EDITORIAL
European Heart Journal (2016) 37, 568–571
doi:10.1093/eurheartj/ehv545
Family or SNPs: what counts for hereditary risk
of coronary artery disease?
Heribert Schunkert*
Deutsches Herzzentrum München and Technische Universität München, Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart Alliance, Munich, Germany
Online publish-ahead-of-print 15 October 2015
This editorial refers to ‘Risk prediction by genetic risk
scores for coronary heart disease is independent of selfreported family history’†, by H. Tada et al., on page 561.
Twin studies document that the individual genetic constitution contributes to the risk of almost all human traits.1 With respect to coronary artery disease (CAD), i.e. globally the predominant lethal
disease, such twin studies revealed that about half of the overall
risk to suffer from a myocardial infarction is inherited.2 The foremost challenges arising from these facts are two-fold: first, is it possible to improve individual risk estimates by adding genetic measures
to prediction tools and, secondly, is it possible to neutralize some of
this genetic risk?
The initial detection of genetic factors contributing to coronary
risk dates back to the finding that a positive family history is an independent predictor for a first- and even a second-degree relative of
an individual who had suffered from myocardial infarction at a young
age.3 – 5 This conclusion is further strengthened by the study of Tada
et al. reported in this issue of the journal.6 The focus of the present
study was to compare the clinical values of family history and common risk alleles, which had been identified in recent genome-wide
association studies (GWAS) for CAD,7 – 10 in predicting coronary
events.6
Refinement of genetic risk scores
In addition to family history, the authors studied two genetic risk
scores based on previous GWAS findings for CAD with respect
to prediction of coronary events in the Malmö Diet and Cancer
Study, a large cohort prospectively followed for 15 years.6 The
data support three relevant conclusions: (i) genetic risk
scores strongly associate with coronary risk; (ii) genetic risk
scores discriminate coronary risk particularly well in middle-aged
subjects; and (iii) risk prediction offered by a genetic risk score is
independent from the information conferred by a positive family
history.
The first and second conclusions drawn from the study by Tada
et al. are in line with previous studies,11 – 14 which accompanied the
increasing number of single nucleotide polymorphisms (SNPs)
found to affect CAD risk with genome-wide significance (i.e.
achieved a P ,5 × 10 – 8).7 – 10 It is worth mentioning that genetic
risk scores based on GWAS findings may be associated not only
with coronary events but also with sudden cardiac death, which
makes any predictive value even more relevant.14 The most recent
analysis of GWAS increased the number of genome-wide significant
SNPs for CAD to 58;10 27 and 50 of these were included in genetic risk scores evaluated by Tada et al.6 Somewhat disappointingly—but in line with previous studies 11 – 15—risk prediction was
only marginally improved by adding the genetic risk scores to a model that included age, gender, and other traditional risk factors including family history. Indeed, the c-statistics did not change much and
the percentage of individuals (17%) correctly re-classified based
on the genetic risk score was only modest. While this improvement
was in addition to 20% of individuals reclassified based on the family
history alone and thus affected overall a large number of individuals,
it was sobering to realize that categorical risk classification above
and below 7.5% of 10-year risk, i.e. a cut-off for implementation
of therapeutic measures, was not substantially improved by the genetic risk score.15 Future analysis need to evaluate whether the restriction to genome-wide significant SNPs—as done by Tada
et al.—is meaningful or whether inclusion of 1000, 5000, 10 000,
or even 100 000 top SNPs in a genetic risk score more profoundly
enhances prediction.
It is interesting to read that the genetic risk score offered better
risk prediction in younger individuals. In fact, comparing the highest
with the lowest quintile of the genetic risk score, Tada et al. observed a 2.4-fold difference in coronary events during 15 years of
follow-up in subjects younger than 57 years of age (P ¼ 7.5 ×
10 – 11).6 Such an enormous difference goes beyond the discriminatory effects of most traditional risk factors such as hypertension
or diabetes. Similar data have been reported for premature cases
of incident and prevalent CAD by Hughes et al. and the
The opinions expressed in this article are not necessarily those of the Editors of the European Heart Journal or of the European Society of Cardiology.
†
doi:10.1093/eurheartj/ehv462.
* Corresponding author. Deutsches Herzzentrum München and Technische Universität München, Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart
Alliance, Lazarettstraße 36, D-80636 Munich, Germany. Tel: +49 8912184073, Fax: +49 8912184013, Email: [email protected]
Published on behalf of the European Society of Cardiology. All rights reserved. & The Author 2015. For permissions please email: [email protected].
Editorial
CARDIoGRAM Consortium.8,12 Given that genetic risk scores have
to deliver better discrimination of risk before such tools can be considered for routine clinical decision-making, this aspect should be evaluated in more depth by future studies. It can be anticipated that
studying larger panels of SNPs in middle-aged subjects at intermediate
risk may finally sharpen decision-making in a clinically relevant fashion.
In contrast, it may be futile to employ genetic risk scores in elderly
subjects, in whom age—or rather the integral effect of all risk factors
over time—is largely responsible for the coronary risk.
Diverse reflections of genetic risk
The most striking finding from the study by Tada et al. is the fact that
a positive family history and a high genetic risk score confer largely
independent information regarding prediction of future coronary
events (Figure 1). While similar observations have been reported
for the prominent risk locus on chromosome 9p21,16 no previous
569
analysis examined the topic in such a comprehensive fashion. The
obvious question arising from these data is: how can it be explained
that the predictive values of family history and common risk alleles,
albeit that both were significantly associated with each other,6 are
largely non-redundant?
On a critical note, one may argue that the analysis of the Malmö
Diet and Cancer Study is limited by the fact that family history was
self-reported (rather than validated by medical records) and without age restriction (rather than limited to an age at the time of myocardial infarction of ,55 years in male and ,65 years in female
relatives17).6 The predictive value of such self-reported family history may be less precise, and, indeed, its hazard ratio for CAD in
the Malmö Diet and Cancer Study was only 1.43 rather than 1.7 –
2.7, numbers that have been reported, for example, by the Framingham Heart Study or the Newcastle Family History Study.6,17,18
Without validation, some seemingly affected (or non-affected) parents or siblings will be falsely categorized, thereby impairing the
Figure 1 Hereditary risk of coronary artery disease. The figure contrasts the overlapping and different qualities of information represented by a
positive family history and by a high score for common risk alleles. While a self-reported family history represents multiple measurable and nonmeasurable, genetic and non-genetic risk factors to which a family is exposed, it is limited by the lack of precision in reporting definitive coronary
artery disease in family members as well as by dilution of effects from one generation to the next. On the other hand, common risk alleles can be
determined accurately in an individual—but reflect at present only a small spectrum of inherited susceptibility. Positive family history (FH) and
common risk alleles add together in predicting coronary events, particularly in young individuals.6 In the Malmö Diet and Cancer Study, the cumulative incidence of events after 15 years of follow-up in individuals with a positive self-reported FH was 1.34-fold greater than in those without
(P ¼ 6.70 × 10 – 16), and 1.78-fold greater among those in the highest vs. lowest quintile of GRS50 (P ¼ 1.60 × 10 – 21), and those positive for both
risk factors vs. those negative for both had a 2.51-fold greater incidence of events (P ¼ 6.60 × 10 – 28).6 The Manhattan plot (upper right) represents P-values (y-axis) for millions of single nucleotide polymorphisms (SNPs) across all chromosomes (x-axis).8
570
accuracy of reported family history for predicting future events.18,19
Perhaps more importantly, the Malmö Diet and Cancer Study did
not restrict—as a defining criterion for a ‘positive family history’—manifestations of coronary disease to those affected at a
relatively young age.3,17,18,20 Indeed, the younger the age in an affected family member the stronger is condensation of genetic variants and therefore the discrimination of coronary events in
first-degree relatives.18
Nevertheless, it is clear from the work of Tada et al. that a positive
family history and common risk alleles reflect partially different aspects of genetic information.6 Indeed, the two measures of genetic
risk differ by multiple features and thus confer different information
for predicting disease burden (Figure 1). Two obvious examples may
illustrate this fact: the genetic risk scores analysed by Tada et al. do
not map monogenic causes for CAD such as mutations causing familial hypercholesterolaemia or dysfunctional nitric oxide (NO) signalling.20 – 22 a clear strength for taking family history. Vice versa,
irrespective of whether or not a family member inherited a causal
variant, all are labelled equally by a positive family history. In this regard genotyping of variants by an array may have clear advantages.
In addition there are multiple other subtler differences between
the two (Figure 1). Aspects that are much more strongly reflected by
familial background, i.e. family history, include shared environmental
factors (e.g. pollution with fine particles, noise, etc.), dietary habits,
or socio-economic background, but also complex genetic features
such runs of homozygosity.23 Moreover, sharing of the mircobiome
and epigenome, i.e. potential risk factors for CAD, or specific coronary manifestations of coronary disease may be evident in siblings
and thus covered by taking the family history.24 Finally, family members of CAD patients are easily identified in order to put preventive
strategies in place.25
On the other hand, counting of common risk alleles in a genetic
risk score very precisely characterizes a given person. Indeed, the
study by Tada et al. suggests that this information, albeit being arbitrarily restricted to 50 SNPs that had obtained genome-wide significance in large-scale association studies at the time the analysis was
carried out,7 – 10 is even better in predicting future coronary events
than a positive family history.
Interestingly, the finding that common risk alleles and family history reflect different genetic aspects of the trait CAD6 is somewhat
in contrast to data regarding LDL-cholesterol SNPs. In this related
field, a phenocopy of ‘familial hypercholesterolaemia’, a monogenic
disease with autosomal inheritance that is even defined by its positive family history, is often found as a consequence of aggregation of
multiple SNPs with small effect sizes.26
What are the clinical implications of the fact that a positive family
history and a high genetic risk score are additive in predicting coronary risk (Figure 1)? In the long run, with addition of more common
and all known rare variants on specific arrays, direct testing of our
genetic variability may be more informative than taking family history. However, if we want to make use of all hidden familial and molecular heritage, we need to cover both for predicting coronary risk.
Acknowledgements
I wish to thank the authors of the manuscript under discussion for
providing the data and the Kaplan– Meier curves for Figure 1.
Editorial
Conflict of interest: none declared.
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