scholarly journals Dissection of the Genetic Basis of Yield-Related Traits in the Chinese Peanut Mini-Core Collection Through Genome-Wide Association Studies

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaojing Zhou ◽  
Jianbin Guo ◽  
Manish K. Pandey ◽  
Rajeev K. Varshney ◽  
Li Huang ◽  
...  
Keyword(s):  
Core Collection ◽  
Genetic Basis ◽  
Diagnostic Marker ◽  
Gene Annotation ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Mini Core Collection ◽  
Genome Wide ◽  
Population Structure Analysis

Peanut is an important legume crop worldwide. To uncover the genetic basis of yield features and assist breeding in the future, we conducted genome-wide association studies (GWAS) for six yield-related traits of the Chinese peanut mini-core collection. The seed (pod) size and weight of the population were investigated under four different environments, and these traits showed highly positive correlations in pairwise combinations. We sequenced the Chinese peanut mini-core collection using genotyping-by-sequencing approach and identified 105,814 high-quality single-nucleotide polymorphisms (SNPs). The population structure analysis showed essentially subspecies patterns in groups and obvious geographical distribution patterns in subgroups. A total of 79 significantly associated loci (P < 4.73 × 10–7) were detected for the six yield-related traits through GWAS. Of these, 31 associations were consistently detected in multiple environments, and 15 loci were commonly detected to be associated with multiple traits. Two major loci located on chromosomal pseudomolecules A06 and A02 showed pleiotropic effects on yield-related traits, explaining ∼20% phenotypic variations across environments. The two genomic regions were found 46 putative candidate genes based on gene annotation and expression profile. The diagnostic marker for the yield-related traits from non-synonymous SNP (Aradu-A06-107901527) was successfully validated, achieving a high correlation between nucleotide polymorphism and phenotypic variation. This study provided insights into the genetic basis of yield-related traits in peanut and verified one diagnostic marker to facilitate marker-assisted selection for developing high-yield peanut varieties.

Genome-Wide Association Studies of Soybean Seed Hardness in the Chinese Mini Core Collection

2018 ◽  
Vol 36 (4) ◽  
pp. 605-617 ◽  
Author(s):  
Xing Zhang ◽  
Jinming Zhao ◽  
Yuanpeng Bu ◽  
Dong Xue ◽  
Zhangxiong Liu ◽  
...  
Keyword(s):  
Core Collection ◽  
Association Studies ◽  
Soybean Seed ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Mini Core Collection ◽  
Genome Wide ◽  
Seed Hardness

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 of Leaf Angle in Maize

2021 ◽  
Author(s):  
Bo Peng ◽  
Xiaolei Zhao ◽  
Yi Wang ◽  
Chunhui Li ◽  
Yongxiang Li ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Phenotypic Variation ◽  
Genetic Basis ◽  
Genome Wide Association ◽  
Leaf Angle ◽  
Genome Wide Association Studies ◽  
Major Qtls ◽  
Genome Wide ◽  
Population Structure Analysis ◽  
A Genome

Abstract Compact plant-type with small leaf angle has increased canopy light interception, which is conducive to the photosynthesis of the population and higher population yield at high density planting in maize. In this study, a panel of 285 diverse maize inbred lines genotyped with 56,000 SNPs was used to investigate the genetic basis of leaf angle across three consecutive years using a genome-wide association study (GWAS). The leaf angle showed broad phenotypic variation and high heritability across different years. Population structure analysis subdivided the panel into four subgroups that correspond to the four major empirical germplasm origins in China, i.e., Tangsipingtou, Reid, Lancaster and P. When tested with the optimal GWAS model, we found that the Q+K model was the best in reducing false positive. In total, 96 SNPs accounting for 5.54%-10.44% of phenotypic variation were significantly (P<0.0001) associated with leaf angle across three years. According to the linkage disequilibrium decay distance, 96 SNPs were binned in 43 QTLs for leaf angle. Seven major QTLs with R2>8% stably detected in at least two years and BLUP values were clustered in four genomic regions (bins 2.01, 2.07, 5.06, and 10.04). Seven important candidate genes, Zm00001d001961, Zm00001d006348, Zm00001d006463, Zm00001d017618, Zm00001d024919, Zm00001d025018, and Zm00001d025033 were predicted for the seven stable major QTLs, respectively. The markers identified in this study can be used for molecular breeding for leaf angle, and the candidate genes would contribute to further understanding of the genetic basis of leaf angle.

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.

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