ArticleGenetics2008
Selective transcriptional profiling and data analysis strategies for expression quantitative trait loci mapping in outbred F2 populations.
Article in Genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Genetic factors underlying host resistance to Rhipicephalus microplus tick infestation in Braford cattle: a systems biology perspective.Mammalian genome : official journal of the International Mammalian Genome Society · 2024Article
- Integrated Genome-Wide Analysis of MicroRNA Expression Quantitative Trait Loci in Pig Longissimus Dorsi Muscle.Frontiers in genetics · 2021Article
- Genetic control of longissimus dorsi muscle gene expression variation and joint analysis with phenotypic quantitative trait loci in pigs.BMC genomics · 2019Article
- An integrative systems genetics approach reveals potential causal genes and pathways related to obesity.Genome medicine · 2015Article
- Article
- Genome-wide linkage analysis of global gene expression in loin muscle tissue identifies candidate genes in pigs.PloS one · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic analysis of transcriptional profiling experiments is emerging as a promising approach for unraveling genes and pathways that underlie variation of complex biological traits. However, these genetical genomics approaches are currently limited by the high cost of microarrays. We studied five different strategies to optimally select subsets of individuals for transcriptional profiling, including (1) maximizing genetic dissimilarity between selected individuals, (2) maximizing the number of recombination events in selected individuals, (3) selecting phenotypic extremes within inferred genotypes of a previously identified quantitative trait locus (QTL), (4) purely random selection, and (5) profiling animals with the highest and lowest phenotypic values within each family-gender subclass. A simulation study was conducted on the basis of a linkage map and marker genotypes were derived from data on chromosome 6 for 510 F2 animals from an existing pig resource population and on a simulated biallelic QTL with pleiotropic effects on performance and gene expression traits. Bivariate analyses were conducted for selected subset sample sizes of 80, 160, and 240 individuals under three different correlation scenarios between the two traits. The genetic dissimilarity and phenotypic extremes within genotype methods had the smallest mean square error on QTL effects and maximum sensitivity on QTL detection, thereby outperforming all other selection strategies, particularly at the smallest proportion of samples selected for gene expression profiling (80/510).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.