Cited by 105Genetic Associations and EpidemiologyGenomics and Rare DiseasesGenetic Mapping and Diversity in Plants and Animals

Lessons from studying monogenic disease for common disease

Peltonen, Leena, Perola, Markus, Naukkarinen, Jussi, Palotie, Aarno·2006·Human Molecular Genetics

Abstract

The prevailing paradigm for common disease emphasizes the role of common variants predisposing to various rampant health problems, and these genetic risk profiles interact with environmental and life style risk factors triggering the disease process and modifying its progress. However, most of what we know about the molecular background of common diseases is in fact based on what we have learned from rare familial forms of these traits. Further, often the mutations identified in even more rare monogenic diseases sharing some trait components with common diseases have exposed critical new pathways involved in the molecular pathogenesis of common health problems. In this review, we aim to exemplify some of the lessons learned from rare Mendelian forms of diseases that have significantly contributed to our understanding of the genetic background of common diseases and their trait components. Identification and characterization of different mutations in Mendelian variants of common traits have been extremely successful with improved accuracy of genome-wide analyses provided by the Genome Project. These, usually rare single gene disorders masquerade a multifactorial trait in their clinical phenotype, but careful data collection of family history (Mendelian segregation in a family), and detailed clinical examination (the cases are exceptionally severe or deviate otherwise from the typical disease cases) have revealed the exceptionally strong genetic character of the trait. Typically, linkage and association studies in such families have guided to a gene responsible for the disease in those particular families. Usually the mutations identified have been nearly family-specific and their value in clinical diagnosis has remained modest. Neither have they explained the majority of cases at the population level. However, by studying these ‘human allelic knockouts’ a unique view into the pathophysiology involved has been obtained. Table 1 provides a list of examples for which rare Mendelian forms of common diseases have resulted in the identification of disease genes and subsequent functional studies have evidenced their relevance for common traits. Informative examples among them include familial Alzheimer's disease (AD), MODY(s) (maturity onset diabetes of the young), familial combined hyperlipidemia (FCHL) and rare familial forms of epilepsies and migraine. These disorders, characterized by their distinct heritability have opened new avenues in further evaluation of the more common forms of dementia, diabetes, dyslipidemias and common forms of epilepsy and migraine.

Integrity signals

Registry

Crossref · 10.1093/hmg/ddl060

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Unlike common diseases, the clarity of causation for Mendelian disorders usually simplifies generating models to connect genotype to organismal phenotype, and these models have led to critical insights into human health and disease biology.

    “Unlike common diseases, the clarity of causation for Mendelian disorders usually simplifies the task of generating models (including human cells and organoids or rodents) to connect genotype to organismal phenotype; these have led to many critical insights into the biology of health and disease in humans98,99.”

    From A brief history of human disease genetics (2020) · cites this paper as [99]

Cites

Flagged first, then by citations

1 of the 49 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Peltonen, L., Perola, M., Naukkarinen, J., & Palotie, A.
Affiliations
  • University of Helsinki (FI)
  • Broad Institute (US)
Corresponding author
Peltonen, Leena
Venue
Human Molecular Genetics
Published
2006-04-15
Volume
15
Issue
suppl_1
Pages
R67-R74
ISSN
  • 0964-6906 (ISSN-L)
  • 1460-2083
OpenAlex
W2114161752
Cited by
105
References
49
Citations by year
2026: 1 · 2025: 5 · 2024: 2 · 2023: 5 · 2022: 5 · 2021: 5 · 2020: 7 · 2019: 5 · 2018: 9 · 2017: 1 · 2016: 4 · 2015: 6 · 2014: 3 · 2013: 5 · 2012: 5
FWCI
0.85
Type
journal article
Language
en
Open access
closed
Locations
  • Human Molecular Genetics (publishedVersion)
  • PubMed (publishedVersion)
  • STM:n Hallinnonalan avoin julkaisuarkisto (Julkari) (submittedVersion)
Funders
  • Academy of Finland (#213506)
  • National Institutes of Health
  • National Heart, Lung, and Blood Institute (R01HL70150-01A1, R01HL070150)
  • National Institute of Neurological Disorders and Stroke (R01NS43559, R01 NS37675, R01NS043559, R01NS037675)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords
monogenic disorders, common diseases, Mendelian inheritance, rare variants, familial Alzheimer's disease, maturity-onset diabetes of the young, familial combined hyperlipidemia, familial epilepsy, familial migraine, disease gene identification, genetic risk factors, molecular pathogenesis
MeSH terms
Alzheimer Disease, Animals, Blood Pressure, Diabetes Mellitus, Humans, Models, Biological, Mutation, Gene Rearrangement, Genetic Predisposition to Disease, Multifactorial Inheritance, Genetic Diseases, Inborn, Databases, Genetic, Lipid Metabolism

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review