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 (762 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
That part of the genome that corresponds to the complete complement of exons of an organism or cell.
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: Exomes.
Where it fits in the MeSH classification
- Broader topics
- Genome
- See also
- Exons; Transcriptome
Related concepts
Research landscape
PubMed indexes 7,711 records tagged with the MeSH heading “Exome” (counts retrieved 2026-10-29). 1,860 of them (24.1%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 5,925 records.
Reviews account for 560 records (7.3%) and meta-analyses for 72. Randomized controlled trials number 16 (0.2%) 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 | 0 |
| 2000-2009 | 0 |
| 2010-2019 | 5,925 |
| 2020 onward | 1,860 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 560 | 7.3% |
| Meta-analyses | 72 | 0.9% |
| Randomized controlled trials | 16 | 0.2% |
Method: PubMed query "Exome"[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 / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| The mutational constraint spectrum quantified from variation in 141,456 humans | 2020 | Research article Design not recorded in structured metadata | 9,101 | cc by PMID 32461654 · DOI · PMC7334197 |
| Exome sequencing and analysis of 454,787 UK Biobank participants | 2021 | Research article Design not recorded in structured metadata | 959 | cc by PMID 34662886 · DOI · PMC8596853 |
| Exome sequencing and characterization of 49,960 individuals in the UK Biobank | 2020 | Research article Design not recorded in structured metadata | 525 | cc by PMID 33087929 · DOI · PMC7759458 |
| Large-scale exome analyses reveal new rare variant contributions in amyotrophic lateral sclerosis | 2026 | Research article Design not recorded in structured metadata | 15 | cc by PMID 41917433 · DOI · PMC13083253 |
The mutational constraint spectrum quantified from variation in 141,456 humans [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation.
- Closing excerpt
- Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare 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 (169 words)
Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 32461654 · DOI · PMC7334197
Exome sequencing and analysis of 454,787 UK Biobank participants [2]
Abstract reading summary — source excerpts
- Opening excerpt
- A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome.
- Closing excerpt
- We illustrate the ability of exome sequencing to identify gene-trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.
- 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 (206 words)
A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study2. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P ≤ 2.18 × 10-11. Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene-trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 34662886 · DOI · PMC8596853
Exome sequencing and characterization of 49,960 individuals in the UK Biobank [3]
Abstract reading summary — source excerpts
- Opening excerpt
- The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world1.
- Closing excerpt
- Exome sequences from the first 49,960 participants highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community.
- 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 (229 words)
The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world1. Here we describe the release of exome-sequence data for the first 49,960 study participants, revealing approximately 4 million coding variants (of which around 98.6% have a frequency of less than 1%). The data include 198,269 autosomal predicted loss-of-function (LOF) variants, a more than 14-fold increase compared to the imputed sequence. Nearly all genes (more than 97%) had at least one carrier with a LOF variant, and most genes (more than 69%) had at least ten carriers with a LOF variant. We illustrate the power of characterizing LOF variants in this population through association analyses across 1,730 phenotypes. In addition to replicating established associations, we found novel LOF variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical importance, and show that 2% of this population has a medically actionable variant. Furthermore, we characterize the penetrance of cancer in carriers of pathogenic BRCA1 and BRCA2 variants. Exome sequences from the first 49,960 participants highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 33087929 · DOI · PMC7759458
Large-scale exome analyses reveal new rare variant contributions in amyotrophic lateral sclerosis [4]
Abstract reading summary — source excerpts
- Opening excerpt
- Amyotrophic lateral sclerosis (ALS) is a heritable disorder where rare variants with low-to-moderate penetrance are thought to dominate genetic risk.
- Closing excerpt
- These findings highlight new translational targets and show that rare variant analyses capture substantially more genetic risk than common variant genome-wide association studies.
- 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 (158 words)
Amyotrophic lateral sclerosis (ALS) is a heritable disorder where rare variants with low-to-moderate penetrance are thought to dominate genetic risk. To identify such rare variants, we harmonized and analyzed exome data from 22 cohorts, totaling 17,919 individuals with ALS and 200,703 controls across discovery and replication phases. Rare variant analyses identified several new risk genes, with replication confirming association of YKT6 and supporting HTR3C, GBGT1 and KNTC1. We also provide strong, independent validation for genes with limited previous evidence: ARPP21, DNAJC7 and CFAP410. Notably, in ARPP21, we identified a new high-effect variant (p.P747L) and confirmed that p.P563L is an ALS-associated variant leading to an aggressive disease course. Beyond new discoveries, our analyses largely recapitulated the known genetic architecture of ALS, identifying risk variants in over 20% of cases and supporting a cumulative oligogenic risk model. These findings highlight new translational targets and show that rare variant analyses capture substantially more genetic risk than common variant genome-wide association studies.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41917433 · DOI · PMC13083253
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 Exome sequencing as a tool for Mendelian disease gene discovery [5]
- Research article Tumor microenvironment: Challenges and opportunities in targeting metastasis of triple negative breast cancer [6]
- Research article ACMG SF v3.2 list for reporting of secondary findings in clinical exome and genome sequencing: A policy statement of the American College of Medical Genetics and Genomics (ACMG) [7]
- Research article Diagnostic clinical genome and exome sequencing [8]
- Research article The promise of whole-exome sequencing in medical genetics [9]
- Research article Disease gene identification strategies for exome sequencing [10]
References
- Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al.. The mutational constraint spectrum quantified from variation in 141,456 humans. . 2020. PMID 32461654 · DOI · PMC7334197
- Backman JD, Li AH, Marcketta A, Sun D, Mbatchou J, Kessler MD, et al.. Exome sequencing and analysis of 454,787 UK Biobank participants. . 2021. PMID 34662886 · DOI · PMC8596853
- Van Hout CV, Tachmazidou I, Backman JD, Hoffman JD, Liu D, Pandey AK, et al.. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. . 2020. PMID 33087929 · DOI · PMC7759458
- Hop PJ, Kooyman M, Kenna BJ, Zwamborn RAJ, van Eijk KR, Wang Y, et al.. Large-scale exome analyses reveal new rare variant contributions in amyotrophic lateral sclerosis. . 2026. PMID 41917433 · DOI · PMC13083253
- Bamshad MJ, Ng SB, Bigham AW, Tabor HK, Emond MJ, Nickerson DA, et al.. Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet. 2011. PMID 21946919 · DOI
- Deepak KGK, Vempati R, Nagaraju GP, Dasari VR, S N, Rao DN, et al.. Tumor microenvironment: Challenges and opportunities in targeting metastasis of triple negative breast cancer. Pharmacol Res. 2020. PMID 32050092 · DOI
- Miller DT, Lee K, Abul-Husn NS, Amendola LM, Brothers K, Chung WK, et al.. ACMG SF v3.2 list for reporting of secondary findings in clinical exome and genome sequencing: A policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2023. PMID 37347242 · DOI · PMC10524344
- Biesecker LG, Green RC. Diagnostic clinical genome and exome sequencing. N Engl J Med. 2014. PMID 24941179 · DOI
- Rabbani B, Tekin M, Mahdieh N. The promise of whole-exome sequencing in medical genetics. J Hum Genet. 2014. PMID 24196381 · DOI
- Gilissen C, Hoischen A, Brunner HG, Veltman JA. Disease gene identification strategies for exome sequencing. Eur J Hum Genet. 2012. PMID 22258526 · DOI · PMC3330229
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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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.