Genome-Wide Association of Stem Water Soluble Carbohydrates in Bread Wheat

Water soluble carbohydrates (WSC) in stems play an important role in buffering grain yield in wheat against biotic and abiotic stresses; however, knowledge of genes controlling WSC is very limited. We conducted a genome-wide association study (GWAS) using a high-density 90K SNP array to better understand the genetic basis underlying WSC, and to explore marker-based breeding approaches. WSC was evaluated in an association panel comprising 166 Chinese bread wheat cultivars planted in four environments. Fifty two marker-trait associations (MTAs) distributed across 23 loci were identified for phenotypic best linear unbiased estimates (BLUEs), and 11 MTAs were identified in two or more environments. Liner regression showed a clear dependence of WSC BLUE scores on numbers of favorable (increasing WSC content) and unfavorable alleles (decreasing WSC), indicating that genotypes with higher numbers of favorable or lower numbers of unfavorable alleles had higher WSC content. In silico analysis of flanking sequences of trait-associated SNPs revealed eight candidate genes related to WSC content grouped into two categories based on the type of encoding proteins, namely, defense response proteins and proteins triggered by environmental stresses. The identified SNPs and candidate genes related to WSC provide opportunities for breeding higher WSC wheat cultivars.     

Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments

Key message

We identified 27 stable loci associated with agronomic traits in spring wheat using genome-wide association analysis, some of which confirmed previously reported studies. GWAS peaks identified in regions where no QTL for grain yield per se has been mapped to date, provide new opportunities for gene discovery and creation of new cultivars with desirable alleles for improving yield and yield stability in wheat.

Abstract

We undertook large-scale genetic analysis to determine marker-trait associations (MTAs) underlying agronomic and physiological performance in spring wheat using genome-wide association studies (GWAS). Field trials were conducted at seven sites in three countries (Sudan, Egypt, and Syria) over 2–3 years in each country. Twenty-five agronomic and physiological traits were measured on 188 wheat genotypes. After correcting for population structure and relatedness, a total of 245 MTAs distributed over 66 loci were associated with agronomic traits in individual and mean performance across environments respectively; some of which confirmed previously reported loci. Of these, 27 loci were significantly associated with days to heading, thousand kernel weight, grain yield, spike length, and leaf rolling for mean performance across environments. Despite strong QTL by environment interactions, eight of the loci on chromosomes 1A, 1D, 5A, 5D, 6B, 7A, and 7B had pleiotropic effects on days to heading and yield components (TKW, SM^?2, and SNS). The winter-type alleles at the homoeologous VRN1 loci significantly increased days to heading and grain yield in optimal environments, but decreased grain yield in heat prone environments. Top 20 high-yielding genotypes, ranked by additive main effects and multiplicative interaction (AMMI), had low kinship relationship and possessed 4–5 favorable alleles for GY MTAs except two genotypes, Shadi-4 and Qafzah-11/Bashiq-1–2. This indicated different yield stability mechanisms due to potentially favorable rare alleles that are uncharacterized. Our results will enable wheat breeders to effectively introgress several desirable alleles into locally adapted germplasm in developing wheat varieties with high yield stability and enhanced heat tolerance.

     

Genome-wide association mapping identifies markers associated with cane yield components and sucrose traits in the Louisiana sugarcane core collection

Sugarcane is an economically important crop for both food and biofuel industries. Marker-assisted breeding in sugarcane is becoming a reality with the recent development and deployment of markers linked with disease resistance genes. Large linkage disequilibrium in sugarcane makes genome-wide association studies (GWAS) a better alternative to biparental mapping to identify markers associated with agronomic traits. GWAS was conducted on a Louisiana core collection to identify marker-trait associations (MTA) for 11 cane yield and sucrose traits using single nucleotide polymorphism (SNP) and insertion-deletion (Indel) markers. Significant (P < .05) MTAs were identified for all traits where the top ranked markers explained up to 15% of the total phenotypic variation. High correlations (0.732 to 0.999) were observed between sucrose traits and 56 markers were found consistent across multiple traits. These markers following validation in more diverse populations could be used in marker-assisted selection of clones in sugarcane breeding program in Louisiana and elsewhere.     

A genome-wide associate study reveals favorable alleles conferring apical and basal spikelet fertility in wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Kernel number per spike (KNPS) is one of the key factors affecting wheat yield, which can be significantly reduced by lower fertility or sterility of the apical and basal spikelets. In this study, the spikelet number per spike (SNPS), thousand kernel weight (TKW), KNPS, total grain numbers of the top three apical spikelets (GNAS), and total grain numbers of the bottom three basal spikelets (GNBS) of 212 wheat lines were recorded from five different environmental conditions. These 212 accessions were genotyped using the 9K iSelect SNP Beadchip. A total of 3269 SNP markers were used for genome-wide association analysis (GWAS). One hundred twelve significant marker-trait associations (MTAs) were identified. Twenty-two MTAs were identified in at least two environments and two of them showed association with two or more grain setting properties. Different loci showed an additive effect with both GNAS and GNBS being much higher in the lines with more favorite alleles. Two SNP loci, wsnp_Ex_c31799_40545376 and wsnp_BF293620A_Ta_2_3, showed the largest effects on increasing KNPS through improved fertility of apical and basal spikelets, respectively. These MTAs have the potential to be used in future marker-assisted selection.     

Genome-wide association and validation of key loci for yield-related traits in wheat founder parent Xiaoyan 6

Xiaoyan 6, one of the most important founder parents in wheat, possesses many superior agronomic traits and has played a crucial role in Chinese wheat breeding programs. In this study, a panel of 66 elite wheat accessions derived from Xiaoyan 6 was planted in four growing seasons; genome-wide association study (GWAS) was performed for six yield-related traits using the wheat 90K genotyping assay. A total of 803 significant marker-trait associations (MTAs) that explained up to 35.0% of the phenotypic variation were detected. Of these, the locus QTkw-5B which contains 19 MTAs for thousand kernel weight (TKW) was consistently detected in three growing seasons and confirmed in a recombinant inbred line (RIL) population by developing simple sequence repeats (SSR) and kompetitive allele-specific PCR (KASP) markers. The locus QPh-3A containing eight repetitive MTAs for plant height (PH) was consistently identified in all the four growing seasons and validated in a RIL population by developing SSR markers. The transmission of Xiaoyan 6 allele indicated that the favorite allele of QPh-3A was strongly selected in breeding programs. Comparing with previous studies, QTkw-5B and QPh-3A should be novel QTL. The locus QFss-2D for fertile spikelet number per spike (FSS) was identified and then validated in three bi-parental populations. This locus controlled various spike-related traits and may be a key spike polymorphic locus. This study could provide insight into dissecting yield-related traits in the breeding population and reliable molecular markers that might be valuable for marker-assisted selection in wheat high-yield breeding programs.     

GWAS for plant growth stages and yield components in spring wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.) harvested in three regions of Kazakhstan

Background

Spring wheat is the largest agricultural crop grown in Kazakhstan with an annual sowing area of 12 million hectares in 2016. Annually, the country harvests around 15 million tons of high quality grain. Despite environmental stress factors it is predicted that the use of new technologies may lead to increases in productivity from current levels of 1.5 to up to 3 tons per hectare. One way of improving wheat productivity is by the application of new genomic oriented approaches in plant breeding projects. Genome wide association studies (GWAS) are emerging as powerful tools for the understanding of the inheritance of complex traits via utilization of high throughput genotyping technologies and phenotypic assessments of plant collections. In this study, phenotyping and genotyping data on 194 spring wheat accessions from Kazakhstan, Russia, Europe, and CIMMYT were assessed for the identification of marker-trait associations (MTA) of agronomic traits by using GWAS.

Results

Field trials in Northern, Central and Southern regions of Kazakhstan using 194 spring wheat accessions revealed strong correlations of yield with booting date, plant height, biomass, number of spikes per plant, and number of kernels per spike. The accessions from Europe and CIMMYT showed high breeding potential for Southern and Central regions of the country in comparison with the performance of the local varieties. The GGE biplot method, using average yield per plant, suggested a clear separation of accessions into their three breeding origins in relationship to the three environments in which they were evaluated. The genetic variation in the three groups of accessions was further studied using 3245 polymorphic SNP (single nucleotide polymorphism) markers. The application of Principal Coordinate analysis clearly grouped the 194 accessions into three clades according to their breeding origins. GWAS on data from nine field trials allowed the identification of 114 MTAs for 12 different agronomic traits.

Conclusions

Field evaluation of foreign germplasm revealed its poor yield performance in Northern Kazakhstan, which is the main wheat growing region in the country. However, it was found that EU and CIMMYT germplasm has high breeding potential to improve yield performance in Central and Southern regions. The use of Principal Coordinate analysis clearly separated the panel into three distinct groups according to their breeding origin. GWAS based on use of the TASSEL 5.0 package allowed the identification of 114 MTAs for twelve agronomic traits. The study identifies a network of key genes for improvement of yield productivity in wheat growing regions of Kazakhstan.

     

小麦芒基因定位及其与农艺性状的相关性分析

芒是位于植物穗上的针状结构,广泛存在于禾本科作物水稻、小麦、高粱和大麦中,不同作物芒的结构存在差异.小麦中,芒对提高穗光合效率和产量、防鸟、抗虫及抗逆有重要作用.前人已经对抑制小麦芒发育的主要基因进行了定位和遗传分析,4个主效基因中仅有B1(Tipped1)基因被克隆.本研究基于人工群体云南3号和偃展1号BC3F6群体...     

Polyphenol oxidase genes as integral part of the evolutionary history of domesticated tetraploid wheat

Studying and understanding the genetic basis of polyphenol oxidases (PPO)-related traits plays a crucial role in genetic improvement of crops.A tetraploid wheat collection (T. turgidum ssp., TWC) was analyzed using the 90K wheat SNP iSelect assay and phenotyped for PPO activity. A total of 21,347 polymorphic SNPs were used to perform genome-wide association analysis (GWA) in TWC and durum wheat sub-groups, detecting 23 and 85 marker-trait associations (MTA). In addition, candidate genes responsible for PPO activity were predicted. Based on the 23 MTAs detected in TWC, two haplotypes associated with low and high PPO activity were identified. Four SNPs were developed and validated providing one reliable marker (IWB75732) for marker assisted selection. The 23 MTAs were used to evaluate the genetic divergence (F_ST > 0.25) between the T. turgidum subspecies, providing new information important for understanding the domestication process of Triticum turgidum ssp. and in particular of ssp. carthlicum.     

Contribution of Genome-Wide Association Studies to Scientific Research: A Pragmatic Approach to Evaluate Their Impact

The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets. We chose multiple sclerosis (MS) as a paradigm disease and assumed that, in pre-GWAS candidate-gene studies, the knowledge behind the choice of each gene reflected the understanding of the disease prior to the advent of GWAS. Importantly, this knowledge was based mainly on non-genetic, phenotypic grounds. We performed single-gene and pathway-oriented comparisons of old and new knowledge in MS by confronting an unbiased list of candidate genes in pre-GWAS association studies with those genes exceeding the genome-wide significance threshold in GWAS published from 2007 on. At the single gene level, the majority (94 out of 125) of GWAS-discovered variants had never been contemplated as plausible candidates in pre-GWAS association studies. The 31 genes that were present in both pre- and post-GWAS lists may be of particular interest in that they represent disease-associated variants whose pathogenetic relevance is supported at the phenotypic level (i.e. the phenotypic information that steered their selection as candidate genes in pre-GWAS association studies). As such they represent attractive therapeutic targets. Interestingly, our analysis shows that some of these variants are targets of pharmacologically active compounds, including drugs that are already registered for human use. Compared with the above single-gene analysis, at the pathway level GWAS results appear more coherent with previous knowledge, reinforcing some of the current views on MS pathogenesis and related therapeutic research. This study presents a pragmatic approach that helps interpret and exploit GWAS knowledge.     

Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P _meta<1×10⁻⁴, including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available.     

Introduction to Deep Sequencing and Its Application to Drug Addiction Research with a Focus on Rare Variants

Through linkage analysis, candidate gene approach, and genome-wide association studies (GWAS), many genetic susceptibility factors for substance dependence have been discovered such as the alcohol dehydrogenase gene (ALDH2) for alcohol dependence (AD) and nicotinic acetylcholine receptor (nAChR) subunit variants on chromosomes 8 and 15 for nicotine dependence (ND). However, these confirmed genetic factors contribute only a small portion of the heritability responsible for each addiction. Among many potential factors, rare variants in those identified and unidentified susceptibility genes are supposed to contribute greatly to the missing heritability. Several studies focusing on rare variants have been conducted by taking advantage of next-generation sequencing technologies, which revealed that some rare variants of nAChR subunits are associated with ND in both genetic and functional studies. However, these studies investigated variants for only a small number of genes and need to be expanded to broad regions/genes in a larger population. This review presents an update on recently developed methods for rare-variant identification and association analysis and on studies focused on rare-variant discovery and function related to addictions.     

Multiple sclerosis as a polygenic disease: An update

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. The observed type of heredity associated with MS is characteristic of polygenic diseases, which arises from a joint contribution of a number of independently acting or interacting polymorphic genes. Recently to identify the genes responsible for genetic predisposition to MS two main approaches have been applied: (1) analysis of association of individual “candidate genes” with the disease and (2) analysis of the wide spectrum of chromosomal loci (whole genome screen) linkage with the disease in families with several MS patients. In the last two years, a new method, which borrowed the best approaches of the previous studies, genome-wide association screening (GWAS), which is based on the modern high-throughput DNA analysis, has been developed. This review describes replicated (validated) results for individual genes and DNA loci located on the majority of chromosomes obtained using these three strategies as well as data on association of MS with allelic combinations of various genes.     

Unifying candidate gene and GWAS Approaches in Asthma

The first genome wide association study (GWAS) for childhood asthma identified a novel major susceptibility locus on chromosome 17q21 harboring the ORMDL3 gene, but the role of previous asthma candidate genes was not specifically analyzed in this GWAS. We systematically identified 89 SNPs in 14 candidate genes previously associated with asthma in >3 independent study populations. We re-genotyped 39 SNPs in these genes not covered by GWAS performed in 703 asthmatics and 658 reference children. Genotyping data were compared to imputation data derived from Illumina HumanHap300 chip genotyping. Results were combined to analyze 566 SNPs covering all 14 candidate gene loci. Genotyped polymorphisms in ADAM33, GSTP1 and VDR showed effects with p-values <0.0035 (corrected for multiple testing). Combining genotyping and imputation, polymorphisms in DPP10, EDN1, IL12B, IL13, IL4, IL4R and TNF showed associations at a significance level between p = 0.05 and p = 0.0035. These data indicate that (a) GWAS coverage is insufficient for many asthma candidate genes, (b) imputation based on these data is reliable but incomplete, and (c) SNPs in three previously identified asthma candidate genes replicate in our GWAS population with significance after correction for multiple testing in 14 genes.     

A genome-wide association study of wheat yield and quality-related traits in southwest China

Wheat (Triticum aestivum L.) is one of the most productive and important crops and its yield potential and quality characteristics are tightly linked with the global food security. In this study, genome-wide association study (GWAS) was conducted for yield and quality-related traits. Based on the high-density wheat 90K Illumina iSelect SNP Array, 192 bread wheat lines from southwest China, including 25 synthetic hexaploid wheat lines, 80 landraces, and 87 cultivars were analyzed. Association analysis results indicated that there were 57, 27, 30, and 34 SNPs associated with plant height (PH), grain protein content (GPC), thousand kernel weight (TKW), and SDS sedimentation volume (SSV) have been detected, respectively. Then, integrating RNA-Seq with bioinformatics analysis, 246 candidate genes (102 for GPC, 52 for TKW, and 92 for SSV) were found. Further analysis indicated that one up-regulated and two down-regulated expression genes affect GPC. Additionally, two haplotypes significantly affecting PH were detected in a 2.2-Mb genome region encompassing a gene which encoded an ubiquitin-specific protease, TaUBP24. The functional markers of TaUBP24 have been developed, which could be used for marker-assisted selection to improve wheat quality and yield.     

Genome-wide association mapping reveals key genomic regions for physiological and yield-related traits under salinity stress in wheat (Triticum aestivum L.)

A genome-wide association study (GWAS) was conducted using six different multi-locus GWAS models and 35K SNP array to demarcate genomic regions underlying reproductive stage salinity tolerance. Marker-trait association analysis was performed for salt tolerance indices (STI) of 11 morpho-physiological traits, and the actual concentrations of Na⁺ and K⁺, and the Na⁺/K⁺ ratio in flag leaf. A total of 293 significantly associated quantitative trait nucleotides (QTNs) for 14 morpho-physiological traits were identified. Of these 293 QTNs, 12 major QTNs with R² ≥ 10.0% were detected in three or more GWAS models. Novel major QTNs were identified for plant height, number of effective tillers, biomass, grain yield, thousand grain weight, Na⁺ and K⁺ content, and the Na⁺/K⁺ ratio in flag leaf. Moreover, 48 candidate genes were identified from the associated genomic regions. The QTNs identified in this study could potentially be targeted for improving salinity tolerance in wheat.     

Genome-wide association mapping of genomic regions associated with phenotypic traits in Canadian western spring wheat

We recently reported genomic regions associated with resistance to four wheat diseases and insensitivity to three Pyrenophora tritici-repentis toxins in an association mapping panel consisting of 81 diverse Canadian western spring wheat (Triticum aestivum L.) cultivars. Here, we report genomic regions and SNPs associated with days to heading, plant maturity, plant height, test weight (grain volume weight), grain yield, and grain protein content in the same population using genome-wide association studies (GWAS). The 81 spring wheat cultivars were evaluated for the above six traits across six environments and genotyped with 19,919 polymorphic SNPs and 14 gene-specific markers. Using mixed liner model and a threshold of p ≤ 3.1 × 10^?4, we identified a total of 139 significant marker-trait associations that were mapped at 19 genomic regions on 11 chromosomes for heading (3 regions), maturity (2), plant height (3), test weight (3), grain yield (6), and grain protein (2). Each region consisted of clusters of markers ranging from 2 to 33 and individually explained from 4.5 to 26.1% of the phenotypic variation averaged over six environments. Some the genomic regions identified in the present study are novel, while others, such as the regions for grain protein on 1B, days to heading on 5A, plant height on 4B, and test weight on 7A, were located close to either known genes or QTLs reported in previous studies, but direct comparisons in some cases were challenging due to lack of common set of markers and reliable physical positions among the different studies. Results from this study provide additional information to wheat researchers developing improved spring wheat cultivars.     

Genetic dissection of wheat panicle traits using linkage analysis and a genome-wide association study

Key message

Coincident regions on chromosome 4B for GW, on 5A for SD and TSS, and on 3A for SL and GNS were detected through an integration of a linkage analysis and a genome-wide association study (GWAS). In addition, six stable QTL clusters on chromosomes 2D, 3A, 4B, 5A and 6A were identified with high PVE% on a composite map.

Abstract

The panicle traits of wheat, such as grain number per spike and 1000-grain weight, are closely correlated with grain yield. Superior and effective alleles at loci related to panicles developments play a crucial role in the progress of molecular improvement in wheat yield breeding. Here, we revealed several notable allelic variations of seven panicle-related traits through an integration of genome-wide association mapping and a linkage analysis. The linkage analysis was performed using a recombinant inbred line (RIL) population (173 lines of F_8:9) with a high-density genetic map constructed with 90K SNP arrays, Diversity Arrays Technology (DArT) and simple sequence repeat (SSR) markers in five environments. Thirty-five additive quantitative trait loci (QTL) were discovered, including eleven stable QTLs on chromosomes 1A, 2D, 4B, 5B, 6B, and 6D. The marker interval between EX_C101685 and RAC875_C27536 on chromosome 4B exhibited pleiotropic effects for GW, SL, GNS, FSN, SSN, and TSS, with the phenotypic variation explained (PVE) ranging from 5.40 to 37.70%. In addition, an association analysis was conducted using a diverse panel of 205 elite wheat lines with a composite map (24,355 SNPs) based on the Illumina Infinium assay in four environments. A total of 73 significant marker-trait associations (MTAs) were detected for panicle traits, which were distributed across all wheat chromosomes except for 4D, 5D, and 6D. Consensus regions between RAC875_C27536_611 and Tdurum_contig4974_355 on chromosome 4B for GW in multiple environments, between QTSS5A.7-43 and BS00021805_51 on 5A for SD and TSS, and between QSD3A.2-164 and RAC875_c17479_359 on 3A for SL and GNS in multiple environments were detected through linkage analysis and a genome-wide association study (GWAS). In addition, six stable QTL clusters on chromosomes 2D, 3A, 4B, 5A, and 6A were identified with high PVE% on a composite map. This study provides potentially valuable information on the dissection of yield-component traits and valuable genetic alleles for molecular-design breeding or functional gene exploration.

     

Contribution of Genome-Wide Association Studies to Scientific Research: A Bibliometric Survey of the Citation Impacts of GWAS and Candidate Gene Studies Published during the Same Period and in the Same Journals

In genetic epidemiology, genome-wide association studies (GWAS) are used to rapidly scan a large set of genetic variants and thus to identify associations with a particular trait or disease. The GWAS philosophy is different to that of conventional candidate-gene-based approaches, which directly test the effects of genetic variants of potentially contributory genes in an association study. One controversial question is whether GWAS provide relevant scientific outcomes by comparison with candidate-gene studies. We thus performed a bibliometric study using two citation metrics to assess whether the GWAS have contributed a capital gain in knowledge discovery by comparison with candidate-gene approaches. We selected GWAS published between 2005 and 2009 and matched them with candidate-gene studies on the same topic and published in the same period of time. We observed that the GWAS papers have received, on average, 30±55 citations more than the candidate gene papers, 1 year after their publication date, and 39±58 citations more 2 years after their publication date. The GWAS papers were, on average, 2.8±2.4 and 2.9±2.4 times more cited than expected, 1 and 2 years after their publication date; whereas the candidate gene papers were 1.5±1.2 and 1.5±1.4 times more cited than expected. While the evaluation of the contribution to scientific research through citation metrics may be challenged, it cannot be denied that GWAS are great hypothesis generators, and are a powerful complement to candidate gene studies.