Whole-Genome Sequencing Uncovers the Genetic Basis of Chronic Mountain Sickness in Andean Highlanders  Dan Zhou, Nitin Udpa, Roy Ronen, Tsering Stobdan,

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Genetics of Chronic Mountain Sickness in Andean Highlanders Nov. 2, 2013 Roy Ronen University of California, San Diego.
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Whole-Genome Sequencing Uncovers the Genetic Basis of Chronic Mountain Sickness in Andean Highlanders  Dan Zhou, Nitin Udpa, Roy Ronen, Tsering Stobdan, Junbin Liang, Otto Appenzeller, Huiwen W. Zhao, Yi Yin, Yuanping Du, Lixia Guo, Rui Cao, Yu Wang, Xin Jin, Chen Huang, Wenlong Jia, Dandan Cao, Guangwu Guo, Jorge L. Gamboa, Francisco Villafuerte, David Callacondo, Jin Xue, Siqi Liu, Kelly A. Frazer, Yingrui Li, Vineet Bafna, Gabriel G. Haddad  The American Journal of Human Genetics  Volume 93, Issue 3, Pages 452-462 (September 2013) DOI: 10.1016/j.ajhg.2013.07.011 Copyright © 2013 The American Society of Human Genetics Terms and Conditions

Figure 1 Profile of the Only Two Candidate Regions that Are Significant in Non-CMS versus CMS and Non-CMS versus MXL Tests One of the statistics in which both of these regions are significant (Sπ,MXL) is plotted across chromosome 12. Five distinct regions exceed the 0.1% FDR threshold–the two highlighted in light blue do not have a major frequency differential between the non-CMS and MXL populations, whereas the one highlighted in pink is similar in other controls. The remaining two are considered prioritized and highlighted in green. The SNP frequencies in the area encompassing these two, part of q13.11, are plotted in the middle. In this plot, the two prioritized regions are highlighted in white, and other regions are shaded in gray. As can be seen, in both regions, there is an almost complete fixation of a haplotype in the non-CMS population that is at a much lower frequency in all lowlander and maladapted controls. The American Journal of Human Genetics 2013 93, 452-462DOI: (10.1016/j.ajhg.2013.07.011) Copyright © 2013 The American Society of Human Genetics Terms and Conditions

Figure 2 Hypoxia Response of Top Candidate Genes in Non-CMS and CMS Cells Fibroblast cells were derived from skin biopsies obtained from the subjects with and without CMS. Two non-CMS and two CMS cell lines were treated with 1.5% O2 for 24 hr. The expression levels of SENP1, ANP32D, and PFKM were measured by quantitative real-time PCR. Compared to the normoxia controls (represented by the green line), hypoxia treatment induced a significant downregulation of SENP1, ANP32D, and PFKM in non-CMS cells (blue bars). In contrast, hypoxia treatment upregulated the expression of SENP1 and ANP32D in the CMS cells (red bars), an opposite effect to the changes observed in the non-CMS cells (∗p < 0.05, nonparametric Wilcoxon rank test). Each bar represents mean ± SEM of two measurements in duplicate. The American Journal of Human Genetics 2013 93, 452-462DOI: (10.1016/j.ajhg.2013.07.011) Copyright © 2013 The American Society of Human Genetics Terms and Conditions

Figure 3 Downregulation of Human SENP1 and ANP32D Orthologs in Drosophila Enhances Survival under Hypoxic Condition da-Gal4 driver was used for ubiquitously knocking down the candidate gene by crossing with respective UAS-RNAi lines. Respective eclosion rates were observed at 21% and 5% O2 (Table S2). (A) CG32110-RNAi when crossed with da-Gal4 significantly increases the eclosion rate at 5% O2. The results were consistent in three RNAi lines targeting the same human SENP1 ortholog gene CG32110 (∗p < 0.005, unpaired t test). (B) The differences were also significant for eclosion rates (%) of the F1 progeny for the two Mapmodulin-RNAi fly lines (∗p < 0.005, unpaired t test). Each bar represents mean ± SEM of eclosion rate. The w1118 and da-Gal4 stocks were tested and used as background controls. The genotypes of each RNAi line are provided in Table S4. The American Journal of Human Genetics 2013 93, 452-462DOI: (10.1016/j.ajhg.2013.07.011) Copyright © 2013 The American Society of Human Genetics Terms and Conditions

Figure 4 The Impact of Sequence Assay Type on Signals of Selection (A–C) Sπ values across chromosome 19 in subjects without CMS, compared to the MXL population, when applied to (A) variants present in our WGS study, (B) variants included in targets from whole-exome sequencing, and (C) the subset of ∼1 M variants from genotyping. The red line represents a genome-wide, 0.1% FDR. Highlighted in green is the genomic region of PBX4, LPAR2, and GMIP, one of the 11 strongest candidate regions in our study. (D–F) SNP frequency profiles of the region highlighted in green for non-CMS (blue) compared to MXL (brown, inverted) for (D) WGS, (E) whole-exome sequencing, and (F) genotyping. As can be seen, the strong signal present via WGS is reduced drastically in genotyping and entirely absent by exome sequencing. The American Journal of Human Genetics 2013 93, 452-462DOI: (10.1016/j.ajhg.2013.07.011) Copyright © 2013 The American Society of Human Genetics Terms and Conditions