Dataset Information

Identification of quantitative trait loci underlying proteome variation in human lymphoblastoid cells.

ABSTRACT: Population-based variability in protein expression patterns, especially in humans, is often observed but poorly understood. Moreover, very little is known about how interindividual genetic variation contributes to protein expression patterns. To begin to address this, we describe elements of technical and biological variations contributing to expression of 544 proteins in a population of 24 individual human lymphoblastoid cell lines that have been extensively genotyped as part of the International HapMap Project. We determined that expression levels of 10% of the proteins were tightly correlated to cell doubling rates. Using the publicly available genotypes for these lymphoblastoid cell lines, we applied a genetic association approach to identify quantitative trait loci associated with protein expression variation. Results identified 24 protein forms corresponding to 15 proteins for which genetic elements were responsible for >50% of the expression variation. The genetic variation associated with protein expression levels were located in cis with the gene coding for the transcript of the protein for 19 of these protein forms. Four of the genetic elements identified were coding non-synonymous single nucleotide polymorphisms that resulted in migration pattern changes in the two-dimensional gel. This is the first description of large scale proteomics analysis demonstrating the direct relationship between genome and proteome variations in human cells.

SUBMITTER: Garge N

PROVIDER: S-EPMC2938086 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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Identification of quantitative trait loci underlying proteome variation in human lymphoblastoid cells.

Garge Nikhil N Pan Huaqin H Rowland Megan D MD Cargile Benjamin J BJ Zhang Xinxin X Cooley Phillip C PC Page Grier P GP Bunger Maureen K MK

Molecular & cellular proteomics : MCP 20100223 7

Population-based variability in protein expression patterns, especially in humans, is often observed but poorly understood. Moreover, very little is known about how interindividual genetic variation contributes to protein expression patterns. To begin to address this, we describe elements of technical and biological variations contributing to expression of 544 proteins in a population of 24 individual human lymphoblastoid cell lines that have been extensively genotyped as part of the Internation ...[more]

PMID: 20179311

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Project description:Distinct floral pollination syndromes have emerged multiple times during the diversification of flowering plants. For example, in western North America, a hummingbird pollination syndrome has evolved more than 100 times, generally from within insect-pollinated lineages. The hummingbird syndrome is characterized by a suite of floral traits that attracts and facilitates pollen movement by hummingbirds, while at the same time discourages bee visitation. These floral traits generally include large nectar volume, red flower colour, elongated and narrow corolla tubes and reproductive organs that are exerted from the corolla. A handful of studies have examined the genetic architecture of hummingbird pollination syndrome evolution. These studies find that mutations of relatively large effect often explain increased nectar volume and transition to red flower colour. In addition, they suggest that adaptive suites of floral traits may often exhibit a high degree of genetic linkage, which could facilitate their fixation during pollination syndrome evolution. Here, we explore these emerging generalities by investigating the genetic basis of floral pollination syndrome divergence between two related Penstemon species with different pollination syndromes--bee-pollinated P. neomexicanus and closely related hummingbird-pollinated P. barbatus. In an F2 mapping population derived from a cross between these two species, we characterized the effect size of genetic loci underlying floral trait divergence associated with the transition to bird pollination, as well as correlation structure of floral trait variation. We find the effect sizes of quantitative trait loci for adaptive floral traits are in line with patterns observed in previous studies, and find strong evidence that suites of floral traits are genetically linked. This linkage may be due to genetic proximity or pleiotropic effects of single causative loci. Interestingly, our data suggest that the evolution of floral traits critical for hummingbird pollination was not constrained by negative pleiotropy at loci that show co-localization for multiple traits.

Dataset Information

Identification of quantitative trait loci underlying proteome variation in human lymphoblastoid cells.

Publications

Identification of quantitative trait loci underlying proteome variation in human lymphoblastoid cells.

Similar Datasets

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