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ppe.12418

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doi: 10.1111/ppe.12418
1
Commentary
Genetic Association Family-Based Studies and Preeclampsia
Claire Infante-Rivard
Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Montreal, QC, Canada
Preeclampsia has been termed a disease of theories;
the latter have evolved over the years but the condition remains of uncertain aetiology. Preeclampsia is
now considered a syndrome with different phenotypes (mild, severe, early onset, late onset, maternal,
placental, recurrent).1 Overall prevalence figures
range from <1% to 9%. Together, these observations
(disease heterogeneity, complex and diverse mechanisms, and relatively low frequency) pose a challenge
for the conduct of robust genetic and epidemiological
studies. Despite these challenges, the weight of the
consequences justifies additional studies: for the foetus these could be, for example, preterm delivery, foetal growth restriction, respiratory problems, with
possible premature cardiovascular disease later in life.
Moreover, it is believed that there are future cardiovascular and renal consequences for women with a
history of preeclampsia.2,3
Preeclampsia aggregates in families. One relevant
question is how much of the familial aggregation can
be attributed to genetic causes. Heritability is a
parameter measured to answer this question. It has
been reported to be approximately 55%, with 30–35%
due to the mother and 20% to the foetus.4 Heritability
is computed from phenotypic data and can vary
according to studies; nevertheless, here, it provides a
strong enough indication to pursue a genetic line of
investigation for preeclampsia.
In this issue of the journal, Bauer et al.5 explore the
role of candidate genes and their polymorphisms
(single-nucleotide polymorphisms, SNP) on the development of preeclampsia.
The study involved a considerable amount of work
and expertise. Nevertheless, one may argue that the
conclusions seem quite limited. In particular, one has
to consider whether using a case and control duo
design to study genetic associations is worth the
Correspondence:
Claire Infante-Rivard, Department of Epidemiology, Biostatistics
and Occupational Health, Faculty of Medicine, McGill
University, Montreal, Canada.
Email: claire.infante-rivard@mcgill.ca
© 2017 John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2017, , –
additional effort. Other more traditional study aspects
may also have contributed to the apparent paucity of
findings.
Among the many possible mechanistic pathways
for preeclampsia, two were selected: the nitric oxide
and the heme oxygenase pathways. Known polymorphic genes involved in these pathways were genotyped. However, most of the selected variants were
not among the frequently studied ones since 1990,
and among those that were (e.g. NOS3), these had not
been found to be associated with preeclampsia.6 The
mechanistically plausible thrombophilia (in particular
the F5 and F2 genes) and angiogenic pathways would
have seemed more promising.
The main contribution of the Bauer et al. study5
rests on the use of a family-based design consisting of
dyads defined as case duos (mothers with preeclampsia and their offspring) as well as control duos (unaffected mothers and their offspring). Whereas pedigree
studies measure linkage (transmission within families), both case–control and family-based association
studies evaluate the association between a phenotypic
condition and some SNP (transmission across families). On the other hand, family-based studies usually
involve more subjects to ascertain and more measures
to take than a case–control study, which necessarily
impacts on feasibility and cost. Therefore, their advantages need to be considered.
Among the family-based association studies, the
case-parent trio has the most advantages. In reference
to the above distinction between linkage versus association, trio studies can test the composite null hypothesis of linkage and association. However, their main
advantage is with respect to internal validity as they
are robust against the principal source of confounding
in genetic association studies namely population
structure. Population structure can be understood as
an ethnic/genetic background that causes both the
distribution of the exposure (the studied SNP) and the
outcome. In the case–control study, the expected
under the null is computed assuming the distribution
of alleles is the same in cases and controls, and
2
C. Infante-Rivard
therefore, the expected in the cases is derived from the
common distribution. Whereas in case-parent trios,
the expected is derived from parents’ genotypes based
on Mendel’s laws. The parents’ non-transmitted alleles serve as controls for the affected offspring. Therefore, there can be no confounding from differences in
ethnic background between the affected offspring and
his/her controls because these are the non-transmitted
alleles from the parents. Assuming no measurement
error or selection bias, the risk estimates in trio studies
are causal because alleles to the offspring (i.e. the
exposure) are assumed perfectly randomised according to Mendel’s laws. On the other hand, a comparison
of unrelated cases and controls is always susceptible
to bias from population structure.
Unfortunately, the population structure bias can
also affect the case-mother/control-mother duos as
used in Bauer et al.5 It has been shown that case duos
will not maintain nominal type I error for the genotype relative risk estimates if there is population stratification.7 Adding different types of controls to help
estimate the mating type frequencies (defined below)
as done in this study does not necessarily help as differences in these frequencies arise between (here)
mothers of cases and mothers of controls. Therefore,
case and control duos are not as robust to population
stratification as case-parent trios. It may be difficult to
obtain DNA samples from fathers, but the birth period is possibly the easiest one to do so and the additional effort may be worthwhile. This is particularly
relevant given that the trio design requires less samples than the case and control duo design and would
appear to have some important advantages. Alternatively, there are analytical methods to detect population structure in association studies and to control for
it in the analysis (e.g. with principal components analysis); unfortunately, not all control methods can be
applied with candidate gene studies because neutral
SNP have not been genotyped. Despite a solid quality
control assessment, this limitation was applicable to
the Bauer et al. study; however, other analyses were
not indicative of a population structure bias.
With case-parent trios, the log-linear model (Poisson
regression) is the prevalent model of analysis.
Genotype relative risks for maternal and offspring
genotypes are estimated using the paternal genotype
and alternative control genotypes, if included in
hybrid designs,8 to estimate the mating type frequencies. These frequencies are the possible combinations
of the mother, father and child genotypes. This
additional information (mating type frequency, allele
frequency) in the log-linear model probably confers it
more power and greater efficiency than say the logistic regression.7 The Bauer et al. study used this analysis model with its advantages, although case duos are
generally not as powerful as case trios. However,
including both mother and child genotypes in the
model as opposed to a separate analysis of these genotypes can address confounding from maternal genotype on the foetal genotype.
Covariate main effects can be estimated using the
log-linear model in duo designs, whereas the caseparent trio does not readily lend itself to the inclusion
of additional non-genetic covariates. Nevertheless,
this is not as straightforward as in logistic regression.
The present paper did not analyse non-genetic covariates together with genetic factors in the log-linear
model.
The Mendelian inheritance ratio assumption is the
basis of many genetic analyses. It can be adjusted for
with a transmission ratio distortion offset or the inclusion of control trios in the log-linear model.9,10 Such
adjustment is exceedingly uncommon in genetic association studies possibly because there is scepticism
about the existence of these distortions (but, of course,
they will not be observed if not investigated). In this
study, control duos were mainly included to increase
power and obtain better parameter estimation.
The family-based association studies provide an
opportunity to study genetic aspects such as parentof-origin effects and maternal foetal genotype interactions.7 This is particularly relevant with preeclampsia.
Interaction as mismatch between maternal and foetal
genotypes can have a negative impact (e.g. Rh incompatibility). A preliminary analysis of interaction was
done and results were negative.5 Despite a large
study, power probably remained an issue.
Study replication, rarely done in non-genetic epidemiological studies, was carried out here. Despite
similar allele frequencies in the original and replication samples, results remain unconfirmed.5 Whether
this was because of a different analysis, sample size,
or lack of transportability, remains to be understood.
Finally, the study 5 is not without a potential for selection bias (at entry and on follow-up in the original
cohort). Conditioning on initial and sustained participation, spurious associations could have resulted.
This is a distinct issue from missing genotypes among
sample subjects which are reasonably assumed missing at random conditional on disease status and
© 2017 John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2017, , –
Commentary
observed genotypes and imputed using an EM
algorithm in the software used for the analysis.
In summary, the design, the analysis, and the
quality control features are very strong in the Bauer
et al. study. Therefore, the largely null results may
suggest that the SNP in the selected pathways do
not in effect play an important role in preeclampsia. However, a future confirmatory study could
benefit, including for internal validity, from collecting paternal DNA (ideally from fathers of case and
control offspring) to use in a related and feasible
design such as trios.
About the author
Claire Infante-Rivard is Professor of Epidemiology at
McGill University in Montréal, Canada. Her work
focuses on environmental and genetic factors as they
relate to childhood diseases, in particular childhood
leukemia and other cancers, and adverse pregnancy
outcomes. She is board-certified in community medicine. Dr. Infante-Rivard serves on the editorial board
of Paediatric and Perinatal Epidemiology.
References
1 Umesawa M, Kobashi G. Epidemiology of hypertensive
disorders in pregnancy: prevalence, risk factors, predictors
and prognosis. Hypertension Research 2017; 40:213–220.
2 Sones JL, Davisson RL. Preeclampsia, of mice and women.
Physiological Genomics 2016; 48:565–572.
© 2017 John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2017, , –
3
3 Grandi SM, Vallee-Pouliot K, Reynier P, Eberg M, Platt RW,
Arel R, et al. Hypertensive disorders in pregnancy and the
risk of subsequent cardiovascular disease. Paediatric and
Perinatal Epidemiology 2017; 31:412–421.
4 Cnattingius S, Reilly M, Pawitan Y, Lichtenstein P. Maternal
and fetal genetic factors account for most of familial
aggregation of preeclampsia: A population-based Swedish
cohort study. American Journal of Medical Genetics 2004;
130A:365–371.
5 Bauer AE, Avery CL, Shi M, Weinberg CR, Olshan AF,
Harmon QE, et al. A family-based study of carbon monoxide
and nitric oxide signaling genes and preeclampsia. Epub
ahead of print.
6 Staines-Urias E, Paez MC, Doyle P, Dudbridge F,
Serrano NC, Ioannidis JPA, et al. Genetic association studies
in pre-eclampsia: systematic meta-analyses and field
synopsis. International Journal of Epidemiology 2012;
41:1764–1775.
7 Ainsworth HF, Unwin J, Jamison DL, Cordell HJ.
Investigation of maternal effects, maternal-fetal interactions
and parent-of-origin effects (imprinting), using mothers and
their offspring. Genetic Epidemiology 2011; 35:19–45.
8 Infante-Rivard C, Mirea L, Bull SB. Combining case-control
and case-trio data from the same population in genetic
association analyses: overview of approaches and illustration
with a candidate gene study. American Journal of Epidemiology
2009; 170:657–664.
9 Huang LO, Labbe A, Infante-Rivard C. Transmission ratio
distortion: review of concept and implications for genetic
association studies. Human Genetics 2013; 132:245–263.
10 Huang LO, Infante-Rivard C, Labbe A. Analysis of caseparent trios using a log-linear model with adjustment for
transmission ratio distortion. Frontiers in Genetics 2016; 7:
Article 155. doi:10.3389/fgene.2016.00155.
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