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De Graeve, F.

Publications and source records attributed to De Graeve, F..

2 recordsLinked to original sources

Independent genetic mapping experiments identify diverse molecular determinants of host adaptation in a generalist herbivore

Interactions between plants and herbivores promote evolutionary change. Studying the evolution of herbivore mechanisms aimed to cope with diUerent host plant species is a critical intersection between evolutionary biology and sustainable pest management. Generalist herbivores are of particular interest, as hybridization between genetically distinct populations can increase the standing genetic variation and therefore the adaptive potential of the species. Tetranychus urticae is a generalist arthropod known for its adaptive potential, evidenced in its immense host range and ability to develop metabolic resistance to xenobiotics. However, the molecular underpinnings associated with the potential of host adaptation and the consequences of host adaptation in this and many other pests remain elusive. Here, we use two independent, empirical approaches to identify and map the genetic basis of host plant performance and adaptation in genetically distinct populations of T. urticae. In the first approach, we subject a genetically diverse mite population to tomato selection and map genomic regions linked to the phenotypic evolution of increased reproductive performance. In the second approach, we map genomic regions responsible for performance on tomato by comparing the genomes of pooled individuals from a F2 backcross between populations with high and low reproductive performance. Both approaches revealed specific and shared genomic regions associated with host plant performance and adaptation and key candidate genes were identified. Our findings highlight the power of spider mite genetic approaches to identify the complex genetic basis of host adaptation in a generalist herbivore.

evolutionary biology↗

MAGE enables population level RNAseq driven genotyping and (differential) allelic divergence detection in healthy kidney and carcinoma

Decreasing sequencing costs have instigated large-scale RNAseq experiments, yet genetic polymorphisms in such data remain poorly exploited. Currently, allele-specific expression (ASE) studies focus almost exclusively on genetic variants explaining expression differences (cis-eQTLs), largely ignoring other ASE effects. The latter are typically associated with higher variance in expression of both copies of a gene, here called Allelic Divergence (AD). We therefore developed an RNAseq-driven population-level beta-binomial mixture model for (differential) AD detection. The model simultaneously enables RNAseq-driven genotyping, which outperforms alternative RNA genotyping methods when applied on healthy kidney data from The Cancer Genome Atlas. Moreover, we identify well-known non-cis-eQTL ASE, e.g. random monoallelic expression of HLA and immunoglobulin genes in healthy kidney, as well as allele-specific aberrations in clear cell kidney carcinoma, including long-range epigenetic silencing of protocadherins, copy-number alterations, and loss of imprinting. These methods are available as the Modeller of Allelic Gene Expression (MAGE) tool suite: https://biobix.github.io/MAGE/.

bioinformatics↗