Anti-EPAS1 monoclonal antibody, clone Hif2alpha237

Objective.-The purpose of this study was to test the hypothesis that polymorphisms in the endothelial PAS domain protein 1 (EPAS1) gene are associated with the susceptibility to high altitude pulmonary edema (HAPE) in Han Chinese. Methods.-This study enrolled 153 HAPE patients (HAPE-p), matched with Han Chinese resistant to HAPE (HAPE-r) and local highland Tibetans from Yushu earthquake construction population in Qinghai where the altitude is more than 3500 m above sea level. The polymorphism of EPAS1 chr2:46441523(hg18) was genotyped by polymerase chain reaction restriction fragment length polymorphism and confirmed by DNA sequencing. Results.-The frequencies of EPAS1 chr2:46441523(hg18) polymorphism C allele were significantly higher in the HAPE-p group than in the HAPE-r group (P < .001), but the frequencies of heterozygous C/G were significantly higher in the HAPE-r group than in the HAPE-p group (P < .001). Moreover, the frequencies of the EPAS1 chr2:46441523(hg18) polymorphism G allele were significantly higher in the highland Tibetan group than in the HAPE-p and HAPE-r groups. Conclusions.-The EPAS1 chr2:46441523(hg18) polymorphism C is strongly associated with susceptibility to HAPE in Han Chinese, and the EPAS1 chr2:46441523(hg18) polymorphism G is present at high frequency and may be associated with high altitude adaptation in the Tibetans.

Human populations around the world encounter various environmental challenges and, consequently, develop genetic adaptations to different selection forces. Identifying the differences in natural selection between populations is critical for understanding the roles of specific genetic variants in evolutionary adaptation. Although numerous methods have been developed to detect genetic loci under recent directional selection, a probabilistic solution for testing and quantifying selection differences between populations is lacking. Here we report the development of a probabilistic method for testing and estimating selection differences between populations. By use of a probabilistic model of genetic drift and selection, we showed that logarithm odds ratios of allele frequencies provide estimates of the differences in selection coefficients between populations. The estimates approximate a normal distribution, and variance can be estimated using genome-wide variants. This allows us to quantify differences in selection coefficients and to determine the confidence intervals of the estimate. Our work also revealed the link between genetic association testing and hypothesis testing of selection differences. It therefore supplies a solution for hypothesis testing of selection differences. This method was applied to a genome-wide data analysis of Han and Tibetan populations. The results confirmed that both the EPAS1 and EGLN1 genes are under statistically different selection in Han and Tibetan populations. We further estimated differences in the selection coefficients for genetic variants involved in melanin formation and determined their confidence intervals between continental population groups. Application of the method to empirical data demonstrated the outstanding capability of this novel approach for testing and quantifying differences in natural selection.