Integrating gene variation and expression to understand complex traits

Cloney, Ross

doi:10.1038/nrg.2016.18

Cited by 24 publications

(19 citation statements)

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“…Genome-wide association studies (GWAS) have revolutionized the genetics research of complex traits and diseases over the last decade [1,2]. As these studies become more common thanks to initiatives such as UK Biobank [3] and international genetics research consortia, it is increasingly necessary to build user-friendly computational tools to allow researchers without strong expertise in the area to annotate, analyse and share results originating from such studies.…”

Section: Motivationmentioning

confidence: 99%

Complex-Traits Genetics Virtual Lab: A community-driven web platform for post-GWAS analyses

Cuéllar-Partida

Lundberg

Campos-González

et al. 2019

Preprint

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BackgroundGenome-wide association studies (GWAS) are an important method for mapping genetic variation underlying complex traits and diseases. Tools to visualize, annotate and analyse results from these studies can be used to generate hypotheses about the molecular mechanisms underlying the associations.FindingsThe Complex-Traits Genetics Virtual Lab (CTG-VL) integrates over a thousand publicly-available GWAS summary statistics, a suite of analysis tools, visualization functions and diverse data sets for genomic annotations. CTG-VL also makes available results from gene, pathway and tissue-based analyses from over 1,500 complex-traits allowing to assess pleiotropy not only at the genetic variant level but also at the gene, pathway and tissue levels. In this manuscript, we showcase the platform by analysing GWAS summary statistics of mood swings derived from UK Biobank. Using analysis tools in CTG-VL we highlight hippocampus as a potential tissue involved in mood swings, and that pathways including neuron apoptotic process may underlie the genetic associations. Further, we report a negative genetic correlation with educational attainment rG = −0.41 ± 0.018 and a potential causal effect of BMI on mood swings OR = 1.01 (95% CI = 1.00–1.02). Using CTG-VL’s database, we show that pathways and tissues associated with mood swings are also associated with neurological traits including reaction time and neuroticism, as well as traits such age at menopause and age at first live birth.ConclusionsCTG-VL is a platform with the most complete set of tools to carry out post-GWAS analyses. The CTG-VL is freely available at https://genoma.io as an online web application.

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Section: Motivationmentioning

confidence: 99%

Complex-Traits Genetics Virtual Lab: A community-driven web platform for post-GWAS analyses

Cuéllar-Partida

Lundberg

Campos-González

et al. 2019

Preprint

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“…Over the last decade, transcriptomic studies have been commonly conducted on small-scale experimental populations to identify genes involved in biological processes underlying complex traits and diseases. Genomic variants affect complex phenotypes often through modulating gene expression16, therefore integrating genomic variants and gene expression data could contribute to a better understanding of the genetic architecture underlying the trait variation16. Compared to most existing pathway annotation databases ( e.g ., Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes), these transcriptomic studies could provide more reliable gene clusters that are functionally related to traits of interest9.…”

mentioning

confidence: 99%

Integrating Sequence-based GWAS and RNA-Seq Provides Novel Insights into the Genetic Basis of Mastitis and Milk Production in Dairy Cattle

Fang

Sahana

et al. 2017

Sci Rep

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Connecting genome-wide association study (GWAS) to biological mechanisms underlying complex traits is a major challenge. Mastitis resistance and milk production are complex traits of economic importance in the dairy sector and are associated with intra-mammary infection (IMI). Here, we integrated IMI-relevant RNA-Seq data from Holstein cattle and sequence-based GWAS data from three dairy cattle breeds (i.e., Holstein, Nordic red cattle, and Jersey) to explore the genetic basis of mastitis resistance and milk production using post-GWAS analyses and a genomic feature linear mixed model. At 24 h post-IMI, genes responsive to IMI in the mammary gland were preferentially enriched for genetic variants associated with mastitis resistance rather than milk production. Response genes in the liver were mainly enriched for variants associated with mastitis resistance at an early time point (3 h) post-IMI, whereas responsive genes at later stages were enriched for associated variants with milk production. The up- and down-regulated genes were enriched for associated variants with mastitis resistance and milk production, respectively. The patterns were consistent across breeds, indicating that different breeds shared similarities in the genetic basis of these traits. Our approaches provide a framework for integrating multiple layers of data to understand the genetic architecture underlying complex traits.

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“…Systems biology integrating various "omics" technologies has facilitated the discovery of candidate genes conferring complex trait variation (e.g., Civelek & Lusis, 2014;Cloney, 2016;Palit et al, 2020). Transcriptomics, proteomics, and metabolomics are well suited to handle complex traits by quantitatively and dynamically determining the activities of transcripts, proteins, and metabolites over a time course.…”

Section: Dissecting the Genetic Basis Of Plant Climate Change Adaptationmentioning

confidence: 99%

Plant adaptation to climate change—Where are we?

Anderson

Song

2020

J of Sytematics Evolution

126

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Climate change poses critical challenges for population persistence in natural communities, for agriculture and environmental sustainability, and for food security. In this review, we discuss recent progress in climatic adaptation in plants. We evaluate whether climate change exerts novel selection and disrupts local adaptation, whether gene flow can facilitate adaptive responses to climate change, and whether adaptive phenotypic plasticity could sustain populations in the short term. Furthermore, we discuss how climate change influences species interactions. Through a more in‐depth understanding of these eco‐evolutionary dynamics, we will increase our capacity to predict the adaptive potential of plants under climate change. In addition, we review studies that dissect the genetic basis of plant adaptation to climate change. Finally, we highlight key research gaps, ranging from validating gene function to elucidating molecular mechanisms, expanding research systems from model species to other natural species, testing the fitness consequences of alleles in natural environments, and designing multifactorial studies that more closely reflect the complex and interactive effects of multiple climate change factors. By leveraging interdisciplinary tools (e.g., cutting‐edge omics toolkits, novel ecological strategies, newly developed genome editing technology), researchers can more accurately predict the probability that species can persist through this rapid and intense period of environmental change, as well as cultivate crops to withstand climate change, and conserve biodiversity in natural systems.

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Integrating gene variation and expression to understand complex traits

Cited by 24 publications

References 1 publication

Complex-Traits Genetics Virtual Lab: A community-driven web platform for post-GWAS analyses

Complex-Traits Genetics Virtual Lab: A community-driven web platform for post-GWAS analyses

Integrating Sequence-based GWAS and RNA-Seq Provides Novel Insights into the Genetic Basis of Mastitis and Milk Production in Dairy Cattle

Plant adaptation to climate change—Where are we?

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