The genetic basis of chronic mountain sickness

Abstract

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 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, 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.

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 π 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.

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.

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.

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.