The interplay of family history and genome-wide polygenic risk scores across 24 common diseases

Scientists stated that family history (FH) is a risk factor for many non-communicable diseases. Given that FH captures several genetic and non-genetic familial risks, it has been used for risk stratification and monitoring health status. For instance, FH is applied for the determination of risks of breast cancer and analyzing the possibility of rheumatic disease in patients with inflammatory arthritis. 

Study: Systematic comparison of family history and polygenic risk across 24 common diseases. Image Credit: SurfsUp/Shutterstock
Study: Systematic comparison of family history and polygenic risk across 24 common diseases. Image Credit: SurfsUp/Shutterstock

Background

It has been challenging to determine inherited disease risk from FH because many individuals are often diagnosed with common diseases without FH. Scientists have stated that the accuracy of FH for determining diseases is low. This is because of many factors, including bias or sensitivity to wordings of the questionnaires/queries that cause misinterpretation of risk. Researchers stated that the decrease in average family size in most developed countries has caused FH to be less informative. 

Advancements in algorithmic methods and genome-wide genetic testing have enabled the development of a personalized approach for measuring genetic susceptibility via polygenic risk scores (PRS). PRSs have determined numerous genetic loci for almost all common diseases. This was made possible by analyzing data from large-scale genetic screenings, which compared allele frequencies of thousands of diseases and healthy controls.

Although previous studies revealed that individual diseases exhibit fairly independent effects of PRS and first-degree FH, data are scarce regarding their causal link or overlaps across different familial risks on a genetic basis.

A new study

A new study posted on the medRxiv* preprint server has investigated the interplay of FH and genome-wide PRS. In this observational study, researchers used the FinnGen study Data Freeze 7 to obtain data on 306,418 adults. These data were associated with disease-based and epidemiological cohorts and hospital biobanks.

In the current study, researchers explored the dynamics of three types of FH across 24 common diseases, i.e., related to first-degree FH (FH1st), second-degree FH (FH2nd), and parental cause of death (FHP). The study candidates, i.e., the index patients and their relatives, were selected from nationwide healthcare registries. Scientists selected the 24 diseases based on the publications available in genome-wide association studies (GWAS).

Scientists systematically constructed disease-specific PRS for 24 diseases. They covered many non-communicable diseases in adults and compared the PRS and FH. The authors reported both PRS and FH to be independent and not interchangeable measures of genetic susceptibility. They provided complimentary and independent information on all susceptible inherited diseases studied.

Key findings

The PRS was able to explain, on average,10% of the effect of FH1st; however, FH1st explained only 3% of the PRSs. Researchers found that PRSs do not depend on early- and late-onset of FH. The PRS stratified the risk in individuals with and without positive FH. This study reports that a PRS is associated with considerably elevated risk, while a low PRS balanced the effect of FH.

The result of the current study is in line with previous studies that reported moderate attenuation in the effect of FH adjusting for PRS in cancer, depression, and cardiometabolic diseases. This study has presented novel data that supports the use of PRS to enhance the risk assessment of several diseases of public health.

Scientists explained the reason behind the independent effects of PRS and FH. They stated that FH considers only non-genetic exposures and behaviors shared by families. However, in the case of PRS, it captures every person's unique combinations of common, disease-associated genetic variants, which relatives do not share. Although PRSs can be studied throughout life, irrespective of phases of life, FS strongly depends on disease events that occurred in relatives in real conditions. 

The key finding of this study, i.e., the independent effect of PRS and FH, is also consistent with previous studies that reported the occurrence of breast and ovarian cancers in BRCA1 and BRCA2 mutation carriers. With FH information, researchers revealed that PRSs could be effectively used for risk assessment of prostate, breast, and colorectal cancer. Currently, clinicians use FH to assess the risk of glaucoma in patients with ocular hypertension. FH is commonly used for assessing the risk of coronary artery disease and type 2 diabetes. A higher PRS may identify individuals who can benefit from preventive treatments for both diseases.

Conclusion

The current study has many strengths, including a systematic and comprehensive assessment of FH using large data obtained from FinnGen. This allowed a systematic comparison of polygenic risk and FH across 24 diseases. A second strength of the study is that the results have been further validated via quantitative genetic theory. One of the key limitations of this study is that it is restricted to individuals of European ancestry. It reports that the effects of FH and genome-wide PRSs are mostly independent. The authors further report that polygenic risk and FH are not interchangeable measures of genetic susceptibility. Instead, they offer complementary information, which can be effectively utilized for assessing inherited risk.

*Important notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:
Dr. Priyom Bose

Written by

Dr. Priyom Bose

Priyom holds a Ph.D. in Plant Biology and Biotechnology from the University of Madras, India. She is an active researcher and an experienced science writer. Priyom has also co-authored several original research articles that have been published in reputed peer-reviewed journals. She is also an avid reader and an amateur photographer.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Bose, Priyom. (2022, July 11). The interplay of family history and genome-wide polygenic risk scores across 24 common diseases. News-Medical. Retrieved on August 13, 2022 from https://www.news-medical.net/news/20220711/The-interplay-of-family-history-and-genome-wide-polygenic-risk-scores-across-24-common-diseases.aspx.

  • MLA

    Bose, Priyom. "The interplay of family history and genome-wide polygenic risk scores across 24 common diseases". News-Medical. 13 August 2022. <https://www.news-medical.net/news/20220711/The-interplay-of-family-history-and-genome-wide-polygenic-risk-scores-across-24-common-diseases.aspx>.

  • Chicago

    Bose, Priyom. "The interplay of family history and genome-wide polygenic risk scores across 24 common diseases". News-Medical. https://www.news-medical.net/news/20220711/The-interplay-of-family-history-and-genome-wide-polygenic-risk-scores-across-24-common-diseases.aspx. (accessed August 13, 2022).

  • Harvard

    Bose, Priyom. 2022. The interplay of family history and genome-wide polygenic risk scores across 24 common diseases. News-Medical, viewed 13 August 2022, https://www.news-medical.net/news/20220711/The-interplay-of-family-history-and-genome-wide-polygenic-risk-scores-across-24-common-diseases.aspx.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
Post a new comment
Post
You might also like...
Tracking a Pathogen through its Genome: the challenges and opportunities of the global governance of genomic surveillance