Status and advances in mining for blackleg (Leptosphaeria maculans) quantitative resistance (QR) in oilseed rape (Brassica napus)

Key message Quantitative resistance (QR) loci discovered through genetic and genomic analyses are abundant in the Brassica napus genome, providing an opportunity for their utilization in enhancing blackleg resistance. Abstract Quantitative resistance (QR) has long been utilized to manage blackleg in Brassica napus (canola, oilseed rape), even before major resistance genes (R-genes) were extensively explored in breeding programmes. In contrast to R-gene-mediated qualitative resistance, QR reduces blackleg symptoms rather than completely eliminating the disease. As a polygenic trait, QR is controlled by numerous genes with modest effects, which exerts less pressure on the pathogen to evolve; hence, its effectiveness is more durable compared to R-gene-mediated resistance. Furthermore, combining QR with major R-genes has been shown to enhance resistance against diseases in important crops, including oilseed rape. For these reasons, there has been a renewed interest among breeders in utilizing QR in crop improvement. However, the mechanisms governing QR are largely unknown, limiting its deployment. Advances in genomics are facilitating the dissection of the genetic and molecular underpinnings of QR, resulting in the discovery of several loci and genes that can be potentially deployed to enhance blackleg resistance. Here, we summarize the efforts undertaken to identify blackleg QR loci in oilseed rape using linkage and association analysis. We update the knowledge on the possible mechanisms governing QR and the advances in searching for the underlying genes. Lastly, we lay out strategies to accelerate the genetic improvement of blackleg QR in oilseed rape using improved phenotyping approaches and genomic prediction tools.

An Accidental Genetic Epidemiologist

I briefly describe my early life and how, through a series of serendipitous events, I became a genetic epidemiologist. I discuss how the Elston–Stewart algorithm was discovered and its contribution to segregation, linkage, and association analysis. New linkage findings and paternity testing resulted from having a genotyping lab. The different meanings of interaction—statistical and biological—are clarified. The computer package S.A.G.E. (Statistical Analysis for Genetic Epidemiology), based on extensive method development over two decades, was conceived in 1986, flourished for 20 years, and is now freely available for use and further development. Finally, I describe methods to estimate and test hypotheses about familial correlations, and point out that the liability model often used to estimate disease heritability estimates the heritability of that liability, rather than of the disease itself, and so can be highly dependent on the assumed distribution of that liability.

The Mega2R package: R tools for accessing and processing genetic data in common formats

The standalone C++ Mega2 program has been facilitating data-reformatting for linkage and association analysis programs since 2000. Support for more analysis programs has been added over time. Currently, Mega2 converts data from several different genetic data formats (including PLINK, VCF, BCF, and IMPUTE2) into the specific data requirements for over 40 commonly-used linkage and association analysis programs (including Mendel, Merlin, Morgan, SHAPEIT, ROADTRIPS, MaCH/minimac3). Recently, Mega2 has been enhanced to use a SQLite database as an intermediate data representation. Additionally, Mega2 now stores bialleleic genotype data in a highly compressed form, like that of the GenABEL R package and the PLINK binary format. Our new Mega2R package now makes it easy to load Mega2 SQLite databases directly into R as data frames. In addition, Mega2R is memory efficient, keeping its genotype data in a compressed format, portions of which are only expanded when needed. Mega2R has functions that ease the process of applying gene-based tests by looping over genes, efficiently pulling out genotypes for variants within the desired boundaries. We have also created several more functions that illustrate how to use the data frames: these permit one to run the pedgene package to carry out gene-based association tests on family data, to run the SKAT package to carry out gene-based association tests, to output the Mega2R data as a VCF file and related files (for phenotype and family data), and to convert the data frames into GenABEL format. The Mega2R package enhances GenABEL since it supports additional input data formats (such as PLINK, VCF, and IMPUTE2) not currently supported by GenABEL. The Mega2 program and the Mega2R R package are both open source and are freely available, along with extensive documentation, from https://watson.hgen.pitt.edu/register for Mega2 and https://CRAN.R-project.org/package=Mega2R for Mega2R.

The Mega2R package: R tools for accessing and processing genetic data in common formats

The standalone C++ Mega2 program has been facilitating data-reformatting for linkage and association analysis programs since 2000. Support for more analysis programs has been added over time. Currently, Mega2 converts data from several different genetic data formats (including PLINK, VCF, BCF, and IMPUTE2) into the specific data requirements for over 40 commonly-used linkage and association analysis programs (including Mendel, Merlin, Morgan, SHAPEIT, ROADTRIPS, MaCH/minimac3). Recently, Mega2 has been enhanced to use a SQLite database as an intermediate data representation. Additionally, Mega2 now stores bialleleic genotype data in a highly compressed form, like that of the GenABEL R package and the PLINK binary format. Our new Mega2R package now makes it easy to load Mega2 SQLite databases directly into R as data frames. In addition, Mega2R is memory efficient, keeping its genotype data in a compressed format, portions of which are only expanded when needed. Mega2R has functions that ease the process of applying gene-based tests by looping over genes, efficiently pulling out genotypes for variants within the desired boundaries. We have also created several more functions that illustrate how to use the data frames: these permit one to run the pedgene package to carry out gene-based association tests on family data, to run the SKAT package to carry out gene-based association tests, to output the Mega2R data as a VCF file and related files (for phenotype and family data), and to convert the data frames into GenABEL format. The Mega2R package enhances GenABEL since it supports additional input data formats (such as PLINK, VCF, and IMPUTE2) not currently supported by GenABEL. The Mega2 program and the Mega2R R package are both open source and are freely available, along with extensive documentation, from https://watson.hgen.pitt.edu/register for Mega2 and https://CRAN.R-project.org/package=Mega2R for Mega2R.