Mapping QTLs controlling the biosynthesis of maltose in soybean

The present study was carried out to identify genomic regions associated with maltose in 2 F2 populations through assessment of sugars using HPLC and genotyping using SSR markers across the genome. SSR markers, Sat_216 (chr 12) and Satt681 (chr 6) in F2 population I and Sat_105 (chr 20) in F2 population II showed significant (P< 0.5) association with maltose content through single marker analysis (SMA) with LOD score of 3.18 (R2 =9.7), 2.54 (R2 =6.8), and 3.54 (R2 =10.4), respectively. Composite interval mapping analysis (CIM) let to identify different QTLs (other than SMA) for maltose content on chr 11, chr 13 and chr 17 in F2 population I while chr 6 and chr15 in F2 population II. QTLs identified for maltose content are in proximity of known functional genes responsible for degradation of starch into maltose. QTLs identified for maltose in the study may be deployed for improving efficiency of marker assisted breeding for development of soybean genotypes with high levels of this sugar.

Mapping QTL hotspots associated with weed competitive traits in backcross population derived from Oryza sativa L. and O. glaberrima Steud.

To improve grain yield under direct seeded and aerobic conditions, weed competitive ability of a rice genotype is a key desirable trait. Hence, understanding and dissecting weed competitive associated traits at both morphological and molecular level is important in developing weed competitive varieties. In the present investigation, the QTLs associated with weed competitive traits were identified in BC1F2:3 population derived from weed competitive accession of O. glaberrima (IRGC105187) and O. sativa cultivar IR64. The mapping population consisting of 144 segregating lines were phenotyped for 33 weed competitive associated traits under direct seeded condition. Genetic analysis of weed competitive traits carried out in BC1F2:3 population showed significant variation for the weed competitive traits and predominance of additive gene action. The population was genotyped with 81 genome wide SSR markers and a linkage map covering 1423 cM was constructed. Composite interval mapping analysis identified 72 QTLs linked to 33 weed competitive traits which were spread on the 11 chromosomes. Among 72 QTLs, 59 were found to be major QTLs (> 10% PVE). Of the 59 major QTLs, 38 had favourable allele contributed from the O. glaberrima parent. We also observed nine QTL hotspots for weed competitive traits (qWCA2a, qWCA2b, qWCA2c, qWCA3, qWCA5, qWCA7, qWCA8, qWCA9, and qWCA10) wherein several QTLs co-localised. Our study demonstrates O. glaberrima species as potential source for improvement for weed competitive traits in rice and identified QTLs hotspots associated with weed competitive traits.

Age-dependent QTL affecting body weight in gilthead seabream (Sparus aurata L.)

We examined 24 maternal half-sib families of gilthead seabream to identify quantitative trait loci (QTL) associated with body weight at four time points during a production cycle. 57 brooders and 637 offspring were genotyped for 14 informative microsatellite markers, spanning linkage groups 1 and 21. The QTL detection method was based on half-sib interval mapping analysis through a linear regression approach. One QTL was found significant at all time points in linkage group 1, with its effect having different profile across time, and one QTL in linkage group 21 that seems to impact body weight at a later growth stage of the species. Current results verified previously published QTL for growth in the above linkage groups, using a different genetic background of seabream. These QTL can be considered as valuable candidates for use in marker-assisted selective breeding programs, aiming at high rates of genetic improvement for growth in S. aurata.