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Yang, C. J.

Publications and source records attributed to Yang, C. J..

10 recordsLinked to original sources

Mutation breeding for quantitative traits in the era of genomic selection

Mutation breeding creates new genetic variation by inducing random mutations and screening for mutants with desirable phenotypes, whereas selective breeding exploits variants that already segregate in breeding populations. The two approaches have rarely been combined, and whether genomic selection (GS) can capture polygenic variation induced by mutagenesis has not been tested. We evaluated GS in two public panels of advanced mutants (190 wheat and 74 tomato lines) and in simulated biparental and multiparental populations derived from mutants. In both panels, predictive abilities (up to 0.39 in wheat and 0.45 in tomato) came entirely from variants shared among lines, which in wheat formed chromosome segments segregating from the parental cultivar. Variants private to single lines, where induced mutations lie, carried no predictive signal. In simulated crosses in which mutations segregate, GS outperformed marker-assisted selection (MAS) of the largest mutation when mutations were numerous and their effects balanced (genetic value 2.84 vs 0.45 after two cycles), but not when unfavorable mutations dominated. In simulated multiparental populations, mutant founders increased genetic gain over five cycles of GS by 1.3- to 6.8-fold when mutational variance was large, but by only 4-5% when it was small relative to standing variation. Induced polygenic variation therefore becomes accessible to GS once mutations are shared among relatives through crossing, which offers a route to integrate mutation breeding into pre-breeding programmes.

genetics↗

Opportunities to improve the recommendation of plant varieties under the Recommended List (RL) system

Recommended List (RL) is the UK plant variety recommendation system introduced in 1944 for supporting growers in making decisions on variety choices. The current analysis for RL is largely based on published works on trial designs and statistical analyses in the 1980s. Given the statistical advances that have been developed and adopted elsewhere, it is timely to review and update the methods for data analysis in RL. In addition, threats from climate change challenge the prediction of variety performance in future environments. Better variety recommendations, particularly for matching varieties to specific environments can be achieved through improved modeling of effects from genetics, environments and genetic by environment interactions. Here, we evaluated grain yield data from 153 spring barley varieties that were trialed for RL from 2002 to 2019. Our results show that the available methods for predicting variety performance are poor and inconsistent across environments without any clearly superior method. However, these shortcomings can be easily overcome by switching the statistical model for analyzing RL data to one that fits variety effects as random and accounts for genetic relationships among varieties. Lastly, we also discuss other possible approaches for analyzing RL data and highlight the relevance of genomics in both variety registration and recommendation.

genetics↗

Instinct to insight: Neural correlates of ethological strategy learning

In ethological behaviors like parenting, animals innately follow stereotyped patterns of choices to decide between uncertain outcomes but can learn to modify their strategies to incorporate new information. For example, female mice in a T-maze instinctively use spatial-memory to search for pups where they last found them but can learn more efficient strategies employing pup-associated acoustic cues. We uncovered neural correlates for transitioning between these innate and learned strategies. Auditory cortex (ACx) was required during learning. ACx firing at the nest increased with learning and correlated with subsequent search speed but not outcome. Surprisingly, ACx suppression rather than facilitation during search was more prognostic of correct sound-cued outcomes - even before adopting a sound-cued strategy. Meanwhile medial prefrontal cortex encoded the last pup location, but this decayed as the spatial-memory strategy declined. Our results suggest a neural competition between a weakening spatial-memory and strengthening sound-cued neural representation to mediate strategy switches.

neuroscience↗

Analysis of historical selection in winter wheat

Winter wheat is a major crop with a rich selection history in the modern era of crop breeding. Genetic gains across economically important traits like yield have been well characterized and are the major force driving its production. Winter wheat is also an excellent model for analyzing historical genetic selection. As a proof of concept, we analyze two major collections of winter wheat varieties that were bred in western Europe from 1916 to 2010, namely the Triticeae Genome (TG) and WAGTAIL panels, which include 333 and 403 varieties respectively. We develop and apply a selection mapping approach, Regression of Alleles on Years (RALLY), in these panels, as well as in simulated populations. RALLY maps loci under sustained historical selection by using a simple logistic model to regress allele counts on years of variety release. To control for drift-induced allele frequency change, we develop a hybrid approach of genomic control and delta control. Within the TG panel, we identify 22 significant RALLY quantitative selection loci (QSLs) and estimate the local heritabilities for 12 traits across these QSLs. By correlating predicted marker effects with RALLY regression estimates, we show that alleles whose frequencies have increased over time are heavily biased towards conferring positive yield effect, but negative effects in flowering time, lodging, plant height and grain protein content. Altogether, our results (1) demonstrate the use of RALLY to identify selected genomic regions while controlling for drift, and (2) reveal key patterns in the historical selection in winter wheat and guide its future breeding. Key MessageModelling of the distribution of allele frequency over year of variety release identifies major loci involved in historical breeding of winter wheat.

genetics↗

Domestication Reshaped the Genetic Basis of Inbreeding Depression in a Maize Landrace Compared to its Wild Relative, Teosinte

Inbreeding depression is the reduction in fitness and vigor resulting from mating of close relatives observed in many plant and animal species. The extent to which the genetic load of mutations contributing to inbreeding depression is due to rare large-effect variation versus potentially more common variants with very small individual effects is unknown and may be affected by population history. We compared the effects of outcrossing and self-fertilization on 18 traits in a landrace population of maize, which underwent a population bottleneck during domestication, and a neighboring population of its wild relative teosinte. Inbreeding depression was greater in maize than teosinte for 15 of 18 traits, congruent with the greater segregating genetic load predicted from sequence data in the maize population. For many traits - and more commonly in maize - genetic variation among selffertilized families was less than expected based on additive and dominance variance estimated in outcrossed families, suggesting that a negative covariance between additive and homozygous dominance effects limits the variation available to selection under partial inbreeding. We identified quantitative trait loci (QTL) representing large-effect rare variants carried by only a single parent, which were more important in teosinte than maize. Teosinte also carried more putative juvenile-acting lethal variants identified by segregation distortion. These results suggest a mixture of mostly polygenic, smalleffect recessive variation underlying inbreeding depression, with an additional contribution from rare larger-effect variants that was more important in teosinte but depleted in maize following to the domestication bottleneck. Purging associated with the maize domestication bottleneck may have selected against large effect variants, but polygenic load is harder to purge and segregating mutational burden increased in maize compared to teosinte.

evolutionary biology↗

Crafting for a better MAGIC: systematic design and test for multiparental advanced generation inter-cross population.

Multiparental advanced generation inter-cross (MAGIC) populations are valuable crop resources with a wide array of research uses including genetic mapping of complex traits, management of genetic resources and breeding of new varieties. Multiple founders are crossed to create a rich mosaic of highly recombined founder genomes in the MAGIC recombinant inbred lines (RILs). Many variations of MAGIC population designs exist; however, a large proportion of the currently available populations have been created empirically and based on similar designs. In our evaluations of five MAGIC populations, we found that the choice of designs has a large impact on the recombination landscape in the RILs. The most popular design used in many MAGIC populations has been shown to have a bias in recombinant haplotypes and low level of unique recombinant haplotypes, and therefore is not recommended. To address this problem and provide a remedy for the future, we have developed the "magicdesign" R package for creating and testing any MAGIC population design via simulation. A Shiny app version of the package is available as well. Our "magicdesign" package provides a unifying tool and a framework for creativity and innovation in MAGIC population designs. For example, using this package, we demonstrate that MAGIC population designs can be found which are very effective in creating haplotype diversity without the requirement for very large crossing programmes. Further, we show that interspersing cycles of crossing with cycles of selfing is effective in increasing haplotype diversity. These approaches are applicable in species which are hard to cross or in which resources are limited.

genetics↗

Overcoming barriers to the registration of new varieties

Distinctness, Uniformity and Stability (DUS) is an intellectual property system introduced in 1961 by the International Union for the Protection of New Varieties of Plants (UPOV) for safeguarding the investment and rewarding innovation in developing new plant varieties. Despite the rapid advancement in our understanding of crop biology over the past 60 years, the DUS system has not changed and is still dependent upon a set of morphological traits for testing candidate varieties. As the demand for more plant varieties increases, the barriers to registration of new varieties become more acute and thus require urgent review to the system. To highlight the challenges and remedies in the current system, we evaluated a comprehensive panel of 805 UK barley varieties that span the entire history of DUS testing. Our findings reveal the system deficiencies and provide evidence for a shift towards a robust genomics enabled registration system for new crop varieties.

genetics↗

Origin Specific Genomic Selection: a simple process to optimize the favourable contribution of parents to progeny

Modern crop breeding is in constant demand for new genetic diversity as part of the arms race with genetic gain. The elite gene pool has limited genetic variation and breeders are trying to introduce novelty from unadapted germplasm, landraces and wild relatives. For polygenic traits, currently available approaches to introgression are not ideal, as there is a demonstrable bias against exotic alleles during selection. Here, we propose a partitioned form of genomic selection, called Origin Specific Genomic Selection (OSGS), where we identify and target selection on favourable exotic alleles. Briefly, within a population derived from a bi-parental cross, we isolate alleles originating from the elite and exotic parents, which then allows us to separate out the predicted marker effects based on the allele origins. We validated the usefulness of OSGS using two nested association mapping (NAM) datasets: barley NAM (elite-exotic) and maize NAM (elite-elite), as well as by computer simulation. Our results suggest that OSGS works well in bi-parental crosses, and it is possible to extend the approach to broader multi-parental populations.

genetics↗

TeoNAM: a nested association mapping population for domestication and agronomic trait analysis in maize

Recombinant inbred lines (RILs) are an important resource for mapping genes controlling complex traits in many species. While RIL populations have been developed for maize, a maize RIL population with multiple teosinte inbred lines as parents has been lacking. Here, we report a teosinte nested association mapping population (TeoNAM), derived from crossing five teosinte inbreds to the maize inbred line W22. The resulting 1257 BC1S4 RILs were genotyped with 51,544 SNPs, providing a high-density genetic map with a length of 1540 cM. On average, each RIL is 15% homozygous teosinte and 8% heterozygous. We performed joint linkage mapping (JLM) and genome-wide association study (GWAS) for 22 domestication and agronomic traits. A total of 255 QTLs from JLM were identified with many of these mapping to known genes or novel candidate genes. TeoNAM is a useful resource for QTL mapping for the discovery of novel allelic variation from teosinte. TeoNAM provides the first report that PROSTRATE GROWTH1, a rice domestication gene, is also a QTL associated with tillering in teosinte and maize. We detected multiple QTLs for flowering time and other traits for which the teosinte allele contributes to a more maize-like phenotype. Such QTL could be valuable in maize improvement.

genetics↗

Neural mechanisms driving hunger-induced changes in sensory perception and behavior in Caenorhabditis elegans

Animals integrate changes in external and internal environments to generate behavior. While neural circuits detecting external cues have been mapped, less is known about how internal states like hunger are integrated into behavioral outputs. We use the nematode C. elegans to decode how changes in internal nutritional status affects chemosensory behaviors. We show that acute food deprivation leads to a reversible decline in repellent, but not attractant, sensitivity. This behavioral change requires two conserved transcription factors MML-1 (Mondo A) and HLH-30 (TFEB), both of which translocate from the intestinal nuclei to the cytoplasm upon food deprivation. Next, we identify insulin-like peptides INS-23 and INS-31 as candidate ligands relaying food-status signals from the intestine to other tissues. Furthermore, we show that ASI chemosensory neurons use the DAF-2 insulin receptor, PI-3 Kinase, and the mTOR complex to integrate these intestine-released peptides. Together, our study shows how internal food status signals are integrated by transcription factors and intestine-neuron signaling to generate flexible behaviors. Author SummaryWe have all experienced behavioral changes when we are hungry - the pang in our stomach can cause us to behave erratically. In particular, hungry animals, including humans, are known to pursue behaviors that involve higher risk compared to when they are well-fed. Here we explore the molecular details of this behavior in the invertebrate animal model C. elegans. This behavior, termed sensory integration, shows that C. elegans display reduced copper sensitivity when hungry. Copper is toxic and repellant to C. elegans; reduced avoidance indicates that these animals use riskier food search behaviors when they are hungry. Luckily, like us, this behavioral change is reversible upon re-feeding. This hunger-induced behavioral change is not due to increased attraction to food or depletion of fat stores, but rather insulin signaling between the intestine and specific neurons. We use genetic tools, microscopy, and behavioral tests to determine that this risky behavior involves sensation of "lack of food" in the intestine, release of signaling molecules, and engagement with sensory neurons. Our work highlights new and potentially evolutionarily conserved ways in which intestinal cells and neurons communicate leading to largescale behavioral change, providing further support for the importance of the gut-brain-axis.

neuroscience↗