Development of Permanent Mapping Populations RILs in Diploid A Genome

Recombinant inbred lines(RILs) serve as powerful tools for genetic mapping.RILs are obtained by crossing two inbred lines followed by repeated selfing or sib-mating to create a set of new

QTL Mapping of Grain Weight Trait in Rice

To provide new experimental materials for QTL analysis of rice yield trait, we constructed a mapping population of 150 1ines （recombination inbred lines, R1L） derived from a cross between rice varieties V20B and CPSLO17, and localized QTLs and evaluated the genetic effects in the two parents and 150 RILs for thousand-grain weight trait by using internal mapping method of software MapQTL5 combining thousand-grain weight phenotypic data of the RILs. The results showed that a new QTL （qTGW-3） related to thousand-grain weight trait was detected. Individual QTL （LOD=4.14） explained 11.9% of the observed phenotypic variance. And the QTL alleles came from the parent V20B.

QTL Analysis of Cold-tolerance at Seedling Stage in Rice

To provided the experimental materials for identifying and cloning the quantitative trait loci （QTLs） of cold tolerance at the seedling stage, the authors analyzed QTLs and evaluated the genetic effects of two parents and a mapping population of 213 lines （recombination inbred lines, RILs） derived from a cross between IR24 and Asominori for cold tolerance at the seedling stage with dead seedling rate by using software QTL IciMapping 4.0, based on a genetic linkage map constructed with 141 SSR molecular markers. The QTLs qCTS -6, qCTS -1 1 and qCTS -1 2 related to cold tolerance at the seedling stage were detected on chromosome 6, 11 and 12, respectively. Individual QTLs （LOD-3.194 3, LOD： 4.688 2, LOD-3.797 0） explained 5.662 7%, 8.549 6% and 12.787 7% of the observed phenotypic variance, respectively. All of the three detected QTLs alleles came from cold-tolerant parent Asominori.

Population spatialization with pixel-level attribute grading by considering scale mismatch issue in regression modeling

Population spatialization is widely used for spatially downscaling census population data to finer-scale.The core idea of modern population spatialization is to establish the association between ancillary data and population at the administrative-unit-level(AUlevel)and transfer it to generate the gridded population.However,the statistical characteristic of attributes at the pixel-level differs from that at the AU-level,thus leading to prediction bias via the cross-scale modeling(i.e.scale mismatch problem).In addition,integrating multi-source data simply as covariates may underutilize spatial semantics,and lead to incorrect population disaggregation;while neglecting the spatial autocorrelation of population generates excessively heterogeneous population distribution that contradicts to real-world situation.To address the scale mismatch in downscaling,this paper proposes a Cross-Scale Feature Construction(CSFC)method.More specifically,by grading pixel-level attributes,we construct the feature vector of pixel grade proportions to narrow the scale differences in feature representation between AU-level and pixel-level.Meanwhile,fine-grained building patch and mobile positioning data are utilized to adjust the population weighting layer generated from POI-density-based regression modeling.Spatial filtering is furtherly adopted to model the spatial autocorrelation effect of population and reduce the heterogeneity in population caused by pixel-level attribute discretization.Through the comparison with traditional feature construction method and the ablation experiments,the results demonstrate significant accuracy improvements in population spatialization and verify the effectiveness of weight correction steps.Furthermore,accuracy comparisons with WorldPop and GPW datasets quantitatively illustrate the advantages of the proposed method in fine-scale population spatialization.

Yield-related QTLs and Their Applications in Rice Genetic Improvement

Grain yield is one of the most important indexes in rice breeding, which is governed by quantitative trait loci （QTLs）. Different map- ping populations have been used to explore the QTLs controlling yield related traits. Primary populations such as F2 and recombi- nant inbred line populations have been widely used to discover QTLs in rice genome-wide, with hundreds of yield-related QTLs detected. Advanced populations such as near isogenic lines （NILs） are efficient to further fine-map and clone target QTLs. NILs for primarily identified QTLs have been proposed and confirmed to be the ideal population for map-based cloning. To date, 20 QTLs directly affecting rice grain yield and its components have been cloned with NIL-F2 populations, and 14 new grain yield QTLs havebeen validated in the NILs. The molecular mechanisms＇of＇a continuous/y increasing number of genes are being unveiled, which aids in the understanding of the formation of grain yield. Favorable alleles for rice breeding have been ＇mined＇ from natural cultivars and wild rice by association analysis of known functional genes with target trait performance. Reasonable combination of favorable alleles has the potential to increase grain yield via use of functional marker assisted selection.

Toward understanding genetic mechanisms of complex traits in rice

Rice is the primary carbohydrate staple cereal feeding the world population. Many genes, known as quantitative trait loci （QTLs）, con- trol most of the agronomically important traits in rice. The identification of QTLs controlling agricultural traits is vital to increase yield and meet the needs of the increasing human population, but the progress met with challenges due to complex QTL inheritance. To date, many QTLs have been detected in rice, including those responsible for yield and grain quality; salt, drought and submergence tolerance; disease and insect resistance; and nutrient utilization efficiency. Map-based cloning techniques have enabled scientists to successfully fine map and clone approximately seventeen QTLs for several traits. Additional in-depth functional analyses and characterizations of these genes will provide valuable assistance in rice molecular breeding.