Literature DB >> 24642866

Shared and unique signals of high-altitude adaptation in geographically distinct Tibetan populations.

Tana Wuren1, Tatum S Simonson2, Ga Qin1, Jinchuan Xing3, Chad D Huff3, David J Witherspoon3, Lynn B Jorde3, Ri-Li Ge1.   

Abstract

Recent studies have used a variety of analytical methods to identify genes targeted by selection in high-altitude populations located throughout the Tibetan Plateau. Despite differences in analytic strategies and sample location, hypoxia-related genes, including EPAS1 and EGLN1, were identified in multiple studies. By applying the same analytic methods to genome-wide SNP information used in our previous study of a Tibetan population (n = 31) from the township of Maduo, located in the northeastern corner of the Qinghai-Tibetan Plateau (4200 m), we have identified common targets of natural selection in a second geographically and linguistically distinct Tibetan population (n = 46) in the Tuo Tuo River township (4500 m). Our analyses provide evidence for natural selection based on iHS and XP-EHH signals in both populations at the p<0.02 significance level for EPAS1, EGLN1, HMOX2, and CYP17A1 and for PKLR, HFE, and HBB and HBG2, which have also been reported in other studies. We highlight differences (i.e., stratification and admixture) in the two distinct Tibetan groups examined here and report selection candidate genes common to both groups. These findings should be considered in the prioritization of selection candidate genes in future genetic studies in Tibet.

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Year:  2014        PMID: 24642866      PMCID: PMC3958363          DOI: 10.1371/journal.pone.0088252

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


Introduction

Native highlanders have specific traits that enable their survival despite the stress imposed by decreased oxygen availability at altitude. Several regions of the genome, including those that harbor hypoxia-sensing and -regulated genes, were recently identified as putative targets for high-altitude adaptation in Tibetans [1]–[11]. Two of these regions contain genes involved in hypoxia sensing and response: the EPAS1 gene, which encodes the hypoxia inducible factor (HIF)-2α subunit, and the EGLN1gene, which encodes a proline hydroxylase, PHD2, that regulates hypoxia-induced factors in an oxygen-dependent manner. Variation in the EPAS1 region is associated with hemoglobin concentration ([Hb]) in Tibetan populations examined in two separate studies [2], [10], and haplotypes at loci containing EGLN1 and PPARA (peroxisome proliferator activated receptor alpha) are associated with [Hb] in Tibetans from the Maduo township in the northeast section of the Qinghai-Tibetan Plateau [7] and in a sample of Tibetans located throughout the Plateau [12]. EPAS1 and PPARA haplotypes in Tibetans from the Tuo Tuo River examined here are associated with elevated serum lactate and free fatty acid concentrations, respectively, providing further support for important biological roles [13]. In addition to these phenotype-associated selection targets, many other genes have been reported as strong targets of selection in other studies and are likely associated with additional adaptive traits in Tibetan populations [6]. Here we highlight selection signals identified at the p<0.02 empirical significance level in previously examined Maduo [7] and Tuo Tuo River [13] Tibetan populations using the same analytical strategies. Tibetans inhabit a vast area of the Qinghai-Tibetan Plateau, which spans approximately 1.5 million square km (0.96 million square miles). At least three major Tibetan dialects are spoken among these geographically distinct groups (Amdo, U-Tsang, and Kham in the northeastern, southwestern, and southeastern regions of the Plateau, respectively [14]), suggesting potential genetic isolation among different Tibetan populations. The demographic history of these populations is, however, highly debated. Archaeological evidence suggests that the ancestors of present-day Tibetan groups migrated to the Qinghai-Tibetan Plateau at various times, ranging from 25,000 to 5,000 years ago [15]. Patterns of genome-wide SNPs support a single-route migration into this region [8], although analyses of mitochondrial DNA variation suggest that different migrations, dating to pre- and post- Last Glacial Maximum, contributed to genetic variation observed among present-day inhabitants [16], [17]. Neighboring populations have also mixed with various Tibetan groups located on the periphery of the plateau, contributing to present-day levels of variation in these regions [8]. While various studies have focused on a few select candidate gene regions [1], [4], [5], many genetic loci have probably been targeted by selection in high-altitude populations (see review [6]). In order to evaluate whether our previously reported selection targets [7] are significant in a different Tibetan group [13], we carried out genome-wide SNP-based selection scans in a linguistically distinct population from a different region of the Qinghai-Tibetan Plateau. Several of the same haplotypes exhibit extreme signals of selection in this second population, highlighting genomic regions that warrant further investigation in genetic studies of high-altitude adaptation in Tibet. We also determined which regions with extreme selection signals contain hypoxia-associated microRNAs. We report differences in the prevalence of admixture and prevalence of mitochondrial haplogroups in two distinct Tibetan groups, highlighting the value of studying different Tibetan groups and accounting for varied genetic backgrounds.

Results

Stratification of Tibetan populations

We used pair-wise allele sharing distances to calculate FST [18] among Tibetan populations and neighboring Asian groups. The two Tibetan populations exhibit the least amount of genetic differentiation from each other (FST = 0.004) compared to other Asian populations (HapMap Chinese [CHB] and Japanese [JPT] [19], and Buryat and Deedu Mongolians [20], [21]. Tuo Tuo River and Maduo Tibetans are similarly differentiated from the Hap Map CHB (FST = 0.014 and 0.012, respectively), which are commonly used for genetic comparisons (Table S1). To determine the extent of population structure among Tibetans and neighboring populations, we used the program Admixture [22] to examine the group-specific proportion of each individual's genome (Figure 1). Each of the Tibetan populations, as well as Buryat Mongolians, Deedu Mongolians, HapMap CHB, and HapMap JPT populations, form a distinct group. As previously reported, Maduo Tibetans exhibit components associated with Han Chinese (CHB), suggesting either recent admixture or may reflect different founder populations in this group [7]. Individuals from the Tuo Tuo River population do not, however, exhibit this signal, likely reflecting the more insulated geographic location of this group (Figure 1).
Figure 1

Map of two sample locations and mtDNA haplogroup frequencies from Maduo (represented by the furthest northeast) and Tuo Tuo River regions.

Numbers 1 and 2 represent sample locations for Maduo [7] and Tuo Tuo River sample locations, respectively.

Map of two sample locations and mtDNA haplogroup frequencies from Maduo (represented by the furthest northeast) and Tuo Tuo River regions.

Numbers 1 and 2 represent sample locations for Maduo [7] and Tuo Tuo River sample locations, respectively.

High-altitude selection candidate genes identified in two distinct Tibetan populations

We employed the same analytic strategies (iHS [23] and XP-EHH [24] statistics) used in our previous study of a Tibetan high-altitude adaptation to identify mutual targets of selection in a second linguistically distinct Tibetan population. By applying the same analytical methods in both populations, we provide further evidence for positive selection in each of two distinct Tibetan populations at the p<0.02 significance level for EPAS1, EGLN1, HMOX2, and CYP17A1 gene regions (Table 1) and further support for adaptation involving PKLR, HFE, and HBB and HBG2 genes identified in other recent studies. The first population described [7] is a group of Qinghai-Tibetans from Maduo who speak the Amdo Tibetan dialect (one of the three major Tibetan dialects spoken on the Qinghai-Tibetan Plateau); the second population examined, who speak the Kham dialect, are from the Tuo Tuo River area [13] (Figure 1). In order to identify high-altitude specific selection candidates, genomic regions with iHS significance levels at p<0.02 identified in our other Asian populations were excluded from analysis as described previously [7]. Of the selection candidate genes reported in our previous study of Maduo Tibetans [7], seven were identified at the p<0.02 significance level in Tuo Tuo River Tibetans and/or have been reported as strong selection candidates in other studies of Tibetan adaptation to altitude (Table 1).
Table 1

Selection signals identified at the empirical top 2% significance level in Tibetans from Maduo (as reported in Simonson et al. 2010) and Tuo Tuo River Tibetans and/or reported in other studies of human adaptation to high altitude as referenced.

GeneChromosome200 kb BinP valueSelection scanReferences for candidate genes
EGLN1 Chr111471.23E-03XP-EHH Maduo [3], [7], [8], [9], [10], [11], [12]
11481.54E-04XP-EHH Maduo
9.83E-03iHS Maduo
9.45E-03iHS Tuo Tuo River
EPAS1 Chr22311.54E-03XP-EHH Maduo [1], [3], [7], [8], [9], [10]
2321.03E-02XP-EHH Maduo
4.09E-03iHS Tuo Tuo River
PPARA Chr222249.00E-03iHS Maduo [7], [25]
CYP17A1 Chr105227.09E-03iHS Maduo [7]
1.40E-02iHS Tuo Tuo River
HMOX2 Chr16221.29E-03iHS Maduo [7], [11]
7.08E-04iHS Tuo Tuo River
1.24E-03XP-EHH Tuo Tuo River
PKLR Chr17674.33E-03iHS Tuo Tuo River [10]
2.00E-02iHS Maduo [7]
HBB and HBG2 Chr11260.0158 [7], [10]
HFE Chr61311.37E-02iHS Tuo Tuo River [10]

The 200 kb bin refers to the genomic position on the chromosome listed in the second column (positions based on Hg18) from which the selection signal emanates. The empirical p value of this region, which contains the selection candidate gene, is based on the selection analysis performed in the population specified.

The 200 kb bin refers to the genomic position on the chromosome listed in the second column (positions based on Hg18) from which the selection signal emanates. The empirical p value of this region, which contains the selection candidate gene, is based on the selection analysis performed in the population specified.

Genotype-phenotype analysis of hemoglobin concentration

The putatively adaptive EPAS1 genomic region is associated with [Hb] in two independent studies of high-altitude adaptation in Tibetans [2], [10]. While an extreme signal of selection near the EPAS1 gene was detected in both the Maduo and Tuo Tuo River Tibetan populations, the EPAS1 haplotype did not exhibit an association with [Hb] in Maduo Tibetans as previously reported [7], nor was there a significant association in Tuo Tuo River Tibetans (n = 36) examined here (Table S3; Figure S1). It is likely that our modest sample size yields reduced power to detect phenotype-genotype associations (Figure S2). In our previous study of Maduo Tibetans, we identified associations between EGLN1 and PPARA selected regions and [Hb] [7]. There is no association between the EGLN1 and PPARA haplotypes and [Hb] in the 36 Tuo Tuo River Tibetans examined here (Table S3; Figure S1). We speculate that genetic heterogeneity may influence genotype-phenotype relationships as a result of the genetic admixture that was detected in Maduo but not Tuo Tuo River Tibetans (Figure 2). It will be important to test, in a larger sample, whether admixture differences underlie reduced power to detect a signal in this modest sample (Figure S2).
Figure 2

Population structure of Tibetans and neighboring populations.

Individual grouping inferred by Admixture with k = 6, arranged by population. Each vertical bar represents an individual's genome. The colors correspond to the proportion of an individual's ancestry derived from one of the k groups.

Population structure of Tibetans and neighboring populations.

Individual grouping inferred by Admixture with k = 6, arranged by population. Each vertical bar represents an individual's genome. The colors correspond to the proportion of an individual's ancestry derived from one of the k groups.

Non-protein coding regions of the genome highlighted in both Tibetan groups

In order to determine whether non-genic microRNAs not previously examined as a priori candidates are contained within our top selection candidates, we examined the intersection between the top two percent of iHS and XP-EHH selection candidate regions and genomic locations that contain any of 54 hypoxia-related miRNAs [25]. Interestingly, the PPARA selection candidate region identified in our previous study contains a hypoxia-associated miRNA (Table S2) that may be involved in high-altitude response, although this region was not identified as a top selection candidate in the Tuo Tuo River Tibetans. A recent report of adaptation in Ethiopian highlanders also reports an extreme signal of selection near PPARA [26], although it is unclear whether the hypoxia-related miRNA in this region is involved in adaptation of either Tibetan or Ethiopian highland groups.

Mitochondrial DNA analyses highlight variation in Maduo and Tuo Tuo River Tibetan populations

Entire mtDNA control region sequence and genotyping of present-day inhabitants of the Tibetan Plateau indicate distinct patterns of variation among Tibetan groups [16], [27]. In order to determine whether the two Tibetan populations exhibit differences in mitochondrial DNA (mtDNA) haplogroups, we genotyped 12 haplotype-defining SNPs in18 and 40 individuals from Maduo and Tuo Tuo River (Figure 1), respectively. Consistent with previously reported frequencies for Tibeto-Burman groups, the most common major mtDNA haplogroup is M (94% and 67.5% for Maduo and TTR, respectively), although a small proportion of haplogroup N is also detected in our samples. M9, which is found at elevated frequency at high-altitude inhabitants in Tibet and India [28], is predominant in both Maduo and Tuo Tuo River populations (Table 2). The sub-M haplogroups D4, M9E, and M8 are most prevalent among Maduo Tibetans, whereas M9E and G are the most common mtDNA haplogroups within the TTR sample. Haplogroup A, which exhibits greater diversity in the southern region of the Qinghai-Tibetan Plateau [17], is detected among Tibetan inhabitants of the Tuo Tuo River area (8%) and Maduo Tibetans who reside further north (7%). Haplogroup B, a major group common among Native American populations, is also present in Tuo Tuo River Tibetans (TTR: 10%). The power to detect mtDNA variants at marginal frequencies (<10%) is limited in our modest sample of mtDNA from the Maduo population, and additional sample collection will be necessary to place these samples in the context of global mtDNA variation and determine precise genetic relationships among these populations.
Table 2

Mitochondrial DNA haplogroup allele frequencies in Maduo and Tuo Tuo River Tibetans.

MtDNA HaplogroupMM8M9ECD4D5GNABFHR
TTR Tibetan0.100.150.050.200.050.130.030.080.100.050.030.05
Maduo Tibetan0.220.280.390.060.06

Discussion

Recent reports of high-altitude adaptation in Tibetans are based on a range of analytical methods applied to different Tibetan populations located throughout the Qinghai-Tibetan Plateau [1], [4]–[6]. While some of the selection candidate genes reported by these studies are the same, there are also selection targets unique to each study. This could be due in part to differences in analytical methods, which range from sequence-based examination of allele-frequency differences between Tibetan and non-Tibetan groups to genome-wide analyses of extended SNP haplotype variation [1], [4]–[6]. Highly differentiated SNPs in the EPAS1 gene region are related to relatively lower [Hb] in two independent studies of high-altitude adaptation in Tibet [2], [10], and recent sequencing efforts have identified two variants in the first exon of EGLN1 that are highly differentiated in Tibetans [12], [29]. Another study of the EGLN1 region shows that two polymorphisms within the first intron are found at elevated frequency (71%) in a high-altitude population from India and are associated with EGLN1 expression and high-altitude pulmonary edema (HAPE) in this population [30]. These SNPs are in complete linkage disequilibrium (r2 = 1.0) with the putatively adaptive selected haplotypes identified in both of the Tibetan populations examined here, providing additional evidence for an adaptive role of EGLN1 across the Qinghai-Tibetan Plateau. Interestingly, EGLN1 was identified as a target of selection in Andean highlanders, although it is unclear whether the putatively adaptive variants are the same as those reported in Tibetan highlanders [3], [31]. A study of highland Daghestani populations also indicates highly differentiated intronic SNPs in a well-conserved region of EGLN1 [32]. While EPAS1 [2], [3], [7], [9]–[11], EGLN1 [3], [7], [9]–[11], [31], and PPARA [7], [26] have been identified as selection candidates in one or more studies of altitude adaptation and/or are associated with Tibetan putatively adaptive phenotypes, several other selection candidates have also been identified in multiple studies. The HMOX2 gene, a heme oxygenase involved in HIF-independent hypoxia sensing, was a top selection candidate in our Maduo and Tuo Tuo River populations and was also reported as a selection candidate in a pooled sample of 50 Tibetan samples collected from various regions of the Qinghai-Tibetan Plateau [11]. Furthermore, PKLR and HBB/HBG2, respectively identified in the Tuo Tuo River and Maduo Tibetan populations at the p<0.02 level, were also reported as top selection candidates in an independent examination of genetic variation in 50 Tibetan exomes [10]. Variants in the CYP17A1 gene, contained within a top selection candidate region identified in both Maduo and Tuo Tuo River populations, are associated with hypertension in European and Asian populations [33]–[35]. While these selection candidates have not been associated with the phenotypes measured thus far, they should be considered as strong candidates for future studies of genetic and physiological adaptations. Considering the lack of differentiation detected through analysis of the protein-coding regions of Han Chinese and Tibetan samples [10], it is also possible that many genetic targets of selection are in non-coding, regulatory regions of the genome. Our analyses suggest natural selection on a miRNA near the PPARA gene, highlighting regulatory variation as a potential factor underlying adaptive advantages within Tibetan genomes. Differences among studies published to date could also result from variation in the genetic background of Tibetan groups. Such factors could influence the extent to which selection signals are captured (the early stages of a selection sweep versus those that are fixed or nearly fixed in the population) or the potential for selection events to occur in different groups. In order to better understand differences in Tibetan adaptation to altitude, it will be necessary to fully characterize population histories in various Tibetan groups, determine the precise functional variants and timing of selection events, and evaluate additional phenotypic differences related to adaptive functional variants.

Conclusions

Selection and association signals identified in Tibetan populations may be influenced by the demographic history of these groups and by the choice of analytic methods. Despite genetic heterogeneity between Tibetan groups as shown here, several putatively adaptive genetic variants are common to Maduo and Tuo Tuo River Tibetans. Further genetic and phenotypic characterizations of physiological traits (e.g., oxygen transport, maternal/fetal responses during pregnancy) are required to determine if the remaining selection targets highlighted here are of biological significance, whether they are related to or independent of [Hb], and how combinations of such factors influence the overall physiology of native highlanders living in the Tibetan Plateau.

Materials and Methods

DNA sample collection

We extracted DNA from whole blood samples obtained from 85 highland natives (non-smokers, no chronic diseases) residing in the Tuo Tuo River township in Qinghai Province (∼4,500 m).

Ethics Statement

Participants provided written agreement as indicated by a signature or mark on a sheet of paper that described the study in their native language. Consent was obtained for all participants, and this study was approved by the Institutional Review Board at the High Altitude Medical Research Institute (Xining, Qinghai, People's Republic of China).

SNP genotyping

We employed the Affymetrix 6.0 SNP Array technology (>900,000 SNPs) to genotype 70 DNA samples at Capital Bio Corporation (Beijing, China). Default parameters for the Birdseed algorithm (version 2) were used to determine genotypes for all samples (Affymetrix, Santa Clara, CA, USA). Genotypic data were analyzed using the Affymetrix Genotyping Console 3.1 (Affymetrix) and included all autosomes but excluded the X and Y chromosomes and the mitochondrial genome.

Estimates of relatedness

We attempted to exclude all first-degree relatives who visited the clinic from our study. We used the ERSA software package [36] to determine genetic relatedness among all individuals examined and excluded one member of any pair exhibiting relatedness closer than second cousins. Based on these criteria, a total of 46 unrelated individuals were included in the analyses. The genotype data of the 46 individuals are available at http://jorde-lab.genetics.utah.edu/ under published data.

Principal components analysis

We performed principal components analysis on genetic distances as previously reported [21]. This analysis included two Tibetan populations from distinct regions and other Asian groups such as the HapMap CHB and JPT populations (CHB = Chinese in Beijing, China; JPT = Japanese in Tokyo, Japan). The CEU (U.S. Utah residents with ancestry from northern and western Europe) and YRI (YRI = Yoruba in Ibadan, Nigeria) HapMap populations provide context for the patterns of variation observed among these populations [19].

Functional candidate gene list

We had previously generated a list of genes likely related to high-altitude adaptation based on Gene Ontology and Panther Pathway categories as described in [7] and examined the intersection between these candidates and selection candidate genes identified at the p<0.02 level. Potential candidate genes identified in the mitochondrial genome and on the X chromosome were not considered for this study.

Admixture analysis

A model-based algorithm implemented in ADMIXTURE [22] was used to determine the genetic ancestries of each individual in a given number of populations without using information about population designation. To eliminate the effects of SNPs that are in linkage disequilibrium (r2 = 1.0), we first filtered out SNPs that had r2>0.2 within 100 kb using PLINK [37], as recommended by the authors of ADMIXTURE.

Selection analyses

We performed the iHS [23] and XP-EHH [24] tests of selection on phased data estimated by the Beagle software package as previously described [7]. These tests are based on extended haplotype homozygosity and measure the reduction in haplotype diversity based on the probability that, as distance from a focal SNP increases, two extended haplotypes are the same. XP-EHH compares across populations (in this case, Tuo Tuo River Tibetans and HapMap Han Chinese and Japanese); iHS compares within-population profiles based on the focal SNP's ancestral state (derived or ancestral). We focused on the selection candidate genes contained within 200 kb regions significant at the p<0.02 level in either test. We further excluded regions where the iHS test was significant at this level in neighboring populations as previously described [7].

Phenotype collection

Hemoglobin concentration, hematocrit, and percent oxygen saturation were determined from venous blood samples using the Mindray Hematology Analyzer (BC-2300, Shenzhen, People's Republic of China) and the Pulse Oximeter (Ohmeda 3700 Pulse Oximeter, Datex-Ohmeda, Boulder, Colorado, USA), respectively.

Genotype-phenotype association

We identified the putatively advantageous haplotypes as previously described [7] and tested whether the three-SNP allele haplotype exhibiting the most extreme iHS scores within each 200 kb genomic region were associated with [Hb] in each population and both populations combined. Stepwise linear regression (MATLAB R2010a) was used to detect significant relationships between these genotypes and [Hb]. The relationships between , , and haplotypes and [Hb] for Tuo Tuo River Tibetans (shown in open circles) and Maduo Tibetans (closed circles). The number of PPARA haplotype copies, previously associated with [Hb] in Maduo Tibetans (p<0.0005; Simonson et al. 2010), is associated with [Hb] when data from both populations are combined (p<0.02). (TIF) Click here for additional data file. Statistical power to detect an association of [Hb] with a haplotype. Simulated data sets were constructed with varying sample size (n = 30–500), assuming that the putatively selected haplotype at one locus decreases [Hb] by e g/dl when present in two copies and e/2 if present in one copy (additive model, e = 0.5, 1.0, 2.0). [Hb] was simulated as a normally-distributed variable with mean 19.6 and standard deviation of 1.6 g/dl, as observed in the Tuo Tuo River sample, and the effect of adaptive haplotype copies was added to that variate for each individual. The frequency f of the adaptive haplotype was set at 0.65, 0.75 or 0.85, per the legend. To mirror the actual tests performed, haplotypes with no effect on [Hb] were simulated for two additional loci (haplotype frequency of 0.65 for both). Genotypes were assigned in Hardy-Weinberg equilibrium. Ages were assigned from a normal distribution, mean 37 years and standard deviation 11.5, then truncated to the range of 18–68, mirroring the observed distribution. Sex was assigned randomly with a 50/50 ratio. Multiple stepwise linear regression was performed using the five simulated predictors: age, sex and haplotype copies at three loci (as used in Table S3). Power to detect a significant association of [Hb] with the simulated adaptive haplotype was estimated as the fraction of 1000 iterations for each parameter set that yielded a significant result at the alpha = 0.5 level. Effect size e has the largest impact on statistical power. Haplotype frequency has a modest influence (Tuo Tuo River EGLN1, EPAS1, and PPARA frequencies = 0.68, 0.81, and 0.77, respectively). Considering our modest sample size, it will be necessary to collect more data from the Tuo Tuo River population to achieve greater power to detect genotype-phenotype associations. (TIF) Click here for additional data file. F (DOCX) Click here for additional data file. Intersection of selection candidate regions and hypoxia-related miRNAs. (DOCX) Click here for additional data file. Multiple stepwise linear regression including age, sex, and three haplotypes previously identified as selection candidates in Tibetans ( , , and ) in Tuo Tuo River Tibetans. Gender was the only significant (p<0.01) predictor in this analysis (F = 14.82, P<0.0005). (DOCX) Click here for additional data file.
  35 in total

1.  Genetic evidence for high-altitude adaptation in Tibet.

Authors:  Tatum S Simonson; Yingzhong Yang; Chad D Huff; Haixia Yun; Ga Qin; David J Witherspoon; Zhenzhong Bai; Felipe R Lorenzo; Jinchuan Xing; Lynn B Jorde; Josef T Prchal; RiLi Ge
Journal:  Science       Date:  2010-05-13       Impact factor: 47.728

2.  PLINK: a tool set for whole-genome association and population-based linkage analyses.

Authors:  Shaun Purcell; Benjamin Neale; Kathe Todd-Brown; Lori Thomas; Manuel A R Ferreira; David Bender; Julian Maller; Pamela Sklar; Paul I W de Bakker; Mark J Daly; Pak C Sham
Journal:  Am J Hum Genet       Date:  2007-07-25       Impact factor: 11.025

3.  Covariances of relatives stemming from a population undergoing mixed self and random mating.

Authors:  C C Cockerham; B S Weir
Journal:  Biometrics       Date:  1984-03       Impact factor: 2.571

4.  Recapitulation of two genomewide association studies on blood pressure and essential hypertension in the Korean population.

Authors:  Kyung-Won Hong; Hyun-Seok Jin; Ji-Eun Lim; Sangsoo Kim; Min Jin Go; Bermseok Oh
Journal:  J Hum Genet       Date:  2010-04-23       Impact factor: 3.172

5.  Identifying signatures of natural selection in Tibetan and Andean populations using dense genome scan data.

Authors:  Abigail Bigham; Marc Bauchet; Dalila Pinto; Xianyun Mao; Joshua M Akey; Rui Mei; Stephen W Scherer; Colleen G Julian; Megan J Wilson; David López Herráez; Tom Brutsaert; Esteban J Parra; Lorna G Moore; Mark D Shriver
Journal:  PLoS Genet       Date:  2010-09-09       Impact factor: 5.917

Review 6.  A microRNA component of the hypoxic response.

Authors:  R Kulshreshtha; R V Davuluri; G A Calin; M Ivan
Journal:  Cell Death Differ       Date:  2008-01-25       Impact factor: 15.828

7.  Genome-wide detection and characterization of positive selection in human populations.

Authors:  Pardis C Sabeti; Patrick Varilly; Ben Fry; Jason Lohmueller; Elizabeth Hostetter; Chris Cotsapas; Xiaohui Xie; Elizabeth H Byrne; Steven A McCarroll; Rachelle Gaudet; Stephen F Schaffner; Eric S Lander; Kelly A Frazer; Dennis G Ballinger; David R Cox; David A Hinds; Laura L Stuve; Richard A Gibbs; John W Belmont; Andrew Boudreau; Paul Hardenbol; Suzanne M Leal; Shiran Pasternak; David A Wheeler; Thomas D Willis; Fuli Yu; Huanming Yang; Changqing Zeng; Yang Gao; Haoran Hu; Weitao Hu; Chaohua Li; Wei Lin; Siqi Liu; Hao Pan; Xiaoli Tang; Jian Wang; Wei Wang; Jun Yu; Bo Zhang; Qingrun Zhang; Hongbin Zhao; Hui Zhao; Jun Zhou; Stacey B Gabriel; Rachel Barry; Brendan Blumenstiel; Amy Camargo; Matthew Defelice; Maura Faggart; Mary Goyette; Supriya Gupta; Jamie Moore; Huy Nguyen; Robert C Onofrio; Melissa Parkin; Jessica Roy; Erich Stahl; Ellen Winchester; Liuda Ziaugra; David Altshuler; Yan Shen; Zhijian Yao; Wei Huang; Xun Chu; Yungang He; Li Jin; Yangfan Liu; Yayun Shen; Weiwei Sun; Haifeng Wang; Yi Wang; Ying Wang; Xiaoyan Xiong; Liang Xu; Mary M Y Waye; Stephen K W Tsui; Hong Xue; J Tze-Fei Wong; Luana M Galver; Jian-Bing Fan; Kevin Gunderson; Sarah S Murray; Arnold R Oliphant; Mark S Chee; Alexandre Montpetit; Fanny Chagnon; Vincent Ferretti; Martin Leboeuf; Jean-François Olivier; Michael S Phillips; Stéphanie Roumy; Clémentine Sallée; Andrei Verner; Thomas J Hudson; Pui-Yan Kwok; Dongmei Cai; Daniel C Koboldt; Raymond D Miller; Ludmila Pawlikowska; Patricia Taillon-Miller; Ming Xiao; Lap-Chee Tsui; William Mak; You Qiang Song; Paul K H Tam; Yusuke Nakamura; Takahisa Kawaguchi; Takuya Kitamoto; Takashi Morizono; Atsushi Nagashima; Yozo Ohnishi; Akihiro Sekine; Toshihiro Tanaka; Tatsuhiko Tsunoda; Panos Deloukas; Christine P Bird; Marcos Delgado; Emmanouil T Dermitzakis; Rhian Gwilliam; Sarah Hunt; Jonathan Morrison; Don Powell; Barbara E Stranger; Pamela Whittaker; David R Bentley; Mark J Daly; Paul I W de Bakker; Jeff Barrett; Yves R Chretien; Julian Maller; Steve McCarroll; Nick Patterson; Itsik Pe'er; Alkes Price; Shaun Purcell; Daniel J Richter; Pardis Sabeti; Richa Saxena; Stephen F Schaffner; Pak C Sham; Patrick Varilly; David Altshuler; Lincoln D Stein; Lalitha Krishnan; Albert Vernon Smith; Marcela K Tello-Ruiz; Gudmundur A Thorisson; Aravinda Chakravarti; Peter E Chen; David J Cutler; Carl S Kashuk; Shin Lin; Gonçalo R Abecasis; Weihua Guan; Yun Li; Heather M Munro; Zhaohui Steve Qin; Daryl J Thomas; Gilean McVean; Adam Auton; Leonardo Bottolo; Niall Cardin; Susana Eyheramendy; Colin Freeman; Jonathan Marchini; Simon Myers; Chris Spencer; Matthew Stephens; Peter Donnelly; Lon R Cardon; Geraldine Clarke; David M Evans; Andrew P Morris; Bruce S Weir; Tatsuhiko Tsunoda; Todd A Johnson; James C Mullikin; Stephen T Sherry; Michael Feolo; Andrew Skol; Houcan Zhang; Changqing Zeng; Hui Zhao; Ichiro Matsuda; Yoshimitsu Fukushima; Darryl R Macer; Eiko Suda; Charles N Rotimi; Clement A Adebamowo; Ike Ajayi; Toyin Aniagwu; Patricia A Marshall; Chibuzor Nkwodimmah; Charmaine D M Royal; Mark F Leppert; Missy Dixon; Andy Peiffer; Renzong Qiu; Alastair Kent; Kazuto Kato; Norio Niikawa; Isaac F Adewole; Bartha M Knoppers; Morris W Foster; Ellen Wright Clayton; Jessica Watkin; Richard A Gibbs; John W Belmont; Donna Muzny; Lynne Nazareth; Erica Sodergren; George M Weinstock; David A Wheeler; Imtaz Yakub; Stacey B Gabriel; Robert C Onofrio; Daniel J Richter; Liuda Ziaugra; Bruce W Birren; Mark J Daly; David Altshuler; Richard K Wilson; Lucinda L Fulton; Jane Rogers; John Burton; Nigel P Carter; Christopher M Clee; Mark Griffiths; Matthew C Jones; Kirsten McLay; Robert W Plumb; Mark T Ross; Sarah K Sims; David L Willey; Zhu Chen; Hua Han; Le Kang; Martin Godbout; John C Wallenburg; Paul L'Archevêque; Guy Bellemare; Koji Saeki; Hongguang Wang; Daochang An; Hongbo Fu; Qing Li; Zhen Wang; Renwu Wang; Arthur L Holden; Lisa D Brooks; Jean E McEwen; Mark S Guyer; Vivian Ota Wang; Jane L Peterson; Michael Shi; Jack Spiegel; Lawrence M Sung; Lynn F Zacharia; Francis S Collins; Karen Kennedy; Ruth Jamieson; John Stewart
Journal:  Nature       Date:  2007-10-18       Impact factor: 49.962

8.  A second generation human haplotype map of over 3.1 million SNPs.

Authors:  Kelly A Frazer; Dennis G Ballinger; David R Cox; David A Hinds; Laura L Stuve; Richard A Gibbs; John W Belmont; Andrew Boudreau; Paul Hardenbol; Suzanne M Leal; Shiran Pasternak; David A Wheeler; Thomas D Willis; Fuli Yu; Huanming Yang; Changqing Zeng; Yang Gao; Haoran Hu; Weitao Hu; Chaohua Li; Wei Lin; Siqi Liu; Hao Pan; Xiaoli Tang; Jian Wang; Wei Wang; Jun Yu; Bo Zhang; Qingrun Zhang; Hongbin Zhao; Hui Zhao; Jun Zhou; Stacey B Gabriel; Rachel Barry; Brendan Blumenstiel; Amy Camargo; Matthew Defelice; Maura Faggart; Mary Goyette; Supriya Gupta; Jamie Moore; Huy Nguyen; Robert C Onofrio; Melissa Parkin; Jessica Roy; Erich Stahl; Ellen Winchester; Liuda Ziaugra; David Altshuler; Yan Shen; Zhijian Yao; Wei Huang; Xun Chu; Yungang He; Li Jin; Yangfan Liu; Yayun Shen; Weiwei Sun; Haifeng Wang; Yi Wang; Ying Wang; Xiaoyan Xiong; Liang Xu; Mary M Y Waye; Stephen K W Tsui; Hong Xue; J Tze-Fei Wong; Luana M Galver; Jian-Bing Fan; Kevin Gunderson; Sarah S Murray; Arnold R Oliphant; Mark S Chee; Alexandre Montpetit; Fanny Chagnon; Vincent Ferretti; Martin Leboeuf; Jean-François Olivier; Michael S Phillips; Stéphanie Roumy; Clémentine Sallée; Andrei Verner; Thomas J Hudson; Pui-Yan Kwok; Dongmei Cai; Daniel C Koboldt; Raymond D Miller; Ludmila Pawlikowska; Patricia Taillon-Miller; Ming Xiao; Lap-Chee Tsui; William Mak; You Qiang Song; Paul K H Tam; Yusuke Nakamura; Takahisa Kawaguchi; Takuya Kitamoto; Takashi Morizono; Atsushi Nagashima; Yozo Ohnishi; Akihiro Sekine; Toshihiro Tanaka; Tatsuhiko Tsunoda; Panos Deloukas; Christine P Bird; Marcos Delgado; Emmanouil T Dermitzakis; Rhian Gwilliam; Sarah Hunt; Jonathan Morrison; Don Powell; Barbara E Stranger; Pamela Whittaker; David R Bentley; Mark J Daly; Paul I W de Bakker; Jeff Barrett; Yves R Chretien; Julian Maller; Steve McCarroll; Nick Patterson; Itsik Pe'er; Alkes Price; Shaun Purcell; Daniel J Richter; Pardis Sabeti; Richa Saxena; Stephen F Schaffner; Pak C Sham; Patrick Varilly; David Altshuler; Lincoln D Stein; Lalitha Krishnan; Albert Vernon Smith; Marcela K Tello-Ruiz; Gudmundur A Thorisson; Aravinda Chakravarti; Peter E Chen; David J Cutler; Carl S Kashuk; Shin Lin; Gonçalo R Abecasis; Weihua Guan; Yun Li; Heather M Munro; Zhaohui Steve Qin; Daryl J Thomas; Gilean McVean; Adam Auton; Leonardo Bottolo; Niall Cardin; Susana Eyheramendy; Colin Freeman; Jonathan Marchini; Simon Myers; Chris Spencer; Matthew Stephens; Peter Donnelly; Lon R Cardon; Geraldine Clarke; David M Evans; Andrew P Morris; Bruce S Weir; Tatsuhiko Tsunoda; James C Mullikin; Stephen T Sherry; Michael Feolo; Andrew Skol; Houcan Zhang; Changqing Zeng; Hui Zhao; Ichiro Matsuda; Yoshimitsu Fukushima; Darryl R Macer; Eiko Suda; Charles N Rotimi; Clement A Adebamowo; Ike Ajayi; Toyin Aniagwu; Patricia A Marshall; Chibuzor Nkwodimmah; Charmaine D M Royal; Mark F Leppert; Missy Dixon; Andy Peiffer; Renzong Qiu; Alastair Kent; Kazuto Kato; Norio Niikawa; Isaac F Adewole; Bartha M Knoppers; Morris W Foster; Ellen Wright Clayton; Jessica Watkin; Richard A Gibbs; John W Belmont; Donna Muzny; Lynne Nazareth; Erica Sodergren; George M Weinstock; David A Wheeler; Imtaz Yakub; Stacey B Gabriel; Robert C Onofrio; Daniel J Richter; Liuda Ziaugra; Bruce W Birren; Mark J Daly; David Altshuler; Richard K Wilson; Lucinda L Fulton; Jane Rogers; John Burton; Nigel P Carter; Christopher M Clee; Mark Griffiths; Matthew C Jones; Kirsten McLay; Robert W Plumb; Mark T Ross; Sarah K Sims; David L Willey; Zhu Chen; Hua Han; Le Kang; Martin Godbout; John C Wallenburg; Paul L'Archevêque; Guy Bellemare; Koji Saeki; Hongguang Wang; Daochang An; Hongbo Fu; Qing Li; Zhen Wang; Renwu Wang; Arthur L Holden; Lisa D Brooks; Jean E McEwen; Mark S Guyer; Vivian Ota Wang; Jane L Peterson; Michael Shi; Jack Spiegel; Lawrence M Sung; Lynn F Zacharia; Francis S Collins; Karen Kennedy; Ruth Jamieson; John Stewart
Journal:  Nature       Date:  2007-10-18       Impact factor: 49.962

9.  Genome-wide association study of blood pressure and hypertension.

Authors:  Daniel Levy; Georg B Ehret; Kenneth Rice; Germaine C Verwoert; Lenore J Launer; Abbas Dehghan; Nicole L Glazer; Alanna C Morrison; Andrew D Johnson; Thor Aspelund; Yurii Aulchenko; Thomas Lumley; Anna Köttgen; Ramachandran S Vasan; Fernando Rivadeneira; Gudny Eiriksdottir; Xiuqing Guo; Dan E Arking; Gary F Mitchell; Francesco U S Mattace-Raso; Albert V Smith; Kent Taylor; Robert B Scharpf; Shih-Jen Hwang; Eric J G Sijbrands; Joshua Bis; Tamara B Harris; Santhi K Ganesh; Christopher J O'Donnell; Albert Hofman; Jerome I Rotter; Josef Coresh; Emelia J Benjamin; André G Uitterlinden; Gerardo Heiss; Caroline S Fox; Jacqueline C M Witteman; Eric Boerwinkle; Thomas J Wang; Vilmundur Gudnason; Martin G Larson; Aravinda Chakravarti; Bruce M Psaty; Cornelia M van Duijn
Journal:  Nat Genet       Date:  2009-05-10       Impact factor: 38.330

10.  A map of recent positive selection in the human genome.

Authors:  Benjamin F Voight; Sridhar Kudaravalli; Xiaoquan Wen; Jonathan K Pritchard
Journal:  PLoS Biol       Date:  2006-03-07       Impact factor: 8.029

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  18 in total

Review 1.  Altitude Adaptation: A Glimpse Through Various Lenses.

Authors:  Tatum S Simonson
Journal:  High Alt Med Biol       Date:  2015-06       Impact factor: 1.981

2.  Cardiac responses to hypoxia and reoxygenation in Drosophila.

Authors:  Rachel Zarndt; Sarah Piloto; Frank L Powell; Gabriel G Haddad; Rolf Bodmer; Karen Ocorr
Journal:  Am J Physiol Regul Integr Comp Physiol       Date:  2015-09-16       Impact factor: 3.619

3.  Genetic signatures of high-altitude adaptation in Tibetans.

Authors:  Jian Yang; Zi-Bing Jin; Jie Chen; Xiu-Feng Huang; Xiao-Man Li; Yuan-Bo Liang; Jian-Yang Mao; Xin Chen; Zhili Zheng; Andrew Bakshi; Dong-Dong Zheng; Mei-Qin Zheng; Naomi R Wray; Peter M Visscher; Fan Lu; Jia Qu
Journal:  Proc Natl Acad Sci U S A       Date:  2017-04-03       Impact factor: 11.205

4.  Low haemoglobin concentration in Tibetan males is associated with greater high-altitude exercise capacity.

Authors:  T S Simonson; G Wei; H E Wagner; T Wuren; G Qin; M Yan; P D Wagner; R L Ge
Journal:  J Physiol       Date:  2015-06-25       Impact factor: 5.182

Review 5.  High-altitude adaptation in humans: from genomics to integrative physiology.

Authors:  Priti Azad; Tsering Stobdan; Dan Zhou; Iain Hartley; Ali Akbari; Vineet Bafna; Gabriel G Haddad
Journal:  J Mol Med (Berl)       Date:  2017-09-26       Impact factor: 4.599

Review 6.  Fifteen years of genomewide scans for selection: trends, lessons and unaddressed genetic sources of complication.

Authors:  Ryan J Haasl; Bret A Payseur
Journal:  Mol Ecol       Date:  2015-09-16       Impact factor: 6.185

7.  Increased blood-oxygen binding affinity in Tibetan and Han Chinese residents at 4200 m.

Authors:  T S Simonson; G Wei; H E Wagner; T Wuren; A Bui; J M Fine; G Qin; F G Beltrami; M Yan; P D Wagner; Ri Li Ge
Journal:  Exp Physiol       Date:  2014-08-28       Impact factor: 2.969

8.  Genetic variants at the EGLN1 locus associated with high-altitude adaptation in Tibetans are absent or found at low frequency in highland Andeans.

Authors:  Erica C Heinrich; Lu Wu; Elijah S Lawrence; Amy M Cole; Cecilia Anza-Ramirez; Francisco C Villafuerte; Tatum S Simonson
Journal:  Ann Hum Genet       Date:  2019-02-04       Impact factor: 2.180

9.  Evidence of Polygenic Adaptation in the Systems Genetics of Anthropometric Traits.

Authors:  Renato Polimanti; Bao Zhu Yang; Hongyu Zhao; Joel Gelernter
Journal:  PLoS One       Date:  2016-08-18       Impact factor: 3.240

10.  Shared Genetic Signals of Hypoxia Adaptation in Drosophila and in High-Altitude Human Populations.

Authors:  Aashish R Jha; Dan Zhou; Christopher D Brown; Martin Kreitman; Gabriel G Haddad; Kevin P White
Journal:  Mol Biol Evol       Date:  2015-11-17       Impact factor: 16.240

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