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Genomewide association study of ionomic traits on diverse soybean populations from germplasm collections.

ABSTRACT: The elemental content of a soybean seed is a determined by both genetic and environmental factors and is an important component of its nutritional value. The elemental content is chemically stable, making the samples stored in germplasm repositories an intriguing source of experimental material. To test the efficacy of using samples from germplasm banks for gene discovery, we analyzed the elemental profile of seeds from 1,653 lines in the USDA Soybean Germplasm Collection. We observed large differences in the elemental profiles based on where the lines were grown, which lead us to break up the genetic analysis into multiple small experiments. Despite these challenges, we were able to identify candidate single nucleotide polymorphisms (SNPs) controlling elemental accumulation as well as lines with extreme elemental accumulation phenotypes. Our results suggest that elemental analysis of germplasm samples can identify SNPs in linkage disequilibrium to genes, which can be leveraged to assist in crop improvement efforts.

SUBMITTER: Ziegler G

PROVIDER: S-EPMC6508489 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Genomewide association study of ionomic traits on diverse soybean populations from germplasm collections.

Ziegler Greg G Nelson Randall R Granada Stephanie S Krishnan Hari B HB Gillman Jason D JD Baxter Ivan I

Plant direct 20180115 1

The elemental content of a soybean seed is a determined by both genetic and environmental factors and is an important component of its nutritional value. The elemental content is chemically stable, making the samples stored in germplasm repositories an intriguing source of experimental material. To test the efficacy of using samples from germplasm banks for gene discovery, we analyzed the elemental profile of seeds from 1,653 lines in the USDA Soybean Germplasm Collection. We observed large diff ...[more]

PMID: 31245681

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Project description:Observable qualitative traits are relatively stable across environments and are commonly used to evaluate crop genetic diversity. Recently, molecular markers have largely superseded describing phenotypes in diversity surveys. However, qualitative descriptors are useful in cataloging germplasm collections and for describing new germplasm in patents, publications, and/or the Plant Variety Protection (PVP) system. This research focused on the comparative analysis of standardized cotton traits as represented within the National Cotton Germplasm Collection (NCGC). The cotton traits are named by 'descriptors' that have non-numerical sub-categories (descriptor states) reflecting the details of how each trait manifests or is absent in the plant. We statistically assessed selected accessions from three major groups of Gossypium as defined by the NCGC curator: (1) "Stoneville accessions (SA)," containing mainly Upland cotton (Gossypium hirsutum) cultivars; (2) "Texas accessions (TEX)," containing mainly G. hirsutum landraces; and (3) Gossypium barbadense (Gb), containing cultivars or landraces of Pima cotton (Gossypium barbadense). For 33 cotton descriptors we: (a) revealed distributions of character states for each descriptor within each group; (b) analyzed bivariate associations between paired descriptors; and (c) clustered accessions based on their descriptors. The fewest significant associations between descriptors occurred in the SA dataset, likely reflecting extensive breeding for cultivar development. In contrast, the TEX and Gb datasets showed a higher number of significant associations between descriptors, likely correlating with less impact from breeding efforts. Three significant bivariate associations were identified for all three groups, bract nectaries:boll nectaries, leaf hair:stem hair, and lint color:seed fuzz color. Unsupervised clustering analysis recapitulated the species labels for about 97% of the accessions. Unexpected clustering results indicated accessions that may benefit from potential further investigation. In the future, the significant associations between standardized descriptors can be used by curators to determine whether new exotic/unusual accessions most closely resemble Upland or Pima cotton. In addition, the study shows how existing descriptors for large germplasm datasets can be useful to inform downstream goals in breeding and research, such as identifying rare individuals with specific trait combinations and targeting breakdown of remaining trait associations through breeding, thus demonstrating the utility of the analytical methods employed in categorizing germplasm diversity within the collection.

Project description:BackgroundAlcohol use (both quantity and dependence) is moderately heritable, and genomewide association studies (GWAS) have identified risk genes in European, African, and Asian populations. The most reproducibly identified risk genes affect alcohol metabolism. Well-known functional variants at the gene encoding alcohol dehydrogenase B and other alcohol dehydrogenases affect risk in European and African ancestry populations. Similarly, variants mapped to these same genes and a well-known null variant that maps to the gene that encodes aldehyde dehydrogenase 2 (ALDH2) also affect risk in various Asian populations. In this study, we completed the first GWAS for 3 traits related to alcohol use in a Thai population recruited initially for studies of methamphetamine dependence.MethodsAll subjects were evaluated with the Thai version of the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA). A total of 1,045 subjects were available for analysis. Three traits were analyzed: flushing, maximum number of alcoholic beverages consumed in any lifetime 24-hour period ("MAXDRINKS"), and DSM-IV alcohol dependence criterion count. We also conducted a pleiotropy analysis with major depression, the only other psychiatric trait where summary statistics from a large-scale Asian-population GWAS are available.ResultsAll 3 traits showed genomewide significant association with variants near ALDH2, with significance ranging from 2.01 × 10-14 (for flushing; lead single nucleotide polymorphism (SNP) PTPN11* rs143894582) to pmeta = 5.80 × 10-10 (for alcohol dependence criterion count; lead SNP rs149212747). These lead SNPs flank rs671 and span a region of over a megabase, illustrating the need for prior biological information in identifying the actual effect SNP, rs671. We also identified significant pleiotropy between major depression and flushing.ConclusionsThese results are consistent with prior findings in Asian populations and add new information regarding alcohol use-depression pleiotropy.

Project description:Common bean (bean) is one of the most important legume crops, and mapping genes for yield and yield-related traits is essential for its improvement. However, yield is a complex trait that is typically controlled by many loci in crop genomes. The objective of this research was to identify regions in the bean genome associated with yield and a number of yield-related traits using a collection of 121 diverse bean genotypes with different yields. The beans were evaluated in replicated trials at two locations, over two years. Significant variation among genotypes was identified for all traits analyzed in the four environments. The collection was genotyped with the BARCBean6K_3 chip (5,398 SNPs), two yield/antiyield gene-based markers, and seven markers previously associated with resistance to common bacterial blight (CBB), including a Niemann-Pick polymorphism (NPP) gene-based marker. Over 90% of the single-nucleotide polymorphisms (SNPs) were polymorphic and separated the panel into two main groups of small-seeded and large-seeded beans, reflecting their Mesoamerican and Andean origins. Thirty-nine significant marker-trait associations (MTAs) were identified between 31 SNPs and 15 analyzed traits on all 11 bean chromosomes. Some of these MTAs confirmed genome regions previously associated with the yield and yield-related traits in bean, but a number of associations were not reported previously, especially those with derived traits. Over 600 candidate genes with different functional annotations were identified for the analyzed traits in the 200-Kb region centered on significant SNPs. Fourteen SNPs were identified within the gene model sequences, and five additional SNPs significantly associated with five different traits were located at less than 0.6 Kb from the candidate genes. The work confirmed associations between two yield/antiyield gene-based markers (AYD1m and AYD2m) on chromosome Pv09 with yield and identified their association with a number of yield-related traits, including seed weight. The results also confirmed the usefulness of the NPP marker in screening for CBB resistance. Since disease resistance and yield measurements are environmentally dependent and labor-intensive, the three gene-based markers (CBB- and two yield-related) and quantitative trait loci (QTL) that were validated in this work may be useful tools for simplifying and accelerating the selection of high-yielding and CBB-resistant bean cultivars.

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Genomewide association study of ionomic traits on diverse soybean populations from germplasm collections.

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Genomewide association study of ionomic traits on diverse soybean populations from germplasm collections.

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