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Genetic Profile

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

Citations verified against PubMedNot yet clinician-reviewed10 referencesApproximately 7 min read

A set of characteristics unique to the DNA of an individual organism or population obtained by analyzing small samples of biological material such as body tissues or fluids, used especially in the context of predicting susceptibil

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 (1,324 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

A set of characteristics unique to the DNA of an individual organism or population obtained by analyzing small samples of biological material such as body tissues or fluids, used especially in the context of predicting susceptibility to a particular disease. For example, some populations have identifiable genetic profiles which put them at higher risk for cancer. Distinguish from genetic background and genetic variation.

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: Genetic Profiles; Profile, Genetic; Profiles, Genetic; Genotype Profile; Genotype Profiles; Profile, Genotype; Profiles, Genotype.

Where it fits in the MeSH classification

Broader topics
Genetic Background

Related concepts

  • Genetic Background
  • Genetic Variation

Research landscape

PubMed indexes 375 records tagged with the MeSH heading “Genetic Profile” (counts retrieved 2026-10-29). 278 of them (74.1%) were published from 2020 onward, and the busiest period so far is 2020 onward with 278 records.

Reviews account for 38 records (10.1%) and meta-analyses for 0. Randomized controlled trials number 1 (0.3%) 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: 104 records; 2020 onward: 278 records0through 199902000-20091042010-20192782020 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
2010-2019104
2020 onward278
Share of records by publication typeReviews: 10.1 percent; Meta-analyses: 0.0 percent; Randomized controlled trials: 0.3 percentReviews10.1% (38)Meta-analyses0.0% (0)Randomized controlled trials0.3% (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 375 PubMed records
TypeRecordsShare
Reviews3810.1%
Meta-analyses00.0%
Randomized controlled trials10.3%

Method: PubMed query "Genetic Profile"[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
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
Need for high-resolution Genetic Analysis in iPSC: Results and Lessons from the ForIPS Consortium
Popp B, Krumbiegel M, Grosch J, Sommer A, Uebe S, Kohl Z, et al.
2018Research article
Design not recorded in structured metadata
68cc by
PMID 30464253 · DOI · PMC6249203
Large-scale genetic profiling of hereditary ataxias in China: implications for a stepwise molecular diagnostic strategy
Yu JY, Sun YM, Dong Y, Wu JJ, Zhu CH, Du YC, et al.
2026Research article
Design not recorded in structured metadata
0cc by
PMID 42806408 · DOI · PMC13621623

Prevalence and genetic profiles of isoniazid resistance in tuberculosis patients: A multicountry analysis of cross-sectional data [1]

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 [2]

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

Need for high-resolution Genetic Analysis in iPSC: Results and Lessons from the ForIPS Consortium [3]

Research article Popp B, Krumbiegel M, Grosch J, Sommer A, Uebe S, Kohl Z, et al. · · 2018 · cited 68 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Genetic integrity of induced pluripotent stem cells (iPSCs) is essential for their validity as disease models and for potential therapeutic use.
Closing excerpt
We show that high-throughput sequencing has value beyond SNV detection and the requirement to individually evaluate each clone.
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 (150 words)

Genetic integrity of induced pluripotent stem cells (iPSCs) is essential for their validity as disease models and for potential therapeutic use. We describe the comprehensive analysis in the ForIPS consortium: an iPSC collection from donors with neurological diseases and healthy controls. Characterization included pluripotency confirmation, fingerprinting, conventional and molecular karyotyping in all lines. In the majority, somatic copy number variants (CNVs) were identified. A subset with available matched donor DNA was selected for comparative exome sequencing. We identified single nucleotide variants (SNVs) at different allelic frequencies in each clone with high variability in mutational load. Low frequencies of variants in parental fibroblasts highlight the importance of germline samples. Somatic variant number was independent from reprogramming, cell type and passage. Comparison with disease genes and prediction scores suggest biological relevance for some variants. We show that high-throughput sequencing has value beyond SNV detection and the requirement to individually evaluate each clone.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 30464253 · DOI · PMC6249203

Large-scale genetic profiling of hereditary ataxias in China: implications for a stepwise molecular diagnostic strategy [4]

Research article Yu JY, Sun YM, Dong Y, Wu JJ, Zhu CH, Du YC, et al. · · 2026

Abstract reading summary — source excerpts
Opening excerpt
BackgroundHereditary ataxias are clinically and genetically heterogeneous, which complicates clinical management, genetic counseling, and scientific research.
Closing excerpt
This study provides an evidence base for early molecular diagnosis, prioritization of therapeutic development, and adaptive design of diagnostic tools for hereditary ataxias.
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 (321 words)

BackgroundHereditary ataxias are clinically and genetically heterogeneous, which complicates clinical management, genetic counseling, and scientific research. Efficient molecular diagnosis is essential but requires molecular epidemiological considerations due to the variable genetic landscapes across regions and populations. In China, despite the established predominance of spinocerebellar ataxia type 3 (SCA3), comprehensive data on the genetic landscape of hereditary ataxias are limited and increasingly outdated, leaving insufficient evidence to support an epidemiology-informed molecular diagnostic strategy and its clinical implementation as a diagnostic algorithm.MethodsWe retrospectively established a large, real-world, comprehensive cohort of hereditary ataxias (the HARMONY cohort) recruited from two medical centers in the Yangtze River Delta region of China (2008-2024) for genetic profiling. Molecular diagnoses were categorized into four classifications ('Definitive', 'Probable', 'Unclear', 'Undiagnosed') by integrating previously identified causative variants with results from systematic genetic testing in etiologically undetermined probands, and the associated variants were mapped onto genetic landscapes.ResultsA total of 2096 probands were included in the HARMONY cohort, achieving a molecular diagnostic yield of 84.9%, with 1715 (81.8%) 'Definitive' and 65 (3.1%) 'Probable' positive diagnoses, alongside 87 (4.2%) 'Unclear' and 229 (10.9%) 'Undiagnosed' cases. While the genetic landscape was dominated by variants underlying SCA3 (1249; 59.6%), with SCA2 (150; 7.2%), SCA1 (139; 6.6%), SCA6 (51; 2.4%), putative SCA36 (36; 1.7%), and SCA12 (35; 1.7%) constituting the major subtypes, the genetic profiling revealed a long tail of rare genotypes. Onset age distributions were characterized for genotypes with ≥ 5 probands. No probands were found to carry expansions in ZFHX3, BEAN1, DAB1, THAP11, or FXN.ConclusionsGenetic profiling of the HARMONY cohort revealed a skewed genetic landscape dominated by a few common repeat-expansion subtypes, alongside a markedly heterogeneous long tail of rare etiologies. The holistic profile supports a stepwise molecular diagnostic strategy, under which we propose a YRD (Yangtze River Delta)-oriented candidate diagnostic algorithm. This study provides an evidence base for early molecular diagnosis, prioritization of therapeutic development, and adaptive design of diagnostic tools for hereditary ataxias.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42806408 · DOI · PMC13621623

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 The Intriguing Carbapenemases of Pseudomonas aeruginosa: Current Status, Genetic Profile, and Global Epidemiology [5]
    Hammoudi Halat D, Ayoub Moubareck C · Yale J Biol Med · 2022 · cited 61 times (Europe PMC)
  • Research article Harnessing heterogeneity in type 2 diabetes mellitus [6]
    Philipson LH · Nat Rev Endocrinol · 2020 · cited 24 times (Europe PMC)
  • Research article GLOBAL ENDOCRINOLOGY: Geographical variation in the profile of RET variants in patients with medullary thyroid cancer: a comprehensive review [7]
    Maciel RMB, Maia AL · Eur J Endocrinol · 2021 · cited 17 times (Europe PMC)
  • Research article Genetic profiling in acute myeloid leukemia: a path to predicting treatment outcome [8]
    Visani G, Loscocco F, Isidori A, Piccaluga PP · Expert Rev Hematol · 2018 · cited 14 times (Europe PMC)
  • Research article Is There Value in Molecular Profiling of Soft-Tissue Sarcoma? [9]
    Italiano A · Curr Treat Options Oncol · 2018 · cited 13 times (Europe PMC)
  • Research article Neuropathophysiology, Genetic Profile, and Clinical Manifestation of Mucolipidosis IV-A Review and Case Series [10]
    Jezela-Stanek A, Ciara E, Stepien KM · Int J Mol Sci · 2020 · cited 12 times (Europe PMC)

References

  1. 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
  2. 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
  3. Popp B, Krumbiegel M, Grosch J, Sommer A, Uebe S, Kohl Z, et al.. Need for high-resolution Genetic Analysis in iPSC: Results and Lessons from the ForIPS Consortium. . 2018. PMID 30464253 · DOI · PMC6249203
  4. Yu JY, Sun YM, Dong Y, Wu JJ, Zhu CH, Du YC, et al.. Large-scale genetic profiling of hereditary ataxias in China: implications for a stepwise molecular diagnostic strategy. . 2026. PMID 42806408 · DOI · PMC13621623
  5. 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
  6. Philipson LH. Harnessing heterogeneity in type 2 diabetes mellitus. Nat Rev Endocrinol. 2020. PMID 31831872 · DOI · PMC8035709
  7. Maciel RMB, Maia AL. GLOBAL ENDOCRINOLOGY: Geographical variation in the profile of RET variants in patients with medullary thyroid cancer: a comprehensive review. Eur J Endocrinol. 2021. PMID 34757920 · DOI
  8. Visani G, Loscocco F, Isidori A, Piccaluga PP. Genetic profiling in acute myeloid leukemia: a path to predicting treatment outcome. Expert Rev Hematol. 2018. PMID 29792762 · DOI
  9. Italiano A. Is There Value in Molecular Profiling of Soft-Tissue Sarcoma?. Curr Treat Options Oncol. 2018. PMID 30523434 · DOI
  10. Jezela-Stanek A, Ciara E, Stepien KM. Neuropathophysiology, Genetic Profile, and Clinical Manifestation of Mucolipidosis IV-A Review and Case Series. Int J Mol Sci. 2020. PMID 32604955 · DOI · PMC7348969

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

375
PubMed records on this heading
74%
published since 2020
38
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 BackgroundGenetics and Genomics
  • Genetic TestingGenetics and Genomics
  • Mendelian Randomization AnalysisClinical Research
  • Genetic EngineeringGenetics and Genomics
  • Genetic Risk ScoreGenetics and Genomics

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