scholarly journals Fast and Powerful Genome Wide Association Analysis of Dense Genetic Data with High Dimensional Imaging Phenotypes

2017 ◽  
Author(s):  
Habib Ganjgahi ◽  
Anderson M. Winkler ◽  
David C. Glahn ◽  
John Blangero ◽  
Brian Donohue ◽  
...  
Keyword(s):  
Mixed Models ◽  
Multiple Testing ◽  
Genetic Basis ◽  
Genome Wide Association ◽  
High Dimensional ◽  
Mixed Effect ◽  
Mixed Effect Models ◽  
Genome Wide ◽  
The Brain ◽  
Linear Mixed Effect Models

ABSTRACTGenome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes presents enormous challenges in terms of computational intensity and the need to account multiple testing in both the imaging and genetic domain. Here we present method that makes mixed models practical with high-dimensional traits by a combination of a transformation applied to the data and model, and the use of a non-iterative variance component estimator. With such speed enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic.

scholarly journals Noise reduction in genome-wide perturbation screens using linear mixed-effect models

2011 ◽  
Vol 27 (16) ◽  
pp. 2173-2180 ◽  
Author(s):  
Danni Yu ◽  
John Danku ◽  
Ivan Baxter ◽  
Sungjin Kim ◽  
Olena K. Vatamaniuk ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Mixed Effect Models ◽  
Genome Wide ◽  
Linear Mixed Effect Models

scholarly journals Pain and the Emotional Brain: Affective Rather than Cognitive Processes Drive the Cortical Encoding of Pain

2021 ◽  
Author(s):  
Astrid Mayr ◽  
Anne Stankewitz ◽  
Stephanie Irving ◽  
Viktor Witkovsky ◽  
Enrico Schulz
Keyword(s):  
Pain Intensity ◽  
Cognitive Processes ◽  
The Body ◽  
Cold Pain ◽  
Mixed Effect ◽  
Mixed Effect Models ◽  
Motivational Aspect ◽  
Pain Ratings ◽  
The Brain ◽  
Linear Mixed Effect Models

The experience of pain has been dissociated into two interwoven aspects: a sensory-discriminative aspect assessed in ratings of pain intensity and an affective-motivational aspect assessed in ratings of unpleasantness. In a pain attenuation paradigm, participants were asked to evaluate applied cold pain. The majority of the trials showed a distinct rating: Some trials were rated higher for unpleasantness, others were rated higher for intensity. Using linear mixed effect models on single trials, we related the variable difference between unpleasantness and intensity ratings to functional MRI data. The direct comparison revealed a stronger relationship between cortical data and pain ratings for unpleasantness. No region showed a stronger effect for pain intensity. The present study underlines the importance of the emotional-affective aspects of pain-related cortical processes in the brain. These findings reflect the biological function of the pain system to prevent harm and to preserve the physical integrity of the body.

scholarly journals Genome-Wide Association Studies Based on Equine Joint Angle Measurements Reveal New QTL Affecting the Conformation of Horses

Genes ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 370 ◽  
Author(s):  
Annik Imogen Gmel ◽  
Thomas Druml ◽  
Rudolf von Niederhäusern ◽  
Tosso Leeb ◽  
Markus Neuditschko
Keyword(s):  
Joint Angle ◽  
Association Studies ◽  
Genome Wide Association ◽  
Cranial Morphology ◽  
Genome Wide Association Studies ◽  
Joint Angles ◽  
Mineral Density ◽  
Mixed Effect ◽  
Stifle Joint ◽  
Genome Wide

The evaluation of conformation traits is an important part of selection for breeding stallions and mares. Some of these judged conformation traits involve joint angles that are associated with performance, health, and longevity. To improve our understanding of the genetic background of joint angles in horses, we have objectively measured the angles of the poll, elbow, carpal, fetlock (front and hind), hip, stifle, and hock joints based on one photograph of each of the 300 Franches-Montagnes (FM) and 224 Lipizzan (LIP) horses. After quality control, genome-wide association studies (GWASs) for these traits were performed on 495 horses, using 374,070 genome-wide single nucleotide polymorphisms (SNPs) in a mixed-effect model. We identified two significant quantitative trait loci (QTL) for the poll angle on ECA28 (p = 1.36 × 10−7), 50 kb downstream of the ALX1 gene, involved in cranial morphology, and for the elbow joint on ECA29 (p = 1.69 × 10−7), 49 kb downstream of the RSU1 gene, and 75 kb upstream of the PTER gene. Both genes are associated with bone mineral density in humans. Furthermore, we identified other suggestive QTL associated with the stifle joint on ECA8 (p = 3.10 × 10−7); the poll on ECA1 (p = 6.83 × 10−7); the fetlock joint of the hind limb on ECA27 (p = 5.42 × 10−7); and the carpal joint angle on ECA3 (p = 6.24 × 10−7), ECA4 (p = 6.07 × 10−7), and ECA7 (p = 8.83 × 10−7). The application of angular measurements in genetic studies may increase our understanding of the underlying genetic effects of important traits in equine breeding.

scholarly journals Comprehensive Genome-Wide Association Analysis Reveals the Genetic Basis of Root System Architecture in Soybean

2020 ◽  
Vol 11 ◽  
Author(s):  
Waldiodio Seck ◽  
Davoud Torkamaneh ◽  
François Belzile
Keyword(s):  
Root System ◽  
System Architecture ◽  
Phenotypic Variation ◽  
Genetic Basis ◽  
Genome Wide Association Study ◽  
Primary Root ◽  
Root System Architecture ◽  
Genome Wide Association ◽  
Nucleotide Polymorphisms ◽  
Genome Wide

Increasing the understanding genetic basis of the variability in root system architecture (RSA) is essential to improve resource-use efficiency in agriculture systems and to develop climate-resilient crop cultivars. Roots being underground, their direct observation and detailed characterization are challenging. Here, were characterized twelve RSA-related traits in a panel of 137 early maturing soybean lines (Canadian soybean core collection) using rhizoboxes and two-dimensional imaging. Significant phenotypic variation (P < 0.001) was observed among these lines for different RSA-related traits. This panel was genotyped with 2.18 million genome-wide single-nucleotide polymorphisms (SNPs) using a combination of genotyping-by-sequencing and whole-genome sequencing. A total of 10 quantitative trait locus (QTL) regions were detected for root total length and primary root diameter through a comprehensive genome-wide association study. These QTL regions explained from 15 to 25% of the phenotypic variation and contained two putative candidate genes with homology to genes previously reported to play a role in RSA in other species. These genes can serve to accelerate future efforts aimed to dissect genetic architecture of RSA and breed more resilient varieties.

Association of fine particulate matter with glucose and lipid metabolism: a longitudinal study in young adults

2021 ◽  
pp. oemed-2020-107039
Author(s):  
Jingyi Qin ◽  
Wei Xia ◽  
Gaodao Liang ◽  
Shunqing Xu ◽  
Xiuge Zhao ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Lipid Metabolism ◽  
Longitudinal Study ◽  
Fine Particulate Matter ◽  
Healthy Adults ◽  
Mixed Effect ◽  
Glucose And Lipid Metabolism ◽  
Mixed Effect Models ◽  
Fine Particulate ◽  
Linear Mixed Effect Models

ObjectivesThis study aimed to evaluate whether PM2.5 exposure in a highly polluted area (>100 µg/m3) affects glucose and lipid metabolism in healthy adults.MethodsWe recruited 110 healthy adults in Baoding city, Hebei, China, and followed them up between 2017 and 2018. Personal air samplers were used to monitor personal PM2.5 levels. Eight glucose and lipid metabolism parameters were quantified. We performed the linear mixed-effect models to investigate the relationships between PM2.5 and glucose and lipid metabolism parameters. Stratified analyses were further performed according to sex and body mass index (BMI).ResultsThe concentration of PM2.5 was the highest in spring, with a median of 232 μg/m3 and the lowest in autumn (139 μg/m3). After adjusting for potential confounders, we found that for each twofold increase in PM2.5, the median of insulin concentration decreased by 5.89% (95% CI −10.91% to −0.58%; p<0.05), and ox-LDL increased by 6.43% (95% CI 2.21% to 10.82%; p<0.05). Stratified analyses indicated that the associations were more pronounced in females, overweight and obese participants.ConclusionsExposure to high PM2.5 may have deleterious effects on glucose and lipid metabolism. Females, overweight and obese participants are more vulnerable.

scholarly journals Genome-Wide Association Study (GWAS) For Cold Tolerance at The Bud Burst Stage in Rice Using SNP Markers

2021 ◽  
Author(s):  
Caijing Li ◽  
Jindong Liu ◽  
Jianxin Bian ◽  
Tao Jin ◽  
Baoli Zou ◽  
...  
Keyword(s):  
Low Temperature ◽  
Association Study ◽  
Cold Tolerance ◽  
Genetic Basis ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Bud Burst ◽  
Rice Varieties ◽  
Rice Landraces ◽  
Genome Wide

Abstract Background: Rice is a crop that is very sensitive to low temperature, and its morphological development and production are greatly affected by low temperature. Therefore, understanding the genetic basis of cold tolerance in rice is of great significance for mining favorable genes and cultivating excellent rice varieties. However, there were limited studies focusing on cold tolerance at the bud burst stage, therefore, considerable attention should be paid to the genetic basis of cold tolerance at the bud burst stage (CTBB).Results: In this study, a natural population consisting of 211 rice landraces collected from 15 provinces of China and other countries were firstly used to evaluate the cold tolerance at the bud burst stage. Population structure analysis showed that this population divided into three groups and was rich in genetic diversity. Our evaluation results confered that the japonica rice was more tolerance to cold at the bud burst stage than indica rice. Genome-wide association study (GWAS) were performed through the phenotypic data of 211 rice landraces and 36,727 SNPs dataset under a mixed linear model, and 12 QTLs (P < 0.0001) were identified according to the seedling survival rate (SSR) treated at 4 ℃, in which there are five QTLs (qSSR2-2, qSSR3-1, qSSR3-2, qSSR3-3 and qSSR9) which were co-located with previous studies, and seven QTLs (qSSR2-1, qSSR3-4, qSSR3-5, qSSR3-6, qSSR3-7, qSSR4 and qSSR7) which were reported for the first time. Among these QTLs, qSSR9, harboring the highest-peak SNP, explained biggest phenotypic variation. Through bioinformatics analysis, five genes (LOC_Os09g12440, LOC_Os09g12470, LOC_Os09g12520, LOC_Os09g12580 and LOC_Os09g12720) were nominated as candidates for qSSR9. Conclusion: This natural population consisting of 211 rice landraces with high density SNPs will serve as a better choice for identifying rice QTLs/genes in future, and the detected QTLs associated with cold tolerance in rice bud burst stage will be conducive to further mining favorable genes and breeding of rice varieties under cold stress.

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Uk Biobank ◽  
The Core ◽  
Genome Wide ◽  
Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

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