Transferability of Marker Trait Associations in Wheat Is Disturbed Mainly by Genotype × Year Interaction

Examining Automatic Stereotyping From a Propositional Perspective: Is Automatic Stereotyping Sensitive to Relational and Validity Information?

Personality and Social Psychology Bulletin ◽

10.1177/01461672211024121 ◽

2021 ◽

pp. 014616722110241

Author(s):

Tal Moran ◽

Jamie Cummins ◽

Jan De Houwer

Keyword(s):

Total N ◽

Relational Information ◽

Truth Values ◽

Alternative Perspective ◽

Automatic Stereotyping ◽

Reference Person ◽

Trait Associations

Research on automatic stereotyping is dominated by the idea that automatic stereotyping reflects the activation of (group–trait) associations. In two preregistered experiments (total N = 391), we tested predictions derived from an alternative perspective that suggests that automatic stereotyping is the result of the activation of propositional representations that, unlike associations, can encode relational information and have truth values. Experiment 1 found that automatic stereotyping is sensitive to the validity of information about pairs of traits and groups. Experiment 2 showed that automatic stereotyping is sensitive to the specific relations (e.g., whether a particular group is more or less friendly than a reference person) between pairs of traits and groups. Interestingly, both experiments found a weaker influence of validity/relational information on automatic stereotyping than on non-automatic stereotyping. We discuss the implications of these findings for research on automatic stereotyping.

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Analysis of Genetic Variation and Enhancement of Salt Tolerance in French Pea (Pisum Sativum L.)

International Journal of Molecular Sciences ◽

10.3390/ijms19082433 ◽

2018 ◽

Vol 19 (8) ◽

pp. 2433 ◽

Cited By ~ 31

Author(s):

Mohamed El-Esawi ◽

Abdullah Al-Ghamdi ◽

Hayssam Ali ◽

Aisha Alayafi ◽

Jacques Witczak ◽

...

Keyword(s):

Fatty Acids ◽

Antioxidant Enzymes ◽

Salt Tolerance ◽

Pisum Sativum ◽

Crude Oil ◽

Oil Content ◽

High Salinity ◽

Aflp Markers ◽

Pisum Sativum L ◽

Trait Associations

Pisum sativum L. (field pea) is a crop of a high nutritional value and seed oil content. The characterization of pea germplasm is important to improve yield and quality. This study aimed at using fatty acid profiling and amplified fragment length polymorphism (AFLP) markers to evaluate the variation and relationships of 25 accessions of French pea. It also aimed to conduct a marker-trait associations analysis using the crude oil content as the target trait for this analysis, and to investigate whether 5-aminolevulinic acid (ALA) could enhance salt tolerance in the pea germplasm. The percentage of crude oil of the 25 pea genotypes varied from 2.6 to 3.5%, with a mean of 3.04%. Major fatty acids in all of the accessions were linoleic acid. Moreover, the 12 AFLP markers used were polymorphic. The cluster analysis based on fatty acids data or AFLP data divided the 25 pea germplasm into two main clusters. The gene diversity of the AFLP markers varied from 0.21 to 0.58, with a mean of 0.41. Polymorphic information content (PIC) of pea germplasm varied from 0.184 to 0.416 with a mean of 0.321, and their expected heterozygosity (He) varied from 0.212 to 0.477 with a mean of 0.362. The AFLP results revealed that the Nain Ordinaire cultivar has the highest level of genetic variability, whereas Elatius 3 has the lowest level. Three AFLP markers (E-AAC/M-CAA, E-AAC/M-CAC, and E-ACA/M-CAG) were significantly associated with the crude oil content trait. The response of the Nain Ordinaire and Elatius 3 cultivars to high salinity stress was studied. High salinity (150 mM NaCl) slightly reduced the photosynthetic pigments contents in Nain Ordinaire leaves at a non-significant level, however, the pigments contents in the Elatius 3 leaves were significantly reduced by high salinity. Antioxidant enzymes (APX—ascorbate peroxidase; CAT—catalase; and POD—peroxidase) activities were significantly induced in the Nain Ordinaire cultivar, but non-significantly induced in Elatius 3 by high salinity. Priming the salt-stressed Nain Ordinaire and Elatius 3 plants with ALA significantly enhanced the pigments biosynthesis, antioxidant enzymes activities, and stress-related genes expression, as compared to the plants stressed with salt alone. In conclusion, this study is amongst the first investigations that conducted marker-trait associations in pea, and revealed a sort of correlation between the diversity level and salt tolerance.

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National Pork Producers Council Maternal Line National Genetic Evaluation Program: A comparison of sow longevity and trait associations with sow longevity1

Journal of Animal Science ◽

10.2527/jas.2005-499 ◽

2006 ◽

Vol 84 (9) ◽

pp. 2590-2595 ◽

Cited By ~ 33

Author(s):

T. Serenius ◽

K. J. Stalder ◽

T. J. Baas ◽

J. W. Mabry ◽

R. N. Goodwin ◽

...

Keyword(s):

Genetic Evaluation ◽

Maternal Line ◽

Evaluation Program ◽

Sow Longevity ◽

Trait Associations

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Marker-trait associations in two-rowed spring barley accessions from Kazakhstan and the USA

PLoS ONE ◽

10.1371/journal.pone.0205421 ◽

2018 ◽

Vol 13 (10) ◽

pp. e0205421 ◽

Cited By ~ 5

Author(s):

Yuliya Genievskaya ◽

Shyryn Almerekova ◽

Burabai Sariev ◽

Vladimir Chudinov ◽

Laura Tokhetova ◽

...

Keyword(s):

Spring Barley ◽

The Usa ◽

Trait Associations

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Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

10.1101/2021.07.05.450786 ◽

2021 ◽

Author(s):

Milton Pividori ◽

Sumei Lu ◽

Binglan Li ◽

Chun Su ◽

Matthew E. Johnson ◽

...

Keyword(s):

Gene Expression ◽

Association Studies ◽

Expression Patterns ◽

Cell Types ◽

Joint Analysis ◽

Genetic Associations ◽

Disease Etiology ◽

Mediating Role ◽

Gene Modules ◽

Trait Associations

Understanding how dysregulated transcriptional processes result in tissue-specific pathology requires a mechanistic interpretation of expression regulation across different cell types. It has been shown that this insight is key for the development of new therapies. These mechanisms can be identified with transcriptome-wide association studies (TWAS), which have represented an important step forward to test the mediating role of gene expression in GWAS associations. However, due to pervasive eQTL sharing across tissues, TWAS has not been successful in identifying causal tissues, and other methods generally do not take advantage of the large amounts of RNA-seq data publicly available. Here we introduce a polygenic approach that leverages gene modules (genes with similar co-expression patterns) to project both gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. We observed that diseases were significantly associated with gene modules expressed in relevant cell types, such as hypothyroidism with T cells and thyroid, hypertension and lipids with adipose tissue, and coronary artery disease with cardiomyocytes. Our approach was more accurate in predicting known drug-disease pairs and revealed stable trait clusters, including a complex branch involving lipids with cardiovascular, autoimmune, and neuropsychiatric disorders. Furthermore, using a CRISPR-screen, we show that genes involved in lipid regulation exhibit more consistent trait associations through gene modules than individual genes. Our results suggest that a gene module perspective can contextualize genetic associations and prioritize alternative treatment targets when GWAS hits are not druggable.

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MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

10.1101/2020.04.17.047225 ◽

2020 ◽

Cited By ~ 2

Author(s):

Arjun Bhattacharya ◽

Yun Li ◽

Michael I. Love

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Brain Tissue ◽

Predictive Models ◽

Association Studies ◽

Genetic Regulation ◽

Breast Tumors ◽

Risk Genes ◽

Complex Interactions ◽

Trait Associations

ABSTRACTTraditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or other molecular effects underlying the SNP-gene association. Here, we outline multi-omics strategies for transcriptome imputation from germline genetics to allow more powerful testing of gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final predictive model of gene expression, along with local SNPs. In the second extension, we assess distal-eQTLs (SNPs associated with genes not in a local window around it) for their mediation effect through mediating biomarkers local to these distal-eSNPs. Distal-eSNPs with large indirect mediation effects are then included in the transcriptomic prediction model with the local SNPs around the gene of interest. Using simulations and real data from ROS/MAP brain tissue and TCGA breast tumors, we show considerable gains of percent variance explained (1-2% additive increase) of gene expression and TWAS power to detect gene-trait associations. This integrative approach to transcriptome-wide imputation and association studies aids in identifying the complex interactions underlying genetic regulation within a tissue and important risk genes for various traits and disorders.AUTHOR SUMMARYTranscriptome-wide association studies (TWAS) are a powerful strategy to study gene-trait associations by integrating genome-wide association studies (GWAS) with gene expression datasets. TWAS increases study power and interpretability by mapping genetic variants to genes. However, traditional TWAS consider only variants that are close to a gene and thus ignores important variants far away from the gene that may be involved in complex regulatory mechanisms. Here, we present MOSTWAS (Multi-Omic Strategies for TWAS), a suite of tools that extends the TWAS framework to include these distal variants. MOSTWAS leverages multi-omic data of regulatory biomarkers (transcription factors, microRNAs, epigenetics) and borrows from techniques in mediation analysis to prioritize distal variants that are around these regulatory biomarkers. Using simulations and real public data from brain tissue and breast tumors, we show that MOSTWAS improves upon traditional TWAS in both predictive performance and power to detect gene-trait associations. MOSTWAS also aids in identifying possible mechanisms for gene regulation using a novel added-last test that assesses the added information gained from the distal variants beyond the local association. In conclusion, our method aids in detecting important risk genes for traits and disorders and the possible complex interactions underlying genetic regulation within a tissue.

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