Biology subjects

Bentley, A. R.

Publications and source records attributed to Bentley, A. R..

17 recordsLinked to original sources

bioRxiv2025

Nitrogen dynamics and yield performance of an elite bread wheat line with BNI capacity expressed in an alkaline soil

Wheat with biological nitrification inhibition (BNI) has been shown to be effective in acidic soils. BNI expressions in alkaline soil have not been documented in field studies. We present the first observation of BNI effects in alkaline soil trials (pH 8.6-8.7) of elite spring wheat that carries the Lr#N short arm from Leymus racemosus. BNI was expressed as lower soil nitrate in three trials conducted in an irrigated wheat system in northwestern Mexico. In one year, we observed 24-37% lower nitrate in soil of the translocation line compared to the control after the second ammonium-N fertilizer application. Lower nitrate was observed in the row and in the furrow. The largest difference between the control and the translocation line was approximately one month after the second ammonium-N split application (73-77% less nitrate). Potential nitrification rates were 27-32% lower in soil from the translocation line compared to the control, one week after a high ammonium-N application. Higher flag leaf nitrate concentration of the translocation line could be related to the strongly reduced soil nitrate. The translocation line took longer to reach anthesis and flowering. In two experiments, the translocation line was equivalent in terms of grain yield, whereas in one experiment it produced lower yields, with fewer grains per spike and reduced number of spikes per area. Protein content and grain N uptake of the translocation line were similar to the control. We conclude that BNI reduces nitrification with a spatially and temporally significant impact under alkaline soil and high N input conditions. This finding points to the potential major environmental benefits that could be achieved also in non-acidic soil spring wheat systems worldwide. The effect of the Lr#N short arm on yield quantity and quality in other elite lines needs further investigation.

plant biology↗

bioRxiv2022

Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture in long-term genetic gains in wheat

Cereal crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, focus on selection for yield has negatively impacted other important traits. To better understand selection within a breeding context, and how it might be optimised, we analysed genotypic and phenotypic data from a diverse, 16-founder wheat multi-parent advanced generation inter-cross (MAGIC) population. Compared to single-trait models, multi-trait ensemble genomic prediction models increased prediction accuracy for almost 90% of traits, improving grain yield prediction accuracy by 3-52%. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10-36%. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimised long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits, which involved optimisation of antagonistic trait relationships. We found multi-trait selection indices could be used to optimise undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability. Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model, accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes and provide insights into mechanisms for continued genetic gain in a limited genepool and optimisation of multiple traits for crop improvement.

genetics↗

bioRxiv2022

Multilocation trials identify stable high yielding spring bread and durum wheat cultivars in Mexico

Determining the stability and consistency of grain yield performance requires accurate evaluation of genotypes in different environments. In Mexico, annual national spring wheat irrigated trials are conducted to assess elite bread and durum wheat performance in different testing environments (TEs) in the main wheat-growing areas. These trials provide data supporting release of new cultivars and aim to also address Mexican wheat value chain grain needs. In this study we analyzed 30 bread and durum wheat trial results from the 2012/13 and 2013/14 growing cycles conducted across TEs in northwest, north and central Mexico. Environmental variability (location, sowing timing, and irrigation schemes) across the national spring wheat irrigated trials enabled genotype by environment interaction to be effectively evaluated. We identified genotypes with high and stable grain yield across TEs of the wheat-growing areas of Mexico. The bread cultivars Bacorehuis F2015 and Borlaug100 F2014, and the durum cultivars Barobampo C2015, CONASIST C2015 and Anatoly C2011 were high yielding and gave stable performance in most of the TEs. This analysis demonstrates the utility of multi-year, multi-environment testing and analysis to identify improved wheat cultivars to meet wheat production demand in Mexico.

plant biology↗

bioRxiv2022

ENVIROME-WIDE ASSOCIATIONS ENHANCE MULTI-YEAR GENOME-BASED PREDICTION OF HISTORICAL WHEAT BREEDING DATA

Linking high-throughput environmental data (enviromics) into genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (GxE). This study developed a data-driven approach based on Environment-Phenotype Associations (EPA) aimed at recycling important GxE information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes; (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. Then, the EPA outcomes were integrated into multi-environment GP models through a new single-step GBLUP. The wheat trial data used included 36 locations, 8 years and 3 target populations of environments (TPE) in India. Four prediction scenarios and 6 kernel-models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. However, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the GxE kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of GxE prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as "covariable selection" unfeasible because GxE is year-germplasm specific. We propose that the EPA effectively serves as a "reinforcement learner" algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.

genetics↗

bioRxiv2022

A wheat chromosome segment substitution line series supports characterisation and use of progenitor genetic variation

Genome-wide introgression and substitution lines have been developed in many plant species, enhancing mapping precision, gene discovery and the identification and exploitation of variation from wild relatives. Created over multiple generations of crossing and/or backcrossing accompanied by marker-assisted selection, the resulting introgression lines are a fixed genetic resource. In this study we report the development of spring wheat chromosome segment substitution lines generated to systematically capture genetic variation from tetraploid (Triticum turgidum ssp dicoccoides) and diploid (Aegilops tauschii) progenitor species. Generated in a common genetic background over four generations of backcrossing, the material is a base resource for the mapping and characterisation of wheat progenitor variation. To facilitate further exploitation the final population was genetically characterised using a high- density genotyping array and a range of agronomic and grain traits assessed to demonstrate the the potential use of the populations for trait localisation in wheat.

genetics↗

bioRxiv2022

A pandemic clonal lineage of the wheat blast fungus

Wheat, the most important food crop, is threatened by a blast disease pandemic. Here, we show that a clonal lineage of the wheat blast fungus recently spread to Asia and Africa following two independent introductions from South America. Through a combination of genome analyses and laboratory experiments, we show that the decade-old blast pandemic lineage can be controlled by the Rmg8 disease resistance gene and is sensitive to strobilurin fungicides. However, we also highlight the potential of the pandemic clone to evolve fungicide-insensitive variants and sexually recombine with African lineages. This underscores the urgent need for genomic surveillance to track and mitigate the spread of wheat blast outside of South America, and to guide pre-emptive wheat breeding for blast resistance.

evolutionary biology↗

bioRxiv2021

Over-expression of the brassinosteroid gene TaDWF4 increases wheat productivity under low and sufficient nitrogen through enhanced carbon assimilation

There is a strong pressure to reduce nitrogen (N) fertiliser inputs while maintaining or increasing current cereal crop yields. Brassinosteroids, (BR), are a group of phytohormones essential for plant growth and development, that have been demonstrated to regulate several agronomic traits. DWF4 encodes a cytochrome P450 that catalyses a rate-limiting step in BR synthesis. We show that overexpression of the dominant shoot expressed homoeologue TaDWF4-B in wheat can increase plant productivity by up to 105% under a range of N levels on marginal soils, resulting in increased N use efficiency (NUE). We show that a two to four-fold increase in TaDWF4 transcript levels enhances the responsiveness of genes regulated by N. The productivity increases seen were primarily due to the maintenance of photosystem II operating efficiency and carbon assimilation in plants when grown under limiting N conditions and not an overall increase in photosynthesis capacity. The increased biomass production and yield per plant in TaDWF4 OE lines could be linked to modified carbon partitioning and changes in expression pattern of the growth regulator Target Of Rapamycin, offering a route towards breeding for sustained yield and lower N inputs.

plant biology↗

bioRxiv2020

GWAS in Africans identifies novel lipids loci and demonstrates heterogenous association within Africa

BackgroundSerum lipids are biomarkers of cardiometabolic disease risk, and understanding the genomic factors contributing to their distribution has been of considerable interest. Large genome-wide association studies (GWAS) have identified over 150 lipids loci; however, GWAS of Africans (AF) are rare. Given the genomic diversity among those of African ancestry, it is expected that a GWAS in Africans could identify novel lipids loci. While GWAS have been conducted in African Americans (AA), such studies are not proxies for studies in continental Africans due to the drastically different environmental context. Therefore, we conducted a GWAS of 4,317 Africans enrolled in the Africa America Diabetes Mellitus study. Methods and ResultsWe used linear mixed models of the inverse normal transformations of covariate-djusted residuals of high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC), total cholesterol (CHOL), triglycerides (TG), and TG/HDLC, with adjustment for three principal components and the random effect of relatedness. Replication of loci associated at p<5x10-8 was attempted in 9,542 AA. Meta-analysis of AF and AA was also conducted. We also conducted analyses that excluded the relatively small number of East Africans. We evaluated known lipids loci in Africans using both exact replication and "local" replication, which accounts for interethnic differences in linkage disequilibrium. In our main analysis, we identified 23 novel associations in Africans. Of the 14 of these that were able to be tested in AA, two associations replicated (GPNMB-TG and ENPP1-TG). Two additional novel loci were discovered upon meta-analysis with AA (rs138282551-TG and TLL2-CHOL). Analyses considering only those with predominantly West African ancestry (Nigeria, Ghana, and AA) yielded new insights: ORC5-LDLC and chr20:60973327-CHOL. ConclusionsWhile functional work will be useful to confirm and understand the biological mechanisms underlying these associations, this study demonstrates the utility of conducting large-scale genomic analyses in Africans for discovering novel loci. The functional significance of some of these loci in relation to lipids remains to be elucidated, yet some have known connections to lipids pathways. For instance, rs147706369 (intronic, TLL2) alters a regulatory motif for sterol regulatory element-binding proteins (SREBPs), which are a family of transcription factors that control the expression of a range of enzymes involved in cholesterol, fatty acid, and triglyceride synthesis.

genomics↗

bioRxiv2020

Multi-ancestry genome-wide gene-sleep interactions identify novel loci for blood pressure

Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups using 1 degree of freedom (1df) interaction and 2df joint tests. Primary multi-ancestry analyses in 62,969 individuals in stage 1 identified 3 novel loci that were replicated in an additional 59,296 individuals in stage 2, including rs7955964 (FIGNL2/ANKRD33) showing significant 1df interactions with long sleep duration and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) showing significant 1df interactions with short sleep duration (Pint < 5x10-8). Secondary ancestry-specific two-stage analyses and combined stage 1 and 2 analyses additionally identified 23 novel loci that need external replication, including 3 and 5 loci showing significant 1df interactions with long and short sleep duration, respectively (Pint < 5x10-8). Multiple genes mapped to our 26 novel loci have known functions in sleep-wake regulation, nervous and cardiometabolic systems. We also identified new gene by long sleep interactions near five known BP loci ([≤]1Mb) including NME7, FAM208A, MKLN1, CEP164, and RGL3/ELAVL3 (Pint < 5x10-8). This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.

genomics↗

bioRxiv2020

Genetic Risk Scores for Cardiometabolic Traits in Sub-Saharan African Populations

There is growing support for the use of genetic risk scores (GRS) in routine clinical settings. Due to the limited diversity of current genomic discovery samples, there are concerns that the predictive power of GRS will be limited in non-European ancestry populations. Here, we evaluated the predictive utility of GRS for 12 cardiometabolic traits in sub-Saharan Africans (AF; n=5200), African Americans (AA; n=9139), and European Americans (EA; n=9594). GRS were constructed as weighted sums of the number of risk alleles. Predictive utility was assessed using the additional phenotypic variance explained and increase in discriminatory ability over traditional risk factors (age, sex and BMI), with adjustment for ancestry-derived principal components. Across all traits, GRS showed upto a 5-fold and 20-fold greater predictive utility in EA relative to AA and AF, respectively. Predictive utility was most consistent for lipid traits, with percent increase in explained variation attributable to GRS ranging from 10.6% to 127.1% among EA, 26.6% to 65.8% among AA, and 2.4% to 37.5% among AF. These differences were recapitulated in the discriminatory power, whereby the predictive utility of GRS was 4-fold greater in EA relative to AA and up to 44-fold greater in EA relative to AF. Obesity and blood pressure traits showed a similar pattern of greater predictive utility among EA. This work demonstrates the poorer performance of GRS in AF and highlights the need to improve representation of multiethnic populations in genomic studies to ensure equitable clinical translation of GRS. Key MessagesO_LIGenetic Risk Score (GRS) prediction is markedly poorer in sub-Saharan Africans compared with African Americans and European Americans C_LIO_LITo ensure equitable clinical translation of GRS, there is need need to improve representation of multiethnic populations in genomic studies C_LI

genomics↗

bioRxiv2020

Grain number and genotype drive nitrogen-dependent yield response in the C4 model Setaria italica (L.) P. Beauv.

Fertiliser nitrogen (N) drives crop yields and requires the breeding and selection of cultivars that are inherently highly N responsive. For major cereal crops such as wheat (Triticum aestivum L.) breeding over time has led to enhanced N use in modern cultivars however there remains a gap in understanding the N responsiveness of minor cereals grains, many of which are highly relevant to global food security. Here we investigate response to increasing N availability in a diverse population of Setaria italica (L., foxtail millet) accessions demonstrating that N-driven yield increase is dependent on grain number rather than individual grain weight. Within the population, some accessions responded strongly to increased N availability while others show little yield improvement under high N. Genetic markers were generated to enable investigation of N responsiveness at a genome-wide level, highlighting likely underlying causal loci, especially for grains per plant. Despite the lack of response in terms of yield increase, a non-responsive accession shows a strong transcriptional response suggesting different metabolic functioning under high vs low N. Our results show major differences in N responsiveness in S. italica and provide novel insight into the genetic and molecular basis for this variation. One sentence summaryNitrogen dependent yield response in Setaria italica L. is driven by grain number and genotypes with low N yield responsive genotypes being more transcriptionally dynamic under varied N levels post-flowering compared to high N yield responsive genotypes.

plant biology↗

bioRxiv2019

Deriving stratified effects from joint models investigating Gene-Environment Interactions

BackgroundModels including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and marginal (i.e not accounting for interaction) models.\n\nResultsIn this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the marginal model using summary statistics from the \"joint\" model. We performed simulation studies to assess our estimators accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators retained accuracy after filtering SNPs by sample size to mitigate potential bias.\n\nConclusionsThese analyses demonstrated the accuracy of our method in estimating both stratified and marginal summary statistics from a joint model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/J2S.

bioinformatics↗

bioRxiv2019

A Novel Type 2 Diabetes Locus in sub-Saharan Africans, ZRANB3, is Implicated in Beta Cell Proliferation

Genome analysis of diverse human populations has contributed to the identification of novel genomic loci for diseases of major clinical and public health impact. Here, we report the largest genome-wide analysis of type 2 diabetes (T2D) in sub-Saharan Africans, an understudied ancestral group. We analyzed ~18 million autosomal SNPs in 5,231 individuals from Nigeria, Ghana and Kenya. TCF7L2 rs7903156 was the most significant locus (p=7.288 x 10-13). We identified a novel genome wide significant locus: ZRANB3 (Zinc Finger RANBP2-Type Containing 3, lead SNP chr2:136064024, T allele frequency=0.034, p=2.831x10-9). Knockdown of the zebrafish ortholog resulted in reduction in pancreatic beta cell number in the developing organism, suggesting a potential mechanism for its effect on glucose hemostasis. We also showed transferability in our study of 32 established T2D loci. Our findings provide evidence of a novel candidate T2D locus and advance understanding of the genetics of T2D in non-European ancestry populations.

genetics↗

bioRxiv2019

Genome-wide Interaction Studies by the CHARGE Gene-Lifestyle Interactions Working Group: what we have learned and what is coming next.

The CHARGE Gene-Lifestyle Interactions Working Group is a unique initiative formed to improve our understanding of the role and biological significance of gene-environment interactions in human traits and diseases. The consortium published several multi-ancestry genome-wide interaction studies (GWIS) involving up to 610,475 individuals for three lipids and four blood pressure traits while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and GxE interactions across phenotype-exposure-population trios, and to derive new insights on the potential mechanistic underlying GxE through in-silico functional analyses. Our comparative analysis shows first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted a negligible correlation between main and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell-types. In these analyses, we found multiple instances of heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work we identified potential biases in methods used to jointly meta-analyses genetic and interaction effects. We performed a series of simulations to characterize these limitations and to provide the community with guideline for future GxE studies.

genetics↗

bioRxiv2019

Multi-ancestry analysis of gene-sleep interactions in 126,926 individuals identifies multiple novel blood lipid loci that contribute to our understanding of sleep-associated adverse blood lipid profile

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To provide new insights in the biology of sleep-associated adverse lipid profile, we conducted multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identified 49 novel lipid loci, and 10 additional novel lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identified new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The novel gene-sleep interactions had a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explained 4.25% of the variance in triglyceride concentration. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.

genomics↗

bioRxiv2018

The effects of training population design on genomic prediction accuracy in wheat

Genomic selection offers several routes for increasing genetic gain or efficiency of plant breeding programs. In various species of livestock there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeders equation. Accurate predictions of genomic breeding value are central to this and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable accurate predictions.\n\nTo quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and triparental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25,000 segregating single nucleotide polymorphism markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Within cross genomic prediction accuracies of yield BLUEs were 0.125 - 0.127 using two different cross-validation approaches, and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasize the importance of the training set design in relation to the genetic material to which the resulting prediction model is to be applied.

genetics↗

bioRxiv2018

Resistance gene discovery and cloning by sequence capture and association genetics

Genetic resistance is the most economic and environmentally sustainable approach for crop disease protection. Disease resistance (R) genes from wild relatives are a valuable resource for breeding resistant crops. However, introgression of R genes into crops is a lengthy process often associated with co-integration of deleterious linked genes1, 2 and pathogens can rapidly evolve to overcome R genes when deployed singly3. Introducing multiple cloned R genes into crops as a stack would avoid linkage drag and delay emergence of resistance-breaking pathogen races4. However, current R gene cloning methods require segregating or mutant progenies5-10, which are difficult to generate for many wild relatives due to poor agronomic traits. We exploited natural pan-genome variation in a wild diploid wheat by combining association genetics with R gene enrichment sequencing (AgRenSeq) to clone four stem rust resistance genes in <6 months. RenSeq combined with diversity panels is therefore a major advance in isolating R genes for engineering broad-spectrum resistance in crops.

genomics↗