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: 3 complete abstracts (525 words), 9 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 constitution or condition of the body which makes the tissues react in special ways to certain extrinsic stimuli and thus tends to make the individual more than usually susceptible to certain diseases.
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: Diathesis; Diatheses; Susceptibility, Disease; Disease Susceptibilities; Susceptibilities, Disease.
Where it fits in the MeSH classification
- Broader topics
- Disease Attributes; Body Constitution
- Narrower topics
- Genetic Predisposition to Disease
Research landscape
PubMed indexes 215,821 records tagged with the MeSH heading “Disease Susceptibility” (counts retrieved 2026-10-31). 47,983 of them (22.2%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 94,532 records.
Reviews account for 47,699 records (22.1%) and meta-analyses for 10,572. Randomized controlled trials number 1,240 (0.6%) 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.
Show the data
| Period | Records |
|---|---|
| through 1999 | 15,626 |
| 2000-2009 | 57,680 |
| 2010-2019 | 94,532 |
| 2020 onward | 47,983 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 47,699 | 22.1% |
| Meta-analyses | 10,572 | 4.9% |
| Randomized controlled trials | 1,240 | 0.6% |
Method: PubMed query "Disease Susceptibility"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-31.
Reading the evidence in anatomy and physiology
Anatomy describes how body structures are built and physiology describes how they normally work, so most findings here are descriptive or mechanistic rather than evidence about treatment. Much of what is known comes from dissection, imaging, tissue studies and experiments in animals, and measurements in healthy volunteers can differ from those in people with disease or at different ages. Normal ranges vary between individuals and populations. Check whether a result was measured in people or in another species, the age and health of the participants, and the method used.
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 / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Cell-type-resolved genetic variation shapes inflammatory bowel disease risk | 2026 | Research article Design not recorded in structured metadata | 11 | cc by PMID 42236949 · DOI · PMC13441992 |
| GWAS on short tandem repeats identifies genetic mechanisms in Alzheimer's disease | 2026 | Research article Design not recorded in structured metadata | 5 | cc by PMID 42243151 · DOI · PMC13237113 |
| Genetic association and machine learning improve the prediction of type 1 diabetes risk | 2026 | Research article Design not recorded in structured metadata | 5 | cc by PMID 42062540 · DOI · PMC13175890 |
Cell-type-resolved genetic variation shapes inflammatory bowel disease risk [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Most genetic variants associated with complex diseases lie in non-coding regions1, complicating efforts to identify effector genes and relevant cell types.
- Closing excerpt
- Our results provide a mechanistic map that links genetic risk to specific genes and cell types in IBD, and a generalized framework for interpretation of GWAS loci using single-cell eQTL mapping of disease-relevant tissues in complex diseases.
- 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 (177 words)
Most genetic variants associated with complex diseases lie in non-coding regions1, complicating efforts to identify effector genes and relevant cell types. Here we map cis-expression quantitative trait loci (eQTLs) across 2.2 million single cells using intestinal biopsies and blood from 421 individuals, including 125 with inflammatory bowel disease (IBD). Cell-type-level eQTLs were more distal to transcription start sites, enriched in enhancers, less likely to regulate the nearest gene, and more than 3.5-fold more likely to colocalize with IBD loci detected in genome-wide association studies (GWASs) than eQTLs detected at tissue-level resolution. We nominate effector genes at more than half of known IBD loci, including MAML2, PSEN2 and ZMIZ1 in myeloid cells, implicating reduced Notch signalling in intestinal immune dysfunction. We also identify Wnt-regulated genes, including MYC, in epithelial stem and progenitor cells, suggesting that impaired renewal contributes to barrier breakdown. Our results provide a mechanistic map that links genetic risk to specific genes and cell types in IBD, and a generalized framework for interpretation of GWAS loci using single-cell eQTL mapping of disease-relevant tissues in complex diseases.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42236949 · DOI · PMC13441992
GWAS on short tandem repeats identifies genetic mechanisms in Alzheimer's disease [2]
Abstract reading summary — source excerpts
- Opening excerpt
- GWAS typically focus on SNPs, often excluding complex genetic variants, such as short tandem repeats.
- Closing excerpt
- Aligning our top short tandem repeats with DNA methylation and transcriptome profiles from human brain samples suggests that several short tandem repeats may unfold their effects by impacting gene expression.
- 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 (197 words)
GWAS typically focus on SNPs, often excluding complex genetic variants, such as short tandem repeats. Here, we report the results of GWAS analyses systematically assessing the role of short tandem repeats, both imputed and directly genotyped by whole genome sequencing, on risk for Alzheimer's disease in a large collection of ~330,000 individuals (3287 cases; 47,048 Alzheimer's disease-by-proxy cases, 283,111 controls) from the UK biobank. Using short tandem repeat genotype data, we identify 15 independent loci showing evidence for genome-wide significant association with Alzheimer's disease risk. While most identified loci had already been highlighted by SNP-based GWAS, we detect short tandem repeat-based signals near the genes SNX32 (chr. 11q13) and WSB1 (chr. 17q11). In addition, we delineate several other loci where short tandem repeats (and not SNPs) either represent the lead signal (ABCA7) or make substantial contributions to the SNP-driven associations (HLA-DRB1, MINDY/ADAM10, and APOE). Heritability analyses estimate that short tandem repeats account for at least 3% of the total phenotypic variance of Alzheimer's disease in this dataset. Aligning our top short tandem repeats with DNA methylation and transcriptome profiles from human brain samples suggests that several short tandem repeats may unfold their effects by impacting gene expression.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42243151 · DOI · PMC13237113
Genetic association and machine learning improve the prediction of type 1 diabetes risk [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Type 1 diabetes (T1D) has a large genetic component, and expanded genetic studies of T1D can enhance biological and therapeutic discovery and improve risk prediction.
- Closing excerpt
- Overall, improved genetic discovery and prediction will have wide clinical, therapeutic and research applications for T1D.
- 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 (151 words)
Type 1 diabetes (T1D) has a large genetic component, and expanded genetic studies of T1D can enhance biological and therapeutic discovery and improve risk prediction. Here we performed genome-wide genetic association and fine-mapping analyses in 20,355 T1D and 797,363 nondiabetic individuals of European ancestry and in 10,107 T1D and 19,639 nondiabetic individuals at the MHC locus, which identified 160 risk signals. We trained a machine learning model, T1GRS, to predict T1D using genetic risk, which improved classification in Europeans and performed similarly in African Americans, compared to previous scores. T1GRS particularly improved prediction in T1D, with fewer high-risk HLA haplotypes and more complex risk profiles, and revealed 154 nonlinear interactions between MHC and non-MHC loci. Finally, we identified four genetic subclusters based on T1GRS features with significant differences in age of onset and diabetic complications. Overall, improved genetic discovery and prediction will have wide clinical, therapeutic and research applications for T1D.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42062540 · DOI · PMC13175890
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 Stroke genetics informs drug discovery and risk prediction across ancestries [4]
- Research article GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19 [5]
- Research article Multi-ancestry meta-analysis and fine-mapping in Alzheimer's disease [6]
- Research article Genome-wide analyses identify 30 loci associated with obsessive-compulsive disorder [7]
- Research article Genome-wide association studies and Mendelian randomization analyses provide insights into the causes of early-onset colorectal cancer [8]
- Research article GWAS meta-analysis of psoriasis identifies new susceptibility alleles impacting disease mechanisms and therapeutic targets [9]
References
- Alegbe T, Harris BT, Fachal L, Ramirez-Navarro L, Tutert M, Krzak M, et al.. Cell-type-resolved genetic variation shapes inflammatory bowel disease risk. . 2026. PMID 42236949 · DOI · PMC13441992
- Gmelin D, Ohlei O, Aslam MM, Junge MP, Parkkinen L, Mullin K, et al.. GWAS on short tandem repeats identifies genetic mechanisms in Alzheimer's disease. . 2026. PMID 42243151 · DOI · PMC13237113
- McGrail C, Sears TJ, Griffin EN, Ghaben AL, Smadbeck P, Flannick J, et al.. Genetic association and machine learning improve the prediction of type 1 diabetes risk. . 2026. PMID 42062540 · DOI · PMC13175890
- Mishra A, Malik R, Hachiya T, Jürgenson T, Namba S, Posner DC, et al.. Stroke genetics informs drug discovery and risk prediction across ancestries. Nature. 2022. PMID 36180795 · DOI · PMC9524349
- Pairo-Castineira E, Rawlik K, Bretherick AD, Qi T, Wu Y, Nassiri I, et al.. GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19. Nature. 2023. PMID 37198478 · DOI · PMC10208981
- Lake J, Warly Solsberg C, Kim JJ, Acosta-Uribe J, Makarious MB, Li Z, et al.. Multi-ancestry meta-analysis and fine-mapping in Alzheimer's disease. Mol Psychiatry. 2023. PMID 37198259 · DOI · PMC10615750
- Strom NI, Gerring ZF, Galimberti M, Yu D, Halvorsen MW, Abdellaoui A, et al.. Genome-wide analyses identify 30 loci associated with obsessive-compulsive disorder. Nat Genet. 2025. PMID 40360802 · DOI · PMC12165847
- Laskar RS, Qu C, Huyghe JR, Harrison T, Hayes RB, Cao Y, et al.. Genome-wide association studies and Mendelian randomization analyses provide insights into the causes of early-onset colorectal cancer. Ann Oncol. 2024. PMID 38408508 · DOI · PMC11213623
- Dand N, Stuart PE, Bowes J, Ellinghaus D, Nititham J, Saklatvala JR, et al.. GWAS meta-analysis of psoriasis identifies new susceptibility alleles impacting disease mechanisms and therapeutic targets. Nat Commun. 2025. PMID 40021644 · DOI · PMC11871359
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 2026-10-31.
See how EvidenceLib is built. Spotted an error? Email support@evidencehelix.com.
Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.