In developing countries, the sweetpotato, (L.) Lam. [Formula: see text], is an important autopolyploid species, both socially and economically. However, quantitative trait loci (QTL) mapping has remained limited due to its genetic complexity. Current fixed-effect models can fit only a single QTL and are generally hard to interpret. Here, we report the use of a random-effect model approach to map multiple QTL based on score statistics in a sweetpotato biparental population ('Beauregard' × 'Tanzania') with 315 full-sibs. Phenotypic data were collected for eight yield component traits in six environments in Peru, and jointly adjusted means were obtained using mixed-effect models. An integrated linkage map consisting of 30,684 markers distributed along 15 linkage groups (LGs) was used to obtain the genotype conditional probabilities of putative QTL at every centiMorgan position. Multiple interval mapping was performed using our R package QTLpoly and detected a total of 13 QTL, ranging from none to four QTL per trait, which explained up to 55% of the total variance. Some regions, such as those on LGs 3 and 15, were consistently detected among root number and yield traits, and provided a basis for candidate gene search. In addition, some QTL were found to affect commercial and noncommercial root traits distinctly. Further best linear unbiased predictions were decomposed into additive allele effects and were used to compute multiple QTL-based breeding values for selection. Together with quantitative genotyping and its appropriate usage in linkage analyses, this QTL mapping methodology will facilitate the use of genomic tools in sweetpotato breeding as well as in other autopolyploids.
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http://dx.doi.org/10.1534/genetics.120.303080 | DOI Listing |
Sci Adv
January 2025
College of Life Science and Technology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China.
Alzheimers Dement
December 2024
Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: The FunGen-xQTL project has significantly advanced genetics by developing and exploring novel quantitative trait loci (QTL) types in human brains, enriching our understanding of complex neurological disease etiology. We broadened the scope of epigenomic QTL analysis, integrating histone acetylation QTLs (haQTLs) and methylation QTLs (mQTLs) that affect multiple histone acetylation peaks or methylation CpG sites spatially. Additionally, we investigated a new category of splicing QTLs (sQTLs) implicated in nonsense-mediated decay (NMD).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Background: To gain a deeper understanding of underlying molecular mechanisms in genomic regions associated with Alzheimer's disease (AD), the National Institute on Aging (NIA) launched the Alzheimer's Disease Sequencing Project (ADSP) Functional Genomics Consortium (FunGen-AD) in 2021.
Method: The first effort of this collaboration, coordinated by the NIA Genetics of Alzheimer's Disease Data Storage Site (NIAGADS), aggregated functional genomics (FG) data from 5 cohorts, including ∼3,000 samples of European (EA) and African ancestries (AA). We used this data to map Quantitative Trait Loci (xQTL) on AD-specific human tissues and cells, providing insights into how non-coding genetic variants contribute to AD risk.
Alzheimers Dement
December 2024
Stanford University, Palo Alto, CA, USA.
Background: Genome-wide association studies (GWAS) have identified thousands of genomic regions associated with complex diseases but understanding the underlying causal mechanisms remains a significant challenge. The FunGen-xQTL project has addressed this by generating and harmonizing molecular quantitative trait loci (xQTL) across multiple layers of molecular traits in human brains, cerebrospinal fluid, and blood-derived cells relevant to neurodegenerative disorders. Existing approaches for integrating xQTL data with GWAS have typically focused on individual molecular traits in individual QTL layers.
View Article and Find Full Text PDFFront Plant Sci
December 2024
Nuclear Institute for Agriculture and Biology College, Pakistan Institute of Engineering and Applied Science (NIAB-C, PIEAS), Faisalabad, Pakistan.
Accessing the underlying genetics of complex traits, especially in small grain pulses is an important breeding objective for crop improvement. Genome-wide association studies (GWAS) analyze thousands of genetic variants across several genomes to identify links with specific traits. This approach has discovered many strong associations between genes and traits, and the number of associated variants is expected to continue to increase as GWAS sample sizes increase.
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