How to discover a metabolic pathway? An update on gene identification in aliphatic glucosinolate biosynthesis, regulation and transport

2014 ◽  
Vol 395 (5) ◽  
pp. 529-543 ◽  
Author(s):  
Lea Møller Jensen ◽  
Barbara Ann Halkier ◽  
Meike Burow

Abstract Identification of enzymes, regulators and transporters involved in different metabolic processes is the foundation to understand how organisms function. There are, however, many difficulties in identifying candidate genes as well as in proving their in vivo roles. In this review, we describe different approaches utilized in Arabidopsis thaliana to identify gene candidates and experiments required to prove the function of a given candidate. For example, we use the production of methionine-derived aliphatic glucosinolates that represent major defence compounds in A. thaliana. Nearly all biosynthetic genes, as well as the first sets of regulators and transporters, have been identified. An array of approaches, i.e. classical mapping, quantitative trait loci (QTL) mapping, eQTL mapping, co-expression, genome wide association studies (GWAS), mutant screens and phylogenetic analyses, has been exploited to increase the number of identified genes. Here we summarize the lessons learned from the different approaches used over the years with the aim to help designing and combining new approaches in the future.

2018 ◽  
Author(s):  
Omer Weissbrod ◽  
Daphna Rothschild ◽  
Elad Barkan ◽  
Eran Segal

Recent studies indicate that the gut microbiome is partially heritable, motivating the need to investigate microbiome-host genome associations via microbial genome-wide association studies (mGWAS). Existing mGWAS demonstrate that microbiome-host genotypes associations are typically weak and are spread across multiple variants, similar to associations often observed in genome-wide association studies (GWAS) of complex traits. Here we reconsider mGWAS by viewing them through the lens of GWAS, and demonstrate that there are striking similarities between the challenges and pitfalls faced by the two study designs. We further advocate the mGWAS community to adopt three key lessons learned over the history of GWAS: (a) Adopting uniform data and reporting formats to facilitate replication and meta-analysis efforts; (b) enforcing stringent statistical criteria to reduce the number of false positive findings; and (c) considering the microbiome and the host genome as distinct entities, rather than studying different taxa and single nucleotide polymorphism (SNPs) separately. Finally, we anticipate that mGWAS sample sizes will have to increase by orders of magnitude to reproducibly associate the host genome with the gut microbiome.


2009 ◽  
Vol 10 (2) ◽  
pp. 161-163 ◽  
Author(s):  
James J Crowley ◽  
Patrick F Sullivan ◽  
Howard L McLeod

2013 ◽  
Vol 98 (7) ◽  
pp. E1278-E1282 ◽  
Author(s):  
Yong-Jun Liu ◽  
Lei Zhang ◽  
Yufang Pei ◽  
Christopher J. Papasian ◽  
Hong-Wen Deng

2018 ◽  
Author(s):  
Omer Weissbrod ◽  
Daphna Rothschild ◽  
Elad Barkan ◽  
Eran Segal

Recent studies indicate that the gut microbiome is partially heritable, motivating the need to investigate microbiome-host genome associations via microbial genome-wide association studies (mGWAS). Existing mGWAS demonstrate that microbiome-host genotypes associations are typically weak and are spread across multiple variants, similar to associations often observed in genome-wide association studies (GWAS) of complex traits. Here we reconsider mGWAS by viewing them through the lens of GWAS, and demonstrate that there are striking similarities between the challenges and pitfalls faced by the two study designs. We further advocate the mGWAS community to adopt three key lessons learned over the history of GWAS: (a) Adopting uniform data and reporting formats to facilitate replication and meta-analysis efforts; (b) enforcing stringent statistical criteria to reduce the number of false positive findings; and (c) considering the microbiome and the host genome as distinct entities, rather than studying different taxa and single nucleotide polymorphism (SNPs) separately. Finally, we anticipate that mGWAS sample sizes will have to increase by orders of magnitude to reproducibly associate the host genome with the gut microbiome.


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