Richardson, T. | bioRxiv Science

bioRxiv2018

The wheat Sr22, Sr33, Sr35 and Sr45 genes confer resistance against stem rust in barley

In the last 20 years, stem rust caused by the fungus Puccinia graminis f. sp. tritici (Pgt), has re-emerged as a major threat to wheat and barley cultivation in Africa and Europe. In contrast to wheat with 82 designated stem rust (Sr) resistance genes, barleys genetic variation for stem rust resistance is very narrow with only seven resistance genes genetically identified. Of these, only one locus consisting of two genes is effective against Ug99, a strain of Pgt which emerged in Uganda in 1999 and has since spread to much of East Africa and parts of the Middle East. The objective of this study was to assess the functionality, in barley, of cloned wheat Sr genes effective against Ug99. Sr22, Sr33, Sr35 and Sr45 were transformed into barley cv. Golden Promise using Agrobacterium-mediated transformation. All four genes were found to confer effective stem rust resistance. The barley transgenics remained susceptible to the barley leaf rust pathogen Puccinia hordei, indicating that the resistance conferred by these wheat Sr genes was specific for Pgt. Cloned Sr genes from wheat are therefore a potential source of resistance against wheat stem rust in barley.

plant biology↗

bioRxiv2017

PhenoSpD: an atlas of phenotypic correlations and a multiple testing correction for the human phenome

BackgroundIdentifying phenotypic correlations between complex traits and diseases can provide useful etiological insights. Restricted access to individual-level phenotype data makes it difficult to estimate large-scale phenotypic correlation across the human phenome. State-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate phenotypic correlation using genome-wide association study (GWAS) summary statistics.\n\nResultsHere, we present an integrated R toolkit, PhenoSpD, to 1) apply metaCCA (or LD score regression) to estimate phenotypic correlations using GWAS summary statistics; and 2) to utilize the estimated phenotypic correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest it is possible to estimate phenotypic correlation using samples with only a partial overlap, but as overlap decreases correlations will attenuate towards zero and multiple testing correction will be more stringent than in perfectly overlapping samples. In a case study, PhenoSpD using GWAS results suggested 324.4 independent tests among 452 metabolites, which is close to the 296 independent tests estimated using true phenotypic correlation. We further applied PhenoSpD to estimated 7,503 pair-wise phenotypic correlations among 123 metabolites using GWAS summary statistics from Kettunen et al. and PhenoSpD suggested 44.9 number of independent tests for theses metabolites.\n\nConclusionPhenoSpD integrates existing methods and provides a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics, which is particularly valuable for post-GWAS analysis of complex molecular traits.\n\nAvailabilityR code and documentation for PhenoSpD V1.0.0 is available online (https://github.com/MRCIEU/PhenoSpD).

bioinformatics↗