ArticleBMC genetics2015
Characterization of the biological processes shaping the genetic structure of the Italian population.
Article in BMC genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 14 citations in OpenAlex.
- "Yet the Problem Remains": Why Genetic Determinism Still Haunts Biomedical Research.Bioethics · 2026Article
- Helena's Many Daughters: More Mitogenome Diversity behind the Most Common West Eurasian mtDNA Control Region Haplotype in an Extended Italian Population Sample.International journal of molecular sciences · 2022Article
- The mitogenome portrait of Umbria in Central Italy as depicted by contemporary inhabitants and pre-Roman remains.Scientific reports · 2020Article
- Y-chromosome and Surname Analyses for Reconstructing Past Population Structures: The Sardinian Population as a Test Case.International journal of molecular sciences · 2019Article
- Population structure of modern-day Italians reveals patterns of ancient and archaic ancestries in Southern Europe.Science advances · 2019Article
- Impact of demography and population dynamics on the genetic architecture of human longevity.Aging · 2018Article
- Complex interplay between neutral and adaptive evolution shaped differential genomic background and disease susceptibility along the Italian peninsula.Scientific reports · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundThe genetic structure of human populations is the outcome of the combined action of different processes such as demographic dynamics and natural selection. Several efforts toward the characterization of population genetic architectures and the identification of adaptation signatures were recently made. In this study, we provide a genome-wide depiction of the Italian population structure and the analysis of the major determinants of the current existing genetic variation.
resultsWe defined and characterized 210 genomic loci associated with the first Principal Component calculated on the Italian genotypic data and correlated to the North-south genetic gradient. Using a gene-enrichment approach we identified the immune function as primarily involved in the Italian population differentiation and we described a locus on chromosome 13 showing combined evidence of North-south diversification in allele frequencies and signs of recent positive selection. In this region our bioinformatics analysis pinpointed an uncharacterized long intergenic non-coding (lincRNA), whose expression appeared specific for immune-related tissues suggesting its relevance for the immune function.
conclusionsOur study, combining population genetic analyses with biological insights provides a description of the Italian genetic structure that in future could contribute to the evaluation of complex diseases risk in the population context.
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.