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The Genetic Basis of Chronic Mountain Sickness
Roy Ronen, Dan Zhou, Vineet Bafna and Gabriel G. Haddad
Physiology 29:403-412, 2014. doi:10.1152/physiol.00008.2014
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PHYSIOLOGY 29: 403– 412, 2014; doi:10.1152/physiol.00008.2014
Roy Ronen,1 Dan Zhou,2
Vineet Bafna,3* and
Gabriel G. Haddad2,4,5*
The Genetic Basis of Chronic Mountain
Sickness
1
Chronic mountain sickness (CMS) is a disease that affects many high-altitude
dwellers, particularly in the Andean Mountains in South America. The hallmark
symptom of CMS is polycythemia, which causes increased risk of pulmonary
hypertension and stroke (among other symptoms). A prevailing hypothesis in
Bioinformatics & Systems Biology Graduate Program,
University of California San Diego, La Jolla, California;
2
Department of Pediatrics, Division of Respiratory Medicine,
University of California San Diego, La Jolla, California;
3
Department of Computer Science and Engineering, University
of California San Diego, La Jolla, California; 4Department of
Neurosciences, University of California San Diego, La Jolla,
California; and 5Rady Children’s Hospital, San Diego, California
*V. Bafna and G. G. Haddad contributed equally to this work.
high-altitude medicine is that CMS results from a population-specific “maladaptation” to the hypoxic conditions at high altitude. In contrast, the prevalence of CMS is very low in other high-altitude populations (e.g., Tibetans and
Ethiopians), which are seemingly well adapted to hypoxia. In recent years,
concurrent with the advent of genomic technologies, several studies have
investigated the genetic basis of adaptation to altitude. These studies have
identified several candidate genes that may underlie the adaptation, or maladaptation. Interestingly, some of these genes are targeted by known drugs,
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raising the possibility of new treatments for CMS and other ischemic diseases.
We review recent discoveries, alongside the methodologies used to obtain
them, and outline some of the challenges remaining in the field.
More than 140 million people around the globe
reside at high altitude (ⱖ3,000 m) in locations such
as the Ethiopian Highlands in Africa, the Himalaya Mountains in Asia, and the Andes Mountain
Range in South America. Although there is no
doubt that the elevation in these regions represents stressful environmental conditions, chiefly
due to low environmental O2, adaptation of highaltitude dwellers has varied qualitatively and
quantitatively. For example, a higher hemoglobin
concentration and lower oxygen saturation have
been observed in Andean highlanders compared
with Tibetans or Ethiopians at similar altitude (3,
5). Furthermore, infants of Tibetans have a higher
birth weight and arterial O2 saturation compared
with infants of other populations (Han Chinese) at
similar altitudes (3). In fact, a sizeable percentage
of individuals in these populations (as much as
16% in certain regions, especially males) are maladapted and are thus threatened by the low levels
of inspired O2 to this day (31). The primary manifestation of this maladaptation to high altitudes is
chronic mountain sickness (CMS) or Monge’s disease, first described by Carlos Monge in the Andes
in 1925 (29). It is characterized by polycythemia
(hematocrit ⬎ 65%) and hypoxemia (O2 ⬍ 85%),
both of which improve upon descent from altitude.
The most frequent symptoms and signs of CMS are
headache, dizziness, breathlessness, palpitations,
sleep disturbance, mental fatigue, and confusion
1548-9213/14 ©2014 Int. Union Physiol. Sci./Am. Physiol. Soc.
(30). People affected by CMS often suffer from
stroke and myocardial infarction in early adulthood, mostly due to increased blood viscosity
and tissue hypoxia. Why some are affected by
this disease and not others has so far been a
mystery. Differences in adaptation patterns between the high-altitude populations suggest that
there are, at least in part, distinct genetic mechanisms underlying the adaptations.
Despite this genetic basis having been proposed
for many years, it is only in the past few years that
our understanding of human adaptation to high
altitude has accelerated (1, 6, 22, 34, 37, 39, 42, 46,
47, 51). These rapid discoveries have mostly resulted from the advent of genomic technologies,
particularly deep sequencing, as well as the concurrent developments in computational genetics.
Understanding the molecular basis of high-altitude
adaptation and maladaptation will also provide us
with a unique handle on genes that are important
for human health and disease. This is particularly
true of conditions where oxygen deprivation plays
a major etiological role, such as arterial pulmonary
hypertension, myocardial ischemia and injury,
stroke, and polycythemia (to name a few). It is
possible that learning about regions of the human
genome that evolved over many thousands of years
to allow adaptation to hypoxia will lead to a better
understanding of adaptation in humans subjected
to other stresses.
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Table 1. Genes identified via association with a hypoxia-tolerance phenotype
Study
Phenotype
Population(s)
Assay
Gene Candidates
Simonson et al. (39)
Hb
Tibet (n ⫽ 31)
EPAS1, PPARA
Yi et al. (47)
Hb, erythrocyte
Tibet (n ⫽ 50)
Beall et al. (6)
Hb
Scheinfeldt et al. (37)
Hb
Tibet (n ⫽ 70)
Tibet (n ⫽ 91)
Ethiopia (n ⫽ 42)
Alkorta-Aranburu et al. (1)
Hb, O2 saturation
Genotyping array
(Affymetrix 6.0)
Exome sequence
(NimbleGen)
Genotyping array
(Illumina 0.5M)
Genotyping array
(Illumina 1M)
Genotyping array
(Illumina)
Ethiopia (n ⫽ 260)
Amhara (102H, 60L)
Oromo (63H, 35L)
EPAS1
EPAS1
ARNT2, THRB
RORA*COL6A1*SLC30A9* HGF*
*Not genome-wide significant after multiple testing correction. H and L, high- and low-altitude individuals, respectively.
A Genomic Approach to
Understanding Chronic Mountain
Sickness
Adaptation to hypoxia (as well as the diagnosis of
CMS) is often measured using related phenotypes,
such as blood oxygen saturation, Hb, or hematocrit
levels (5, 30). From available data, we can surmise
that the different populations all adapted separately: their phenotypic values show distinct inherited traits. It is possible that a founder effect may
have been at work when the populations first migrated, and standing genetic variation in the
founders was subjected to selective constraints
leading to this variable adaptation. Although most
high-altitude populations in the world are welladapted, in some regions (e.g., the Andes), 10 –20%
of the male population is threatened by the CMS
syndrome (31). Such populations provide us with a
unique opportunity to study the nature of adaptation by contrasting the genetics of well adapted
individuals and those with CMS (51). CMS is believed to arise, at least partly, due to an excessive
production of red blood cells (RBC). By increasing
RBC production, humans attempt to mitigate the
effect of low environmental oxygen via increased
oxygen carrying capacity in the blood. Hence, CMS
is considered an adaptation to life under chronic
hypoxia at altitude. Indeed, sea-level-dwelling humans visiting high altitudes show a similar, albeit
more modest, response. However, a long-term
consequence of this increase in RBC production is
increased blood viscosity, leading to vascular
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sludging and increasing the likelihood of vascular
occlusion, stroke, myocardial ischemia, and infarcts in early adulthood. It also results in uneven
blood flow through the lungs, increasing the ventilation-perfusion mismatch and leading to hypoxemia, contrary effects to what is desired. In trying
to adapt, the organism has effectively responded in
a way that is adverse to its survival and well being
(mal-adaptation) at high altitude (51). Therefore,
the genetic basis for CMS can be investigated in the
context of adaptation to hypoxic environments at
high altitudes, and our survey keeps that perspective. FIGURE 1A describes a generic strategy that is
implicit in many highlander studies. The first steps
of this strategy, correlating genotypes and phenotypes and/or scanning the genome for signatures
of natural selection, reveal candidate genes. Due to
many factors that may confound this step, candidate genes must then be validated using various
approaches. Our review focuses on these two steps,
where current studies suggest a complex, multigenic adaptation.
Genome-Wide Association Studies and
Disease Traits
Association tests measure correlation between genotype segregation and a phenotype, and can be
applied to any of the many hypoxia-related phenotypes (e.g., Hb levels). Different studies have
explored this with different designs involving the
choice of phenotype, population (mainly Tibetan,
Ethiopian, and Andean highlanders), genotyping
technology, and statistical methods. It was recognized early on that there are population-specific
differences in highlander phenotypes. For instance, Tibetan highlanders have lower Hb levels
but also lower O2 saturation levels compared with
Andeans (3). Hanaoka et al. (18) showed that serum erythropoietin (EPO) levels in Sherpas at 3,440
m was equal to that in non-Sherpas at a much
lower altitude, indicating that Sherpas have a resistance response to EPO levels. Moore (30) and
Beall (4, 5, 7) review many relevant traits across
highlander populations, including hematocrit and
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In this review, we highlight several recent studies
(including ours) dealing with the genetic underpinnings of high-altitude adaptation or mal-adaptation. CMS is a maladaptation to high altitude, and
any understanding of CMS will likely shed light
on relevant genetic and physiological mechanisms. We evaluate the methods used in previous
studies and the results obtained. In addition, we
highlight some of the remaining open questions
in the field.
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FIGURE 1. Proposed workflow for hypoxia-related therapeutics and schematic genealogical tree
A: a proposed workflow for hypoxia related therapeutics, starting with genetic samples and ending with candidate therapeutic targets. B: schematic genealogical tree illustrating the evolution of a non-recombining genomic fragment across three
populations, one of which migrates to high altitude (HA population) and undergoes genetic adaptation, whereas the others
remain at low altitudes (LA and Outgroup populations). The bottom of the tree (leaves) represents individuals sampled from
the current generation, whereas the upper sections reflect the past genealogy. In the HA population, hypoxia imposes positive natural selection on the beneficial allele (blue star), increasing its frequency (in the non-CMS group) at the expense of
individuals carrying the maladapted allele (CMS). As long as phenotypic variation persists in the adaptive trait (e.g., Hb levels
are still variable in the HA population, meaning the selective sweep is ongoing), genetic association may find variants associated with the trait. However, after the trait reaches fixation or given small effect sizes and/or smaller cohorts, genome-wide
association (GWA) is unlikely to reveal the adaptive genes. Neutrality tests can be used to pinpoint genomic regions under
selection in both settings (i.e., pre- and postfixation, and given a smaller sample). These tests utilize properties of the genealogical tree. The LSBL/PBS tests approximate the branch length leading to the MRCA of the HA population, which is unusually high in regions under selection (see long branch with blue SNPs). Tajima’s ␲ uses the mean allelic heterogeneity, which
is unusually low in regions under selection (since HA individuals are genetically similar given their relatively recent MRCA).
The iHS/EHH tests use haplotype homozygosity, which is unusually high and spans longer regions under selection (most variation in HA individuals, shown as SNPs on the path from MRCA to the present HA individuals, is common to the entire HA
sample). Common practice is to genotype a population sample, followed by imputation from a nearby, and densely sequenced, reference population (e.g., the LA population). Because imputation relies on conserved linkage disequilibrium (LD)
between target and reference populations, and LD is strongly altered by selective sweeps, imputation will be inaccurate in
regions evolving under strong selection. This further illustrates the importance of WGS. MCRA, most recent common
ancestor.
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zoomed in on EPAS1. Yi et al. (47) focused on genes
annotated by the ontology term “response to hypoxia.” Beall et al. (6) looked carefully at sites
around the EPAS1 gene and validated them
through independent cohorts. Scheinfeldt et al.
(37) and Alkorta-Aranburu et al. (1) studied Ethiopian highlanders and reported many interesting
candidate genes; however, their results do not
achieve genome-wide significance after Bonferroni
correction for any SNPs located near genes. Yet,
they validated through secondary means and identified a number of novel genes, suggesting a very
different adaptive response in Ethiopians compared with Tibetan highlanders. In summary, association tests (particularly with ascertained SNPs)
must be applied to larger populations, validated on
independent cohorts, or focused on a reduced set
of candidate regions. A viable approach to identifying an unbiased list of candidate genes is through
searching for genomic signatures of natural selection, discussed next.
Natural Selection and Disease Pathogenesis
Given the strong selective constraints stemming
from low environmental oxygen, identifying genetic signatures of natural selection in highlander
populations provides us with an alternative approach to genotype/phenotype association for
candidate gene detection. FIGURE 1B provides a
schematic of the evolutionary history of a short
(non-recombining) chromosomal segment under
positive selection. The bottom (“leaves”) of the
evolutionary tree corresponds to the genomic
region in extant individuals of different subpopulations, whereas the top (“root”) of the tree represents the region in the most recent common
ancestor (MRCA). Mutations (green or blue circles)
on a specific lineage are inherited by all its descendants. The highlander (HA) migration exerts a
selective constraint. Consequently, individuals carrying a beneficial allele (blue star) rise rapidly in
frequency in the population, outcompeting other
individuals. These individuals 1) present the nonCMS phenotype; 2) have a recent common ancestor, with all other non-CMS individuals sharing a
longer than average branch of common mutations
(i.e., blue circles); and 3) have had limited time to
differentiate (i.e., few mutation and recombination
events), leading to a lack of allelic diversity and
long homogenous haplotypes in the region. Statistical tests capturing these characteristics have been
used to identify regions under selection (Table 2).
For example, LSBL/PBS (38, 47) and FST (21) were
used by Yi et al. (47), Bigham et al. (9), AlkortaAranburu et al. (1), Zhou et al. (51), and Udpa et al.
(42) to approximate the branch length leading to
MRCA. Zhou et al. (51) and Udpa et al. (42) used multiple tests to measure allelic diversity (or lack thereof).
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hemoglobin levels, O2 saturation, arterial O2 content, ventilatory response to hypoxia, exhaled NO,
and pulmonary vasoconstriction. In our context,
individuals with and without CMS can be used in a
case-control association study, with the caveat that
there may be significant population substructure
[see Figure S3 of Zhou et al. (51)]. Most association
studies to date have focused on Hb levels due to
ease of measurement and its correlation with other
traits of interest. In a typical genome-wide association study (GWAS), the association between sampled genotypes and a specific trait is measured in
case-control cohorts using statistical tests (see Table 1). The merits of different statistical tests have
been previously reviewed (23, 45) and will not be
discussed here.
In the domain of genomic technologies, we now
have a multitude of options, including candidate
gene sequencing, SNP genotyping arrays, whole
exome sequencing (WES), and whole genome sequencing (WGS). Each of these options has difficult
trade-offs, arguably making this the single most
important decision in the study-design process.
For example, genotyping arrays sample a large corpus (up to several million) of genomic loci but suffer
from a serious problem of ascertainment bias [recently summarized by Lachance and Tishkoff (24)
and illustrated schematically in FIGURE 1B]. Normally, conserved haplotype structure between populations implies that, even if only a small collection of
SNPs is sampled (e.g., by array), most alleles can be
inferred from a previously sequenced reference population (10). Although generally accurate, this strategy (genotype imputation) may fail in genomic
regions affected by strong selection (blue dots in
FIGURE 1B), since the haplotype structure is highly
sensitive to selective sweeps and is expected to diverge in such regions. Although some [e.g., Yi et al.
(47)] have used whole exome sequencing to overcome such issues, this technology does not sample
the vast noncoding portions of the genome, including regulatory and many noncoding RNA regions.
Indeed, one of the most important sites discovered to
date was in an intron of the EPAS1 gene not specifically targeted by the exon array.
A second problem, unrelated to the technology
domain, is the reduced power stemming from
the multigenic response to hypoxia. As with
many other complex traits, most published GWA
studies to date have failed to achieve genomewide significance after multiple testing correction. Finally, adaptive alleles fix in the population,
reducing phenotypic variation in their respective
traits, and consequently reducing the power of association testing. Therefore, researchers have focused on candidate genes with known physiology
(mainly HIF pathway genes; see Table 1). Simonson et al. (39) used a short list of five regions and
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Table 2. Genes identified via tests of selection
Study
Population(s)
Assay
Bigham et al.* (9)
Tibet (n ⫽ 49)
Andes (n ⫽ 49)
Tibet (n ⫽ 31)
Genotyping array (Affymetrix 6.0)
Gene Candidates
*Complete gene set not listed due to space limitations.
Simonson et al. (39) and Scheinfeldt et al. (37) used the
iHS test (36, 44) to compute the decay of haplotype
similarity. These tests may be confounded by recent
admixture of populations, which can also lead to lack of
allelic diversity in some cases. Recently, an admixturecorrection was proposed by Huerta-Sanchez et al. (22)
to refine the analysis.
Taken together, these studies reveal the complexity of understanding hypoxia adaptation. On
one hand, small sample sizes, low effect, and reduced phenotypic diversity (after fixation of a
beneficial allele) make it difficult to achieve genome-wide significance in association studies. Indeed, with the exception of the EPAS1 genes in
Tibetans, few genes have been identified with genome-wide significance. On the other hand, recent
population admixture and complex demographic
histories may confound tests of selection. We believe that, with further refinement, both methodologies will likely yield additional insights.
Another major issue is the choice of genetic assaying technology. Genotype arrays are designed to exploit haplotype blocks by directly assaying only select
SNPs from the existing variation (so-called “tag
SNPs”), and then using imputation (10) to fill in missing genotypes. Yet, Udpa et al. (42) and Zhou et al.
(51) demonstrated that highlander populations may
have a different haplotype structure specifically in
areas under selection, and that many regions with
strong signature of selection would be missed
by genotype arrays or exome sequencing (FIGURE 2).
Instead, these studies used WGS from small population samples of highlanders. In a study of Andean
highlanders with CMS and non-CMS phenotypes,
Zhou et al. (51) identified 11 regions genome-wide
with significant haplotype frequency differentials between the CMS and non-CMS individuals, which are
consistent with selective sweeps. Two distinct regions contained genes that had fly orthologs and
could be validated in a model organism system (an
erythropoiesis regulator, SENP1, and an oncogene,
ANP32D). These studies illustrate the potential of
whole genome sequencing in identifying the genetic
basis of CMS and long-term hypoxia adaptation in
general. Although the studies by Zhou et al. (51) and
Udpa et al. (42) illustrate the advantages of WGS, it is
currently too expensive for use on large cohorts.
Therefore, a cost-effective design may be created using tests of selection on WGS of a smaller cohort,
followed by genotyping and association tests on a
larger cohort. The identified genes would be candidates for secondary validation, followed by functional testing.
From Association to Causative
Genes and Pathways
Independent Cohorts
Most highlander studies to date are not powered to
achieve genome-wide significance for association,
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Tibet: EGLN1, EPAS1 Andes: EGLN1,
TH, NOS2A, PRKAA1
Simonson et al. (39)
Genotyping array (Affymetrix 6.0)
EPAS1, EGLN1, CYP2E1, EDNRA,
ANGPTL4, CAMK2D, HMOX2,
CYP17A1, PPARA, PTEN
Yi et al.* (47)
Tibet (n ⫽ 50)
Exome sequence (NimbleGen)
EPAS1, HBB, HBG2, FANCA, PKLR,
HFE
Beall et al. (6)
Tibet (n ⫽ 35)
Illumina Quad (0.5M)
EPAS1
Xu et al. (46)
Tibet (n ⫽ 46)
Genotyping array (Affymetrix 6.0)
EPAS1, EGLN1
Peng et al.* (34)
Tibet (n ⫽ 1334)
Genotyping array (Affymetrix 6.0)
EPAS1, EGLN1
Scheinfeldt et al.* (37)
Ethiopia
Genotyping array
CBARA1, ARHGAP15, RNF216,
Amhara HA (n ⫽ 28)
(Illumina 1M)
SYNJ2, NAT2, AIMP1, VAV3, ARNT2,
Aari/Hamer LA (n ⫽ 19)
THRB
Alkorta-Aranburu et al.* (1) Ethiopia (n ⫽ 260)
Genotyping array (Illumina)
CUL3, ADRBK1, CORO1B, ASF1B,
Amhara (102H, 60L)
MAPKAPK2, ADH6, SLC30A9,
Oromo (63H, 35L)
TMEM33
Huerta-Sanchez et al.* (22) Ethiopia
Genotyping array (Illumina Omni 1M) BHLHE41, CASP1, SMURF2
Tigray-Amhara (n ⫽ 47)
Oromo (n ⫽21)
Zhou et al. (51)
Andes
Whole genome sequencing (Illumina) SENP1, ANP32D
CMS (n ⫽ 10)
non-CMS (n ⫽ 10)
Udpa et al. (42)
Ethiopia
Whole genome sequencing (Illumina) CIC, LIPE, PAFAH1B3, EDNRB
Amhara (n ⫽ 7)
Oromo (n ⫽ 6)
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even when large population cohorts have been
used [e.g., Peng et al. (34)]. Therefore, many studies
seek to replicate their findings on independent
cohorts from the same population. Good examples
of this are the EPAS1 and EGLN1 genes, which have
been shown repeatedly as important for hypoxia
adaptation in Tibetans, pointing to the essential
role of the HIF pathway. Interestingly, these genes
appear to play a less substantial role in Andeans
and have not been observed in Ethiopians (see
Tables 1 and 2). In general, even genes that appear
repeatedly in multiple highlander cohorts require
further investigation to elucidate their specific biological role.
Model Organisms
To validate the effects of genes found via genomic
scans for association or selection, animal models
may be of great value (42, 51). In the context of
CMS, the idea is to determine the functional impact of genetic variants identified as potentially
significant in both affected (CMS) and unaffected
(non-CMS) individuals. Drosophila melanogaster
provides a powerful in vivo model to dissect the
genetic mechanisms that contribute to human disease (8, 15, 33), including aging (17, 28), neurological and cardiac disease (13, 25, 27), cancer (35, 43),
and the mechanisms underlying hypoxia tolerance
or susceptibility (2, 50, 52). Several genes obtained
from human studies of high-altitude adaptation
(and maladaptation) have orthologs in the Drosophila genome and could thus be tested for effects
on hypoxia tolerance. For example, Zhou et al. (51)
observed a dramatic increase in survival when the
expression of candidate genes SENP1 and ANP32D
(obtained from genomic tests of selection) was reduced in flies under hypoxic conditions, indicating
a likely role for these genes in human adaptation to
high altitude (see FIGURE 3). Indeed, SENP1 is
known to regulate erythropoiesis, and Senp1⫺/⫺
mice die of anemia in early life. If CMS pathogenesis
is even partially caused by abnormal polycythemia,
then SENP1 may be of prime importance, potentially
linking erythropoiesis to the pathogenesis of CMS.
Although to the best of our knowledge the model
organism work done by Zhou et al. (51) and Udpa
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FIGURE 2. The effects of sequence assay on genome-wide scans for selection
A and B: test statistic values on chromosome 19, when taking into account all variants discovered by WGS (A) or only the subset
found in a common ⬃1M SNP genotyping array (B) (1% FDR computed separately based on the genome-wide distribution of test
statistic values). Highlighted in green is 1 of the 11 significant peaks reported in our laboratory’s study (51), which does not exceed
the 1% FDR using only genotype data. C and D: SNP frequency profiles of the highlighted (green) region in non-CMS (blue) compared with MXL (brown, inverted) showing all variants from WGS (C) or only the subset present in genotyping (D). WGS reveals
many variants in the region, allowing a robust estimate of the allele frequency distribution, whereas genotyping detects only a
handful of alleles, making inference of adaptive evolution difficult. However, genotyping studies in large populations can be used
to validate the frequency differences obtained via WGS of smaller cohorts. Figure adapted from Zhou et al. (51), with permission
from Elsevier.
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et al. (42) represents the only such work to validate
results from studies of genetic variation in highaltitude human populations, other models have
also been used in studying acute or chronic hypoxic stress (20, 40, 49). Zebrafish and C. elegans
have been used, mostly to dissect the genetic basis
of response to hypoxic stress and genetic predisposition. These model systems could be very useful
to corroborate findings from human studies where
cellular and molecular studies are difficult to
perform.
In Vitro Models
Naively, one might expect that similar adaptive
genes should surface among populations experiencing similar selective stresses. To some extent
this is true, as is the case for EGLN1 that exhibits
a strong signature of selection in both the Andean and the Tibetan populations (see Table 2).
Yet, a striking observation from Tables 1 and 2 is
the relative lack of overlap in candidate adaptive
genes among the different populations. Partially,
this may result from technical limitations in the
respective studies (meaning the true overlap may
be greater than currently observed). Nevertheless, we believe the small overlap stems chiefly
from the structure and connectivity of the underlying genetic networks. Differently put, although different genes are involved across
different populations, it is plausible that similar
mechanisms and/or pathways are at play. For
instance, different loss of function (LOF) mutations in critical genes in a pathway, or different
mutations disrupting regulatory sites of such
genes, may suffice to mediate the chronic hypoxia response.
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Another possible approach for validation of candidate genes obtained from statistical tests of association or selection is to study the relevant
phenotypic effects in vitro. Zhou et al. (51) determined the expression levels of candidate genes in
fibroblasts (obtained from skin biopsies) placed
under decreasing levels of O2. Furthermore, a similar approach can be applied to reprogrammed iPS
cells. This can be very useful toward replicating the
disease in a dish, as has been done before in other
cases (11). As shown by Zhou et al. (51), such in
vitro models are able to capture aspects of CMS. In
this way, we may better understand the effect of
genetic variants on phenotype in the condition
when the disease is manifested.
Functional Networks
In FIGURE 4, we show a genetic network constructed by GeneMania (32) from the genes reported
in the studies appearing in Tables 1 and 2. The
FIGURE 3. Experimental validation in a model system of candidate genes for human
high-altitude adaptation
Downregulation of human SENP1 and ANP32D orthologs in Drosophila enhances survival under hypoxia. The da-Gal4 driver was used to ubiquitously knock down the individual candidate genes by
crossing with respective UAS-RNAi lines. Eclosion rates were then measured at 21% and 5% O2.
A: significant increase in eclosion rate under 5% O2 in three RNAi lines targeting the same human
SENP1 ortholog (CG32110). B: the differences in eclosion rates were also significant in the two lines
targeting the human ANP32D ortholog (Mapmodulin). Each bar represents mean 5 SE of eclosion
rate. The w1118 and da-Gal4 stocks were tested and used as background controls. Figure adapted
from Zhou et al. (51), with permission from Elsevier.
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network includes connections representing physical
(protein-protein) interactions as well as known pathways. GeneMania reports a statistically significant
enrichment of several relevant biological processes,
including response to hypoxia (FDR ⫽ 2.28 ⫻ 10⫺6),
blood circulation (FDR ⫽ 2.06 ⫻ 10⫺3), endothelial
cell proliferation (FDR ⫽ 4.76 ⫻ 10⫺3), and response to oxidative stress (FDR ⫽ 9.98 ⫻ 10⫺3). In
addition, we note that to a certain extent the network segregates into components that correspond
to underlying physiological processes. Importantly, genes observed across different populations
are often present in the same component, further
supporting the hypothesis of similarity at the process level.
Future Directions
Although there is little doubt that genetic factors
underlie human adaptation to high altitude, there
is a paucity of investigations into the role of epigenetics in these adaptations. There is reason to suspect that the harsh environmental stress at high
altitudes may cause changes in DNA methylation
or histone modification. Indeed, a study by Hartley
et al. (19) showed experimentally that major epigenetic changes occur in a culture system using
primary neurons. Moreover, preliminary experiments from our laboratory have recently demonstrated that even a short period of hypoxia induces
many changes in DNA methylation that last for
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FIGURE 4. GeneMania (32) network constructed from candidate genes for adaptation to hypoxia
The network contains two types of edges: physical interaction (red) and known pathways (blue), and includes genes from Tables 1 and 2 (green,
blue, and yellow circles), and additional genes with direct connections (gray circles). Genes with no connecting edges are not shown. Genes are
shaded according to the geographical region in which they were identified. Note that many genes from the hypoxia response pathway are directly
implicated in multiple populations. The hypoxia response directly affects metabolism. Specifically, the transcription factor HIF1A also upregulates
Angiopoietin-like protein 4 (26), which in turn regulates the PPAR-dependent expression of LIPE. Other genes impacted by hypoxia involve the vascular system, such as the vasoconstrictor EDNRB, and MAP kinase 2, which influences pulmonary vascular permeability (12). The Fanconi anemia
complex genes [which also complex with Spectrin (SPTA)] are key members of a DNA repair pathway that are regulated by hypoxic stress. In addition, the FANCG gene interacts with cytochrome P450 protein CYP2E1 (14). Together, the studies demonstrate the complex, multi-locus adaptation
to hypoxia achieved by different populations.
410
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This work was supported in part by National Institutes of
Health (NIH) Grant 1P01 HL-098053 to G.G.H., NSF grant
CCF-1115206 to V.B., and NIH grants U54-HL-108460 and
1P01 HD-070494 to V.B. V.B. was also supported in part by
NIH Grant 5RO1-HG-004962.
No conflicts of interest, financial or otherwise, are declared by the author(s).
Author contributions: R.R. and D.Z. performed experiments; R.R., D.Z., V.B., and G.G.H. analyzed data; R.R.,
D.Z., V.B., and G.G.H. interpreted results of experiments;
R.R., D.Z., and V.B. prepared figures; R.R., D.Z., V.B., and
G.G.H. drafted manuscript; R.R., D.Z., V.B., and G.G.H.
edited and revised manuscript; R.R., D.Z., V.B., and G.G.H.
approved final version of manuscript; D.Z., V.B., and
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