Imaged‐based phenotyping accelerated QTL mapping and qtl × environment interaction analysis of testa colour in peanut ( Arachis hypogaea )

2021 ◽  
Author(s):  
Shengzhong Zhang ◽  
Xiaohui Hu ◽  
Huarong Miao ◽  
Ye Chu ◽  
Fenggao Cui ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Arachis Hypogaea ◽  
Interaction Analysis ◽  
Environment Interaction ◽  
Testa Colour

scholarly journals QTL mapping and QTL × Environment interaction analysis of pod and seed related traits in cultivated peanut (<italic>Arachis hypogaea</italic> L.)

2021 ◽  
Vol 47 (10) ◽  
pp. 1874-1890
Author(s):  
Xin-Hao MENG ◽  
Jing-Nan ZHANG ◽  
Shun-Li CUI ◽  
Y. Chen Charles ◽  
Guo-Jun MU ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Arachis Hypogaea ◽  
Interaction Analysis ◽  
Environment Interaction ◽  
Cultivated Peanut

scholarly journals QTL mapping and QTL × environment interaction analysis of multi-seed pod in cultivated peanut (Arachis hypogaea L.)

2019 ◽  
Vol 7 (2) ◽  
pp. 249-260 ◽  
Author(s):  
Liang Wang ◽  
Xinlei Yang ◽  
Shunli Cui ◽  
Guojun Mu ◽  
Xingming Sun ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Arachis Hypogaea ◽  
Interaction Analysis ◽  
Arachis Hypogaea L ◽  
Cultivated Peanut

QTL Mapping and Interaction Analysis of Seed Weight per Plant in Soybean among Different Environments

2013 ◽  
Vol 39 (6) ◽  
pp. 1021 ◽  
Author(s):  
Dong-Mei FAN ◽  
Dian-Jun SUN ◽  
Zhan-Zhou MA ◽  
Chun-Yan LIU ◽  
Zhen YANG ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Seed Weight ◽  
Interaction Analysis

scholarly journals Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hae-Un Jung ◽  
Won Jun Lee ◽  
Tae-Woong Ha ◽  
Ji-One Kang ◽  
Jihye Kim ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Environmental Factors ◽  
Body Mass ◽  
Bayes Factor ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Interaction Analysis ◽  
Carbohydrate Intake ◽  
Calorie Intake ◽  
Environment Interaction

AbstractMultiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10−6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10−6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10−9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10−10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene–environment interaction affecting disease.

scholarly journals Deciphering Main Climate and Edaphic Components Driving Oat Adaptation to Mediterranean Environments

2021 ◽  
Vol 12 ◽  
Author(s):  
Francisco J. Canales ◽  
Gracia Montilla-Bascón ◽  
Luis M. Gallego-Sánchez ◽  
Fernando Flores ◽  
Nicolas Rispail ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Agronomic Traits ◽  
Interaction Analysis ◽  
Variation Partitioning ◽  
Environment Interaction ◽  
Edaphic Factors ◽  
Mediterranean Environments ◽  
Wide Range ◽  
Northern Regions ◽  
Edaphic Variables

Oat, Avena sativa, is an important crop traditionally grown in cool-temperate regions. However, its cultivated area in the Mediterranean rim steadily increased during the last 20 years due to its good adaptation to a wide range of soils. Nevertheless, under Mediterranean cultivation conditions, oats have to face high temperatures and drought episodes that reduce its yield as compared with northern regions. Therefore, oat crop needs to be improved for adaptation to Mediterranean environments. In this work, we investigated the influence of climatic and edaphic variables on a collection of 709 Mediterranean landraces and cultivars growing under Mediterranean conditions. We performed genotype–environment interaction analysis using heritability-adjusted genotype plus genotype–environment biplot analyses to determine the best performing accessions. Further, their local adaptation to different environmental variables and the partial contribution of climate and edaphic factors to the different agronomic traits was determined through canonical correspondence, redundancy analysis, and variation partitioning. Here, we show that northern bred elite cultivars were not among the best performing accessions in Mediterranean environments, with several landraces outyielding these. While all the best performing cultivars had early flowering, this was not the case for all the best performing landraces, which showed different patterns of adaption to Mediterranean agroclimatic conditions. Thus, higher yielding landraces showed adaptation to moderate to low levels of rain during pre- and post-flowering periods and moderate to high temperature and radiation during post-flowering period. This analysis also highlights landraces adapted to more extreme environmental conditions. The study allowed the selection of oat genotypes adapted to different climate and edaphic factors, reducing undesired effect of environmental variables on agronomic traits and highlights the usefulness of variation partitioning for selecting genotypes adapted to specific climate and edaphic conditions.

Abstract MP11: Systematic Discovery Of Thousands Of Gene-Environment Interactions For Cardiometabolic Serum Biomarkers

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Kenneth E Westerman
Keyword(s):  
Genetic Variants ◽  
Cardiometabolic Risk ◽  
Interaction Analysis ◽  
Environmental Exposures ◽  
Cardiometabolic Health ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
A Genome ◽  
The Mean

Background: Gene-environment interaction (GEI) analysis enables us to understand how genetic variants modify the effects of environmental exposures on cardiometabolic risk factors, providing a foundation for genome-based precision medicine. Ideally, these interactions could be mapped comprehensively across all measured genetic variants, exposures, and outcomes, but this approach is computationally intensive and statistically underpowered. Recent studies have shown that variance-quantitative trait loci (vQTLs), or genetic variants that associate with differential variance of an outcome, are substantially enriched for underlying GEIs. Here, we sought to first identify vQTLs for cardiometabolic traits, then use this smaller genetic search space to uncover novel gene-environment interactions across thousands of environmental exposures. Methods: A two-stage, multi-ancestry analysis was conducted in 355,790 unrelated participants from the UK Biobank. First, we performed a genome-wide vQTL scan for each of 20 serum metabolic biomarkers, including but not limited to lipids, lipoproteins, and glycemic measures. This scan used Levene’s test to identify genetic markers whose genotypes are associated with the variance, rather than the mean, of the biomarker. Next, we collected over 2000 variables corresponding to socioeconomic, dietary, lifestyle, and clinical exposures, and conducted an interaction analysis for each combination of exposure and vQTL-biomarker pair. For each stage, the analysis was initially stratified by ancestry then meta-analyzed to generate the primary set of results. Results: vQTLs were identified at 514 independent regions in the genome, with most of these genetic variants already known to affect the mean biomarker level. In the subsequent gene-environment interaction analysis, we found 2,162 significant interactions passing a stringent significance threshold adjusted for multiple testing ( p < 0.05 / 578 vQTL-biomarker pairs / 2140 exposures = 4х10 -8 ). Some of these expanded on existing findings; for example, genetic marker rs2393775 in the HNF1A gene interacted with education level (as a proxy for socioeconomic status) to influence hsCRP ( p = 1.3х10 -10 ), building on a previous finding that HNF1A variants modify the effect of perceived stress on cardiovascular outcomes. Others highlighted novel biology, such as an interaction between variants near the fatty liver-associated gene TM6SF2 and oily fish intake on total and LDL-cholesterol levels ( p = 6.6х10 -9 ). Conclusions: Our systematic GEI discovery effort identified thousands of interactions that may impact cardiometabolic risk, both expanding on previous research and identifying novel biological mechanisms. This catalog of vQTLs and interactions can inform future mechanistic studies and provides a knowledge base for genome-centered precision approaches to cardiometabolic health.

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