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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

Distribution of RNA expression.Transcript-specific RNA expression values from the Maize Gene Atlas [30] were summed to determine total expression for each gene. The log-transformed distribution of maximum expression values are shown for the entire filtered gene set (solid line) or just genes with GWAS hits within 5 kb of their primary transcripts (dashed line); vertical lines indicate the median of each distribution. The GWAS-hit genes show a slight depletion (∼20%) of low-expressed genes. For comparison, the median expression of maize transcription factors in this dataset (as annotated on Grassius, http://grassius.org/) is indicated by an arrowhead. FPKM, Fragments Per Kilobase of transcript per Million mapped reads.
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pgen-1004845-g006: Distribution of RNA expression.Transcript-specific RNA expression values from the Maize Gene Atlas [30] were summed to determine total expression for each gene. The log-transformed distribution of maximum expression values are shown for the entire filtered gene set (solid line) or just genes with GWAS hits within 5 kb of their primary transcripts (dashed line); vertical lines indicate the median of each distribution. The GWAS-hit genes show a slight depletion (∼20%) of low-expressed genes. For comparison, the median expression of maize transcription factors in this dataset (as annotated on Grassius, http://grassius.org/) is indicated by an arrowhead. FPKM, Fragments Per Kilobase of transcript per Million mapped reads.

Mentions: First, an analysis of expression levels using RNA-seq data from the Maize Gene Atlas [30] reveals a small (∼20%) but highly significant depletion of low-expressed genes (p = 1.30×10−22 by Mann-Whitney test and ≈0 by Kolmogorov-Smirnov test) (Fig. 6). The expression level of these genes is even lower than most transcription factors, which are themselves usually only expressed at a low level, and their depletion among GWAS hits may reflect a lower probability of such rarely expressed genes altering plant phenotype.


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)

Distribution of RNA expression.Transcript-specific RNA expression values from the Maize Gene Atlas [30] were summed to determine total expression for each gene. The log-transformed distribution of maximum expression values are shown for the entire filtered gene set (solid line) or just genes with GWAS hits within 5 kb of their primary transcripts (dashed line); vertical lines indicate the median of each distribution. The GWAS-hit genes show a slight depletion (∼20%) of low-expressed genes. For comparison, the median expression of maize transcription factors in this dataset (as annotated on Grassius, http://grassius.org/) is indicated by an arrowhead. FPKM, Fragments Per Kilobase of transcript per Million mapped reads.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4256217&req=5

pgen-1004845-g006: Distribution of RNA expression.Transcript-specific RNA expression values from the Maize Gene Atlas [30] were summed to determine total expression for each gene. The log-transformed distribution of maximum expression values are shown for the entire filtered gene set (solid line) or just genes with GWAS hits within 5 kb of their primary transcripts (dashed line); vertical lines indicate the median of each distribution. The GWAS-hit genes show a slight depletion (∼20%) of low-expressed genes. For comparison, the median expression of maize transcription factors in this dataset (as annotated on Grassius, http://grassius.org/) is indicated by an arrowhead. FPKM, Fragments Per Kilobase of transcript per Million mapped reads.
Mentions: First, an analysis of expression levels using RNA-seq data from the Maize Gene Atlas [30] reveals a small (∼20%) but highly significant depletion of low-expressed genes (p = 1.30×10−22 by Mann-Whitney test and ≈0 by Kolmogorov-Smirnov test) (Fig. 6). The expression level of these genes is even lower than most transcription factors, which are themselves usually only expressed at a low level, and their depletion among GWAS hits may reflect a lower probability of such rarely expressed genes altering plant phenotype.

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