ArticleBMC genomics2011
Comparison of measures of marker informativeness for ancestry and admixture mapping.
Article in BMC genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed.
- Recomb-Mix: fast and accurate local ancestry inference.Bioinformatics (Oxford, England) · 2025Article
- A Pipeline and Recommendations for Population and Individual Diagnostic SNP Selection in Non-Model Species.Molecular ecology resources · 2025Article
- Identifying low-density, ancestry-informative SNP markers through whole genome resequencing in Indian, Chinese, and wild yak.BMC genomics · 2024Article
- Fast and accurate local ancestry inference with Recomb-Mix.bioRxiv : the preprint server for biology · 2024Article
- Population Pharmacogenomics for Health Equity.Genes · 2023Article
- Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data.Journal of animal science and biotechnology · 2023Article
- Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets.American journal of human genetics · 2023Article
- Evaluating the use of statistical and machine learning methods for estimating breed composition of purebred and crossbred animals in thirteen cattle breeds using genomic information.Frontiers in genetics · 2023Article
- Effects of genetic ancestry and socioeconomic deprivation on ethnic differences in serum creatinine.Gene · 2022Article
- Admixture and breed traceability in European indigenous pig breeds and wild boar using genome-wide SNP data.Scientific reports · 2022Article
- Genomic analysis of the rare British Lop pig and identification of distinctive genomic markers.PloS one · 2022Article
- Genetic Ancestry Inference and Its Application for the Genetic Mapping of Human Diseases.International journal of molecular sciences · 2021Review
- Inflammatory profiles in Chilean Mapuche and non-Mapuche women with gallstones at risk of developing gallbladder cancer.Scientific reports · 2021Article
- Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs.BMC genomics · 2021Article
- Common Data Elements: Critical Assessment of Harmonization between Current Multi-Center Traumatic Brain Injury Studies.Journal of neurotrauma · 2020Observational
- MI-MAAP: marker informativeness for multi-ancestry admixed populations.BMC bioinformatics · 2020Article
- LEI: A Novel Allele Frequency-Based Feature Selection Method for Multi-ancestry Admixed Populations.Scientific reports · 2019Article
- Selection of Optimal Ancestry Informative Markers for Classification and Ancestry Proportion Estimation in Pigs.Frontiers in genetics · 2019Article
- Temporal stability and assignment power of adaptively divergent genomic regions between herring (Ecology and evolution · 2019Article
- Ancient Ancestry Informative Markers for Identifying Fine-Scale Ancient Population Structure in Eurasians.Genes · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundAdmixture mapping is a powerful gene mapping approach for an admixed population formed from ancestral populations with different allele frequencies. The power of this method relies on the ability of ancestry informative markers (AIMs) to infer ancestry along the chromosomes of admixed individuals. In this study, more than one million SNPs from HapMap databases and simulated data have been interrogated in admixed populations using various measures of ancestry informativeness: Fisher Information Content (FIC), Shannon Information Content (SIC), F statistics (FST), Informativeness for Assignment Measure (In), and the Absolute Allele Frequency Differences (delta, δ). The objectives are to compare these measures of informativeness to select SNP markers for ancestry inference, and to determine the accuracy of AIM panels selected by each measure in estimating the contributions of the ancestors to the admixed population.
resultsFST and In had the highest Spearman correlation and the best agreement as measured by Kappa statistics based on deciles. Although the different measures of marker informativeness performed comparably well, analyses based on the top 1 to 10% ranked informative markers of simulated data showed that In was better in estimating ancestry for an admixed population.
conclusionsAlthough millions of SNPs have been identified, only a small subset needs to be genotyped in order to accurately predict ancestry with a minimal error rate in a cost-effective manner. In this article, we compared various methods for selecting ancestry informative SNPs using simulations as well as SNP genotype data from samples of admixed populations and showed that the In measure estimates ancestry proportion (in an admixed population) with lower bias and mean square error.
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.