paralogs but not in other paralogs. Most of the regulator genes are present as triple copies. Accordingly, glucosinolate synthesis and regulation in B. rapa appears to be more complex than that of Arabidopsis. With the isolation and further characterization of the endogenous genes, health-beneficial vegetables or desirable animal feed crops could be developed by metabolically engineering the glucosinolate pathway.

Motivation: Many complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. Although a causal genetic variation may influence a group of highly correlated traits jointly, most of the previous association analyses considered each phenotype separately, or combined results from a set of single-phenotype analyses. Results: We propose a new statistical framework called graph-guided fused lasso to address this issue in a principled way. Our approach represents the dependency structure among the quantitative traits explicitly as a network, and leverages this trait network to encode structured regularizations in a multivariate regression model over the genotypes and traits, so that the genetic markers that jointly influence subgroups of highly correlated traits can be detected with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently, our approach analyzes all of the traits jointly in a single statistical method to discover the genetic markers that perturb a subset of correlated triats jointly rather than a single trait. Using simulated datasets based on the HapMap consortium data and an asthma dataset, we compare the performance of our method with the single-marker analysis, and other sparse regression methods that do not use any structural information in the traits. Our results show that there is a significant advantage in detecting the true causal single nucleotide polymorphisms when we incorporate the correlation pattern in traits using our proposed methods. Availability: Software for GFlasso is available at http://www.sailing.cs.cmu.edu/gflasso.html

Motivation: A fundamental problem in population genetics, which being also of importance to forensic science, is to compute the match probability (MP) that two individuals randomly chosen from a population have identical alleles at a collection of loci. At present, 11–13 unlinked autosomal microsatellite loci are typed for forensic use. In a finite population, the genealogical relationships of individuals can create statistical non-independence of alleles at unlinked loci. However, the so-called product rule, which is used in courts in the USA, computes the MP for multiple unlinked loci by assuming statistical independence, multiplying the one-locus MPs at those loci. Analytically testing the accuracy of the product rule for more than five loci has hitherto remained an open problem. Results: In this article, we adopt a flexible graphical framework to compute multi-locus MPs analytically. We consider two standard models of random mating, namely the Wright–Fisher (WF) and Moran models. We succeed in computing haplotypic MPs for up to 10 loci in the WF model, and up to 13 loci in the Moran model. For a finite population and a large number of loci, we show that the MPs predicted by the product rule are highly sensitive to mutation rates in the range of interest, while the true MPs computed using our graphical framework are not. Furthermore, we show that the WF and Moran models may produce drastically different MPs for a finite population, and that this difference grows with the number of loci and mutation rates. Although the two models converge to the same coalescent or diffusion limit, in which the population size approaches infinity, we demonstrate that, when multiple loci are considered, the rate of convergence in the Moran model is significantly slower than that in the WF model. Availability: A C++ implementation of the algorithms discussed in this article is available at http://www.cs.berkeley.edu/~yss/software.html.