scholarly journals CPNE3 moderates the association between anxiety and working memory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chunhui Chen ◽  
Ziyi Wang ◽  
Chuansheng Chen ◽  
Gui Xue ◽  
Shuzhen Lu ◽  
...  
Keyword(s):  
Working Memory ◽  
Genome Wide Association Study ◽  
Significant Negative Correlation ◽  
Curvilinear Relationship ◽  
Gene Score ◽  
Genome Wide ◽  
Score Analysis ◽  
Gene Level ◽  
And Performance ◽  
The Individual

AbstractMutual influences between anxiety and working memory (WM) have been extensively studied, and their curvilinear relationship resembles the classic Yerkes-Dodson law of arousal and performance. Given the genetic bases of both anxiety and WM, it is likely that the individual differences in the Yerkes-Dodson law of anxiety and WM may have genetic correlates. The current genome wide association study (GWAS) enrolled 1115 healthy subjects to search for genes that are potential moderators of the association between anxiety and WM. Results showed that CPNE3 rs10102229 had the strongest effect, p = 3.38E−6 at SNP level and p = 2.68E−06 at gene level. Anxiety and WM had a significant negative correlation (i.e., more anxious individuals performed worse on the WM tasks) for the TT genotype of rs10102229 (resulting in lower expression of CPNE3), whereas the correlation was positive (i.e., more anxious individuals performed better on the WM tasks) for the CC carriers. The same pattern of results was found at the gene level using gene score analysis. These effects were replicated in an independent sample (N = 330). The current study is the first to report a gene that moderates the relation between anxiety and WM and potentially provides a genetic explanation for the classic Yerkes-Dodson law.

scholarly journals Genome-wide association for heifer reproduction and calf performance traits in beef cattle

Genome ◽  
2015 ◽  
Vol 58 (12) ◽  
pp. 549-557 ◽  
Author(s):  
Everestus C. Akanno ◽  
Graham Plastow ◽  
Carolyn Fitzsimmons ◽  
Stephen P. Miller ◽  
Vern Baron ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genome Wide Association Study ◽  
Average Daily Gain ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Research Centre ◽  
Genome Wide ◽  
A Genome ◽  
Starting Point ◽  
And Performance

The aim of this study was to identify SNP markers that associate with variation in beef heifer reproduction and performance of their calves. A genome-wide association study was performed by means of the generalized quasi-likelihood score (GQLS) method using heifer genotypes from the BovineSNP50 BeadChip and estimated breeding values for pre-breeding body weight (PBW), pregnancy rate (PR), calving difficulty (CD), age at first calving (AFC), calf birth weight (BWT), calf weaning weight (WWT), and calf pre-weaning average daily gain (ADG). Data consisted of 785 replacement heifers from three Canadian research herds, namely Brandon Research Centre, Brandon, Manitoba, University of Alberta Roy Berg Kinsella Ranch, Kinsella, Alberta, and Lacombe Research Centre, Lacombe, Alberta. After applying a false discovery rate correction at a 5% significance level, a total of 4, 3, 3, 9, 6, 2, and 1 SNPs were significantly associated with PBW, PR, CD, AFC, BWT, WWT, and ADG, respectively. These SNPs were located on chromosomes 1, 5–7, 9, 13–16, 19–21, 24, 25, and 27–29. Chromosomes 1, 5, and 24 had SNPs with pleiotropic effects. New significant SNPs that impact functional traits were detected, many of which have not been previously reported. The results of this study support quantitative genetic studies related to the inheritance of these traits, and provides new knowledge regarding beef cattle quantitative trait loci effects. The identification of these SNPs provides a starting point to identify genes affecting heifer reproduction traits and performance of their calves (BWT, WWT, and ADG). They also contribute to a better understanding of the biology underlying these traits and will be potentially useful in marker- and genome-assisted selection and management.

scholarly journals MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors

2020 ◽  
Author(s):  
Justin Williams ◽  
Beisi Xu ◽  
Daniel Putnam ◽  
Andrew Thrasher ◽  
Chunliang Li ◽  
...  
Keyword(s):  
Promoter Activity ◽  
Malignant Neoplasms ◽  
Clinical Value ◽  
Individual Gene ◽  
Learning Framework ◽  
Genome Wide ◽  
Gene Level ◽  
Biological Impacts ◽  
The Individual ◽  
Methylation Patterns

AbstractAlthough genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present MethylationToActivity (M2A), a machine learning framework that uses convolutional neural networks to infer promoter activities (H3K4me3 and H3K27ac enrichment) from DNA methylation patterns for individual genes. Using publicly available datasets in real-world test scenarios, we demonstrate that M2A is highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers, including both solid and hematologic malignant neoplasms.

scholarly journals TWAS results are complementary to and less affected by linkage disequilibrium than GWAS

2021 ◽  
Author(s):  
Delin Li ◽  
Qiang Liu ◽  
Patrick S Schnable
Keyword(s):  
Linkage Disequilibrium ◽  
Association Study ◽  
Phenotypic Variation ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Expression Levels ◽  
Genome Wide ◽  
A Genome ◽  
Gene Level ◽  
Gene Expression Levels

Abstract A genome-wide association study (GWAS) is used to identify genetic markers associated with phenotypic variation. In contrast, a transcriptome-wide association study (TWAS) detects associations between gene expression levels and phenotypic variation. It has previously been shown that in the cross-pollinated species, maize (Zea mays), GWAS and TWAS identify complementary sets of trait-associated genes, many of which exhibit characteristics of true positives. Here, we extend this conclusion to the self-pollinated species, Arabidopsis thaliana and soybean (Glycine max). Linkage disequilibrium (LD) can result in the identification, via GWAS, of false-positive associations. In all three analyzed plant species, most trait-associated genes identified via TWAS are well separated physically from other candidate genes. Hence, TWAS is less affected by LD than is GWAS, demonstrating that TWAS is particularly well suited for association studies in genomes with slow rates of LD decay, such as soybean. TWAS is reasonably robust to the plant organs/tissues used to determine expression levels. In summary, this study confirms that TWAS is a promising approach for accurate gene-level association mapping in plants that is complementary to GWAS, and established that TWAS can exhibit substantial advantages relative to GWAS in species with slow rates of LD decay.

scholarly journals A Twin Study of Breastfeeding With a Preliminary Genome-Wide Association Scan

2014 ◽  
Vol 18 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Lucia Colodro-Conde ◽  
Gu Zhu ◽  
Robert A. Power ◽  
Anjali Henders ◽  
Andrew C. Heath ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Breast Size ◽  
Polygenic Risk Score ◽  
Community Based ◽  
Genome Wide ◽  
Best Fitting ◽  
The One ◽  
The Individual

Breastfeeding has been an important survival trait during human history, though it has long been recognized that individuals differ in their exact breastfeeding behavior. Here our aims were, first, to explore to what extent genetic and environmental influences contributed to the individual differences in breastfeeding behavior; second, to detect possible genetic variants related to breastfeeding; and lastly, to test if the genetic variants associated with breastfeeding have been previously found to be related with breast size. Data were collected from a large community-based cohort of Australian twins, with 3,364 women participating in the twin modelling analyses and 1,521 of them included in the genome-wide association study (GWAS). Monozygotic (MZ) twin correlations (rMZ = 0.52, 95% CI 0.46–0.57) were larger than dizygotic (DZ) twin correlations (rDZ = 0.35, 95% CI 0.25–0.43) and the best-fitting model was the one composed by additive genetics and unique environmental factors, explaining 53% and 47% of the variance in breastfeeding behavior, respectively. No breastfeeding-related genetic variants reached genome-wide significance. The polygenic risk score analyses showed no significant results, suggesting breast size does not influence breastfeeding. This study confers a replication of a previous one exploring the sources of variance of breastfeeding and, to our knowledge, is the first one to conduct a GWAS on breastfeeding and look at the overlap with variants for breast size.

scholarly journals Genome-wide association study of working memory brain activation

2017 ◽  
Vol 115 ◽  
pp. 98-111 ◽  
Author(s):  
Gabriëlla A.M. Blokland ◽  
Angus K. Wallace ◽  
Narelle K. Hansell ◽  
Paul M. Thompson ◽  
Ian B. Hickie ◽  
...  
Keyword(s):  
Working Memory ◽  
Association Study ◽  
Genome Wide Association Study ◽  
Brain Activation ◽  
Genome Wide Association ◽  
Genome Wide

scholarly journals Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection

2018 ◽  
Author(s):  
Alvaro N. Barbeira ◽  
Milton D. Pividori ◽  
Jiamao Zheng ◽  
Heather E. Wheeler ◽  
Dan L. Nicolae ◽  
...  
Keyword(s):  
Complex Traits ◽  
Target Genes ◽  
Association Studies ◽  
Test Methods ◽  
Genome Wide Association Studies ◽  
Individual Level ◽  
Genome Wide ◽  
Gene Level ◽  
The Individual ◽  
The Uk

AbstractIntegration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association test methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restricts our ability to detect associations. Here we propose an efficient statistical method that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes: MulTiXcan. MulTiXcan integrates evidence across multiple panels while taking into account their correlation. We apply our method to a broad set of complex traits available from the UK Biobank and show that we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed an extension to work on summary statistics: S-MulTiXcan, which we show yields highly concordant results with the individual level version. Results from our analysis as well as software and necessary resources to apply our method are publicly available.

scholarly journals MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Williams ◽  
Beisi Xu ◽  
Daniel Putnam ◽  
Andrew Thrasher ◽  
Chunliang Li ◽  
...  
Keyword(s):  
Promoter Activity ◽  
Malignant Neoplasms ◽  
Clinical Value ◽  
Individual Gene ◽  
Learning Framework ◽  
Genome Wide ◽  
Gene Level ◽  
Biological Impacts ◽  
The Individual ◽  
Methylation Patterns

AbstractAlthough genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present MethylationToActivity (M2A), a machine learning framework that uses convolutional neural networks to infer promoter activities based on H3K4me3 and H3K27ac enrichment, from DNA methylation patterns for individual genes. Using publicly available datasets in real-world test scenarios, we demonstrate that M2A is highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers, including both solid and hematologic malignant neoplasms.

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