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1.  The Contribution of Genetic Diversity to Subdivide Populations Living in the Silk Road of China 
PLoS ONE  2014;9(5):e97344.
There are several indigenous ethnic populations along the silk road in the Northwest of China that display clear differences in culture and social customs, perhaps as a result of geographic isolation and different linguistic traditions. However, extensive trade and other interactions probably facilitated the admixture of different gene pools between these populations over the last two millennia. To further explore the evolutionary relationships of the 13 ethnic populations residing in Northwest China and to reveal the features of population admixture, the 9 most-commonly employed CODIS loci (D3S1358, TH01, D5S818, D13S317, D7S820, CSF1PO, vWA, TPOX, FGA) were selected for genotyping and further analysis. Phylogenetic tree and principal component analysis revealed clear pattern of population differentiation between 4 populations living in Sinkiang Uighur Autonomous Region and other 9 populations dwelled in the upper regions of Silk Road. R matrix regression showed high-level gene flow and population admixture dose exist among these ethic populations in the Northwest region of China. Furthermore, the Mantel test suggests that larger percent of genetic variance (21.58% versus 2.3%) can be explained by geographic isolation than linguistic barriers, which matched with the contribution of geographic factors to other world populations.
doi:10.1371/journal.pone.0097344
PMCID: PMC4020837  PMID: 24828511
2.  Correction: A Genome-Wide Linkage and Association Scan Reveals Novel Loci for Hypertension and Blood Pressure Traits 
PLoS ONE  2012;7(6):10.1371/annotation/4415f88f-ab10-44dd-8ba9-1a57ade740c1.
doi:10.1371/annotation/4415f88f-ab10-44dd-8ba9-1a57ade740c1
PMCID: PMC3371059
3.  A Genome-Wide Linkage and Association Scan Reveals Novel Loci for Hypertension and Blood Pressure Traits 
PLoS ONE  2012;7(2):e31489.
Hypertension is caused by the interaction of environmental and genetic factors. The condition which is very common, with about 18% of the adult Hong Kong Chinese population and over 50% of older individuals affected, is responsible for considerable morbidity and mortality. To identify genes influencing hypertension and blood pressure, we conducted a combined linkage and association study using over 500,000 single nucleotide polymorphisms (SNPs) genotyped in 328 individuals comprising 111 hypertensive probands and their siblings. Using a family-based association test, we found an association with SNPs on chromosome 5q31.1 (rs6596140; P<9×10−8) for hypertension. One candidate gene, PDC, was replicated, with rs3817586 on 1q31.1 attaining P = 2.5×10−4 and 2.9×10−5 in the within-family tests for DBP and MAP, respectively. We also identified regions of significant linkage for systolic and diastolic blood pressure on chromosomes 2q22 and 5p13, respectively. Further family-based association analysis of the linkage peak on chromosome 5 yielded a significant association (rs1605685, P<7×10−5) for DBP. This is the first combined linkage and association study of hypertension and its related quantitative traits with Chinese ancestry. The associations reported here account for the action of common variants whereas the discovery of linkage regions may point to novel targets for rare variant screening.
doi:10.1371/journal.pone.0031489
PMCID: PMC3286457  PMID: 22384028
4.  Comparisons of seven algorithms for pathway analysis using the WTCCC Crohn's Disease dataset 
BMC Research Notes  2011;4:386.
Background
Though rooted in genomic expression studies, pathway analysis for genome-wide association studies (GWAS) has gained increasing popularity, since it has the potential to discover hidden disease pathogenic mechanisms by combining statistical methods with biological knowledge. Generally, algorithms or programs proposed recently can be categorized by different types of input data, null hypothesis or counts of analysis stages. Due to complexity caused by SNP, gene and pathway relationships, re-sampling strategies like permutation are always utilized to derive an empirical distribution for test statistics for evaluating the significance of candidate pathways. However, evaluation of these algorithms on real GWAS datasets and real biological pathway databases needs to be addressed before we apply them widely with confidence.
Findings
Two algorithms which use summary statistics from GWAS as input were implemented in KGG, a novel and user-friendly software tool for GWAS pathway analysis. Comparisons of these two algorithms as well as the other five selected algorithms were conducted by analyzing the WTCCC Crohn's Disease dataset utilizing the MsigDB canonical pathways. As a result of using permutation to obtain empirical p-value, most of these methods could control Type I error rate well, although some are conservative. However, the methods varied greatly in terms of power and running time, with the PLINK truncated set-based test being the most powerful and KGG being the fastest.
Conclusions
Raw data-based algorithms, such as those implemented in PLINK, are preferable for GWAS pathway analysis as long as computational capacity is available. It may be worthwhile to apply two or more pathway analysis algorithms on the same GWAS dataset, since the methods differ greatly in their outputs and might provide complementary findings for the studied complex disease.
doi:10.1186/1756-0500-4-386
PMCID: PMC3199264  PMID: 21981765

Results 1-4 (4)