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Association mapping across numerous traits reveals patterns of functional variation in maize.

Wallace JG, Bradbury PJ, Zhang N, Gibon Y, Stitt M, Buckler ES - PLoS Genet. (2014)

Bottom Line: Phenotypic variation in natural populations results from a combination of genetic effects, environmental effects, and gene-by-environment interactions.We also find that genes tagged by GWAS are enriched for regulatory functions and are ∼ 50% more likely to have a paralog than expected by chance, indicating that gene regulation and gene duplication are strong drivers of phenotypic variation.These results will likely apply to many other organisms, especially ones with large and complex genomes like maize.

View Article: PubMed Central - PubMed

Affiliation: Institute for Genomic Diversity, Cornell University, Ithaca, New York, United States of America.

ABSTRACT
Phenotypic variation in natural populations results from a combination of genetic effects, environmental effects, and gene-by-environment interactions. Despite the vast amount of genomic data becoming available, many pressing questions remain about the nature of genetic mutations that underlie functional variation. We present the results of combining genome-wide association analysis of 41 different phenotypes in ∼ 5,000 inbred maize lines to analyze patterns of high-resolution genetic association among of 28.9 million single-nucleotide polymorphisms (SNPs) and ∼ 800,000 copy-number variants (CNVs). We show that genic and intergenic regions have opposite patterns of enrichment, minor allele frequencies, and effect sizes, implying tradeoffs among the probability that a given polymorphism will have an effect, the detectable size of that effect, and its frequency in the population. We also find that genes tagged by GWAS are enriched for regulatory functions and are ∼ 50% more likely to have a paralog than expected by chance, indicating that gene regulation and gene duplication are strong drivers of phenotypic variation. These results will likely apply to many other organisms, especially ones with large and complex genomes like maize.

No MeSH data available.


Related in: MedlinePlus

Number of polymorphisms found and variance explained for each trait.(A) Polymorphisms found per trait. Bars show the mean and standard deviation of markers found per iteration before (light bars) and after (dark bars) filtering for RMIP≥0.05 (see Methods). The number of markers found tends to broadly mirror the genetic complexity of each trait, with metabolic traits having fewer markers found than complex, polygenic traits like plant architecture. The relative complexity within each category is less certain, but the pattern still probably holds to a first degree of approximation. (B) Variance explained per trait. For each trait, a general linear model incorporating a family term (for each of the 25 biparental families in NAM) and all SNPs that passed filtering (dark bars in (A)) was fit to the original Best Linear Unbiased Predictors (BLUPs) for each trait. Bars show the portion of total variance explained by the fitted SNPs as measured by adjusted R2.
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pgen-1004845-g001: Number of polymorphisms found and variance explained for each trait.(A) Polymorphisms found per trait. Bars show the mean and standard deviation of markers found per iteration before (light bars) and after (dark bars) filtering for RMIP≥0.05 (see Methods). The number of markers found tends to broadly mirror the genetic complexity of each trait, with metabolic traits having fewer markers found than complex, polygenic traits like plant architecture. The relative complexity within each category is less certain, but the pattern still probably holds to a first degree of approximation. (B) Variance explained per trait. For each trait, a general linear model incorporating a family term (for each of the 25 biparental families in NAM) and all SNPs that passed filtering (dark bars in (A)) was fit to the original Best Linear Unbiased Predictors (BLUPs) for each trait. Bars show the portion of total variance explained by the fitted SNPs as measured by adjusted R2.

Mentions: The number of polymorphisms identified for each trait varies widely and broadly matches prior assumptions based on the genetic complexity of the traits (Fig. 1). Comparing our results with those of published studies in NAM shows good agreement with the locations of known QTL (S2 Figure).


Association mapping across numerous traits reveals patterns of functional variation in maize.

Wallace JG, Bradbury PJ, Zhang N, Gibon Y, Stitt M, Buckler ES - PLoS Genet. (2014)

Number of polymorphisms found and variance explained for each trait.(A) Polymorphisms found per trait. Bars show the mean and standard deviation of markers found per iteration before (light bars) and after (dark bars) filtering for RMIP≥0.05 (see Methods). The number of markers found tends to broadly mirror the genetic complexity of each trait, with metabolic traits having fewer markers found than complex, polygenic traits like plant architecture. The relative complexity within each category is less certain, but the pattern still probably holds to a first degree of approximation. (B) Variance explained per trait. For each trait, a general linear model incorporating a family term (for each of the 25 biparental families in NAM) and all SNPs that passed filtering (dark bars in (A)) was fit to the original Best Linear Unbiased Predictors (BLUPs) for each trait. Bars show the portion of total variance explained by the fitted SNPs as measured by adjusted R2.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4256217&req=5

pgen-1004845-g001: Number of polymorphisms found and variance explained for each trait.(A) Polymorphisms found per trait. Bars show the mean and standard deviation of markers found per iteration before (light bars) and after (dark bars) filtering for RMIP≥0.05 (see Methods). The number of markers found tends to broadly mirror the genetic complexity of each trait, with metabolic traits having fewer markers found than complex, polygenic traits like plant architecture. The relative complexity within each category is less certain, but the pattern still probably holds to a first degree of approximation. (B) Variance explained per trait. For each trait, a general linear model incorporating a family term (for each of the 25 biparental families in NAM) and all SNPs that passed filtering (dark bars in (A)) was fit to the original Best Linear Unbiased Predictors (BLUPs) for each trait. Bars show the portion of total variance explained by the fitted SNPs as measured by adjusted R2.
Mentions: The number of polymorphisms identified for each trait varies widely and broadly matches prior assumptions based on the genetic complexity of the traits (Fig. 1). Comparing our results with those of published studies in NAM shows good agreement with the locations of known QTL (S2 Figure).

Bottom Line: Phenotypic variation in natural populations results from a combination of genetic effects, environmental effects, and gene-by-environment interactions.We also find that genes tagged by GWAS are enriched for regulatory functions and are ∼ 50% more likely to have a paralog than expected by chance, indicating that gene regulation and gene duplication are strong drivers of phenotypic variation.These results will likely apply to many other organisms, especially ones with large and complex genomes like maize.

View Article: PubMed Central - PubMed

Affiliation: Institute for Genomic Diversity, Cornell University, Ithaca, New York, United States of America.

ABSTRACT
Phenotypic variation in natural populations results from a combination of genetic effects, environmental effects, and gene-by-environment interactions. Despite the vast amount of genomic data becoming available, many pressing questions remain about the nature of genetic mutations that underlie functional variation. We present the results of combining genome-wide association analysis of 41 different phenotypes in ∼ 5,000 inbred maize lines to analyze patterns of high-resolution genetic association among of 28.9 million single-nucleotide polymorphisms (SNPs) and ∼ 800,000 copy-number variants (CNVs). We show that genic and intergenic regions have opposite patterns of enrichment, minor allele frequencies, and effect sizes, implying tradeoffs among the probability that a given polymorphism will have an effect, the detectable size of that effect, and its frequency in the population. We also find that genes tagged by GWAS are enriched for regulatory functions and are ∼ 50% more likely to have a paralog than expected by chance, indicating that gene regulation and gene duplication are strong drivers of phenotypic variation. These results will likely apply to many other organisms, especially ones with large and complex genomes like maize.

No MeSH data available.


Related in: MedlinePlus