Biomedical subjects

Marilyn A L West

Publications and source records attributed to Marilyn A L West.

5 recordsLinked to original sources

PubMed2006

Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis.

The genetic architecture of transcript-level variation is largely unknown. The genetic determinants of transcript-level variation were characterized in a recombinant inbred line (RIL) population (n = 211) of Arabidopsis thaliana using whole-genome microarray analysis and expression quantitative trait loci (eQTL) mapping of transcript levels as expression traits (e-traits). Genetic control of transcription was highly complex: one-third of the quantitatively controlled transcripts/e-traits were regulated by cis-eQTL, and many trans-eQTL mapped to hotspots that regulated hundreds to thousands of e-traits. Several thousand eQTL of large phenotypic effect were detected, but almost all (93%) of the 36,871 eQTL were associated with small phenotypic effects (R(2) < 0.3). Many transcripts/e-traits were controlled by multiple eQTL with opposite allelic effects and exhibited higher heritability in the RILs than their parents, suggesting nonadditive genetic variation. To our knowledge, this is the first large-scale global eQTL study in a relatively large plant mapping population. It reveals that the genetic control of transcript level is highly variable and multifaceted and that this complexity may be a general characteristic of eukaryotes.

Arabidopsis↗

PubMed2006

Identification of QTLs controlling gene expression networks defined a priori.

BACKGROUND: Gene expression microarrays allow the quantification of transcript accumulation for many or all genes in a genome. This technology has been utilized for a range of investigations, from assessments of gene regulation in response to genetic or environmental fluctuation to global expression QTL (eQTL) analyses of natural variation. Current analysis techniques facilitate the statistical querying of individual genes to evaluate the significance of a change in response, also known as differential expression. Since genes are also known to respond as groups due to their membership in networks, effective approaches are needed to investigate transcriptome variation as related to gene network responses. RESULTS: We describe a statistical approach that is capable of assessing higher-order a priori defined gene network response, as measured by microarrays. This analysis detected significant network variation between two Arabidopsis thaliana accessions, Bay-0 and Shahdara. By extending this approach, we were able to identify eQTLs controlling network responses for 18 out of 20 a priori-defined gene networks in a recombinant inbred line population derived from accessions Bay-0 and Shahdara. CONCLUSION: This approach has the potential to be expanded to facilitate direct tests of the relationship between phenotypic trait and transcript genetic architecture. The use of a priori definitions for network eQTL identification has enormous potential for providing direction toward future eQTL analyses.

Arabidopsis↗

PubMed2006

High-density haplotyping with microarray-based expression and single feature polymorphism markers in Arabidopsis.

Expression microarrays hybridized with RNA can simultaneously provide both phenotypic (gene expression) and genotypic (marker) data. We developed two types of genetic markers from Affymetrix GeneChip expression data to generate detailed haplotypes for 148 recombinant inbred lines (RILs) derived from Arabidopsis thaliana accessions Bayreuth and Shahdara. Gene expression markers (GEMs) are based on differences in transcript levels that exhibit bimodal distributions in segregating progeny, while single feature polymorphism (SFP) markers rely on differences in hybridization to individual oligonucleotide probes. Unlike SFPs, GEMs can be derived from any type of DNA-based expression microarray. Our method identifies SFPs independent of a gene's expression level. Alleles for each GEM and SFP marker were ascertained with GeneChip data from parental accessions as well as RILs; a novel algorithm for allele determination using RIL distributions capitalized on the high level of genetic replication per locus. GEMs and SFP markers provided robust markers in 187 and 968 genes, respectively, which allowed estimation of gene order consistent with that predicted from the Col-0 genomic sequence. Using microarrays on a population to simultaneously measure gene expression variation and obtain genotypic data for a linkage map will facilitate expression QTL analyses without the need for separate genotyping. We have demonstrated that gene expression measurements from microarrays can be leveraged to identify polymorphisms across the genome and can be efficiently developed into genetic markers that are verifiable in a large segregating RIL population. Both marker types also offer opportunities for massively parallel mapping in unsequenced and less studied species.

Arabidopsis↗

PubMed2005

Genomic survey of gene expression diversity in Arabidopsis thaliana.

Differential gene expression controls variation in numerous plant traits, such as flowering time and plant/pest interactions, but little is known about the genomic distribution of the determinants of transcript levels and their associated variation. Affymetrix ATH1 GeneChip microarrays representing 22,810 genes were used to survey the transcriptome of seven Arabidopsis thaliana accessions in the presence and absence of exogenously applied salicylic acid (SA). These accessions encompassed approximately 80% of the moderate- to high-frequency nucleotide polymorphisms in Arabidopsis. A factorial design, consisting of three biological replicates per accession for the two treatments at three time points (4, 28, and 52 hr post-treatment), and a total of 126 microarrays were used. Between any pair of Arabidopsis accessions, we detected on average 2234 genes (ranging from 1428 to 3334) that were significantly differentially expressed under the conditions of this experiment, using a split-plot analysis of variance. Upward of 6433 genes were differentially expressed between at least one pair of accessions. These results suggest that analysis of additional genetic, developmental, and environmental conditions may show that a significant fraction of the Arabidopsis genome is differentially expressed. Examination of sequence diversity demonstrated a significant positive association with diversity in gene expression.

Analysis of Variance↗

PubMed2004

Evaluation of AFLPs for germplasm fingerprinting and assessment of genetic diversity in cultivars of tomato (Lycopersicon esculentum L.).

Cultivated tomato (L. esculentum L.) germplasm exhibits limited genetic variation compared with wild Lycopersicon species. Amplified fragment length polymorphism (AFLP) markers were used to evaluate genetic variation among 74 cultivars, primarily from California, and to fingerprint germplasm to determine if cultivar-specific patterns could be obtained. All 74 cultivars were genotyped using 26 AFLP primer combinations; of the 1092 bands scored, 102 AFLP bands (9.3%) were polymorphic. Pair-wise genetic similarity coefficients (Jaccard and Nei-Li) were calculated. Jaccard coefficients varied from 0.16 to 0.98 among cultivar pairs, and 72% of pair-wise comparisons exceeded 0.5. UPGMA (unweighted pair-group method with arithmetic averaging) clustering and principle component analysis revealed four main clusters, I-IV; most modern hybrid cultivars grouped in II, whereas most vintage cultivars grouped in I. Clusters III and IV contained three and two cultivars, respectively. Some groups of cultivars closely related by pedigree exhibited high bootstrap values, but lower values (<50%) were obtained for cluster II and its four subgroups. Unique fingerprints for all 74 cultivars were obtained by a minimum of seven AFLP primer pairs, despite inclusion of some closely related cultivars. This study demonstrated that AFLP markers are effective for obtaining unique fingerprints of, and assessing genetic diversity among, tomato cultivars.

Base Sequence↗