scholarly journals Lessons learned from Genetic Analysis Workshop 17: transitioning from genome-wide association studies to whole-genome statistical genetic analysis

2011 ◽  
Vol 35 (S1) ◽  
pp. S107-S114 ◽  
Author(s):  
Alexander F. Wilson ◽  
Andreas Ziegler
Keyword(s):  
Genetic Analysis ◽  
Genetic Analysis Workshop ◽  
Association Studies ◽  
Lessons Learned ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Genome Wide

scholarly journals Integration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Gabriel Costa Monteiro Moreira ◽  
Clarissa Boschiero ◽  
Aline Silva Mello Cesar ◽  
James M. Reecy ◽  
Thaís Fernanda Godoy ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Association Studies ◽  
Fat Deposition ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Genome Wide

scholarly journals Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services

2020 ◽  
Vol 27 (9) ◽  
pp. 1425-1430
Author(s):  
Inès Krissaane ◽  
Carlos De Niz ◽  
Alba Gutiérrez-Sacristán ◽  
Gabor Korodi ◽  
Nneka Ede ◽  
...  
Keyword(s):  
Web Services ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Cloud Platform ◽  
Human Genomics ◽  
Genome Wide ◽  
Innovative Methodology ◽  
Amazon Web Services

Abstract Objective Advancements in human genomics have generated a surge of available data, fueling the growth and accessibility of databases for more comprehensive, in-depth genetic studies. Methods We provide a straightforward and innovative methodology to optimize cloud configuration in order to conduct genome-wide association studies. We utilized Spark clusters on both Google Cloud Platform and Amazon Web Services, as well as Hail (http://doi.org/10.5281/zenodo.2646680) for analysis and exploration of genomic variants dataset. Results Comparative evaluation of numerous cloud-based cluster configurations demonstrate a successful and unprecedented compromise between speed and cost for performing genome-wide association studies on 4 distinct whole-genome sequencing datasets. Results are consistent across the 2 cloud providers and could be highly useful for accelerating research in genetics. Conclusions We present a timely piece for one of the most frequently asked questions when moving to the cloud: what is the trade-off between speed and cost?

scholarly journals Genome-Wide Association Studies and Whole-Genome Prediction Reveal the Genetic Architecture of KRN in Maize

2020 ◽  
Author(s):  
Yixin An ◽  
Lin Chen ◽  
Yongxiang Li ◽  
Chunhui Li ◽  
Yunsu Shi ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Association Studies ◽  
Genome Wide Association ◽  
Cytokinin Oxidase ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Maize Breeding ◽  
Candidate Gene Association ◽  
Genome Wide ◽  
Genome Prediction

Abstract Background: Kernel row number (KRN) is an important trait for the domestication and improvement of maize. To explore the genetic basis of KRN has great research significance and can provide the valuable information for molecular assisted selection.Results: In this study, one single-locus method (MLM) and six multi-locus methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO) of genome-wide association studies (GWASs) were used to identify significant quantitative trait nucleotides (QTNs) for KRN in an association panel including 639 maize inbred lines that were genotyped by the MaizeSNP50 BeadChip. In three phenotyping environments and with best linear unbiased prediction (BLUP) values, seven GWAS methods revealed different numbers of KRN-associated QTNs, ranging from 11 to 177. Based on these results, seven important regions for KRN located on chromosomes 1, 2, 3, 5, 9, and 10 were identified by at least three methods and in at least two environments. Moreover, 49 genes from the seven regions were expressed in different maize tissues. Among the 49 genes, ARF29 (Zm00001d026540, encoding auxin response factor 29) and CKO4 (Zm00001d043293, encoding cytokinin oxidase protein) were significantly related to KRN based on expression analysis and candidate gene association mapping. Whole-genome prediction (WGP) for KRN was also performed, and we found that the KRN-associated tagSNPs achieved a high prediction accuracy. The best strategy was to integrate the total KRN-associated tagSNPs identified by all GWAS models.Conclusions: These results aid in our understanding of the genetic architecture of KRN and provide useful information for genomic selection for KRN in maize breeding.

scholarly journals Host genetics and microbiome associations from the lens of genome wide association studies

2018 ◽  
Author(s):  
Omer Weissbrod ◽  
Daphna Rothschild ◽  
Elad Barkan ◽  
Eran Segal
Keyword(s):  
Gut Microbiome ◽  
Complex Traits ◽  
Association Studies ◽  
Meta Analysis ◽  
Lessons Learned ◽  
Host Genome ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
History Of

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.

scholarly journals Imputation to whole-genome sequence using multiple pig populations and its use in genome-wide association studies

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Sanne van den Berg ◽  
Jérémie Vandenplas ◽  
Fred A. van Eeuwijk ◽  
Aniek C. Bouwman ◽  
Marcos S. Lopes ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Association Studies ◽  
Genome Wide Association ◽  
Whole Genome Sequence ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Genome Wide

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
Keyword(s):  
Association Studies ◽  
Phylogenetic Analyses ◽  
Lessons Learned ◽  
Genome Wide Association ◽  
Gene Identification ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Aliphatic Glucosinolates ◽  
Biosynthesis Regulation

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.

Sign in / Sign up

Export Citation Format

Share Document