that higher than the Q5DB (only 5.1-5.8). Based on observation, in general the morphologic of the QF3-1 and QF3-2 lines shows mostly similar with the recurrent parent (Q5DB cultivar) such as day to heading, secondary plant number, panicle number, primary plant number, seed per panicle, spikelets per panicle (unpublished data). Noteworthily that in unsalted field, the QF3-1 and QF3-2 lines have exhibited the real yield 7.5- 8.2 ton/hectare, while the Q5DB was shown the real yield 7.1-7.9 ton/hectare. Markers assisted selection (MAS) as a high technology tool for breeding was used in this study, integrated with conventional breeding. The promising rice lines Q5DB-Saltol possesing SaltolQTL with the similar morphologic Q5DB cultivar was selected. The salinity tolerance level of the promising lines was shown as the tolerance check FL478 line. The real yield of the promising line QF3-1 was 6.5- 6.7 ton/hectare to compared with the yield 6.3 ton/hectare of the recipient variety Q5DB on the saline level NaCl= 3 0 / 00 field.
Allelic diversity for five genetic loci (DL, FON4, OsMADS24, OsMADS45 and Spw1) associated with floral organ development were investigated among a small heterogeneous rice population which included one wild species (O. rufipogon Griffiths), one indigenous less popular natural floral organ mutant (O. sativa var. indica cv. Jugal), one indigenous normal line (O. sativa var. indica cv. Bhut- moori) and one improved high yielding line (O. sativa var. indica cv. IR 36). Detailed spikelet mor- phology showed that var. Jugal had variable number (1 - 3) of carpels within a single spikelet which was unique and resulted in variable (1 - 3) number of kernels within a single matured spikelet (grain). The genomic DNA of each investigated line was amplified with primer sequences designed from the selected genetic loci and the derived polymorphism profiles were used for study of allelic diversity for the studied loci. The derived genetic distances among the rice lines were used for dendrogram construction. In constructed dendrogram, the mutant genotype (Jugal) showed highest similarity with the wild rice (O. rufipogon) instead of the rice lines. To verify this finding, the genomic DNA of each studied line was also amplified with four SSR loci, tightly linked to saltolQTL, mapped to rice chromosome 1. The amplified products were screened for polymor- phism and another dendrogram was constructed to reveal the genetic distance among the lines for selected salt tolerance linked SSR loci. In SSR derived dendrogram, the wild rice (O. rufipogon) got totally separated from the all three rice genotypes though all the studied four lines showed equal sensitivity for salt sensitivity in a physiological screening experiment. From the combined expe- riment, it can be concluded that genetic architecture of floral organ development loci in var. Jugal may have some uniqueness which is not present in normal rice but common to O. rufipogon, a spe- cies which is regarded as immediate progenitor of present day modern rice (O. sativa). Though
of Pokkali/ IR29. Bonilla et al (2002) saturated this segment of chro- mosome 1 with RFLP and microsatellite markers using the RIL popula- tion and reported that two microsatellite markers, RM23 and RM140 flanked the SalTolQTL with 16.4 and 10.1 cM distance, respectively. The source of salinity tolerance in population 1 was from IR61920-3B- 22-2-1 (NSIC106) for this study which was donated by the ancient par- ent of this variety TKM6, Kitcheli Chamba or Vallaikar. The source of salinity tolerance of the NILs and RIL populations was used by Niones (2004) and Bonilla et al (2002) was Pokkali, which has been the most commonly-used source of salinity tolerance in rice to date. Interestingly, a QTL was detected in the same region of chromosome 1.
The original IR29/Pokkali QTL study using 80 extreme RILs identified Saltol as the QTL with the highest significance for shoot Na–K ratio with an LOD of 14.5 and R 2 of 64%, based on selective genotyping (Gregorio 1997). A follow-up study categorized the RILs into sensitive and tolerant groups and mapped the position of Saltol between RM23 and RM140 (10.7–12.2 Mb on chromosome 1), and confirmed the effect of the shoot Na – K ratio with an LOD of 6.6 and R 2 of 43% using 54 RILs (Bonilla et al. 2002). While neither of these studies presented the percent variation explained for visual SES tolerance scores or survival, it was assumed that by controlling the key mechanism of Na + /K + homeostasis under stress, Saltol is a major contributor to seedling stage tolerance. The data from the current study confirmed that Saltol contributes to Na + /K + homeostasis with an LOD of 7.6 and R 2 of 27% across the 140 RILs and a 30% decrease in the shoot Na – K ratio, from 1.7 to 1.2 in the IR29/Pokkali backcross lines, while the Saltol effect on SES scores in the QTL population and backcross lines was much smaller. The fact that Saltol affected the Na – K ratio more than other traits supports the possibility that the sodium transporter SKC1 (OsHKT1;5 as in Platten et al. 2006) may be the causal gene underlying the SaltolQTL. SKC1 was found to encode a sodium transporter that helps control Na + /K + homeostasis through unloading of Na + from the xylem (Ren et al. 2005), which has been suggested to function primarily in roots to reduce the amount of Na + ions that are transported to the leaves (Hauser and Horie 2010). Although the SKC1 QTL was originally detected using Nona Bokra, more research is needed to characterize the Pokkali allele at SKC1 to determine if it serves a similar function to maintain Na + /K + homeostasis in the shoots. Interestingly, a recent study identified a QTL for Na–K ratio between 11.1 and 14.6 Mb on chromosome 1 from the upland japonica variety Moroberekan (Haq et al. 2010) suggesting that the Saltol region may have functional significance for salt tolerance across both indica and japonica varieties.
inheritance pattern of sodicity tolerance, difficulties in screening and linkage drag as reported by Jairia et al. (2009). On the other hand, with the recent advancements in molecular biology, a major QTL, the ‘Saltol’ QTL which explains about 64.3-80.2 % of the variability in shoot Na+/K+ ratio at seeding stage has been identified in the rice variety Pokkali, (Krishnendu Chattopadhyay et al., 2014) has been identified to be handy tool for the breeders to selectively introgress the salt tolerance in high yielding salt sensitive rice varieties. Pokkali is the most widely used salt tolerant donor in salinity rice breeding. This major QTL is located in the region of 10.5 to 12.5 Mb of short arm of chromosome 1 of Pokkali and flanked by 21 SSR markers (Niones, 2004). Thomson et al. (2010) identified the tightly linked SSR markers to selectively transfer this QTL into the desired genetic background by which the difficulties in screening for salt tolerance and linkage drag could be overcome. Since SSR markers have been found to be linked to some of the specific traits of interest and used as the tools of biotechnology it is possible to transfer valuable genes of salt tolerance in rice without linkage drag (Mackill et al., 2006).
statistically similar germination percentages. The results clarify the complementary effects of these three QTLs and the suitability of deploying them together for max- imum advantage and stability of the traits. The results also showed the uniqueness of these loci in terms of their specificity to AG and no effect was observed under normal conditions. Deployment of these loci along with those underlying fast emergence and early seedling vigor will provide all-around improvement in the germination of rice and lead to higher robustness and stability of the trait. Eight promising donor lines with different QTL combinations, high AG survival, high germination and white-colored pericarp (Table 5) can be used for popula- tion development and QTL deployment for further AG breeding. Our study also identified qSH1–1 for seedling height on chromosome 1 with peak marker at 22.04 Mb (Additional file 1: Tables S2 and S3). This is a novel locus and it can be used for improving seedling height without manipulating the dwarfing gene “sd1” region (38.38–38.39 Mb). This trait is crucial not only for DSR and AG but also for stresses such as stagnant flooding, for which taller SH is a desirable trait.
The advent of molecular marker technology and molecular linkage genetic maps has made it possible to cha- racterize the performance of individual quantitative trait loci (QTLs). These techniques have been widely ap- plied to identify QTLs controlling yield and related traits in various crops, such as maize -, tomatoes -, and wheat  . QTL analyses have been conducted focusing on traits that are components of grain yield and quality -. A number of QTLs have been identified in rice using various genetic backgrounds in different environments -, which has provided useful information for breeders to improve breeding strat- egies via marker-assisted selection (MAS). The effects of epistasis have been identified by classical quantitative genetic analysis. The existence of digenicepistatic interactions in barley has been demonstrated using morpho- logical markers . Recent QTL mapping studies have revealed that epistatic interactions among multiple loci play an important role in complex quantitative traits such as yield and yield components. In rice, several map- ping populations have been developed to detect epistatic effects onyield and related traits, such as the F 2 , F 2 : 3
The overall phenotypic performance under salt stress reflected by visual SES scores is determined by several key traits, including survival, sodium and potassium concentration and rate of growth. Here we used path analysis to assess the genetic contribution of different mechanisms of salinity tolerance during early vegetative stage in the rice landrace Capsule, to identify key factors associated with salt tolerance. The results suggest that selection should be made based on Na + and K + concen- trations and ratios in plant tissue, and seedling survival to fast track the development of improved salt tolerant varieties. Most of the QTL identified here through single marker analysis were also detected using interval map- ping and composite interval mapping, and were further confirmed through graphical genotyping. Several QTL were identified on chromosomes 1 ( qNa1.1 , qK1.1 , qNaK-R1.1 , qSur1.1 ), 2 ( qNa2.2 ), 3 ( qSES3.1, qNa3.3, qK3.2, qNaK-R3.3, qSur3.2 ), 5 ( qSES5.2, qNa5.4, qNaK- R5.4 ) and 12 ( qSES12.3, qK12.3, qSur12.3 ), that are asso- ciated with tolerance at seedling stage, and the newly mapped loci on chromosomes 1 and 3 are novel. These QTL are good targets for subsequent fine mapping and cloning to develop gene-based SNP markers. Pyramiding these QTL with previously identified loci will help develop highly tolerant varieties for salt affected areas, especially coastal areas where salt stress is a major impediment for rice production during both dry and wet seasons; an effect further worsening with climate change.
cates that panicle development is driven by carbon re- sources although the process itself consumes little assimilate. Indeed, trait expression has to be plastic to ensure balanced source-sink relationships under vari- able resources. Also, Kamiji et al. (2011) reported that N top-dressing during the first stage of panicle develop- ment has a large effect on spikelet production, and genotypic differences in spikelet production could be explained by the crop growth rate during the 14-day period before heading. The sensitivity to carbon supply during this period was confirmed by Lafarge et al. (2010) on rice by looking at the detrimental effect on yield components of a 10-day shading period imposed in the field at early reproductive stage. Shiratsuchi et al. (2007) revealed that yield is correlated with the ratio of spikelet number to tiller dry weight after spikelet differentiation, and Endo-Higashi and Izawa (2011) suggested that pre-floral photosynthate accumulation determines reproductive sink capacity. This was re- cently confirmed by Adriani et al. (2016) analyzing near isogenic lines (NILs) with larger panicles developed by Qi et al. (2008) and Fujita et al. (2009, 2012) carrying qTSN4 on the long arm of chromosome 4, a QTL iden- tified for high total spikelet number (TSN). Adriani et al. (2016) reported that panicle size was strongly af- fected by (i) shading imposed during the reproductive phase in the greenhouse or (ii) change in plant density in the field. These authors also revealed that the effect of qTSN was associated with an earlier tillering cessa- tion and the development of subsequent larger inter- nodes and leaf blades and so was already visible before panicle initiation. Consequently, the panicle sink is not only generated during the reproductive phase, but ap- pears also adjusted to the plant’s (or tiller’s) internal re- sources, which in turn depends on the environment. It also highlights that there is a direct response of panicle development to tiller and plant vigor that can be re- lated, either directly to a reduction in light access and C acquisition through cloudiness or planting density, as also reported on sorghum (Lafarge et al. 2002), or indir- ectly to another abiotic constraint affecting C source-
while deeper and stagnant water with two weeks’ dura- tion and >100 cm in depth can cause damage ranging from 40% to 77% . Traditional varieties adapted to the submergence prone environments are low yielding due to their low tillering ability, long droopy leaves, suscepti- bility to lodging and poor grain quality. Development of submergence tolerant varieties is generally considered as the most effective entry point for improving productivity of rice varieties damaged from typhoon and flash flood, and it is also the cheapest option for farmers. Mackill (2006) proposed that adoption of a completely new vari- ety could take considerable time, whereas the chances of rapid adoption of popular varieties converted through marker assisted backcrossing (MABC) were relatively higher .
To adapt rice plants to these varying Fe toxic conditions, three types of tolerance mechanisms have been proposed. Type I refers to exclusion of Fe 2+ at the root level. Root oxidizing power due to oxygen release or enzymatic oxi- dation is responsible for the oxidization and precipitation of Fe 2+ on the root surface, thus avoiding excess Fe 2+ from uptake into rice shoots (Ando et al. 1983; Green and Etherington 1977). Type II refers to the inclusion but sub- sequent avoidance of Fe 2+ via internal distribution and storage in a less reactive form. Thus, ferritin is a promis- ing candidate protein as it can accommodate up to 4,000 Fe atoms in a safe and bio-available form (Briat et al. 2010). Type III refers to inclusion and tolerance to ROS formed in the Fenton reactions. Anti-oxidants such as ascorbic acid, and reduced glutathione can scavenge ROS (Fang et al. 2001; Gallie 2013), and antioxidant en- zymes, such as superoxide dismutase, peroxidase and catalase reportedly protect plants from ROS damage (Bode et al. 1995; Fang and Kao 2000; Fang et al. 2001).
method. To determine cellular localization, pSORT was used (http://wolfpsort.seq.cbrc.jp/). SignalP (http://www. cbs.dtu.dk/services/SignalP/) was employed to predict N- terminal signal peptides and MEME (Bailey and Gribskov 1998) was used to identify conserved protein motifs in the sequences. Selection pressure among monocupins was determined using codeml from the PAML package (Yang 1997; Yang and Nielsen 2000). A ω >1 indicates diversify- ing selection while a ω<1 indicates purifying selection. Of the six subclasses, only rhicadhesin-like receptor proteins, OXO and OXLP meet the criteria for calculating ω which are based on the following: (a) >50% similarity at the amino acid level, (b) absence of long insertions and deletions, and (c) at least three members. Evidence for monocupin gene expression was derived from the Rice Gene Expression Evidence page from TIGR (http://www. tigr.org/tdb/e2k1/osa1/locus_expression_evidence.shtml) and KOME (http://cdna01.dna.affrc.go.jp/cDNA/). The gene structure and orientation of monocupins were derived from the TIGR Rice Genome database build 5.0.
in Japonica rice backgrounds in field experiments (Terao et al. 2010; Huang et al. 2009; Ookawa et al. 2010). A QTL qGN4.1 with major effect on grain num- ber per panicle was stable across 3 years with high LOD scores of 13, 6.8 and 5.3, explaining 27%, 16% and 12% of phenotypic variation, respectively (Deshmukh et al. 2010). The QTL qGN4.1 was first mapped on the long arm of rice chromosome 4 in two different recom- binant inbred line (RIL) populations (Pusa 1266/Pusa Basmati 1 and Pusa 1266/Jaya) derived from a new plant type (NPT) Indica rice genotype Pusa 1266 (Deshmukh et al. 2010; Marathi et al. 2012). This QTL is co-located with other important QTLs for the num- ber of primary and secondary branches per panicle, number of tillers per plant, and flag leaf length and width, which may be due to either a tight genetic link- age or pleiotropic effects of the same gene on multiple traits (Deshmukh et al. 2010). No major gain in yield potential has been reported with the new plant type (NPT) breeding lines developed by IRRI due to poor grain filling and low biomass production. These draw- backs may be due to low crop growth rate during vege- tative stage in the NPT lines as compared to Indica cultivars (Yamagishi et al. 1996), dense arrangement of spikelets on the panicle (Khush and Peng 1996), a limited number of large vascular bundles for assimilate transport, and source constraint due to early leaf senes- cence (Ladha et al. 1998). The introduction of high grain number trait from new plant type cultivars into recipient lines has also been initiated to broaden the genetic background of the NPT germplasm and refine the original ideotype design for increasing grain filling percentage and biomass production (Peng et al. 1999).
(Mace et al. 2012) and wheat (Hamada et al. 2012; Christopher et al. 2013). In rice (Oryza sativa L.), two QTLs for the root gravitropic response, which is an im- portant component of RGA, have been detected on chromosomes 6 and 10 (Norton and Price 2009). Our research group has also reported three major rice QTLs for RGA, namely DRO1 (DEEPER ROOTING 1), DRO2, and qSOR1 (quantitative trait locus for SOIL SURFACE ROOTING 1), in three different mapping populations (Uga et al. 2011, 2012, 2013b). DRO1 has been detected on chromosome 9 in recombinant inbred lines (IK-RILs) derived from a cross between the shallow-rooting culti- var IR64 and the deep-rooting cultivar Kinandang Patong (Uga et al. 2011). DRO2 has been found on chromosome 4 in three F 2 populations derived from
lation including 1520 individuals derived from the RHL. Three insertion-deletion (InDel) and five single nucleo- tide polymorphism (SNP) markers were developed by comparing the sequences of the parents. Combining the genotype and phenotype of individuals, the QTL was delimited between two InDel markers INDEL7-2 and INDEL7-3 in 27.1 kb interval (Figure 4B). The target region contains 3 predicted genes (LOC_Os07g41180, LOC_Os07g41190 and LOC_Os07g41200) based on Rice Genome Annotation Project Website (http://rice.plant- biology.msu.edu/). Sequence variations of those genes between two parents were identified and expressions at RNA level were analyzed in leaves of the parents at booting stage (Figure 4C; Figure 5). Four SNPs causing amino acid change and 3 SNPs existed in exons and the promoter region, respectively in LOC_Os07g41180 gene. And the gene LOC_Os07g41200 had 2 nonsynonymous SNPs in one exon, 3 SNPs and an InDel in the promoter (Figure 4C). Both genes expressed at significantly different level in PA64s and two NILs (NIL-PA64s-1 and NIL- PA64s-2) compared with 93–11 (Figure 5). There were only 6 SNPs in the promoter of LOC_Os07g41190 gene and no significantly different expression in PA64s and two NILs compared with 93–11. Therefore, LOC_Os07g 41180 and LOC_Os07g41200 were selected candidates for qFLW7.2.
In contrast to the GG approach, transcript profiling of nonrecombinant animals does not allow QTL mapping of the expression levels and therefore cannot differenti- ate between cis- and trans-eQTL. However, co-localiza- tion of a differentially expressed gene and the pQTL can be tested, given that a physical, or genetic, map is avail- able for the genes. This would be equivalent to a cis- eQTL/pQTL co-localization test for the genes under cis control. An approach for sorting out cis- from trans- eQTL in this experimental design consists of first isolat- ing the genomic region with the pQTL in a congenic strain by backcrossing a donor strain to a recipient strain for multiple generations and then testing differen- tial gene expression between the congenic and the reci- pient background strain. Depending on the size of the congenic strain, differentially expressed genes in the donor region are likely to be under cis regulation or alternatively by trans control from linked genes within the limits of the congenic interval. In contrast, differen- tial expression of genes outside the donor region is expected to be regulated, directly or indirectly, by genes located within the congenic interval. Contaminating donor DNA in places outside the congenic donor region could produce false trans-eQTL. However, nonrecombi- nant individuals from an F 2 cross between the congenic
for grain length and width in rice were cloned. One of them, GL7/GW7, has similar effects on grain length and width with opposite allelic directions, controlling grain shape but hardly influencing grain weight (Wang et al., 2015a; Wang et al., 2015b). The other 13 genes affect grain size and weight. Four of them mainly control grain width, including GW2, GS5, qSW5/GW5, and GW8 (Li & Li, 2016). Eight others mainly control grain length, including GS2/GL2, OsLG3, qLGY3/OsLG3b, GS3, GL3.1/qGL3, GL4, TGW6, and GLW7 (Li & Li, 2016; Wu et al., 2017; Yu et al., 2017; Liu et al., 2018; Yu et al., 2018). The remaining one, GW6a, has similar effects on grain length and width with the same allelic direction, and consequently exhibits a larger impact on grain weight (Song et al., 2015). It has been shown that these QTL regulate the prolifer- ation and expansion of cells in spikelet hulls through diversified regulatory pathways. While most of them were involved in independent signaling pathways me- diated by proteasomal degradation, plant hormones and G proteins, a number of genes were found to interact with each other (Yan et al., 2011; Wang et al., 2015a; Liu et al., 2018). These findings have greatly enriched our knowledge on the genetic control of grain size in rice, but much more efforts are needed to fill the gap in understanding the regulatory framework for this critical agronomical trait (Zuo & Li, 2014; Li & Li, 2016).
Quantitative trait loci (QTL) affecting milk production and health of dairy cattle were mapped in a very large Holstein granddaughter design. The analysis included 1794 sons of 14 sires and 206 genetic markers distributed across all 29 autosomes and flanking an estimated 2497 autosomal cM using Kosambi ’s mapping function. All families were analyzed jointly with least-squares (LS) and variance components (VC) methods. A total of 6 QTL exceeding approximate experiment-wise significance thresholds, 24 QTL exceeding suggestive thresholds, and 34 QTL exceeding chromosome-wise thresholds were identified. Significance thresholds were determined via data permutation (for LS analysis) and chi-square distribution (for VC analysis). The average bootstrap confidence interval for the experiment-wise significant QTL was 48 cM. Some chromosomes harbored QTL affecting several traits, and these were always in coupling phase, defined by consistency with genetic correlations among traits. Chromosome 17 likely harbors 2 QTL affecting milk yield, and some other chromosomes showed some evidence for 2 linked QTL affecting the same trait. In each of these cases, the 2 QTL were in repulsion phase in those families appearing to be heterozygous for both QTL, a finding which supports the build-up of linkage disequilibrium due to selection.