Fransen, S. | bioRxiv Science

bioRxiv2019

Genetic basis of plasticity for forage quality traits in response to water deficit in a diverse germplasm panel of alfalfa

Plant phenotypic plasticity is the ability of plants to express different phenotypes in response to environmental variations. Genetic bases by which phenotypic plasticity affects plant adaptation to environmental change remain largely unknown. In the present study, we characterized 26 forage quality traits in a panel of alfalfa 198 accessions in a field trial under water deficit gradient. The regression analysis revealed that the values of fiber-related traits decreased, while those among energy-related traits increased, as water deficit increased. Genetic loci for forage quality traits were investigated by Genome-wide association studies (GWAS) under different levels of water deficit. Genetic loci associated with forage quality traits were identified and compared. Similar regions were found between energy-related traits when grand means were used for GWAS. Most of the associated markers were identified under water deficit, suggesting genetic mechanisms for forage quality traits were differ between well-watered and water stressed plants. Although GWAS on forage quality have been reported, we are the first to address the genetic factors for forage quality traits under water deficit in autotetraploid alfalfa. The information gained from the present study will be useful for the genetic improvement of alfalfa with enhanced drought/salt tolerance while maintaining forage quality.

genetics↗

bioRxiv2018

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

BackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.\n\nMethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.\n\nResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.\n\nConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

cancer biology↗