Download SCHMIDT Genetics and socieconomic inequalities in health

Genetics and Socioeconomic Inequalities in Health in the
Era of Genome-Wide Association Studies
Underlagsrapport til Sosial ulikhet i helse: En norsk kunnskapsoversikt
Börge Schmidt
Institute for Medical Informatics, Biometry and Epidemiology;
University of Duisburg-Essen
2013
1. Introduction
The supposed relationship between genetic factors and social traits has been discussed since the
rediscovery of Mendel's laws at the beginning of the 20th century (Holtzman 2002). In the recent
past, questions have also been asked about the interplay between genetics, health and
socioeconomic status with regard to the well-known inverse association between
socioeconomic key indicators (education, occupation and income) and disease outcomes1
(Mackenbach 2005). The renaissance of the debate is linked to the rapid development in
genome sequencing technology. Due to this, the study of genetic risk factors for many diseases
has become feasible and affordable in large study populations. There are already large sets of
genetic data available, by means of which genetic differences between individuals on the
genome-wide level can be detected with regard to disease-related traits. This provides
opportunities to study the genetic basis of common diseases, which were rather limited when
employing twin, adoption, and other family study designs or classical approaches of molecular
and genetic epidemiology (Guo & Adkins 2008).
Currently, genome-wide data are primarily used in the search for genetic causes of complex
diseases, but also increasingly for the study of social and behavioral traits. As these traits and
their supposed impact on the distribution of opportunities in society are of general interest for
the research on social inequalities, genetic variation and the complex interplay of genetic and
environmental factors are more and more taken into account in this context (Bearman 2008;
Guo 2008).
However, there are only a few empirical studies published in which disease-related genetic
variants (i.e., inter-individual genetic differences, for which a statistical association with a
specific disease or a determinant of disease has already been established) have been screened
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A phenomenon widely known as health inequalities.
for an unequal distribution across socioeconomic groups (Gimeno et al. 2008; Holzapfel et al.
2011), but the number of such studies that aim to explore the genetic contribution to health
inequalities will probably increase in the near future.
The objective of the present work is to summarize the recent debate on the contribution of
genetic factors to the explanation of health inequalities. It starts with an overview of basic
genetics and current methods in genetic epidemiology as well as their application to the study
of behavioral traits (chapter 2). Then, the plausibility of a genetic contribution in terms of
supposed genetic differences between socioeconomic groups is evaluated by incorporating
genetic factors in explanations of health inequalities (chapter 3). In addition, the role of genetic
factors in health inequalities beyond supposed genetic differences between socioeconomic
groups are introduced using the examples of gene–environment interaction and epigenetic
modifications (chapter 4), before general conclusions are given (chapter 5).
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2. The genetic basis of complex traits
2.1 Basics of genetics
Each cell in the human body carries genetic information referred to as the genome. This
information is encoded in the molecules of deoxyribonucleic acid (DNA) and the initial
description of its double helix structure in the 1950s (Watson & Crick 1953) can be regarded
as the starting point of modern genetics. Since this discovery, rapid advances in the exploration
of inter-individual genetic variation and its inheritance mechanisms have made subtle DNA
sequence differences available for current genetic research. The DNA sequence represents the
central element of research, as it contains the genes, which in turn provide the information for
the transcription of ribonucleic acids (RNA). A specific group of RNA, the messenger RNA,
contains the information for the protein synthesis.
Proteins perform important cell functions in the form of, e.g., enzymes, hormones or receptor
molecules, and consist of amino acids. These amino acids are determined by the DNA structure
of genes that are composed of a sequence of four different elements, the nucleotides. Each
nucleotide consists of a phosphate group, the monosaccharide deoxyribose and one of the four
nucleobases adenine (A), thymine (T), guanine (G) and cytosine (C). Their sequence in base
pairs forming the double stranded structure of the DNA, which is usually organized in 23 pairs
of chromosomes in the nucleus of human cells (see Figure 1).
In addition to providing genes for protein synthesis, DNA also carries important information
for other biological functions, e.g., as to whether specific genes are expressed or not and in
which quality and quantity proteins are synthesized. Approximately 20,500 genes are assumed
to be present in the human DNA accounting only for a small portion of the DNA sequence in
total (Clamp et al. 2007). The exact functions of the remaining DNA segments are as yet poorly
understood.
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Figure 1: The structure of DNA and its organization in a chromosome (Source: Leja 2009).
The length of the human DNA sequence adds up to approximately three billion base pairs, of
which in two randomly selected individuals on average 99.9% are arranged identically (The
International HapMap Project 2003). The DNA sequence varies between individuals only at
certain positions, which are also known as genetic polymorphisms (or mutations if the
frequency of the rarest variant is less than 1% in the population). The two or more variants of
such a polymorphism are called the different alleles at the respective position in the DNA
strand. As every human individual has a double set of chromosomes, at each polymorphic
position two alleles occur that determine the genotype of an individual. These alleles can be
either the same (homozygous) or different from each other (heterozygous).
It is the sum of the polymorphic positions in the DNA sequence that constitute genetic variation
between individuals. The systematic identification of genetic polymorphisms is one of the
prerequisites for making genetic variation data available for scientific use in population studies
and to detected genetic associations with a specific trait (also called phenotype). For this
purpose, single nucleotide polymorphisms (SNPs) are of particular interest. A SNP is the
substitution of only a single base at a polymorphic position in the DNA sequence and SNPs
represent the most common type of polymorphisms in the human genome (Frazer et al. 2007;
Abecasis et al. 2012).
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The DNA sequence – including such variations – is transmitted from one generation to the next,
therefore inherited in a biological sense. This process of inheritance follows specific patterns
(see Figure 2). During reproduction, new genetic diversity is created by random rearrangement
of the parental chromosomes. The offspring will receive one of their two sets of chromosomes
from each parent, so that each parent shares half of the genetic makeup with the offspring.
Figure 2: Example of a dominant-recessive pattern of inheritance for a genetic variant with the two alleles A
(dominant) and a (recessive). The father's (square) monogenic phenotype (black) appears in the offspring only if
they are also homozygous for the a allele (Source: modified from North & Martin 2008).
2.2 Genetic factors of disease etiology
Diseases with a genetic basis are roughly distinguished into two groups. First, the monogenic
diseases, which are more or less determined by a single genetic mutation and inherited
according to Mendel's laws. These monogenic diseases include, e.g. Cystic fibrosis (Collins
1992) and Huntington's disease (Walker 2007), for which the disease-causing genetic variants
are rare, i.e. they occur very infrequently in the general population. The causal genetic variant
of a monogenic disease typically has a strong effect on the respective disease phenotype. Thus,
the occurrence of such a disease phenotype depends crucially – in most cases exclusively – on
the disease-causing genetic variant.
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The second group of diseases with a genetic basis is called polygenic, as multiple diseaserelated genetic polymorphisms are involved in their etiology. Especially for complex diseases,
which are caused by a variety of different risk factors, a polygenic basis has been suggested.
These complex and polygenic diseases include, e.g. Type 2 diabetes mellitus, coronary heart
disease or cancer. Various genetic polymorphisms and environmental factors seem to contribute
to their etiology, in which the interaction of genetic factors and the environment also plays an
important role (Thomas 2010). In recent years, by exploring the polygenic basis of several
complex diseases, many highly prevalent genetic variants with low penetrance2 have been
detected. The small effects of single genetic variants on the respective disease phenotypes
reflect the non-deterministic nature of genetic factors in the etiology of complex diseases. As
there is strong evidence that most of the common diseases are complex and polygenic, it has
turned out to be much more complicated to disentangle the relationship between genetic factors
and the respective phenotype than it is for rare monogenic diseases.
Since in the public discourse the strong genetic effects known for monogenic diseases have
been often transferred to any alleged relationships between genotypes and phenotypes, genetic
factors are commonly perceived as deterministic factors (Henderson 2008). But human traits
are only rarely caused by a single genetic variant. The reduction of the actual complexity in the
interplay between environmental and genetic factors can thus lead to the misinterpretation of
genes as the sole determinants of personal destinies (Condit 1999; Conrad & Gabe 1999;
Juengst 2004).
2.3 Identification of disease-related genetic factors
2
Penetrance specifies the probability by which a certain genotype leads to the occurrence of an associated
phenotype, i.e., only a small proportion of the carriers of a disease-related risk allele with low penetrance actually
develop the corresponding disease.
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In many cases, monogenic diseases are affected by a mutation in coding regions of genes. These
mutations directly result in a structural and functional change of the protein synthesis.
Such mutations are then both necessary and sufficient for the development of the disease, which
allows for the assessment of their inheritance patterns by segregation analysis. Here, the
position of the predisposing mutations can be detected using linkage analysis in multigenerational family studies by means of co-segregation of genetic markers with known
positions. The causative mutation can then be accurately determined by sequencing the
identified DNA region and the genes located there. As complex diseases with a polygenic basis
usually do not show clear inheritance patterns, such traditional methods of genetic
epidemiology have contributed only with limited success to the identification of the genetic
variants involved (Guo & Adkins 2008).
To determine the genetic basis of complex diseases, genome-wide association studies (GWAS)
have recently been used to search for statistically significant differences in allele frequencies of
many genome-wide distributed SNPs in large study populations. There are several reasons that
GWAS became a feasible and widely used method in the recent past. An important prerequisite
was the development of the Common Disease - Common Variant model in the mid-1990s
(Lander 1996). In addition, the development of a catalog of common genetic polymorphisms in
the human genome by the initiation of the International HapMap Project (The International
HapMap Project 2003) as well as a huge step forward in the development of powerful
genotyping technologies were also essential (Gunderson et al. 2005; Kennedy et al. 2003).
The assumption of the Common Disease - Common Variant model is that the genetic basis of
frequently occurring diseases is composed of risk alleles with a high frequency in human
populations and that the effects of individual risk alleles on the corresponding disease are
relatively small3. Based on this assumption, the idea was to represent the genome-wide variation
3
According to Hindorff et al. (2009) single SNPs identified even by early GWAS contribute only with a median
odds ratio of 1.33 per risk allele to the etiology of complex diseases.
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with a dense selection of common SNPs. Advantage was taken of the block structure of
neighboring and thus highly correlated SNPs by which the genome is characterized (Gabriel et
al. 2002). This nonrandom correlation between genetic polymorphisms located at different
positions of the same chromosome is also known as linkage disequilibrium (LD). On the
population level, LD is stronger for genetic polymorphisms that are close together on a
chromosome than for those farther apart from each other and polymorphisms of high LD are
inherited together more often than it would be expected from a purely random recombination.
Taking advantage of the resulting block structure of the genome by selecting a set of tag-SNPs,
much of the genome-wide variability can be mapped. Thus, these tag-SNPs represent not only
themselves, but also neighboring polymorphisms not included in the initial SNP selection
(Sebastiani et al. 2003).
In the year 2005, a description of the block structure of the human genome and a catalog of
more than one million SNPs was presented by the International HapMap Project (The
International HapMap Project 2005). On this basis, DNA microarrays were developed to
ascertain the genome-wide variability of individuals in large study populations (Gunderson et
al. 2005). This can be seen as the starting point for the hypothesis-free approach to search for
disease-associated polymorphisms. To date, a huge number of genome-wide associations have
been already reported. As of March 2013, about 8,700 SNPs were identified by GWAS and
published in more than 1,500 papers (Hindorff et al. 2013). It is envisaged that in the near future
DNA microarrays will capture several millions of SNPs as well as other types of genetic
polymorphisms and to include also rare variants (Baker 2010). In addition, studies that
incorporate high-throughput sequencing data, i.e. the complete sequence of the genome, seem
to become feasible within the next decade (Bochud 2012).
To avoid false positive results, however, several potential sources of error have to be considered
when conducting GWAS. One of the challenges is the multiple testing of an increasing number
of SNPs to be analyzed simultaneously. To minimize the number of findings generated by
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chance, very large study populations are needed. In addition, spurious genetic associations may
occur, if in unspecified subgroups of the study population systematic difference in allele
frequencies are present (Marchini et al. 2004). Finally, replication of the GWAS results in
independent study populations is needed to confirm genetic association. Furthermore, it is
important to realize that the mere confirmation of a statistical link does not provide information
on the biological function or plausibility of promising research findings. Many SNPs which
show robust associations with disease traits are located in gene regions with unknown function
or even far away from coding regions (Hindorff et al. 2009). To date, the exact mechanisms by
which SNPs exert their effect are not known for most GWAS findings.
2.4 Genetic factors of behavioral traits and social outcomes
During the last decades, various studies have suggested that a variety of psychological and
behavioral traits have at least partly a genetic basis (Plomin 2008). These research findings
were derived by formal genetic studies on twins, siblings, adoptees or families without
analyzing molecular genetic data, but by the estimation of the trait’s heritability4. The statistical
assumptions of these formal genetic studies have been widely criticized, however, and there is
reasonable doubt about the accuracy of the heritability estimates as well as their interpretation
(Joseph 2012; Kamin & Goldberger 2002; Lewontin 1974; Richardson & Norgate 2005; Rose
et al. 1984; Vitzthum 2003).
Despite this criticism, it is increasingly accepted that genetic factors could have a partial
influence on behavior and thus may indirectly impact on the accumulation of benefits over an
individual's life course (Freese 2008). Until recently, the genetic heterogeneity among
individuals was treated simply as a black box in research on social inequality and the inclusion
of genetic information was completely rejected (Bearman 2008; Guo 2008). As many topics of
4
The estimate for heritability indicates what proportion of the observed variation of a trait is associated with
genetic variance in the respective study population.
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behavioral research have always been of interest to inequality research, methods of genetic
epidemiology get more and more attention (Cherny 2008; Guo & Adkins 2008; North & Martin
2008). It is now hoped that the use of new molecular methods such as GWAS will lead to the
detection of polymorphisms involved in the development of behavioral and social outcomes.
With the conduction of GWAS in behavioral research the Common Disease - Common Variant
model of complex diseases is simply transferred to behavioral traits. Hence, next to the
influence of environmental factors common genetic variants are assumed to build the polygenic
basis for inter-individual differences in behavioral traits with a supposed relevance to an
individual's socioeconomic status. However, social variables are subject to considerable
measurement error and – in the biological sense – further away from the supposed effects of
specific risk alleles than biomedical parameters such as lipid levels or blood pressure. Thus, a
mediation of genetic effects due to influences of the (social) environment is more likely than a
direct genetic effect on social outcomes (Guo 2008). It remains questionable whether simply
transferring the GWAS approach, which has been developed for complex diseases, is useful for
exploring social outcomes, particularly since the inaccuracy of outcome measures is discussed
as a fundamental problem for the further identification of the genetic basis of complex disease
traits in GWAS (Müller et al. 2010).
A first wave of GWAS for various behavioral characteristics such as personality, cognitive
skills and entrepreneurship has already been performed without showing any robust findings
(Butcher et al. 2008; Davies et al. 2011; Davis et al. 2010; Docherty et al. 2010; de Moor et al.
2010; Terracciano et al. 2010; van der Loos et al. 2010). To date, the results of two GWAS for
educational attainment – a key indicator of socioeconomic status – are already published
(Beauchamp et al. 2010; Martin et al. 2011), but were limited in statistical power due to their
relatively small sample size. They also do not report any robust associations with specific
genetic variants. This suggests that – if at all – only genetic variants with very small effect sizes
are associated to such a complex social trait as educational attainment. In addition, these GWAS
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give supporting evidence that an unequal distribution of common genetic variants across
socioeconomic groups – at least to a substantial degree – is highly unlikely.
However, because of the relatively simple implementation of GWAS due to their hypothesisfree approach and the availability of large datasets in the context of international consortia, this
method will remain attractive for the study of behavioral and social outcomes in the future.
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3. Incorporating genetic factors in concepts of explaining health inequalities
3.1 Basic concepts of explaining health inequalities
In the following chapter, basic concepts for the explanation of health inequalities are presented.
By pointing out single causal relationships, they are useful to structure the complex interplay
of the various factors that may contribute to health inequalities, although more sophisticated
models have already been developed (e.g., Mackenbach 2006).
There are currently no elaborate concepts for the explanation of health inequalities available
that adequately incorporate genetic factors. However, the existing basic concepts can be
extended by including genetic factors. This would allow for the examination whether a genetic
contribution to health inequalities by genetic differences between socioeconomic groups is
plausible. Such a contribution of genetic differences to health inequalities would be in effect, if
there was an association of socioeconomic status with specific genetic variants that are in some
way related to disease – or, in other words, an unequal distribution of disease-related risk alleles
across socioeconomic strata.
According to Mackenbach (2005) this would be the case if at least one of the following two
conditions was met: First, certain genetic variants could be associated with socioeconomic
status as well as with disease and thus contribute directly to the development of disease. Second,
certain genetic variants could be associated with socioeconomic status as well as with a
determinant of disease and thus contribute indirectly to the development of disease.
3.2 Direct health selection
With the hypothesis of direct health selection it is assumed that the health status of an individual
determines its socioeconomic status. Thus, health inequalities are explained directly by healthrelated selection mechanisms that influence processes of social mobility, supposing that
individuals with poor health are more likely to move downward in socioeconomic status than
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individuals with good health and vice versa (Blane et al. 1993). Here, present individual
socioeconomic status can be compared to a previous point in the life course (intragenerational
mobility) or compared to the status of the parents (intergenerational mobility).
A genetic variant associated with disease can be easily incorporated into the explanation of
direct health selection, because an association of a disease-related genetic variant with
socioeconomic status would be expected if the genetic variant acts on an individual’s social
mobility by its effect on disease (see Figure 3).
Figure 3: Incorporation of genetic factors in the hypothesis of direct health selection for the explanation of health
inequalities (→ causal direction; ---- undirected association; SES, socioeconomic status).
But the impact of genetic factors in such a model is subject to multiple limitations. First, the
hypothesis of direct health selection does not seem to have much explanatory power by itself,
particularly in relation to common complex diseases. Although there is some empirical
evidence for direct health selection, the contribution to the explanation of health inequalities is
generally low as shown in a number of studies (Dahl & Kjӕrsgaard 1993; Lundberg 1991;
Suhrcke & de Paz Nieves 2011; van de Mheen et al. 1999). One reason for this is that most
common complex diseases show up late in life at an age in which processes of social mobility
are sparse (Blane et al. 1993). Thus, associations of socioeconomic status in childhood or
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adolescence (such as indicated by, e.g. parental socioeconomic status or educational attainment)
with health status in adulthood cannot be explained plausibly by direct health selection (Haan
et al. 1989).
Second, the accumulation of disease-related risk alleles in low socioeconomic groups resulting
from selective pressure would be a process occurring over a period of many generations
(Holtzman 2002). Over this period, a persisting social structure mainly formed by health-related
social mobility would be needed, in which risk allele carriers remain in lower socioeconomic
groups independently of their disease status due to lack of educational and other opportunities
of advancement. However, for almost all western countries this has not been the case, as in the
past century upward social mobility was observed significantly more frequent than downward
social mobility (Breen 2004; Erikson & Goldthorpe 1992). Because of this social mobility
which is unlikely to be based on the occurrence of common diseases, an equal distribution of
disease-related risk alleles across socioeconomic strata is much more plausible (Mackenbach
& Howden-Chapman 2003).
Third, in the recent past health inequalities have shown dynamic trends. For instance, only a
few decades ago the prevalence of coronary heart disease was much higher in upper
socioeconomic groups. Today the exact opposite is observed with coronary heart disease
showing a much higher prevalence in lower socioeconomic groups (Marmot & McDowall
1986). This phenomenon does not fit in the idea of unequally distributed risk alleles resulting
from direct health selection.
Fourth, as already described, common complex diseases are assumed to show a polygenic basis
next to various environmental causes. As the involved risk alleles are usually inherited more or
less independently of each other – particularly if they are located on different chromosomes or
connected only by low LD – it is unlikely that all of an individual’s risk alleles will be
transmitted together to its offspring (Holtzman 2002). As for complex diseases single risk
alleles are usually low-penetrant and a certain number of risk alleles may need to be present in
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combination to produce sufficient genetic effects on disease, single risk alleles would be under
no sufficient selective pressure to produce an unequal distribution across socioeconomic strata.
This lack of selective pressure may be even more pronounced if the clinical manifestation of
disease occurs after the main period of reproductive activity.
Taken together, an association of disease-related risk alleles with socioeconomic status would
only be expected if the respective risk allele was highly penetrant and related to an early onset
disease that inhibits upward social mobility and induces downward social mobility not only in
the current social system but also in the social system of many previous generations. With
regard to common diseases none of these conditions is met in the western countries. Thus, there
is hardly any reason to assume differences in disease-related genetic makeup between
socioeconomic groups.
In theory, there might be an exception with monogenic disorders and their disease-causing
mutations. These highly penetrant genetic variants determine conditions which have sometimes
strong effects on life quality and cognitive function early in the life course. As already
described, however, monogenic disorders are very rare and cannot be regarded to contribute
significantly to health inequalities in general.
To date, only a few empirical studies have been published in which disease-related risk alleles
and their associations with indicators of socioeconomic status have been explored. In the study
of Gimeno et al. (2008) the risk alleles of five SNPs of the CRP gene were examined whether
they are associated with different indicators of socioeconomic status (parental education and
parental occupational status in the participants' childhood) in a finish study population of 1,484
participants with marked social inequalities in blood serum C-reactive protein (CRP) levels.
There was no evidence that the respective risk alleles, which significantly contributed to the
blood serum CRP levels in the study population, were unequally distributed across
socioeconomic strata.
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In the study of Holzapfel et al. (2011) two obesity-related SNPs (associated to the TMEM18
and FTO gene, respectively) were under investigation in 12,425 participants from Germany.
The respective risk alleles were significantly associated to body mass index (BMI) and there
was also a reported association of education and income as indicators of socioeconomic status
with BMI. However, there was no evidence for a genetic association of the investigated SNPs
with socioeconomic status in the study population.
In addition, Schmidt et al. (manuscript submitted for publication) observed health inequalities
in diabetes mellitus in a population-based cohort of 4,655 study participants, but no evidence
for an association of a selection of 11 SNPs robustly related to diabetes with education, income
and paternal occupation as indicators of socioeconomic status. They also replicated the results
of Holzapfel et al. (2011) for the FTO gene, which is also related to diabetes through its effect
on BMI.
Single study results do not allow general statements. However, the lack of empirical findings
in combination with the theoretical considerations already described underline that diseaserelated genetic factors are unlikely to play a role in explaining health inequalities of common
complex diseases by supposed genetic differences between socioeconomic groups. It should
also be mentioned that the plausibility of generating null results if investigating socioeconomic
differences in risk alleles may have led to substantial underreporting and there have been
probably a number of null results obtained that did not find their way to publication.
3.3 Indirect health selection
According to indirect health selection health-related social mobility is not initiated directly by
health status, but indirectly caused by determinants of health and disease. It is assumed that
these determinants – acting early in life – serve as predictors for both, future socioeconomic
status as well as health status in adulthood. In the first instance, the vague notion of the word
"determinant" does not define which factors are involved. However, personal attributes such as
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general cognitive ability, dietary habits, coping strategies or body size as a marker for physical
fitness have often been highlighted in previous discussions of indirect health selection (Blane
et al. 1993; West 1991; Wilkinson 1986). According to the hypothesis of indirect health
selection, these personal traits are considered as a third variable which is responsible for the
covariance of health and socioeconomic status.
Thus, genetic factors could be incorporated if they affect both disease and socioeconomic status
without a mediation of genetic effects on socioeconomic status by disease (see Figure 4). In this
context, genetic factors connected to psychological and behavioral traits (e.g., cognitive skills,
the big five personality traits) have been cited (Mackenbach 2005) and their relevance for status
attainment (Conger & Donnellan 2007; Nettle 2003) as well as for health-related behavior has
been discussed (Batty et al. 2009; Gallacher 2008; Marmot & Kivimäki 2009; Nabi et al. 2008).
Figure 4: Incorporation of genetic factors in the hypothesis of indirect health selection for the explanation of health
inequalities (→ causal direction; ---- undirected association; SES, socioeconomic status).
As already stated, status-related psychological and behavioral traits are highly complex and
assumed to show at best a polygenic basis, although robust GWAS findings for traits such as
personality or cognitive skills are lacking to date (see chapter 2.4). If in the explanation of
indirect health selection a high number of low penetrant genetic variants are supposed to
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contribute to status-related as well as health-related behavioral traits, some of the same
limitations have to be considered as described for the impact of genetic factors in direct health
selection (see chapter 3.2).
One of the most cited examples in the examination of indirect health selection is general
cognitive abilities, i.e. intelligence. In the past, empirical evidence on the genetic basis of
intelligence was mainly derived by the already mentioned and criticized twin studies with
heritability estimates ranging from 0.3 to 0.8 depending on study population and age of study
participants (Boomsma et al. 2002; Plomin 2008). However, despite the progress that has been
made recently by GWAS in detecting common genetic variants associated with complex
diseases, to date no genetic variant has been robustly associated with intelligence (Davies et al.
2011; Chabris et al. 2012). This could be either interpreted as evidence for the contribution of
many genetic variants with small effects (Davis et al. 2010) or simply as the misinterpretation
of heritability estimates as evidence for a strong genetic contribution to inter-individual
differences in intelligence.
Likewise, despite some efforts in conducting GWAS for a range of psychiatric disorders such
as schizophrenia, major depression and bipolar disorder which are supposed to affect
socioeconomic status, only a handful of robust findings have been reported to date (Green et al.
2012; Hamshere et al. 2012; Hek et al. 2013; Rietschel et al. 2012; Ripke et al. 2011, 2012;
Steinberg et al. 2012; Wray et al. 2010). These findings consist of genetic variants that are either
low-penetrant or very rare in populations (Collins & Sullivan 2013) and explain only little of
the inter-individual variation in the respective trait (Crow 2011). Because of the lack of
promising findings, it has even been argued that there is no major genetic contribution to
psychological and behavioral traits at all, including psychiatric disorders (Joseph 2012). As
stated by Crow (2011), “there comes a point at which the genetic skeptic can be pardoned the
suggestion that if the genes are so small and so multiple, what they are hardly matters, the
dividing line between polygenes and no genes is of little practical consequence.” Particularly
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in the light of recent GWAS results, asking for the meaning of smaller and smaller genetic
effects is indeed legitimate, whereas the use of genetic factors in prediction models for
individual outcomes (e.g., Heckman 2007) seems to be rather questionable.
Finally, the accumulation of traits such as intelligence in certain socioeconomic groups seems
to be mainly influenced by non-genetic factors (Holtzman 2005) and it should also kept in mind
that status-related psychological and behavioral traits do not only represent a predictor but also
an outcome of socioeconomic status and life circumstances (Nabi et al. 2008). As there have
been no studies reported that have investigated socioeconomic differences in the few risk alleles
for which associations with psychiatric disorders have been detected, there is no reason to
assume that genetic factors play an important role in a framework of indirect health selection.
3.4 Social causation
The hypothesis of social causation is the main explanation in the current debate on health
inequalities. It states that a low socioeconomic status causes disease and not vice versa.
However, an individual’s socioeconomic status does not directly affect the state of health, but
indirectly through socioeconomic differences in relation to material factors, health behaviors
and psychosocial stressors and resources (Mackenbach 2006). Accordingly, population groups
with low socioeconomic status are affected to a greater extent by adverse living and working
conditions and by a more precarious financial situation (Blane 1997). In addition, adverse health
behaviors and psychosocial stress occur to a higher extent in population groups with a low
socioeconomic status (Brunner & Marmot 2006). These different determinants of health
inequalities are not independent of each other, so that e.g. unfavorable material conditions can
act as psychosocial stressors and thus affect health-related behavior (Mackenbach 2006).
With regard to the incorporation of disease-related genetic factors in the explanation of social
causation a genetic contribution to health inequalities in terms of an unequal distribution of risk
alleles across socioeconomic strata also seems to be unlikely. Since both socioeconomic status
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and disease-related risk alleles act independently on disease, no association between
socioeconomic status and risk alleles would be expected (see Figure 5).
Figure 5: Incorporation of genetic factors in the hypothesis of social causation for the explanation of
health inequalities (→ causal direction; SES, socioeconomic status).
In addition, social causation of disease-related genetic variants also seems to be highly
implausible, as the DNA sequence of an individual is already fixed before its socioeconomic
status starts to develop (Mackenbach 2005). Exceptions are somatic DNA mutations that may
be caused by, e.g., occupational exposures such as radiation or chemical mutagens. However,
these somatic mutations are usually not transmitted to the next generation, making diseasecausing DNA mutations unlikely to accumulate in lower socioeconomic groups as a result of
their higher exposure to mutagens. Even if a mutation in the parents DNA would cause disease
in their (not yet conceived or born) offspring, the cause for the disease to occur would still be
the exposure to mutagens and not the genetic mutation itself.
As social causation is currently the main explanation for health inequalities and does also not
allow for the occurrence of disease-related genetic differences across socioeconomic groups, it
is highly unlikely that genetic differences are in any way accountable for health inequalities.
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4. Genetics and health inequalities beyond supposed genetic differences of
socioeconomic groups
4.1 Genetic factors in the life course perspective
The previously presented explanations of health inequalities and the causal directions described
by them can be integrated into a more complex approach: the life course perspective of health
inequalities (Kuh 2003; Lynch & Davey Smith 2005; Davey Smith 2003). Through this, onesided explanations are attenuated and a more adequate consideration of the mechanisms that
lead to health inequalities over the life course is given.
The focus in the life course perspective is on early life stages with increased susceptibility to
adverse social and environmental factors which are supposed to be connected to health
inequalities in adolescence and adulthood. Accordingly, the social inequality in many late onset
diseases as well as early mortality can be understood as the result of a lifelong accumulation of
social, environmental and genetic health risks which are linked by the strong correlation of the
socioeconomic status of successive life stages (Graham 2002). Findings of GWAS such as
genetic variants related to the FTO gene and their effects on BMI have already been considered
in life course studies (Kaakinen et al. 2010).
As described in the following chapters, there are suggested mechanisms that could be
adequately incorporated in life course studies to allow for a moderation of genetic effects by
socioeconomic status without having both factors in competition to each other. This allows for
a more plausible incorporation of genetic factors which is appropriate to the complex network
of health risks and protective factors that impact on health inequalities.
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4.1 Gene–environment interactions
The concept of gene–environment interactions has been extensively evaluated in the
sociological literature (Freese 2008; Guo 2008; North & Martin 2008) and many studies that
have considered candidate genetic variants to estimate the modification of genetic effects on
behavioral outcomes such as antisocial behavior (Kim-Cohen et al. 2006), cognitive function
(Boardman et al. 2012) and educational continuation (Shanahan et al. 2008) by environmental
factors have been published during the last few years. Results of studies that have investigated
candidate genes often lack consistent findings across different study populations and this has
also been the case for candidate gene–environment interaction studies (Kauffmann & Nadif
2010). However, approaches are emerging that give thought to the lessons learned by GWAS
(Thomas 2010). This will probably lead to more robust findings in the future.
With regard to incorporating gene–environment interactions in the explanation of health
inequalities it could be hypothesized that the effects of genetic variants on disease may be not
of the same size or same direction across socioeconomic groups, but modified by
socioeconomic status and its related conditions such as diet, physical activity and exposure to
toxins. Indeed, a few formal genetic studies already suggest that disease variance explained by
genetic factors is decreasing with higher income and educational levels (Johnson & Krueger
2005; Johnson et al. 2010), but gene–environment interaction studies considering the recent
findings of GWAS for complex disease outcomes are sparse.
However, in some studies the modification of genetic effects by factors related to
socioeconomic status has been explored. For example, in the study of Do et al. (2011) effect
modification of chromosome 9p21 variants on cardiovascular disease by dietary intake has been
investigated in two unrelated study samples. Although a consistent effect of 9p21 genetic
variants on cardiovascular disease was observed among individuals who had a low prudent diet,
the effect was diminished among individuals who consumed a more prudent diet.
Socioeconomic measures have not been included in the study. As associations of diet with
22
socioeconomic status have been reported (James et al. 1997; Davey Smith & Brunner 1997),
diet may thus be hypothesized as a mediator of effect modification by socioeconomic.
Despite the lack of replication for many of the results derived by gene–environment studies, the
idea that genetic factors do not operate independently of an individual’s environment is indeed
plausible and evidence for gene–environment interaction is likely to increase if methodological
challenges have been overcome.
4.2 Epigenetics and health inequalities
There has recently been much debate about the potential of the rapidly expanding field of
epigenetics for explaining how social and environmental factors “get under the skin” to produce
health inequalities (Champagne 2010; Hou et al. 2012; Szyf et al. 2008; Toyokawa et al. 2012).
Epigenetic research broadly concerns molecular mechanisms that modify the expression of
genes while leaving the DNA sequence unchanged (Jaenisch & Bird 2003). The most prominent
examples of such epigenetic mechanisms are those related to DNA methylation, histone
modification and non-coding RNA, which play a crucial role in cellular differentiation.
Epigenetic profiles are inherited biologically, but – in contrast to an individual’s genetic
makeup – are subject to environmentally induced changes during the life course and thus not
entirely fixed at birth. Currently, it is assumed that these induced alterations may persist under
certain circumstances across generations (Foley et al. 2009). This would even allow for
approaches to explain health inequalities in subsequent generations without supposing them to
be fixed by shared genetic factors. Finally, epigenetic modification depicts a mechanism
through which gene-environment interactions may be mediated and they represent an important
feature for getting different phenotypes from the same genotype.
Epigenetic alterations have been already linked to several exposures such as smoking (Breitling
et al. 2011) and alcohol (Zhu et al. 2012) which are in turn more prevalent in groups with low
socioeconomic status (Hiscock et al. 2012; Mulia & Karriker-Jaffe 2012). In addition, a wide
23
range of environmental pollutants, e.g. pesticides, air pollution and lead, have shown
associations to epigenetic alterations (Hou et al. 2012) and it has also been hypothesized that
the social environment contributes to epigenetic alterations via psychosocial factors, e.g. stress
and maternal care (Toyokawa et al. 2012).
Alterations of the epigenome may be some of the earliest cellular events in disease onset and
associations with several complex diseases such as cancer, asthma, allergies, diabetes mellitus,
obesity and psychiatric disorders are suspected (Hatchwell & Greally 2007). A broad range of
conditions may therefore have an impact on epigenetic alterations and subsequently on health
conditions.
Since the widely recognized studies on social factors and their effect on DNA methylation in
rodents (Francis et al. 1999; Meaney 2001; Szyf et al. 2005; Weaver et al. 2004), a few studies
in human populations have reported associations of early-life as well as adult socioeconomic
status with different measures of epigenetic variation such as global and genome-wide DNA
methylation patterns (Borghol et al. 2012; McGuinness et al. 2012; Subramanyam et al. 2013;
Tehranifar et al. 2013). Despite the preliminary character of these studies due to their small
sample size and the methodological challenges that have generally to be considered in
epigenetic epidemiology (Relton & Davey Smith 2012; Heijmans & Mill 2012), their results
give support to the hypothesis that socioeconomic status impact through material, behavioral
and psychosocial factors on epigenetic alteration during the life course to contribute to health
inequalities. Thus, differences in epigenetic profiles driven by unequally distributed
environmental factors may provide the means to explain how socioeconomic determinants
impact on disease etiology without assuming genetic differences between socioeconomic
groups. However, reliable results derived by large prospective and population-based studies
that link socioeconomic status to epigenetic alteration are needed.
24
5. Conclusions
By evaluating the results of recent research in genetic epidemiology and linking them to
explanations of health inequalities it has been shown that it is by no means plausible to expect
genetic differences – whether regarding health-related genetic factors or supposed genetic
factors of behavioral traits – between socioeconomic groups, as sometimes claimed (Lundborg
& Stenberg 2010). To date, there has been no empirical evidence for socioeconomic differences
in known risk alleles such as those recently derived by GWAS.
In contrast to the implausibility of genetic differences as explanation for health inequalities,
gene–environment interaction seems to be a more promising approach to adequately
incorporate genetic factors in inequality research. The shift from supposing genetic differences
as responsible for social outcomes to the more plausible approach of environmental
modification of genetic effects is crucial in understanding the role of genetics in (health)
inequalities. The individual genetic makeup that is fixed at conception does not determine future
socioeconomic status, behavior or abilities, but environments do indeed have a strong impact.
Since genetic risks seem to be equally distributed across socioeconomic groups, detrimental
environments are not. Thus, first implications of research on gene–environment interaction and
epigenetic alteration give support to the long-standing demand of reducing health inequalities
by improving environments of socioeconomic disadvantaged groups through an enhancement
of material circumstances and their psychosocial and behavioral consequences independently
of the individual genetic makeup.
As large GWAS consortia will probably investigate socioeconomic outcomes in the near future,
enhanced statistical power will be achieved that allows for the detection of even tiny differences
in allele frequencies between population subgroups. Due to region-specific differences in the
assessment of socioeconomic indicators as well as cultural differences in their meaning, the
probability of false positive findings is increasing in international consortia that consist of
diverse populations. In addition, as the likelihood of spurious associations is increasing with a
25
decrease in the strength of association (Ioannidis 2005), any findings need to be interpreted
with the utmost caution. A sufficient answer to the question of how such results could be at all
interpreted in a meaningful way is still lacking.
By overemphasizing genetic factors and their role in explaining socioeconomic outcomes,
researchers, health care providers and policy makers may be detracted from approaching the
sources that are much more likely to contribute to health inequalities (Burn et al. 2001;
Holtzman 2006). As research on health inequalities has shown, successful intervention
strategies to improve life circumstances of deprived population groups are probably the best
way for tackling health inequalities (Mackenbach & Stronks 2002; Marmot 2010; Swedish
National Committee for Public Health 2001). In contrast, it remains unclear how information
on an individual’s genetic makeup could be in any way helpful to reduce health inequalities.
For further exploring the role of genetic factors in health inequalities beyond supposed genetic
differences of socioeconomic groups, the life course perspective may be a suitable theoretical
framework. Genetic effects that may be modified and mediated by environmental factors could
be adequately considered in this approach. It is to be hoped, that further advancements in the
methods needed for the investigation of such complex mechanisms as gene–environment
interaction will shed more light on the dynamic network of contributing factors that play a role
in the explanation of health inequalities.
26
6. Summary
The supposed relationship between genetic factors and social traits has been discussed since the
beginning of the 20th century. In the light of new epidemiological methods such as genomewide association studies (GWAS) which are used to establish genetic associations to diseaserelated traits, questions have also been asked about the interplay between genetics, health and
socioeconomic status with regard to social inequalities in health.
A contribution of genetic factors to health inequalities in terms of genetic differences between
socioeconomic groups would be in effect, if there was an association of socioeconomic status
with specific genetic variants that are related to disease, either directly or indirectly. After
evaluating the plausibility of such associations by incorporating genetic factors in basic
explanations of health inequalities (i.e., social causation, direct and indirect health selection),
there is hardly any reason to assume socioeconomic differences in risk allele frequency with
regard to common diseases (e.g., diabetes mellitus, cardiovascular disease, cancer) or other
complex traits (e.g, cognitive abilities, personality, psychiatric disorders) which are next to
environmental factors supposed to be influenced by a large number of genetic variants with
only small effects.
There are already a few empirical studies published that have investigated risk allele
frequencies in socioeconomic groups without finding any evidence for an unequal distribution.
Ever increasing study populations may produce findings of tiny socioeconomic differences in
allele frequencies in the future, however, a sufficient answer to the question of how such results
could be at all interpreted in a meaningful way is still lacking.
In contrast, first implications of research on gene–environment interaction and epigenetic
alteration give support to the long-standing demand of reducing health inequalities by
improving environments of socioeconomic disadvantaged groups through an enhancement of
material circumstances and their psychosocial and behavioral consequences independently of
the individual genetic makeup.
27
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