50 years of rice breeding in Bangladesh: genetic yield trends.

To assess the efficiency of genetic improvement programs, it is essential to assess the genetic trend in long-term data. The present study estimates the genetic trends for grain yield of rice varieties released between 1970 and 2020 by the Bangladesh Rice Research Institute. The yield of the varieties was assessed from 2001-2002 to 2020-2021 in multi-locations trials. In such a series of trials, yield may increase over time due to (i) genetic improvement (genetic trend) and (ii) improved management or favorable climate change (agronomic/non-genetic trend). In both the winter and monsoon seasons, we observed positive genetic and non-genetic trends. The annual genetic trend for grain yield in both winter and monsoon rice varieties was 0.01 t ha-1, while the non-genetic trend for both seasons was 0.02 t ha-1, corresponding to yearly genetic gains of 0.28% and 0.18% in winter and monsoon seasons, respectively. The overall percentage yield change from 1970 until 2020 for winter rice was 40.96%, of which 13.91% was genetic trend and 27.05% was non-genetic. For the monsoon season, the overall percentage change from 1973 until 2020 was 38.39%, of which genetic and non-genetic increases were 8.36% and 30.03%, respectively. Overall, the contribution of non-genetic trend is larger than genetic trend both for winter and monsoon seasons. These results suggest that limited progress has been made in improving yield in Bangladeshi rice breeding programs over the last 50 years. Breeding programs need to be modernized to deliver sufficient genetic gains in the future to sustain Bangladeshi food security.

Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder's equation.

KEY MESSAGE: The integration of new technologies into public plant breeding programs can make a powerful step change in agricultural productivity when aligned with principles of quantitative and Mendelian genetics. The breeder's equation is the foundational application of quantitative genetics to crop improvement. Guided by the variables that describe response to selection, emerging breeding technologies can make a powerful step change in the effectiveness of public breeding programs. The most promising innovations for increasing the rate of genetic gain without greatly increasing program size appear to be related to reducing breeding cycle time, which is likely to require the implementation of parent selection on non-inbred progeny, rapid generation advance, and genomic selection. These are complex processes and will require breeding organizations to adopt a culture of continuous optimization and improvement. To enable this, research managers will need to consider and proactively manage the, accountability, strategy, and resource allocations of breeding teams. This must be combined with thoughtful management of elite genetic variation and a clear separation between the parental selection process and product development and advancement process. With an abundance of new technologies available, breeding teams need to evaluate carefully the impact of any new technology on selection intensity, selection accuracy, and breeding cycle length relative to its cost of deployment. Finally breeding data management systems need to be well designed to support selection decisions and novel approaches to accelerate breeding cycles need to be routinely evaluated and deployed.

Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.

Quality control genotyping for assessment of genetic identity and purity in diverse tropical maize inbred lines.

Quality control (QC) genotyping is an important component in breeding, but to our knowledge there are not well established protocols for its implementation in practical breeding programs. The objectives of our study were to (a) ascertain genetic identity among 2-4 seed sources of the same inbred line, (b) evaluate the extent of genetic homogeneity within inbred lines, and (c) identify a subset of highly informative single-nucleotide polymorphism (SNP) markers for routine and low-cost QC genotyping and suggest guidelines for data interpretation. We used a total of 28 maize inbred lines to study genetic identity among different seed sources by genotyping them with 532 and 1,065 SNPs using the KASPar and GoldenGate platforms, respectively. An additional set of 544 inbred lines was used for studying genetic homogeneity. The proportion of alleles that differed between seed sources of the same inbred line varied from 0.1 to 42.3 %. Seed sources exhibiting high levels of genetic distance are mis-labeled, while those with lower levels of difference are contaminated or still segregating. Genetic homogeneity varied from 68.7 to 100 % with 71.3 % of the inbred lines considered to be homogenous. Based on the data sets obtained for a wide range of sample sizes and diverse genetic backgrounds, we recommended a subset of 50-100 SNPs for routine and low-cost QC genotyping, verified them in a different set of double haploid and inbred lines, and outlined a protocol that could be used to minimize errors in genetic analyses and breeding.

Multiple Small-Effect Alleles of Indica Origin Enhance High Iron-Associated Stress Tolerance in Rice Under Field Conditions in West Africa.

Understanding the genetics of field-based tolerance to high iron-associated (HIA) stress in rice can accelerate the development of new varieties with enhanced yield performance in West African lowland ecosystems. To date, few field-based studies have been undertaken to rigorously evaluate rice yield performance under HIA stress conditions. In this study, two NERICA × O. sativa bi-parental rice populations and one O.sativa diversity panel consisting of 296 rice accessions were evaluated for grain yield and leaf bronzing symptoms over multiple years in four West African HIA stress and control sites. Mapping of these traits identified a large number of QTLs and single nucleotide polymorphisms (SNPs) associated with stress tolerance in the field. Favorable alleles associated with tolerance to high levels of iron in anaerobic rice soils were rare and almost exclusively derived from the indica subpopulation, including the most favorable alleles identified in NERICA varieties. These findings highlight the complex genetic architecture underlying rice response to HIA stress and suggest that a recurrent selection program focusing on an expanded indica genepool could be productively used in combination with genomic selection to increase the efficiency of selection in breeding programs designed to enhance tolerance to this prevalent abiotic stress in West Africa.

Identification of donors for low-nitrogen stress with maize lethal necrosis (MLN) tolerance for maize breeding in sub-Saharan Africa.

After drought, a major challenge to smallholder farmers in sub-Saharan Africa is low-fertility soils with poor nitrogen (N)-supplying capacity. Many challenges in this region need to be overcome to create a viable fertilizer market. An intermediate solution is the development of maize varieties with an enhanced ability to take up or utilize N in severely depleted soils, and to more efficiently use the small amounts of N that farmers can supply to their crops. Over 400 elite inbred lines from seven maize breeding programs were screened to identify new sources of tolerance to low-N stress and maize lethal necrosis (MLN) for introgression into Africa-adapted elite germplasm. Lines with high levels of tolerance to both stresses were identified. Lines previously considered to be tolerant to low-N stress ranked in the bottom 10% under low-N confirming the need to replace these lines with new donors identified in this study. The lines that performed best under low-N yielded about 0. 5 Mg ha-1 (20%) more in testcross combinations than some widely used commercial parent lines such as CML442 and CML395. This is the first large scale study to identify maize inbred lines with tolerance to low-N stress and MLN in eastern and southern Africa.

Rapid breeding and varietal replacement are critical to adaptation of cropping systems in the developing world to climate change.

Plant breeding is a key mechanism for adaptation of cropping systems to climate change. Much discussion of breeding for climate change focuses on genes with large effects on heat and drought tolerance, but phenology and stress tolerance are highly polygenic. Adaptation will therefore mainly result from continually adjusting allele frequencies at many loci through rapid-cycle breeding that delivers a steady stream of incrementally improved cultivars. This will require access to elite germplasm from other regions, shortened breeding cycles, and multi-location testing systems that adequately sample the target population of environments. The objective of breeding and seed systems serving smallholder farmers should be to ensure that they use varieties developed in the last 10 years. Rapid varietal turnover must be supported by active dissemination of new varieties, and active withdrawal of obsolete ones. Commercial seed systems in temperate regions achieve this through competitive seed markets, but in the developing world, most crops are not served by competitive commercial seed systems, and many varieties date from the end of the Green Revolution (the late 1970s, when the second generation of modern rice and wheat varieties had been widely adopted). These obsolete varieties were developed in a climate different than today's, placing farmers at risk. To reduce this risk, a strengthened breeding system is needed, with freer international exchange of elite varieties, short breeding cycles, high selection intensity, wide-scale phenotyping, and accurate selection supported by genomic technology. Governments need to incentivize varietal release and dissemination systems to continuously replace obsolete varieties.

Rapid Cycling Genomic Selection in a Multiparental Tropical Maize Population.

Genomic selection (GS) increases genetic gain by reducing the length of the selection cycle, as has been exemplified in maize using rapid cycling recombination of biparental populations. However, no results of GS applied to maize multi-parental populations have been reported so far. This study is the first to show realized genetic gains of rapid cycling genomic selection (RCGS) for four recombination cycles in a multi-parental tropical maize population. Eighteen elite tropical maize lines were intercrossed twice, and self-pollinated once, to form the cycle 0 (C0) training population. A total of 1000 ear-to-row C0 families was genotyped with 955,690 genotyping-by-sequencing SNP markers; their testcrosses were phenotyped at four optimal locations in Mexico to form the training population. Individuals from families with the best plant types, maturity, and grain yield were selected and intermated to form RCGS cycle 1 (C1). Predictions of the genotyped individuals forming cycle C1 were made, and the best predicted grain yielders were selected as parents of C2; this was repeated for more cycles (C2, C3, and C4), thereby achieving two cycles per year. Multi-environment trials of individuals from populations C0, C1, C2, C3, and C4, together with four benchmark checks were evaluated at two locations in Mexico. Results indicated that realized grain yield from C1 to C4 reached 0.225 ton ha-1 per cycle, which is equivalent to 0.100 ton ha-1 yr-1 over a 4.5-yr breeding period from the initial cross to the last cycle. Compared with the original 18 parents used to form cycle 0 (C0), genetic diversity narrowed only slightly during the last GS cycles (C3 and C4). Results indicate that, in tropical maize multi-parental breeding populations, RCGS can be an effective breeding strategy for simultaneously conserving genetic diversity and achieving high genetic gains in a short period of time.

A study of allelic diversity underlying flowering-time adaptation in maize landraces.

Landraces (traditional varieties) of domesticated species preserve useful genetic variation, yet they remain untapped due to the genetic linkage between the few useful alleles and hundreds of undesirable alleles. We integrated two approaches to characterize the diversity of 4,471 maize landraces. First, we mapped genomic regions controlling latitudinal and altitudinal adaptation and identified 1,498 genes. Second, we used F-one association mapping (FOAM) to map the genes that control flowering time, across 22 environments, and identified 1,005 genes. In total, we found that 61.4% of the single-nucleotide polymorphisms (SNPs) associated with altitude were also associated with flowering time. More than half of the SNPs associated with altitude were within large structural variants (inversions, centromeres and pericentromeric regions). The combined mapping results indicate that although floral regulatory network genes contribute substantially to field variation, over 90% of the contributing genes probably have indirect effects. Our dual strategy can be used to harness the landrace diversity of plants and animals.

Identification of QTL for Early Vigor and Stay-Green Conferring Tolerance to Drought in Two Connected Advanced Backcross Populations in Tropical Maize (Zea mays L.).

We aimed to identify quantitative trait loci (QTL) for secondary traits related to grain yield (GY) in two BC1F2:3 backcross populations (LPSpop and DTPpop) under well-watered (4 environments; WW) and drought stressed (6; DS) conditions to facilitate breeding efforts towards drought tolerant maize. GY reached 5.6 and 5.8 t/ha under WW in the LPSpop and the DTPpop, respectively. Under DS, grain yield was reduced by 65% (LPSpop) to 59% (DTPpop) relative to WW. GY was strongly associated with the normalized vegetative index (NDVI; r ranging from 0.61 to 0.96) across environmental conditions and with an early flowering under drought stressed conditions (r ranging from -0.18 to -0.25) indicative of the importance of early vigor and drought escape for GY. Out of the 105 detected QTL, 53 were overdominant indicative of strong heterosis. For 14 out of 18 detected vigor QTL, as well as for eight flowering time QTL the trait increasing allele was derived from CML491. Collocations of early vigor QTL with QTL for stay green (bin 2.02, WW, LPSpop; 2.07, DS, DTPpop), the number of ears per plant (bins 2.02, 2.05, WW, LPSpop; 5.02, DS, LPSpop) and GY (bin 2.07, WW, DTPpop; 5.04, WW, LPSpop), reinforce the importance of the observed correlations. LOD scores for early vigor QTL in these bins ranged from 2.2 to 11.25 explaining 4.6 (additivity: +0.28) to 19.9% (additivity: +0.49) of the observed phenotypic variance. A strong flowering QTL was detected in bin 2.06 across populations and environmental conditions explaining 26-31.3% of the observed phenotypic variation (LOD: 13-17; additivity: 0.1-0.6d). Improving drought tolerance while at the same time maintaining yield potential could be achieved by combining alleles conferring early vigor from the recurrent parent with alleles advancing flowering from the donor. Additionally bin 8.06 (DTPpop) harbored a QTL for GY under WW (additivity: 0.27 t/ha) and DS (additivity: 0.58 t/ha). R2 ranged from 0 (DTPpop, WW) to 26.54% (LPSpop, DS) for NDVI, 18.6 (LPSpop, WW) to 42.45% (LPSpop, DS) for anthesis and from 0 (DTPpop, DS) to 24.83% (LPSpop, WW) for GY. Lines out-yielding the best check by 32.5% (DTPpop, WW) to 60% (DTPpop, DS) for all population-by-irrigation treatment combination (except LPSpop, WW) identified are immediately available for the use by breeders.

New insights into the genetics of in vivo induction of maternal haploids, the backbone of doubled haploid technology in maize.

Haploids and doubled haploid (DH) inbred lines have become an invaluable tool for maize genetic research and hybrid breeding, but the genetic basis of in vivo induction of maternal haploids is still unknown. This is the first study reporting comparative quantitative trait locus (QTL) analyses of this trait in maize. We determined haploid induction rates (HIR) in testcrosses of a total of 1061 progenies of four segregating populations involving two temperate haploid inducers, UH400 (HIR = 8%) and CAUHOI (HIR = 2%), one temperate and two tropical inbreds with HIR = 0%, and up to three generations per population. Mean HIR of the populations ranged from 0.6 to 5.2% and strongly deviated from the midparent values. One QTL (qhir1) explaining up to p = 66% of the genetic variance was detected in bin 1.04 in the three populations involving a noninducer parent and the HIR-enhancing allele was contributed by UH400. Segregation ratios of loci in bin 1.04 were highly distorted against the UH400 allele in these three populations, suggesting that transmission failure of the inducer gamete and haploid induction ability are related phenomena. In the CAUHOI × UH400 population, seven QTL were identified on five chromosomes, with qhir8 on chromosome 9 having p > 20% in three generations of this cross. The large-effect QTL qhir1 and qhir8 will likely become fixed quickly during inducer development due to strong selection pressure applied for high HIR. Hence, marker-based pyramiding of small-effect and/or modifier QTL influencing qhir1 and qhir8 may help to further increase HIR in maize. We propose a conceptual genetic framework for inheritance of haploid induction ability, which is also applicable to other dichotomous traits requiring progeny testing, and discuss the implications of our results for haploid inducer development.

Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments.

Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F(2)-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F(2)-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.