Biology subjects

McNellie, J. P.

Publications and source records attributed to McNellie, J. P..

3 recordsLinked to original sources

bioRxiv2026

The stability of fatty acid composition in sunflower oil is dependent on environment and affected by structural variation

In sunflower (Helianthus annuus L.), the composition of fatty acids in the seeds, primarily oleic, linoleic, stearic and palmitic acid, is of utmost importance for oil quality. Despite this, the genetic basis of this trait and its interaction with the environment is poorly understood. Understanding this interaction is critical to improvement of sunflower within the context of climate change. In this work, we incorporated fatty acid composition measurements from the sunflower SAM population and eight environments across an extensive geographic cline into GWAS. The SAM panel consists of 287 varieties representing approximately 90% of sunflower diversity, for which 2.2 million high-quality SNPs with a MAF > 5% are available. For increased power, multivariate GWAS was performed with four different inputs: (i) mean fatty acid composition within each environment, (ii) mean fatty acid composition within each environment omitting high oleic varieties, (iii) trait stability within environments quantified by standard errors among replicate samples ( stability) and (iv) Eberhart and Russells {beta} which quantifies trait stabilities across environments ({beta} stability). All four analyses yielded highly significantly associated SNPs. We found that high oleic varieties exhibited high {beta} trait stability, resulting in substantial overlap in markers between analyses (i) and (iv), with signals being fairly consistent between environments in analysis (i). For analyses (ii) and (iii), significant markers tended to vary between trials. For significant SNPs across all analyses, 147 candidate genes were identified, including promising candidates such as 15 fatty acid metabolism genes, 6 heat shock proteins and 22 transcription factors. Lastly, a large introgression consisting of two flanking inverted sequences on Chromosome 5 was found to coincide with stability in the Georgia trial, suggesting a role in FA composition stability under high heat conditions.

plant biology↗

bioRxiv2024

Understanding genetic architecture overcomes tradeoffs between seed quality and insect resistance

The sunflower (Helianthus annuus) pericarp protects the seed within from both abiotic and biotic stresses. Achenes with stronger pericarps are less susceptible to damage from insect feeding. Complicating the genetic improvement of pericarp strength is the negative correlation between pericarp thickness (a component of strength) and oil content. As breeding efforts have increased oil content, there has been a concomitant decrease in pericarp thickness. A logical sunflower improvement goal is to improve oil content while preserving pericarp strength through genetic mechanisms independent of the tradeoffs with pericarp thickness. To determine the genetic basis of oil content, pericarp strength, and thickness, we identified QTL in two populations; the Sunflower Association Mapping panel (Mandel et al., 2011) and a recombinant inbred line (RIL) population derived from a thin pericarp oilseed inbred (HA 467) crossed to a thick pericarp open pollinated variety from Turkiye (PI 170415). A region on chromosome 15 was associated with neighboring QTL for banded moth resistance, oil content, and pericarp thickness, partially underlying the trade-offs among these traits. Additional QTL on chromosome 5 and 14 for pericarp strength provide fewer trade-offs with oil content. QTL for pericarp strength on chromosome 5 and pericarp thickness on chromosome 16 were associated with large structural variants, with candidate gene presence/absence variation between the haplotypes on chromosome 5. Understanding the origin and nature of phenotypic tradeoffs is beneficial to plant biologists and sunflower breeders as they seek to understand the origin and genetic architecture of adaptive and maladaptive traits.

genetics↗

bioRxiv2024

Variant filters using segregation information improve mapping of nectar-production genes in sunflower (Helianthus annuus L.)

Accurate variant calling is critical for identifying the genetic basis of complex traits, yet filters used in variant detection and validation may inadvertently exclude valuable genetic information. In this study, we compare common sequencing depth filters, used to eliminate error-prone variants associated with repetitive regions and technical issues, with a biologically relevant filtering approach that targets expected population-level Mendelian segregation. The resulting variant sets were evaluated in the context of nectar volume QTL mapping in sunflower (Helianthus annuus L.). Our previous research failed to detect a significant interval containing a strong candidate gene for nectar production (HaCWINV2). We removed certain hard filters and implemented a Chi-square goodness-of-fit test to retain variants that segregate according to expected genetic ratios. We hypothesized that this will enhance mapping resolution and capture key genetic regions previously missed. We demonstrate that biologically relevant filtering retains more significant QTL and candidate genes, including HaCWINV2, while removing variants due to technical errors more effectively, and accounted for 48.55% of phenotypic variation. In finding nine putative homologs of Arabidopsis genes with nectary function within QTL regions, we demonstrate that this filtering strategy, which considers biological contexts, has a higher power of true variant detection than the commonly used variant depth filtering strategy. PLAIN LANGUAGE SUMMARYIn genomic research, identifying genetic markers is key to understanding complex traits, but traditional methods for filtering genetic data can sometimes miss important information. In this study, we explored a new data filtering approach for mapping genes related to nectar production in sunflower. We applied a more flexible filtering method that considers how markers are expected to segregate in breeding populations. Our previous work failed to identify an important gene previously hypothesized to be involved in nectar production, likely due to overly strict filtering. Our improved approach identified nine sunflower genes related to nectar production genes in the model species Arabidopsis thaliana, as compared to zero genes identified from the previous filtering strategy. This study highlights the value of using flexible, biologically relevant filtering methods, which can lead to better results in plant genomic studies. CORE IDEASO_LIDiscovering biologically meaningful variants from sequence data requires a careful and critical view of bioinformatic workflows. C_LIO_LIThe use of arbitrary filters can remove significant genomic variation that contributes to the phenotype of interest. C_LIO_LIArbitrary filters can also fail to remove variant call errors. C_LIO_LIA Chi-square filtering strategy based on segregation ratio retained a larger number of valid variants. C_LIO_LIMore candidate regions with putative nectar-related genes and better statistical support were discovered. C_LI

plant biology↗