Dataset Information

Analysis of Extreme Phenotype Bulk Copy Number Variation (XP-CNV) Identified the Association of rp1 with Resistance to Goss's Wilt of Maize.

ABSTRACT: Goss's wilt (GW) of maize is caused by the Gram-positive bacterium Clavibacter michiganensis subsp. nebraskensis (Cmn) and has spread in recent years throughout the Great Plains, posing a threat to production. The genetic basis of plant resistance is unknown. Here, a simple method for quantifying disease symptoms was developed and used to select cohorts of highly resistant and highly susceptible lines known as extreme phenotypes (XP). Copy number variation (CNV) analyses using whole genome sequences of bulked XP revealed 141 genes containing CNV between the two XP groups. The CNV genes include the previously identified common rust resistant locus rp1. Multiple Rp1 accessions with distinct rp1 haplotypes in an otherwise susceptible accession exhibited hypersensitive responses upon inoculation. GW provides an excellent system for the genetic dissection of diseases caused by closely related subspecies of C. michiganesis. Further work will facilitate breeding strategies to control GW and provide needed insight into the resistance mechanism of important related diseases such as bacterial canker of tomato and bacterial ring rot of potato.

SUBMITTER: Hu Y

PROVIDER: S-EPMC5812337 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Analysis of Extreme Phenotype Bulk Copy Number Variation (XP-CNV) Identified the Association of rp1 with Resistance to Goss's Wilt of Maize.

Hu Ying Y Ren Jie J Peng Zhao Z Umana Arnoldo A AA Le Ha H Danilova Tatiana T Fu Junjie J Wang Haiyan H Robertson Alison A Hulbert Scot H SH White Frank F FF Liu Sanzhen S

Frontiers in plant science 20180209

Goss's wilt (GW) of maize is caused by the Gram-positive bacterium Clavibacter michiganensis subsp. nebraskensis (Cmn) and has spread in recent years throughout the Great Plains, posing a threat to production. The genetic basis of plant resistance is unknown. Here, a simple method for quantifying disease symptoms was developed and used to select cohorts of highly resistant and highly susceptible lines known as extreme phenotypes (XP). Copy number variation (CNV) analyses using whol ...[more]

PMID: 29479358

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Project description:BACKGROUND:Cancer associated copy number variation (CNV) events provide important information for identifying patient subgroups and suggesting treatment strategies. Technical and logistical issues, however, make it challenging to accurately detect abnormal copy number events in a cost-effective manner in clinical studies. RESULTS:Here we present CNV Radar, a software tool that utilizes next-generation sequencing read depth information and variant allele frequency patterns, to infer the true copy number status of genes and genomic regions from whole exome sequencing data. Evaluation of CNV Radar in a public multiple myeloma dataset demonstrated that CNV Radar was able to detect a variety of CNVs associated with risk of progression, and we observed >?70% concordance with fluorescence in situ hybridization (FISH) results. Compared to other CNV callers, CNV Radar showed high sensitivity and specificity. Similar results were observed when comparing CNV Radar calls to single nucleotide polymorphism array results from acute myeloid leukemia and prostate cancer datasets available on TCGA. More importantly, CNV Radar demonstrated its utility in the clinical trial setting: in POLLUX and CASTOR, two phase 3 studies in patients with relapsed or refractory multiple myeloma, we observed a high concordance rate with FISH for del17p, a risk defining CNV event (88% in POLLUX and 90% in CASTOR), therefore allowing for efficacy assessments in clinically relevant disease subgroups. Our case studies also showed that CNV Radar is capable of detecting abnormalities such as copy-neutral loss of heterozygosity that elude other approaches. CONCLUSIONS:We demonstrated that CNV Radar is more sensitive than other CNV detection methods, accurately detects clinically important cytogenetic events, and allows for further interrogation of novel disease biology. Overall, CNV Radar exhibited high concordance with standard methods such as FISH, and its success in the POLLUX and CASTOR clinical trials demonstrated its potential utility for informing clinical and therapeutic decisions.

Dataset Information

Analysis of Extreme Phenotype Bulk Copy Number Variation (XP-CNV) Identified the Association of rp1 with Resistance to Goss's Wilt of Maize.

Publications

Analysis of Extreme Phenotype Bulk Copy Number Variation (XP-CNV) Identified the Association of <i>rp1</i> with Resistance to Goss's Wilt of Maize.

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