Strategies for identifying the genetic basis of dyslipidemia: genome-wide association studies vs. the resequencing of extremes

2010 ◽  
Vol 21 (2) ◽  
pp. 123-127 ◽  
Author(s):  
Chiea Chuen Khor ◽  
Denise Li-Meng Goh
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

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.

scholarly journals Genome-wide association studies of cardiac electrical phenotypes

2020 ◽  
Vol 116 (9) ◽  
pp. 1620-1634
Author(s):  
Charlotte Glinge ◽  
Najim Lahrouchi ◽  
Reza Jabbari ◽  
Jacob Tfelt-Hansen ◽  
Connie R Bezzina
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Individual Variability ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Challenges And Opportunities ◽  
Electrocardiographic Parameters

Abstract The genetic basis of cardiac electrical phenotypes has in the last 25 years been the subject of intense investigation. While in the first years, such efforts were dominated by the study of familial arrhythmia syndromes, in recent years, large consortia of investigators have successfully pursued genome-wide association studies (GWAS) for the identification of single-nucleotide polymorphisms that govern inter-individual variability in electrocardiographic parameters in the general population. We here provide a review of GWAS conducted on cardiac electrical phenotypes in the last 14 years and discuss the implications of these discoveries for our understanding of the genetic basis of disease susceptibility and variability in disease severity. Furthermore, we review functional follow-up studies that have been conducted on GWAS loci associated with cardiac electrical phenotypes and highlight the challenges and opportunities offered by such studies.

scholarly journals Genome-Wide Association Studies Reveal the Genetic Basis of Ionomic Variation in Rice

2018 ◽  
Vol 30 (11) ◽  
pp. 2720-2740 ◽  
Author(s):  
Meng Yang ◽  
Kai Lu ◽  
Fang-Jie Zhao ◽  
Weibo Xie ◽  
Priya Ramakrishna ◽  
...  
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Genetic Basis of Tiller Dynamics of Rice Revealed by Genome-Wide Association Studies

Plants ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 1695
Author(s):  
Shuyu Zhao ◽  
Su Jang ◽  
Yoon Kyung Lee ◽  
Dong-Gwan Kim ◽  
Zhengxun Jin ◽  
...  
Keyword(s):  
Linear Regression ◽  
Candidate Genes ◽  
Genetic Basis ◽  
Association Studies ◽  
Tiller Number ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Rice Plants ◽  
Genome Wide ◽  
Productive Tiller

A tiller number is the key determinant of rice plant architecture and panicle number and consequently controls grain yield. Thus, it is necessary to optimize the tiller number to achieve the maximum yield in rice. However, comprehensive analyses of the genetic basis of the tiller number, considering the development stage, tiller type, and related traits, are lacking. In this study, we sequence 219 Korean rice accessions and construct a high-quality single nucleotide polymorphism (SNP) dataset. We also evaluate the tiller number at different development stages and heading traits involved in phase transitions. By genome-wide association studies (GWASs), we detected 20 significant association signals for all traits. Five signals were detected in genomic regions near known candidate genes. Most of the candidate genes were involved in the phase transition from vegetative to reproductive growth. In particular, HD1 was simultaneously associated with the productive tiller ratio and heading date, indicating that the photoperiodic heading gene directly controls the productive tiller ratio. Multiple linear regression models of lead SNPs showed coefficients of determination (R2) of 0.49, 0.22, and 0.41 for the tiller number at the maximum tillering stage, productive tiller number, and productive tiller ratio, respectively. Furthermore, the model was validated using independent japonica rice collections, implying that the lead SNPs included in the linear regression model were generally applicable to the tiller number prediction. We revealed the genetic basis of the tiller number in rice plants during growth, By GWASs, and formulated a prediction model by linear regression. Our results improve our understanding of tillering in rice plants and provide a basis for breeding high-yield rice varieties with the optimum the tiller number.

scholarly journals Genome-wide Association Studies Reveal the Genetic Basis of Natural Ionomic Variation in Rice Grain Oryza Sativa Subsp. Indica

2021 ◽  
Author(s):  
Suong T. Cu ◽  
Nicholas Warnock ◽  
Julie Pasuquin ◽  
Michael Dingkuhn ◽  
James Stangoulis
Keyword(s):  
Genetic Basis ◽  
Agronomic Traits ◽  
Association Studies ◽  
Starch Synthesis ◽  
Grain Filling ◽  
Genome Wide Association ◽  
Rice Grain ◽  
Genome Wide Association Studies ◽  
Potential Candidate ◽  
Genome Wide

Abstract This study presents a comprehensive study of the genetic bases controlling variation in the rice ionome employing genome-wide association studies (GWAS) with a diverse panel of indica accessions, each genotyped with 5.2 million markers. GWAS was performed for twelve elements including B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, P, and Zn and four agronomic traits including days to 50% flowering, grain yield, plant height and thousand grain weight (TGW). GWAS identified 128 loci associated with the grain elements and 57 associated with the agronomic traits. There were sixteen co-localization regions containing QTL for two or more traits. Fourteen grain element quantitative trait loci were stable across growing environments, which can be strong candidates to be used in marker-assisted selection to improve the concentrations of nutritive elements in rice grain. Potential candidate genes were revealed including OsNAS3 controlling the variation of Zn and Co concentrations. The effects of starch synthesis and grain filling on TGW and multiple grain elements were elucidated through the involvement of OsSUS1 and OsGSSB1 genes. Overall, our study provides crucial insights into the genetic basis of ionomic variations in rice and will facilitate improvement in breeding for trace mineral content.

Sign in / Sign up

Export Citation Format

Share Document