scholarly journals Emerging Paradigms in Genomics-Based Crop Improvement

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Abhishek Bohra
Keyword(s):  
High Throughput ◽  
Molecular Breeding ◽  
Recurrent Selection ◽  
Association Studies ◽  
Crop Improvement ◽  
Breeding Value ◽  
Easy Access ◽  
Genome Wide Association Studies ◽  
Multiple Alleles ◽  
Genome Wide

Next generation sequencing platforms and high-throughput genotyping assays have remarkably expedited the pace of development of genomic tools and resources for several crops. Complementing the technological developments, conceptual shifts have also been witnessed in designing experimental populations. Availability of second generation mapping populations encompassing multiple alleles, multiple traits, and extensive recombination events is radically changing the phenomenon of classical QTL mapping. Additionally, the rising molecular breeding approaches like marker assisted recurrent selection (MARS) that are able to harness several QTLs are of particular importance in obtaining a “designed” genotype carrying the most desirable combinations of favourable alleles. Furthermore, rapid generation of genome-wide marker data coupled with easy access to precise and accurate phenotypic screens enable large-scale exploitation of LD not only to discover novel QTLs via whole genome association scans but also to practise genomic estimated breeding value (GEBV)-based selection of genotypes. Given refinements being experienced in analytical methods and software tools, the multiparent populations will be the resource of choice to undertake genome wide association studies (GWAS), multiparent MARS, and genomic selection (GS). With this, it is envisioned that these high-throughput and high-power molecular breeding methods would greatly assist in exploiting the enormous potential underlying breeding by design approach to facilitate accelerated crop improvement.

scholarly journals Genome-wide association studies provide insights into the genetic determination of fruit traits of pear

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ming-Yue Zhang ◽  
Cheng Xue ◽  
Hongju Hu ◽  
Jiaming Li ◽  
Yongsong Xue ◽  
...  
Keyword(s):  
Fruit Quality ◽  
Molecular Breeding ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Fruit Traits ◽  
Stone Cell ◽  
Genome Wide ◽  
Fruit Quality Traits

AbstractPear is a major fruit tree crop distributed worldwide, yet its breeding is a very time-consuming process. To facilitate molecular breeding and gene identification, here we have performed genome-wide association studies (GWAS) on eleven fruit traits. We identify 37 loci associated with eight fruit quality traits and five loci associated with three fruit phenological traits. Scans for selective sweeps indicate that traits including fruit stone cell content, organic acid and sugar contents might have been under continuous selection during breeding improvement. One candidate gene, PbrSTONE, identified in GWAS, has been functionally verified to be involved in the regulation of stone cell formation, one of the most important fruit quality traits in pear. Our study provides insights into the complex fruit related biology and identifies genes controlling important traits in pear through GWAS, which extends the genetic resources and basis for facilitating molecular breeding in perennial trees.

scholarly journals Haplotype-based genome wide association study using a novel SNP-set method : RAINBOW

2019 ◽  
Author(s):  
Kosuke Hamazaki ◽  
Hiroyoshi Iwata
Keyword(s):  
Haplotype Block ◽  
Rare Variants ◽  
Association Studies ◽  
False Positives ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Alleles ◽  
Genome Wide ◽  
Causal Variants ◽  
Novel Snp

AbstractBackgroundDiffculty in detecting rare variants is one of the problems in conventional genome wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set GWAS method named “RAINBOW” and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes.ResultsWe prepared 100 datasets of simulated phenotypic data and real marker genotype data of Oryza sativa subsp. indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. The results of the comparison indicated that the proposed method was able to better control false positives than the others. The proposed method was also excellent at detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset. Moreover, the proposed method showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, especially when the causal variants were located very close to each other and the directions of their effects were opposite.ConclusionThe proposed method, RAINBOW, is especially superior in controlling false positives, detecting causal variants, and detecting nearby causal variants with opposite effects. By using the SNP-set approach as the proposed method, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as the R package and is available at https://github.com/KosukeHamazaki/RAINBOW.

scholarly journals Dissecting meningococcal disease and carriage traits using high throughput phenotypic testing

2020 ◽  
Vol 2 (7A) ◽  
Author(s):  
Megan De Ste Croix ◽  
Dave Neelam ◽  
Neil Oldfield ◽  
Jay Lucidarme ◽  
David Turner ◽  
...  
Keyword(s):  
High Throughput ◽  
Meningococcal Disease ◽  
Association Studies ◽  
Phase Variation ◽  
Genome Wide Association Studies ◽  
Current Policy ◽  
Genome Sequences ◽  
Phenotypic Differences ◽  
Genome Wide ◽  
The Uk

Despite on-going vaccination programmes, Neisseria meningitidis causes over 700 cases of invasive meningococcal disease (IMD) in the UK each year. In 2017-18, the MenW and MenY capsular groups caused 38% of all IMD cases. Current policy is to generate genome sequences of all meningococcal disease isolates. Using this resource, we aim to understand how genetic variation contributes to phenotypic differences between carriage and disease isolates. We are adapting a variety of assays, designed to mimic carriage and disease behaviours, for high throughput phenotypic testing of multiple meningococcal isolates from carriage and cases of IMD. We have selected 335 MenW cc11 and MenY cc23 isolates and are currently testing subsets of isolates in cell culture (CaLu3), growth and biofilm assays. Phenotypic differences will be utilised as input data for Genome Wide Association Studies that aim to identify the specific genomic variants, or combinations of variants, determining observed differences. Genomic data will include whole genome sequences and repeat-mediated phase variation states. Our preliminary data has detected variation in the ability of cc11 and cc23 isolates to disrupt monolayers of CaLu3 cells, indicating that minor genetic differences in phylogentically similar organisms may be physiologically important for both carriage and disease. We will also discuss progress in establishing successful, high-throughput assays for testing multiple isolates.

scholarly journals Causal Haplotype Block Identification in Plant Genome-Wide Association Studies

2021 ◽  
Author(s):  
Xing Wu ◽  
Wei Jiang ◽  
Christopher Fragoso ◽  
Jing Huang ◽  
Geyu Zhou ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Haplotype Block ◽  
Association Studies ◽  
Crop Improvement ◽  
Plant Genome ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Haplotype Blocks ◽  
Genome Wide

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) used in many GWAS that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects. In plants, the relatively small population size in GWAS and the high genetic diversity found many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to infer the causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, and GMMAT in both simulation and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in simulations with high polygenicity. Moreover, it resulted in higher mapping resolution, especially in regions of high LD, by identifying small causal blocks in the larger haplotype block. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA results, and its average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved mapping resolution to facilitate crop improvement.

scholarly journals High-throughput analysis of epistasis in genome-wide association studies with BiForce

2012 ◽  
Vol 28 (15) ◽  
pp. 1957-1964 ◽  
Author(s):  
Attila Gyenesei ◽  
Jonathan Moody ◽  
Colin A.M. Semple ◽  
Chris S. Haley ◽  
Wen-Hua Wei
Keyword(s):  
High Throughput ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Genome Wide

scholarly journals PheLiGe: an interactive database of billions of human genotype–phenotype associations

2020 ◽  
Vol 49 (D1) ◽  
pp. D1347-D1350 ◽  
Author(s):  
Tatiana I Shashkova ◽  
Eugene D Pakhomov ◽  
Denis D Gorev ◽  
Lennart C Karssen ◽  
Peter K Joshi ◽  
...  
Keyword(s):  
Biological Function ◽  
Association Studies ◽  
Complex Trait ◽  
Easy Access ◽  
Genome Wide Association Studies ◽  
Web Interface ◽  
Regional Patterns ◽  
Protein Levels ◽  
Genome Wide ◽  
Causal Genes

Abstract Genome-wide association studies have provided a vast array of publicly available SNP × phenotype association results. However, they are often in disparate repositories and formats, making downstream analyses difficult and time consuming. PheLiGe (https://phelige.com) is a database that provides easy access to such results via a web interface. The underlying database currently stores >75 billion genotype–phenotype associations from 7347 genome-wide and 1.2 million region-wide (e.g. cis-eQTL) association scans. The web interface allows for investigation of regional genotype-phenotype associations across many phenotypes, giving insights into the biological function affected by the variant in question. Furthermore, PheLiGe can compare regional patterns of association between different traits. This analysis can ascertain whether a co-association is due to pleiotropy or linkage. Moreover, comparison of association patterns for a complex trait of interest and gene expression and protein levels can implicate causal genes.

scholarly journals Uncovering Missing Heritability in Rare Diseases

Genes ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 275 ◽  
Author(s):  
Tatiana Maroilley ◽  
Maja Tarailo-Graovac
Keyword(s):  
High Throughput ◽  
Rare Diseases ◽  
High Throughput Sequencing ◽  
Association Studies ◽  
Fold Increase ◽  
Genome Wide Association Studies ◽  
Missing Heritability ◽  
Genetic Changes ◽  
Technological Advances ◽  
Genome Wide

The problem of ‘missing heritability’ affects both common and rare diseases hindering: discovery, diagnosis, and patient care. The ‘missing heritability’ concept has been mainly associated with common and complex diseases where promising modern technological advances, like genome-wide association studies (GWAS), were unable to uncover the complete genetic mechanism of the disease/trait. Although rare diseases (RDs) have low prevalence individually, collectively they are common. Furthermore, multi-level genetic and phenotypic complexity when combined with the individual rarity of these conditions poses an important challenge in the quest to identify causative genetic changes in RD patients. In recent years, high throughput sequencing has accelerated discovery and diagnosis in RDs. However, despite the several-fold increase (from ~10% using traditional to ~40% using genome-wide genetic testing) in finding genetic causes of these diseases in RD patients, as is the case in common diseases—the majority of RDs are also facing the ‘missing heritability’ problem. This review outlines the key role of high throughput sequencing in uncovering genetics behind RDs, with a particular focus on genome sequencing. We review current advances and challenges of sequencing technologies, bioinformatics approaches, and resources.

scholarly journals 026.Endometriosis — linkage, positional cloning and genome wide association

2004 ◽  
Vol 16 (9) ◽  
pp. 26
Author(s):  
G. W. Montgomery ◽  
J. Wicks ◽  
Z. Z. Zhao ◽  
D. R. Nyholt ◽  
N. G. Martin ◽  
...  
Keyword(s):  
High Throughput ◽  
Positional Cloning ◽  
Complex Disease ◽  
Association Studies ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Single Experiment ◽  
Priority List ◽  
Genome Wide

Endometriosis is a complex disease which affects up to 10% of women in their reproductive years. Common symptoms include severe dysmenorrhea and pelvic pain. The disease is associated with subfertility and some malignancies. Genetic and environmental factors both influence endometriosis. The aim of our studies is to identify genetic variation contributing to endometriosis and define pathways leading to disease. We recruited a large cohort of affected sister pair (ASP) families where two sisters have had surgically confirmed disease and conducted a 10�cM genome scan. The results of the linkage analysis identified one chromosomal region with significant linkage and one region of suggestive linkage. The regions implicated by these studies are generally of the order of 20–30�cM and include several hundred genes. Locating the gene or genes contributing to disease within the region is a challenging task. The best approach to the problem is association studies using a high density of SNP markers. The recent development of human SNP maps and high throughput SNP genotyping platforms makes this task easier. We have developed high throughput SNP typing at QIMR using the Sequenom MassARRAY platform. The method allows multiple SNP assays to be genotyped on the same sample in a single experiment. Throughput and genotyping costs depend critically on this level of multiplexing and we routinely genotype 6–8 SNPs in a single assay. We are using bioinformatics and functional approaches to develop a priority list of genes to screen early in the project. SNP markers in these genes are being genotyped using the MassARRAY platform to search for genes contributing to endometriosis. In the future, genome wide association studies with our families may locate additional genes contributing to endometriosis.

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