Despite the current progress in high-throughput, dense genome scans, a major portion of complex traits' heritability still remains unexplained, a phenomenon commonly termed "missing heritability." The negligence of analytical approaches accounting for gene-gene interaction effects, such as statistical epistasis, is probably central to this phenomenon. Here we performed a comprehensive two-way SNP interaction analysis of human episodic memory, which is a heritable complex trait, and focused on 120 genes known to show differential, memory-related expression patterns in rat hippocampus. Functional magnetic resonance imaging was also used to capture genotype-dependent differences in memory-related brain activity. A significant, episodic memory-related interaction between two markers located in potassium channel genes (KCNB2 and KCNH5) was observed (P(nominal combined)=0.000001). The epistatic interaction was robust, as it was significant in a screening (P(nominal)=0.0000012) and in a replication sample (P(nominal)=0.01). Finally, we found genotype-dependent activity differences in the parahippocampal gyrus (P(nominal)=0.001) supporting the behavioral genetics finding. Our results demonstrate the importance of analytical approaches that go beyond single marker statistics of complex traits.
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March 2025
School of Plant, Environmental and Soil Sciences, Louisiana State University Agricultural Center, Baton Rouge, Louisiana, USA.
Genomic selection can enhance the rate of genetic gain of cane and sucrose yield in sugarcane (Saccharum L.), an important industrial crop worldwide. We assessed the predictive ability (PA) for six traits, such as theoretical recoverable sugar (TRS), number of stalks (NS), stalk weight (SW), cane yield (CY), sugar yield (SY), and fiber content (Fiber) using 20,451 single nucleotide polymorphisms (SNPs) with 22 statistical models based on the genomic estimated breeding values of 567 genotypes within and across five stages of the Louisiana sugarcane breeding program.
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December 2024
Department of Plant Sciences, North Dakota State University, Fargo, 58108, USA.
Detecting genome-wide SNP-SNP interactions (epistasis) efficiently is essential to harnessing the vast data now available from modern biobanks. With millions of SNPs and genetic information from hundreds of thousands of individuals, researchers are positioned to uncover new insights into complex disease pathways. However, this data scale brings significant computational and statistical challenges.
View Article and Find Full Text PDFBioData Min
December 2024
School of Computing, Queen's University, 557 Goodwin Hall, 21-25 Union St, Kingston, K7L 2N8, Ontario, Canada.
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December 2024
Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia 30100, Spain.
medRxiv
November 2024
Centre de Recherche du CHUM, and Faculty of Medicine, University of Montreal, QC, Canada.
Initially introduced in 1909 by William Bateson, classic epistasis (genetic variant interaction) refers to the phenomenon that one variant prevents another variant from a different locus from manifesting its effects. The potential effects of genetic variant interactions on complex diseases have been recognized for the past decades. Moreover, It has been studied and demonstrated that leveraging the combined SNP effects within the genetic block can significantly increase calculation power, reducing background noise, ultimately leading to novel epistasis discovery that the single SNP statistical epistasis study might overlook.
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