Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat

Background: Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits (canopy temperature, CT and normalized difference vegetation index, NDVI) to predict 5 complex primary traits (harvest index, HI; grain yield, GY; grain number, GN; spike partitioning index, SPI; fruiting efiiciency, FE) using two cross-validation schemes CV1 and CV2. Results: In this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2 (multi-trait cross validation 2) model improved predictive ability by 4.8 to 138.5% compared to ST-CV1(single-trait cross validation 1). However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model. Conclusions: The study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops

INVESTIGATION INTO NATURAL GAS LIQUEFACTION METHODS, LNG TRANSPORT AND STORAGE

Liquefied Natural Gas (LNG) processes are very new in Turkey. The Government of Turkey, due to diversification of supply and balancing of seasonal load, decided to import LNG from Algeria. The first shipment in Marmara Ereğli import terminal has been carried out in the August the 3 rd, 1994. LNG after regasification will be injected into the main transmission pipeline. The share of LNG in the world natural gas trade was approixmately 22.1% in 1988. According to the forecast, LNG share will be rapidly spreading all over the world in near future. In this paper, treatment, liquefaction, transport, storage, regasification, distribution and utilisation of LNG are examined. Particular attention has given into liquefaction of natural gas

UroVysion Fluorescence in situ hybridization (UroVysion FISH) assay for detecting Turkish bladder cancer patients in voided urine: A preliminary study

Bladder cancer is the fourth most common cancer in men and the fifth most common cancer worldwide. UroVysion FISH has high sensitivity and specificity for urothelial carcinoma detection. We investigated the genetic marker detected by the UroVysion FISH technique in diagnosis of Turkish bladder cancer patients and compared these results with the urine cytology and cystoscopy. Urine specimens were analyzed using UroVysion FISH probes for abnormalities in centromeric chromosomes 3, 7, and 17 and locus-specific 9p21

UroVysion fluorescence in situ hybridization (UroVysion FISH) assay for detection of bladder cancer in voided urine of Turkish patients: a preliminary study

Bladder cancer is the fourth most common cancer in men and the fifth most common cancer worldwide. UroVysion FISH has high sensitivity and specificity for urothelial carcinoma detection. We investigated the genetic marker detected by the UroVysion FISH technique in diagnosis of Turkish bladder cancer patients and compared these results with the urine cytology and cystoscopy. Urine specimens were analyzed using UroVysion FISH probes for abnormalities in centromeric chromosomes 3, 7, and 17 and locus-specific 9p21

Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes

The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat