Genetic epidemiology and public health: hope, hype, and future prospects

doi:10.1016/S0140-6736(05)67601-5

The Lancet

Volume 366, Issue 9495, 22–28 October 2005, Pages 1484-1498

https://doi.org/10.1016/S0140-6736(05)67601-5 Get rights and content

Summary

Genetic epidemiology is a rapidly expanding research field, but the implications of findings from such studies for individual or population health are unclear. The use of molecular genetic screening currently has some legitimacy in certain monogenic conditions, but no established value with respect to common complex diseases. Personalised medical care based on molecular genetic testing is also as yet undeveloped for common diseases. Genetic epidemiology can contribute to establishing the causal nature of environmentally modifiable risk factors, throught the application of mendelian randomisation approaches and thus contribute to appropriate preventive strategies. Technological and other advances will allow the potential of genetic epidemiology to be revealed over the next few years, and the establishment of large population-based resources for such studies (biobanks) should contribute to this endeavour.

Section snippets

Genomic profiling in the prevention and treatment of common diseases

Since the launch of the human genome project the potential of increased genetic knowledge to improve human health has been widely championed.15, 16, 17 In a striking image from his 1999 Shattuck lecture, Francis Collins, of the US National Human Genome Research Institute, described a hypothetical consultation in 2010 in which a 23-year-old man has a high concentration of cholesterol identified during screening and undergoes extensive genetic testing.¹⁸ Table 1 shows the genotypes that are

Genetic screening

Although this series focuses principally on the genetics of common complex diseases, it is genetic screening, mainly for monogenic disorders, that has provided most opportunities for potential interaction between genetics and public health. Genetic screening can take several forms—recessive carrier screening, recessive disease screening, autosomal dominant disease screening, pharmacogenetic risk screening, employment risk screening, and complex genetic disease screening (panel 3).³⁸

The aim of

Genomic profiling and susceptibility to common diseases

The contribution of genetic epidemiology to an evidence base for conventional genetic screening has not yet delivered an appreciable number of targets that can be implemented within programmes. However, as illustrated by Francis Collins' hypothetical patient, the greatest potential benefit from genomic profiling would be in improving common disease prevention. Currently, though, prospects for such prevention are few.⁷⁰ The main problem is that very few common genetic variants are known to

Personalised medicine: hype or hope?

The use of genetic variants as screening tests overlaps with, but is distinct from, the notion of personalised medicine, in which precise treatment protocols are envisaged that depend upon genotypic information. Our concern is with common variants that might influence choice of therapeutic regimen for disease prevention or treatment of common disease, rather than the identification of genes expressed in rare diseases that aid selection of specific treatments,⁷⁹ such as imatinib for Bcr-Abl

Mendelian randomisation: strengthening causal inference in observational epidemiology

The basic aim of aetiological observational epidemiology is to identify modifiable causes of disease and through this to contribute to strategies for prevention. This enterprise has, however, had several setbacks. Observational studies have apparently identified robust associations, which are interpreted as probably or possibly causal, but when they are tested in randomised controlled trials, they have proved illusory.85, 86 Examples include hormone replacement therapy and coronary heart

Biobanks

Several articles have begun to address the features of a good genetic association study.31, 124, 125, 126, 127 Focus on study design has increased because genetic association studies of complex phenotypes have typically failed to discover susceptibility loci or have failed to replicate those findings that initially seemed positive.118, 125, 127, 128, 129, 130, 131, 132 Despite the widespread use of genetic case-control studies, their lack of consistency is a well recognised limitation.118, 128,

The future

The past decade has been an important time for human genetics. Growth in technical capacities and genomic knowledge has been tempered by initial failures to find genes for complex phenotypes with any strategy—linkage or association. Our statistical capacities and our ability to process and interpret data still lag behind the technical capability to produce very large amounts of genomic data. An unfortunate feature of the genomics revolution has been a tendency to hyperbole, leading to

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Rising incidence of heart failure (HF) in the Western world despite advanced clinical care necessitate exploration of further preventive tools and strategies. Lipoprotein(a) [Lp(a)], recognized as one of the major cardiovascular risk factors has also been implicated as a risk factor for HF. However, existing evidence remains inconclusive and that has led us to perform this meta-analysis.
PubMed/Medline, EMBASE and Scopus were systematically searched for studies evaluating an association of Lp(a) with occurrence of HF from inception-till November 2023. Random effects models and I² statistics were used for pooled odds ratio (OR) and heterogeneity assessment. We performed leave one out sensitivity analyses by sequentially removing one study at a time and recalculating the pooled effect size.
Our search rendered in total 360 studies and after final screening this resulted in 7 Mendelian randomization (MR) design. According to the MR analysis, increasing Lp(a) level were significantly associated with increased risk of HF (OR 1.064, 95 % CI: 1.043-1.086, I²= 97.59 %, P < 0.001). In addition, Leave-one-out sensitivity analysis showed that the effect size did not change substantially by removal of any particular study in MR studies and ORs ranged from 1.051 (when excluding Levin) to a maximum of 1.111 (when excluding Wang or Jiang), hereby confirming the association.
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Lipoprotein (a) (Lp[a]) is an established risk factor for atherosclerotic cardiovascular disease (ASCVD). However, data on association of Lp(a) with risk of atrial fibrillation (AF) is still limited. We searched PubMed/Medline, Scopus, and EMBASE for studies evaluating the association of Lp(a) with the occurrence of AF until July 2023. Random effects models and I² statistics were used for pooled odds ratios (OR), and heterogeneity assessments. A subgroup analysis was performed based on the cohort population, and a one-out sensitivity analysis was performed. This meta-analysis comprised 275,647 AF cases and 2,100,172 Lp(a) participants. An increase in Lp(a) was associated with an increased risk of AF in mendelian randomization (MR) studies (OR 1.024, 95% CI: 1.007-1.042, I² = 87.72%, P < 0.001). Leave-one-out sensitivity analysis confirmed equivalent results in MR studies. Subgroup analysis of MR studies revealed a higher risk of AF in the European cohort (OR 1.023, 95% CI: 1.007-1.040, I² = 89.05%, P < 0.001) and a low risk (OR 0.940, 95% CI: 0.893-0.990) in the Chinese population. Meta-analysis of the MR data suggested higher levels of Lp(a) were associated with increased risk of AF. Future robust prospective studies are warranted to validate these findings.
Erratum: Use of Lipoprotein(a) in clinical practice: A biomarker whose time has come. A scientific statement from the National Lipid Association (Journal of Clinical Lipidology (2019) 13(3) (374–392), (S1933287419300868), (10.1016/j.jacl.2019.04.010))
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Lipoprotein(a) [Lp(a)] is a well-recognized, independent risk factor for atherosclerotic cardiovascular disease, with elevated levels estimated to be prevalent in 20% of the population. Observational and genetic evidence strongly support a causal relationship between high plasma concentrations of Lp(a) and increased risk of atherosclerotic cardiovascular disease–related events, such as myocardial infarction and stroke, and valvular aortic stenosis. In this scientific statement, we review an array of evidence-based considerations for testing of Lp(a) in clinical practice and the utilization of Lp(a) levels to inform treatment strategies in primary and secondary prevention.
Genetic influences on treatment-seeking for common mental health problems in the UK biobank
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The majority of those who experience clinical anxiety and/or depressive symptoms in the population do not receive treatment. Studies investigating inequalities in treatment outcomes rarely consider that individuals respond differently to their experience of the environment. Much of our environment is under genetic influence, via our behaviour, whereby individuals actively select their experiences. If genes influence who seeks and receives treatment, selection bias will confound genomic studies of treatment response. Furthermore, if some individuals are at high genetic risk of needing but not commencing treatment, then greater efforts could be made to engage them. The role of common genetic variation on four lifetime treatment-seeking behaviours (treatment-seeking, treatment-receipt, self-help, self-medication with alcohol/drugs) was examined in participants of the UK Biobank (sample size range: 48,106 - 75,322). Treatment-related behaviours were only modestly heritable in these data. Nonetheless, genetic correlations reveal substantial genetic overlap between lifetime treatment-related behaviours and psychiatric disorders, symptoms and behavioural traits. To our knowledge, this is the first study to examine genetic influences on treatment-related behaviours. Further work is required to determine whether genetic factors could be used alongside clinical, social and demographic factors to identify at risk groups and inform strategies which target early intervention.
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SeriesGenetic epidemiology and public health: hope, hype, and future prospects

Summary

Section snippets

Genomic profiling in the prevention and treatment of common diseases

Genetic screening

Genomic profiling and susceptibility to common diseases

Personalised medicine: hype or hope?

Mendelian randomisation: strengthening causal inference in observational epidemiology

Biobanks

The future

Trends Genet

Lancet

Lancet

Lancet

Am J Hum Genet

Prog Brain Res

Lancet

Lancet

Genet Med

Genet Med

Genet Med

Am J Obstet Gynecol

Adv Genet

Clin Chim Acta

Am J Hum Genet

J Clin Epidemiol

Genet Med

Atheroslcerosis

Genet Med

Genet Med

Atherosclerosis

Prev Med

Lancet

Predicting disease using genomics

Nat Rev

Using pharmocogenetics to improve drug safety and efficacy

JAMA

Moving towards individualized medicine with pharmacogenomics

Nature

‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?

Int J Epidemiol

Fibrinogen, C-reactive protein and coronary heart disease: does Mendelian randomization suggest the associations are non-causal?

QJM

Commentary: Mendelian randomization and gene-environment interaction

Int J Epidemiol

Commentary: Development of Mendelian randomisation: from hypothesis test to ‘Mendelian deconfounding’

Int J Epidemiol

Commentary: Katan's remarkable foresight: genes and causality 18 years on

Int J Epidemiol

Mendelian randomization: prospects, potentials, and limitations

Int J Epidemiol

Plasma fibrinogen and fibrinogen genotypes in 4685 cases of myocardial infarction and in 6002 controls: test of causality by “Mendelian randomization”

Circulation

Homocysteine and coronary heart disease

Heart Online

DNA-Based Medicine: Prevention and Therapy

A Personal View of the project

The new genetics in clinical practice

BMJ

Medical and societal consequences of the human genome project

NEJM

Lipid related genes and myocardial infarction in 4685 cases and 3460 controls: discrepancies between genotype, blood lipid concentrations, and coronary disease risk

Int J Epidemiol

Genetic polymorphisms in N-acetyltransferase-2 and Microsomal Expoxide Hydrolase, cumulative cigarette smoking, and lung cancer

Cancer Epidemiol Biomarkers Prev

The APC E1317Q variant in adenomatous polyps and colorectal cancers

Cancer Epidemiol Biomark Prev

Apolipoprotein E epsilon4 count affects age at onset of Alzheimer disease, but not lifetime susceptibility: The Cache County Study

Arch Gen Psychiatry

Risk and protective effects of the APOE gene towards Alzheimer's disease in the Kungsholmen project: variation by age and sex

J Neurol Neurosurg Psychiatry

American College of Medical Genetics/American Society of Human Genetics Working Group on ApoE and Alzheimer disease

JAMA

ELAC2/HPC2 polymorphisms, prostate-specific antigen levels, and prostate cancer

J Natl Cancer Inst

The APC variants I1307K and E1317Q are associated with colorectal tumors, but not always with a family history

Proc Natl Acad Sci USA

The UK National Screening Committee's Criteria for appraising the viability, effectiveness and appropriateness of a screening programme

The International HapMap Project

Nature

Series
Genetic epidemiology and public health: hope, hype, and future prospects