Abstract
A gene integrates the effects of all SNPs in its sequence span, which benefits the genome-wide association study. To explore gene-level variations affecting economic traits in maize, we extended the SNP-based GWAS analysis software Single-RunKing developed by our team to gene-based GWAS, which used the FaST-LMM algorithm to convert the linear mixed model into simple linear model association analysis. An F-test statistic was formulated to test and identify candidate genes. We compared the statistical efficiency of using 80% principal components (EPC), the first principal component (FPC), and all SNP markers (ALLSNP) as independent variables, which predecessors commonly used to integrate SNPs and represent genes. With a Huazhong Agricultural University (HAU) genomic dataset of 2.65M SNPs from 540 maize plants, 34,774 genes were annotated across the whole genome. Genome-wide association studies with 20 agronomic traits were performed using the software developed here. Another maize dataset from the Ames panel (AP) was also analyzed. The EPC method fits the model well and has good statistical efficiency. It not only overcomes the false negative problem when using all SNP markers for analysis (ALLSNP) but also solves the false positive problem of its corresponding simple linear model method EPCLM. Compared with FPC, the EPC method has higher statistical efficiency. A total of 132 quantitative trait genes (QTG) were identified for the 20 traits from HAU maize dataset and one trait of AP maize.
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Claims 5
Claim · model-written summary
The EPC method fit the gene-based GWAS model well and had good statistical efficiency.
- Outcome
- statistical efficiencye0
Zea maysNot reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The EPC method fits the model well and has good statistical efficiency.”
t0“Maize”
Claim · model-written summary
EPC overcame the false-negative problem of ALLSNP.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“It not only overcomes the false negative problem when using all SNP markers for analysis (ALLSNP)”
t0“Maize”
Claim · model-written summary
EPC resolved the false-positive problem of EPCLM.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“but also solves the false positive problem of its corresponding simple linear model method EPCLM.”
t0“Maize”
Claim · model-written summary
EPC had higher statistical efficiency than FPC.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Compared with FPC, the EPC method has higher statistical efficiency.”
t0“Maize”
Claim · model-written summary
The analyses identified 132 quantitative trait genes for the specified maize traits and datasets.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“A total of 132 quantitative trait genes (QTG) were identified for the 20 traits from HAU maize dataset and one trait of AP maize.”