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Genetic Background

Genetics and Genomics · NLM MeSH · updated 2026-10-29 · version 1

Citations verified against PubMedNot yet clinician-reviewed10 referencesApproximately 6 min read

The entire genetic composition of a strain or individual that interacts with a gene of interest.

Table of Contents

1. Definition2. Research landscape3. Reading the evidence4. Open-access studies5. Key reviews6. References7. About this article

Definition and research guide

This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.

Content available: 4 complete abstracts (1,161 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.

Definition

The entire genetic composition of a strain or individual that interacts with a gene of interest.

Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.

Also known as: Background, Genetic; Backgrounds, Genetic; Genetic Backgrounds.

Where it fits in the MeSH classification

Broader topics
Genetic Phenomena
Narrower topics
Genetic Profile
See also
Heredity

Research landscape

PubMed indexes 1,053 records tagged with the MeSH heading “Genetic Background” (counts retrieved 2026-10-29). 662 of them (62.9%) were published from 2020 onward, and the busiest period so far is 2020 onward with 662 records.

Reviews account for 136 records (12.9%) and meta-analyses for 7. Randomized controlled trials number 1 (0.1%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.

PubMed records by publication periodthrough 1999: 0 records; 2000-2009: 0 records; 2010-2019: 405 records; 2020 onward: 662 records0through 199902000-20094052010-20196622020 onward
Records in PubMed indexed with this heading, by publication period. Real counts from NCBI E-utilities; the final period covers only part of the current year. Volume reflects how much has been published, not how strong the evidence is.
Show the data
PeriodRecords
through 19990
2000-20090
2010-2019405
2020 onward662
Share of records by publication typeReviews: 12.9 percent; Meta-analyses: 0.7 percent; Randomized controlled trials: 0.1 percentReviews12.9% (136)Meta-analyses0.7% (7)Randomized controlled trials0.1% (1)
Share of all PubMed records on this heading that carry each publication type. Types overlap with other records, so the shares do not sum to 100%.
Publication-type chart data
Publication types among 1,053 PubMed records
TypeRecordsShare
Reviews13612.9%
Meta-analyses70.7%
Randomized controlled trials10.1%

Method: PubMed query "Genetic Background"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.

Reading the evidence in genetics and genomics

Genetic findings vary widely in strength. A variant linked to a condition in one study may not replicate in other ancestries, and most common variants have small effects that are meaningful only in aggregate. Studies of rare, high-impact variants can support strong causal claims about a gene, while association studies describe statistical links that need functional and independent replication. Check the sample size, the ancestry of participants, whether results were replicated, and whether a laboratory or clinical outcome was actually measured.

Evidence from open-access studies

These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.

Study comparison — selected abstracts, not a systematic review
Study / authorsYearSource label / designEurope PMC citationsLicence / source
Impact of genetic background and experimental reproducibility on identifying chemical compounds with robust longevity effects
Lucanic M, Plummer WT, Chen E, Harke J, Foulger AC, Onken B, et al.
2017Research article
Design not recorded in structured metadata
131cc by
PMID 28220799 · DOI · PMC5321775
Prevalence and genetic profiles of isoniazid resistance in tuberculosis patients: A multicountry analysis of cross-sectional data
Dean AS, Zignol M, Cabibbe AM, Falzon D, Glaziou P, Cirillo DM, et al.
2020Research article
Design not recorded in structured metadata
112cc by
PMID 31961877 · DOI · PMC6974034
Integration of comprehensive genomic profiling, tumor mutational burden, and PD-L1 expression to identify novel biomarkers of immunotherapy in non-small cell lung cancer
Shi Y, Lei Y, Liu L, Zhang S, Wang W, Zhao J, et al.
2021Research article
Design not recorded in structured metadata
79cc by
PMID 33655698 · DOI · PMC7982619
Genetic background sets the trajectory of experimental cancer evolution
Aitken SJ, Connor F, Feig C, Rayner TF, Lukk M, Luft J, et al.
2026Research article
Design not recorded in structured metadata
2cc by
PMID 42486977 · DOI · PMC13600922

Impact of genetic background and experimental reproducibility on identifying chemical compounds with robust longevity effects [1]

Research article Lucanic M, Plummer WT, Chen E, Harke J, Foulger AC, Onken B, et al. · · 2017 · cited 131 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Limiting the debilitating consequences of ageing is a major medical challenge of our time.
Closing excerpt
Our results highlight promising pharmacological leads and demonstrate the importance of assessing lifespans of discrete cohorts across repeat studies to capture biological variation in the search for reproducible ageing interventions.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (147 words)

Limiting the debilitating consequences of ageing is a major medical challenge of our time. Robust pharmacological interventions that promote healthy ageing across diverse genetic backgrounds may engage conserved longevity pathways. Here we report results from the Caenorhabditis Intervention Testing Program in assessing longevity variation across 22 Caenorhabditis strains spanning 3 species, using multiple replicates collected across three independent laboratories. Reproducibility between test sites is high, whereas individual trial reproducibility is relatively low. Of ten pro-longevity chemicals tested, six significantly extend lifespan in at least one strain. Three reported dietary restriction mimetics are mainly effective across C. elegans strains, indicating species and strain-specific responses. In contrast, the amyloid dye ThioflavinT is both potent and robust across the strains. Our results highlight promising pharmacological leads and demonstrate the importance of assessing lifespans of discrete cohorts across repeat studies to capture biological variation in the search for reproducible ageing interventions.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 28220799 · DOI · PMC5321775

Prevalence and genetic profiles of isoniazid resistance in tuberculosis patients: A multicountry analysis of cross-sectional data [2]

Research article Dean AS, Zignol M, Cabibbe AM, Falzon D, Glaziou P, Cirillo DM, et al. · · 2020 · cited 112 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
BackgroundThe surveillance of drug resistance among tuberculosis (TB) patients is central to combatting the global TB epidemic and preventing the spread of antimicrobial resistance.
Closing excerpt
The low prevalence of resistance to pyrazinamide and fluoroquinolones among patients with Hr-TB provides further justification for the recommended modified treatment regimen.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (439 words)

BackgroundThe surveillance of drug resistance among tuberculosis (TB) patients is central to combatting the global TB epidemic and preventing the spread of antimicrobial resistance. Isoniazid and rifampicin are two of the most powerful first-line anti-TB medicines, and resistance to either of them increases the risk of treatment failure, relapse, or acquisition of resistance to other drugs. The global prevalence of rifampicin resistance is well documented, occurring in 3.4% (95% CI 2.5%-4.4%) of new TB patients and 18% (95% CI 7.6%-31%) of previously treated TB patients in 2018, whereas the prevalence of isoniazid resistance at global and regional levels is less understood. In 2018, the World Health Organization (WHO) recommended a modified 6-month treatment regimen for people with isoniazid-resistant, rifampicin-susceptible TB (Hr-TB), which includes rifampicin, pyrazinamide, ethambutol, and levofloxacin. We estimated the global prevalence of Hr-TB among TB patients and investigated associated phenotypic and genotypic drug resistance patterns.Methods and findingsAggregated drug resistance data reported to WHO from either routine continuous surveillance or nationally representative periodic surveys of TB patients for the period 2003-2017 were reviewed. Isoniazid data were available from 156 countries or territories for 211,753 patients. Among these, the global prevalence of Hr-TB was 7.4% (95% CI 6.5%-8.4%) among new TB patients and 11.4% (95% CI 9.4%-13.4%) among previously treated TB patients. Additional data on pyrazinamide and levofloxacin resistance were available from 6 countries (Azerbaijan, Bangladesh, Belarus, Pakistan, the Philippines, and South Africa). There were no cases of resistance to both pyrazinamide and levofloxacin among Hr-TB patients, except for the Philippines (1.8%, 95% CI 0.2-6.4) and Belarus (5.3%, 95% CI 0.1-26.0). Sequencing data for all genomic regions involved in isoniazid resistance were available for 4,563 patients. Among the 1,174 isolates that were resistant by either phenotypic testing or sequencing, 78.6% (95% CI 76.1%-80.9%) had resistance-conferring mutations in the katG gene and 14.6% (95% CI 12.7%-16.8%) in both katG and the inhA promoter region. For 6.8% (95% CI 5.4%-8.4%) of patients, mutations occurred in the inhA promoter alone, for whom an increased dose of isoniazid may be considered. The main limitations of this study are that most analyses were performed at the national rather than individual patient level and that the quality of laboratory testing may vary between countries.ConclusionsIn this study, the prevalence of Hr-TB among TB patients was higher than the prevalence of rifampicin resistance globally. Many patients with Hr-TB would be missed by current diagnostic algorithms driven by rifampicin testing, highlighting the need for new rapid molecular technologies to ensure access to appropriate treatment and care. The low prevalence of resistance to pyrazinamide and fluoroquinolones among patients with Hr-TB provides further justification for the recommended modified treatment regimen.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 31961877 · DOI · PMC6974034

Integration of comprehensive genomic profiling, tumor mutational burden, and PD-L1 expression to identify novel biomarkers of immunotherapy in non-small cell lung cancer [3]

Research article Shi Y, Lei Y, Liu L, Zhang S, Wang W, Zhao J, et al. · · 2021 · cited 79 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
ObjectivesThis study aimed to explore the novel biomarkers for immune checkpoint inhibitor (ICI) responses in non-small cell lung cancer (NSCLC) by integrating genomic profiling, tumor mutational burden (TMB), and expression of programmed death receptor 1 ligand (PD-L1).
Closing excerpt
Our study demonstrated that KMT2C/TP53 co-mutation might be an accurate, cost-effective, and reliable biomarker to predict responses to PD-1 blockade therapy in NSCLC patients and that adding KRAS to the biomarker combination creates a more robust parameter to identify the best responders to ICI therapy.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (414 words)

ObjectivesThis study aimed to explore the novel biomarkers for immune checkpoint inhibitor (ICI) responses in non-small cell lung cancer (NSCLC) by integrating genomic profiling, tumor mutational burden (TMB), and expression of programmed death receptor 1 ligand (PD-L1).Materials and methodsTumor and blood samples from 637 Chinese patients with NSCLC were collected for targeted panel sequencing. Genomic alterations, including single nucleotide variations, insertions/deletions, copy number variations, and gene rearrangements, were assessed and TMB was computed. TMB-high (TMB-H) was defined as ≥10 mutations/Mb. PD-L1 positivity was defined as ≥1% tumor cells with membranous staining. Genomic data and ICI outcomes of 240 patients with NSCLC were derived from cBioPortal.ResultsEGFR-sensitizing mutations, ALK, RET, and ROS1 rearrangements were associated with lower TMB and PD-L1+/TMB-H proportions, whereas KRAS, ALK, RET, and ROS1 substitutions/indels correlated with higher TMB and PD-L1+/TMB-H proportions than wild-type genotypes. Histone-lysine N-methyltransferase 2 (KMT2) family members (KMT2A, KMT2C, and KMT2D) were frequently mutated in NSCLC tumors, and these mutations were associated with higher TMB and PD-L1 expression, as well as higher PD-L1+/TMB-H proportions. Specifically, patients with KMT2C mutations had higher TMB and PD-L1+/TMB-H proportions than wild-type patients. The median progression-free survival (PFS) was 5.47 months (95% CI 2.5-NA) in patients with KMT2C mutations versus 3.17 months (95% CI 2.6-4.27) in wild-type patients (p = 0.058). Furthermore, in patients with NSCLC who underwent ICI treatment, patients with TP53/KMT2C co-mutations had significantly longer PFS and greater durable clinical benefit (HR: 0.48, 95% CI: 0.24-0.94, p = 0.033). TP53 mutation combined with KMT2C or KRAS mutation was a better biomarker with expanded population benefit from ICIs therapy and increased the predictive power (HR: 0.46, 95% CI: 0.26-0.81, p = 0.007).ConclusionWe found that tumors with different alterations in actionable target genes had variable expression of PD-L1 and TMB in NSCLC. TP53/KMT2C co-mutation might serve as a predictive biomarker for ICI responses in NSCLC.Implications for practiceCancer immunotherapies, especially immune checkpoint inhibitors (ICIs), have revolutionized the treatment of non-small cell lung cancer (NSCLC); however, only a proportion of patients derive durable responses to this treatment. Biomarkers with greater accuracy are highly needed. In total, 637 Chinese patients with NSCLC were analyzed using next-generation sequencing and IHC to characterize the unique features of genomic alterations and TMB and PD-L1 expression. Our study demonstrated that KMT2C/TP53 co-mutation might be an accurate, cost-effective, and reliable biomarker to predict responses to PD-1 blockade therapy in NSCLC patients and that adding KRAS to the biomarker combination creates a more robust parameter to identify the best responders to ICI therapy.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 33655698 · DOI · PMC7982619

Genetic background sets the trajectory of experimental cancer evolution [4]

Research article Aitken SJ, Connor F, Feig C, Rayner TF, Lukk M, Luft J, et al. · · 2026 · cited 2 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Human cancers are heterogeneous1.
Closing excerpt
Even modest genetic divergence, comparable with that found across human ancestry groups, can strikingly alter selection pressures during cancer development to shape both cancer risk and the trajectory of tumour evolution.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (161 words)

Human cancers are heterogeneous1. Dissecting how germline genetic variation and environmental factors shape tumour evolution using human datasets is limited by inherent diversity in genetic backgrounds2 and environmental exposures3-5. Here, to overcome these limitations, we re-ran early tumour evolution hundreds of times in diverged inbred mouse strains, generating matched histology and whole-genome and transcriptome sequences. The sex, environment and carcinogenic exposures were all controlled, and the study design allowed us to capture genetic variation comparable with that observed across human populations while exploiting the nested hierarchical structure of strain-litter-animal-tumour relationships. Our analyses reveal that epistatic interactions between genetic background and acquired somatic mutations result in population-specific disease progression, including choice of driver mutations, occurrence of whole-genome duplication and subclonal selection dynamics that mirror both cancer susceptibility and tumour growth rate. Even modest genetic divergence, comparable with that found across human ancestry groups, can strikingly alter selection pressures during cancer development to shape both cancer risk and the trajectory of tumour evolution.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42486977 · DOI · PMC13600922

Key reviews and meta-analyses

The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.

  • Research article Characterization of the Contribution of Genetic Background and Gender to Disease Progression in the SOD1 G93A Mouse Model of Amyotrophic Lateral Sclerosis: A Meta-Analysis [5]
    Pfohl SR, Halicek MT, Mitchell CS · J Neuromuscul Dis · 2015 · cited 106 times (Europe PMC)
  • Research article Occupational and genetic risk factors for osteoarthritis: a review [6]
    Yucesoy B, Charles LE, Baker B, Burchfiel CM · Work · 2015 · cited 91 times (Europe PMC)
  • Research article The genetic background of inflammatory bowel disease: from correlation to causality [7]
    Uniken Venema WT, Voskuil MD, Dijkstra G, Weersma RK, Festen EA · J Pathol · 2017 · cited 88 times (Europe PMC)
  • Research article Identifying breast cancer susceptibility genes - a review of the genetic background in familial breast cancer [8]
    Wendt C, Margolin S · Acta Oncol · 2019 · cited 86 times (Europe PMC)
  • Research article Genes and Diet in the Prevention of Chronic Diseases in Future Generations [9]
    Franzago M, Santurbano D, Vitacolonna E, Stuppia L · Int J Mol Sci · 2020 · cited 74 times (Europe PMC)
  • Research article The Intriguing Carbapenemases of Pseudomonas aeruginosa: Current Status, Genetic Profile, and Global Epidemiology [10]
    Hammoudi Halat D, Ayoub Moubareck C · Yale J Biol Med · 2022 · cited 61 times (Europe PMC)

References

  1. Lucanic M, Plummer WT, Chen E, Harke J, Foulger AC, Onken B, et al.. Impact of genetic background and experimental reproducibility on identifying chemical compounds with robust longevity effects. . 2017. PMID 28220799 · DOI · PMC5321775
  2. Dean AS, Zignol M, Cabibbe AM, Falzon D, Glaziou P, Cirillo DM, et al.. Prevalence and genetic profiles of isoniazid resistance in tuberculosis patients: A multicountry analysis of cross-sectional data. . 2020. PMID 31961877 · DOI · PMC6974034
  3. Shi Y, Lei Y, Liu L, Zhang S, Wang W, Zhao J, et al.. Integration of comprehensive genomic profiling, tumor mutational burden, and PD-L1 expression to identify novel biomarkers of immunotherapy in non-small cell lung cancer. . 2021. PMID 33655698 · DOI · PMC7982619
  4. Aitken SJ, Connor F, Feig C, Rayner TF, Lukk M, Luft J, et al.. Genetic background sets the trajectory of experimental cancer evolution. . 2026. PMID 42486977 · DOI · PMC13600922
  5. Pfohl SR, Halicek MT, Mitchell CS. Characterization of the Contribution of Genetic Background and Gender to Disease Progression in the SOD1 G93A Mouse Model of Amyotrophic Lateral Sclerosis: A Meta-Analysis. J Neuromuscul Dis. 2015. PMID 26594635 · DOI · PMC4652798
  6. Yucesoy B, Charles LE, Baker B, Burchfiel CM. Occupational and genetic risk factors for osteoarthritis: a review. Work. 2015. PMID 24004806 · DOI · PMC4562436
  7. Uniken Venema WT, Voskuil MD, Dijkstra G, Weersma RK, Festen EA. The genetic background of inflammatory bowel disease: from correlation to causality. J Pathol. 2017. PMID 27785786 · DOI
  8. Wendt C, Margolin S. Identifying breast cancer susceptibility genes - a review of the genetic background in familial breast cancer. Acta Oncol. 2019. PMID 30606073 · DOI
  9. Franzago M, Santurbano D, Vitacolonna E, Stuppia L. Genes and Diet in the Prevention of Chronic Diseases in Future Generations. Int J Mol Sci. 2020. PMID 32290086 · DOI · PMC7178197
  10. Hammoudi Halat D, Ayoub Moubareck C. The Intriguing Carbapenemases of Pseudomonas aeruginosa: Current Status, Genetic Profile, and Global Epidemiology. Yale J Biol Med. 2022. PMID 36568831 · PMC9765337

About this article

Sources
NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
Verification
Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
Review state
automated source verified; no human expert review has been recorded yet.
Version
1, last updated 2026-10-29.

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Related research topics

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All 95 articles in Genetics and Genomics →

At a glance

1,053
PubMed records on this heading
63%
published since 2020
136
review articles
1
randomized trials
10
verified references below

Counts come from PubMed on 2026-10-29; they show how much has been written, not how strong the evidence is.

Related topics See all →

  • Genetic ProfileGenetics and Genomics
  • Genetic EngineeringGenetics and Genomics
  • Genetic Risk ScoreGenetics and Genomics
  • Gene ExpressionGenetics and Genomics
  • Gene FlowGenetics and Genomics

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