GWASpro: a high-performance genome-wide association analysis server

A Genome-Wide Association Study of Protein, Oil, and Amino Acid Content in Wild Soybean (Glycine soja)

Soybean (Glycine max L.) is a globally important source of plant proteins, oils, and amino acids for both humans and livestock. Wild soybean (Glycine soja Sieb. and Zucc.), the ancestor of cultivated soybean, could be a useful genetic source for increasing these components in soybean crops. In this study, 96,432 single-nucleotide polymorphisms (SNPs) across 203 wild soybean accessions from the 180K Axiom® Soya SNP array were investigated using an association analysis. Protein and oil content exhibited a highly significant negative correlation, while the 17 amino acids exhibited a highly significant positive correlation with each other. A genome-wide association study (GWAS) was conducted on the protein, oil, and amino acid content using the 203 wild soybean accessions. A total of 44 significant SNPs were associated with protein, oil, and amino acid content. Glyma.11g015500 and Glyma.20g050300, which contained SNPs detected from the GWAS, were selected as novel candidate genes for the protein and oil content, respectively. In addition, Glyma.01g053200 and Glyma.03g239700 were selected as novel candidate genes for nine of the amino acids (Ala, Asp, Glu, Gly, Leu, Lys, Pro, Ser, and Thr). The identification of the SNP markers related to protein, oil, and amino acid content reported in the present study is expected to help improve the quality of selective breeding programs for soybeans.

Medicago truncatula resources to study legume biology and symbiotic nitrogen fixation

Medicago truncatula is a chosen model for legumes towards deciphering fundamental legume biology, especially symbiotic nitrogen fixation. Current genomic resources for M. truncatula include a completed whole genome sequence information for R108 and Jemalong A17 accessions along with the sparse draft genome sequences for other 226 M. truncatula accessions. These genomic resources are complemented by the availability of mutant resources such as retrotransposon (Tnt1) insertion mutants in R108 and fast neutron bombardment (FNB) mutants in A17. In addition, several M. truncatula databases such as small secreted peptides (SSPs) database, transporter protein database, gene expression atlas, proteomic atlas, and metabolite atlas are available to the research community. This review describes these resources and provide information regarding how to access these resources.

Genome-Wide Association Studies of Seven Root Traits in Soybean (Glycine max L.) Landraces

Soybean [Glycine max (L.) Merr.], an important oilseed crop, is a low-cost source of protein and oil. In Southeast Asia and Africa, soybeans are widely cultivated for use as traditional food and feed and industrial purposes. Given the ongoing changes in global climate, developing crops that are resistant to climatic extremes and produce viable yields under predicted climatic conditions will be essential in the coming decades. To develop such crops, it will be necessary to gain a thorough understanding of the genetic basis of agronomic and plant root traits. As plant roots generally lie beneath the soil surface, detailed observations and phenotyping throughout plant development present several challenges, and thus the associated traits have tended to be ignored in genomics studies. In this study, we phenotyped 357 soybean landraces at the early vegetative (V2) growth stages and used a 180 K single-nucleotide polymorphism (SNP) soybean array in a genome-wide association study (GWAS) conducted to determine the phenotypic relationships among root traits, elucidate the genetic bases, and identify significant SNPs associated with root trait-controlling genomic regions/loci. A total of 112 significant SNP loci/regions were detected for seven root traits, and we identified 55 putative candidate genes considered to be the most promising. Our findings in this study indicate that a combined approach based on SNP array and GWAS analyses can be applied to unravel the genetic basis of complex root traits in soybean, and may provide an alternative high-resolution marker strategy to traditional bi-parental mapping. In addition, the identified SNPs, candidate genes, and diverse variations in the root traits of soybean landraces will serve as a valuable basis for further application in genetic studies and the breeding of climate-resilient soybeans characterized by improved root traits.

Genome-Scale Profiling and High-Throughput Analyses Unravel the Genetic Basis of Arsenic Content Variation in Rice

Ionomics, the study of the composition of mineral nutrients and trace elements in organisms that represent the inorganic component of cells and tissues, has been widely studied to explore to unravel the molecular mechanism regulating the elemental composition of plants. However, the genetic factors of rice subspecies in the interaction between arsenic and functional ions have not yet been explained. Here, the correlation between As and eight essential ions in a rice core collection was analyzed, taking into account growing condition and genetic factors. The results demonstrated that the correlation between As and essential ions was affected by genetic factors and growing condition, but it was confirmed that the genetic factor was slightly larger with the heritability for arsenic content at 53%. In particular, the cluster coefficient of japonica (0.428) was larger than that of indica (0.414) in the co-expression network analysis for 23 arsenic genes, and it was confirmed that the distance between genes involved in As induction and detoxification of japonica was far than that of indica. These findings provide evidence that japonica populations could accumulate more As than indica populations. In addition, the cis-eQTLs of AIR2 (arsenic-induced RING finger protein) were isolated through transcriptome-wide association studies, and it was confirmed that AIR2 expression levels of indica were lower than those of japonica. This was consistent with the functional haplotype results for the genome sequence of AIR2, and finally, eight rice varieties with low AIR2 expression and arsenic content were selected. In addition, As-related QTLs were identified on chromosomes 5 and 6 under flooded and intermittently flooded conditions through genome-scale profiling. Taken together, these results might assist in developing markers and breeding plans to reduce toxic element content and breeding high-quality rice varieties in future.

Genome-wide association study of cassava starch paste properties

An understanding of cassava starch paste properties (CSPP) can contribute to the selection of clones with differentiated starches. This study aimed to identify genomic regions associated with CSPP using different genome-wide association study (GWAS) methods (MLM, MLMM, and Farm-CPU). The GWAS was performed using 23,078 single-nucleotide polymorphisms (SNPs). The rapid viscoanalyzer (RVA) parameters were pasting temperature (PastTemp), peak viscosity (PeakVisc), hot-paste viscosity (Hot-PVisc), cool-paste viscosity (Cold-PVisc), final viscosity (FinalVis), breakdown (BreDow), and setback (Setback). Broad phenotypic and molecular diversity was identified based on the genomic kinship matrix. The broad-sense heritability estimates (h²) ranged from moderate to high magnitudes (0.66 to 0.76). The linkage disequilibrium (LD) declined to between 0.3 and 2.0 Mb (r² <0.1) for most chromosomes, except chromosome 17, which exhibited an extensive LD. Thirteen SNPs were found to be significantly associated with CSPP, on chromosomes 3, 8, 17, and 18. Only the BreDow trait had no associated SNPs. The regional marker-trait associations on chromosome 18 indicate a LD block between 2907312 and 3567816 bp and that SNP S18_3081635 was associated with SetBack, FinalVis, and Cold-PVisc (all three GWAS methods) and with Hot-PVisc (MLM), indicating that this SNP can track these four traits simultaneously. The variance explained by the SNPs ranged from 0.13 to 0.18 for SetBack, FinalVis, and Cold-PVisc and from 0.06 to 0.09 for PeakVisc and Hot-PVisc. The results indicated additive effects of the genetic control of Cold-PVisc, FinalVis, Hot-PVisc, and SetBack, especially on the large LD block on chromosome 18. One transcript encoding the glycosyl hydrolase family 35 enzymes on chromosome 17 and one encoding the mannose-p-dolichol utilization defect 1 protein on chromosome 18 were the most likely candidate genes for the regulation of CSPP. These results underline the potential for the assisted selection of high-value starches to improve cassava root quality through breeding programs.

Using precision phenotyping to inform de novo domestication

An update on the use of precision phenotyping to assess the potential of lesser cultivated species as candidates for de novo domestication or similar development for future agriculture.

MMAP: a cloud computing platform for mining the maximum accuracy of predicting phenotypes from genotypes

 

Accurately predicting phenotypes from genotypes holds great promise to improve health management in humans and animals, and breeding efficiency in animals and plants. Although many prediction methods have been developed, the optimal method differs across datasets due to multiple factors, including species, environments, populations and traits of interest. Studies have demonstrated that the number of genes underlying a trait and its heritability are the two key factors that determine which method fits the trait the best. In many cases, however, these two factors are unknown for the traits of interest. We developed a cloud computing platform for Mining the Maximum Accuracy of Predicting phenotypes from genotypes (MMAP) using unsupervised learning on publicly available real data and simulated data. MMAP provides a user interface to upload input data, manage projects and analyses and download the output results. The platform is free for the public to conduct computations for predicting phenotypes and genetic merit using the best prediction method optimized from many available ones, including Ridge Regression, gBLUP, compressed BLUP, Bayesian LASSO, Bayes A, B, Cpi and many more. Users can also use the platform to conduct data analyses with any methods of their choice. It is expected that extensive usage of MMAP would enrich the training data, which in turn results in continual improvement of the identification of the best method for use with particular traits.

Availability and implementation

The MMAP user manual, tutorials and example datasets are available at http://zzlab.net/MMAP.

Supplementary information

Supplementary data are available at Bioinformatics online.

Molecular approaches for improving oil palm for oil

The oil palm, originating from Africa, is the most productive oil crop species. Palm oil is an important source of edible oil. Its current global plantation area is over 23 million ha. The theoretical oil yield potential of the oil palm is 18.2 tons/ha/year. However, current average oil yield is only 3.8 tons/ha/year. In the past 100 years, conventional breeding and improvement of field management played important roles in increasing oil yield. However, conventional breeding for trait improvement was limited by its very long (10-20 years) phenotypic selection cycle, although it improved oil yield by ~10-20% per generation. Molecular breeding using novel molecular technologies will accelerate genetic improvement and may reduce the need to deforest and to use arable land for expanding oil palm plantations, which in turn makes palm oil more sustainable. Here, we comprehensively synthesize information from relevant literature of the technologies, achievements, and challenges of molecular approaches, including tissue culture, haploid breeding, mutation breeding, marker-assisted selection (MAS), genomic selection (GS), and genome editing (GE). We propose the characteristics of ideal palms and suggest a road map to breed ideal palms for sustainable palm oil.

HiGwas: how to compute longitudinal GWAS data in population designs

SUMMARY

Genome-wide association studies (GWAS), particularly designed with thousands and thousands of single-nucleotide polymorphisms (SNPs) (big p) genotyped on tens of thousands of subjects (small n), are encountered by a major challenge of p ≪ n. Although the integration of longitudinal information can significantly enhance a GWAS's power to comprehend the genetic architecture of complex traits and diseases, an additional challenge is generated by an autocorrelative process. We have developed several statistical models for addressing these two challenges by implementing dimension reduction methods and longitudinal data analysis. To make these models computationally accessible to applied geneticists, we wrote an R package of computer software, HiGwas, designed to analyze longitudinal GWAS datasets. Functions in the package encompass single SNP analyses, significance-level adjustment, preconditioning and model selection for a high-dimensional set of SNPs. HiGwas provides the estimates of genetic parameters and the confidence intervals of these estimates. We demonstrate the features of HiGwas through real data analysis and vignette document in the package.

AVAILABILITY AND IMPLEMENTATION

https://github.com/wzhy2000/higwas.

CONTACT

rwu@phs.psu.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.