Defining genomic landscape for identification of potential candidate resistance genes associated with major rice diseases through MetaQTL analysis.

Rice production is severely affected by various diseases such as bacterial leaf blight (BLB), brown spot (BS), false smut (FS), foot rot (FR), rice blast (RB), and sheath blight (SB). In recent years, several quantitative trait loci (QTLs) studies involving different populations have been carried out, resulting in the identification of hundreds of resistance QTLs for each disease. These QTLs can be integrated and analyzed using meta-QTL (MQTL) analysis for better understanding of the genetic architecture underlying multiple disease resistance (MDR). This study involved an MQTL analysis on 661 QTLs (378, 161, 21, 41, 44, and 16 QTLs for SB, RB, BLB, BS, FS, and FR, respectively) retrieved from 50 individual studies published from 1995 to 2021. Of these, 503 QTLs were projected finally onto the consensus map saturated with 6,275 markers, resulting in 73 MQTLs, including 27 MDR-MQTLs conferring resistance to three or more diseases. Forty-seven MQTLs were validated using marker-trait associations identified in published genome-wide association studies. A total of 3,310 genes, including both R and defense genes, were also identified within some selected high-confidence MQTL regions that were investigated further for the syntenic relationship with barley, wheat, and maize genomes. Thirty-nine high-confidence candidate genes were selected based on their expression patterns and recommended for future studies involving functional validation, genetic engineering, and gene editing. Nineteen MQTLs were co-localized with 39 known R genes for BLB and RB diseases. These results could pave the way to utilize candidate genes in a marker-assisted breeding program for MDR in rice.

Genome-Wide Meta-Analysis of QTLs Associated with Root Traits and Implications for Maize Breeding.

Root system architecture (RSA), also known as root morphology, is critical in plant acquisition of soil resources, plant growth, and yield formation. Many QTLs associated with RSA or root traits in maize have been identified using several bi-parental populations, particularly in response to various environmental factors. In the present study, a meta-analysis of QTLs associated with root traits was performed in maize using 917 QTLs retrieved from 43 mapping studies published from 1998 to 2020. A total of 631 QTLs were projected onto a consensus map involving 19,714 markers, which led to the prediction of 68 meta-QTLs (MQTLs). Among these 68 MQTLs, 36 MQTLs were validated with the marker-trait associations available from previous genome-wide association studies for root traits. The use of comparative genomics approaches revealed several gene models conserved among the maize, sorghum, and rice genomes. Among the conserved genomic regions, the ortho-MQTL analysis uncovered 20 maize MQTLs syntenic to 27 rice MQTLs for root traits. Functional analysis of some high-confidence MQTL regions revealed 442 gene models, which were then subjected to in silico expression analysis, yielding 235 gene models with significant expression in various tissues. Furthermore, 16 known genes viz., DXS2, PHT, RTP1, TUA4, YUC3, YUC6, RTCS1, NSA1, EIN2, NHX1, CPPS4, BIGE1, RCP1, SKUS13, YUC5, and AW330564 associated with various root traits were present within or near the MQTL regions. These results could aid in QTL cloning and pyramiding in developing new maize varieties with specific root architecture for proper plant growth and development under optimum and abiotic stress conditions.

Quantitative Trait Loci Mapping for Earliness, Fruit, and Seed Related Traits Using High Density Genotyping-by-Sequencing-Based Genetic Map in Bitter Gourd (Momordica charantia L.).

Bitter gourd (Momordica charantia L.) is an important vegetable crop having numerous medicinal properties. Earliness and yield related traits are main aims of bitter gourd breeding program. High resolution quantitative trait loci (QTLs) mapping can help in understanding the molecular basis of phenotypic variation of these traits and thus facilitate marker-assisted breeding. The aim of present study was to identify genetic loci controlling earliness, fruit, and seed related traits. To achieve this, genotyping-by-sequencing (GBS) approach was used to genotype 101 individuals of F4 population derived from a cross between an elite cultivar Punjab-14 and PAUBG-6. This population was phenotyped under net-house conditions for three years 2018, 2019, and 2021. The linkage map consisting of 15 linkage groups comprising 3,144 single nucleotide polymorphism (SNP) markers was used to detect the QTLs for nine traits. A total of 50 QTLs for these traits were detected which were distributed on 11 chromosomes. The QTLs explained 5.09-29.82% of the phenotypic variance. The highest logarithm of the odds (LOD) score for a single QTL was 8.68 and the lowest was 2.50. For the earliness related traits, a total of 22 QTLs were detected. For the fruit related traits, a total of 16 QTLs and for seed related traits, a total of 12 QTLs were detected. Out of 50 QTLs, 20 QTLs were considered as frequent QTLs (FQ-QTLs). The information generated in this study is very useful in the future for fine-mapping and marker-assisted selection for these traits in bitter gourd improvement program.