Lipoprotein Lipase Gene Polymorphisms and Blood Pressure Levels in the Northern Chinese Han Population

Li, Biao; Ge, Dongliang; Wang, Yuelan; Zhao, Weiyan; Zhou, Xiaoyang; Gu, Dongfeng; Chen, Runsheng

doi:10.1291/hypres.27.373

Cited by 14 publications

(11 citation statements)

References 34 publications

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“…The effects of mutant ANGPTL genes on lipoprotein lipase (LPL) have been studied through in vitro functional studies and in vivo mice studies. LPL has been known to affect glucose metabolism [34], cholesterol level [34]–[37], and blood pressure [38]. This biological evidence strengthens the support of the identified associations.…”

Section: Discussionsupporting

confidence: 74%

A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions

2010

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There is solid evidence that rare variants contribute to complex disease etiology. Next-generation sequencing technologies make it possible to uncover rare variants within candidate genes, exomes, and genomes. Working in a novel framework, the kernel-based adaptive cluster (KBAC) was developed to perform powerful gene/locus based rare variant association testing. The KBAC combines variant classification and association testing in a coherent framework. Covariates can also be incorporated in the analysis to control for potential confounders including age, sex, and population substructure. To evaluate the power of KBAC: 1) variant data was simulated using rigorous population genetic models for both Europeans and Africans, with parameters estimated from sequence data, and 2) phenotypes were generated using models motivated by complex diseases including breast cancer and Hirschsprung's disease. It is demonstrated that the KBAC has superior power compared to other rare variant analysis methods, such as the combined multivariate and collapsing and weight sum statistic. In the presence of variant misclassification and gene interaction, association testing using KBAC is particularly advantageous. The KBAC method was also applied to test for associations, using sequence data from the Dallas Heart Study, between energy metabolism traits and rare variants in ANGPTL 3,4,5 and 6 genes. A number of novel associations were identified, including the associations of high density lipoprotein and very low density lipoprotein with ANGPTL4. The KBAC method is implemented in a user-friendly R package.

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Section: Discussionsupporting

confidence: 74%

A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions

2010

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“…Therefore, there were 12 studies that failed to meet our inclusion/exclusion criteria, including one review article, 22 two population studies, 23,24 three studies investigating other variants in LPL gene [25][26][27] and six linkage studies. 13,14,[28][29][30] Among the remaining, there were five eligible studies for association of S447X variant with hypertension as a binary trait 9,[17][18][19]31 and four eligible studies for association of this variant with blood pressure variation. 10,15,16,32 The baseline characteristics of these eligible studies are summarized in Table 1 (for categorical hypertension association) and Table 2 (for continuous blood pressure association), respectively.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Some studies suggested an association between the 447X variant and low hypertension risk or decreased blood pressure, [15][16][17] whereas other studies found no association at all. 18,19 In retrospect, individual studies with small sample sizes that are known to have low statistical power limited the identification of genetic variability and yielded poor replication record. To systematically overcome this limitation, we metaanalysed all available case-control studies reported in English language aiming to examine the association of LPL gene S447X variant with hypertension.…”

Section: Introductionmentioning

confidence: 99%

Meta-based association of the lipoprotein lipase gene S447X variant with hypertension and blood pressure variation

2010

J Hum Hypertens

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Association of the lipoprotein lipase (LPL) gene S447X variant with hypertension has been investigated extensively, whereas the results are often irreproducible. We therefore conducted a meta-analysis to examine whether S447X variant was associated with hypertension and blood pressure variation. Case-control reports published in English language and humans were identified from MEDLINE, EMBASE and Web of Science search engines as of 10 December 2009. Fixed-effects model was applied to pool data in the absence of between-studies heterogeneity, and random-effects model otherwise. A total of five studies (960 cases and 1145 controls) for hypertension and four studies (n=2777) for blood pressure were included. Compared with 447SS homogeneous carriers, those with 447X variant had a lower risk of hypertension (odds ratio (OR)=0.78; 95% confidence interval (CI): 0.62-0.98; P=0.03), and this effect reached significance under the fixed-effects model (I(2)=30% and P=0.22). Similarly, compared with 447S allele carriers, those with 447X allele carriers also had a lower risk of hypertension (OR=0.79; 95% CI: 0.64-0.98; P=0.03). In case of pregnancy-induced hypertension, no significance was observed (P>0.05). As for blood pressure association, there was no significant difference between 447X variant and 447SS homogeneous carriers for both systolic and diastolic blood pressure in the whole population, even stratified by gender (P>0.05). The Egger test told no publication bias for all associations. This meta-analysis demonstrated that LPL gene S447X variant was significantly associated with hypertension and showed no obvious relation with pregnancy-induced hypertension and blood pressure variation.

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“…LPL has been known to affect glucose metabolism, 33 cholesterol level 34 and blood pressure. 35 Additionally, the association between variants in the ANGPTL4 gene and triglyceride levels has been successfully replicated. 3,36 Sensitivity of MTA to mis-specified sampling mechanisms was extensively evaluated.…”

Section: Discussionmentioning

confidence: 96%

A flexible likelihood framework for detecting associations with secondary phenotypes in genetic studies using selected samples: application to sequence data

Liu

Leal

2011

Eur J Hum Genet

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For most complex trait association studies using next-generation sequencing, in addition to the primary phenotype of interest, many clinically important secondary traits are also available, which can be analyzed to map susceptibility genes. Owing to high sequencing costs, most studies use selected samples, and the sampling mechanisms of these studies can be complicated. When the primary and secondary traits are correlated, analyses of secondary phenotypes can cause spurious associations in selected samples and existing methods are inadequate to adjust for them. To address this problem, a likelihood-based method, MULTI-TRAIT-ASSOCIATION (MTA) was developed. MTA is flexible and can be applied to any study with known sampling mechanisms. It also allows efficient inferences of genetic parameters. To investigate the power of MTA and different study designs, extensive simulations were performed under rigorous population genetic and phenotypic models. It is demonstrated that there are great benefits for analyzing secondary phenotypes in selected samples. In particular, using case-control samples and samples with extreme primary phenotypes can be more powerful than analyzing random samples of equivalent size. One major challenge for sequence-based association studies is that most data sets are not of sufficient size to be adequately powered. By applying MTA, data sets ascertained under distinct mechanisms or targeted at different primary traits can be jointly analyzed to map common phenotypes and greatly increase power. The combined analysis can be performed using freely available data sets from public repositories, for example, dbGaP. In conclusion, MTA will have an important role in dissecting the etiology of complex traits.

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Lipoprotein Lipase Gene Polymorphisms and Blood Pressure Levels in the Northern Chinese Han Population

Cited by 14 publications

References 34 publications

A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions

A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions

Meta-based association of the lipoprotein lipase gene S447X variant with hypertension and blood pressure variation

A flexible likelihood framework for detecting associations with secondary phenotypes in genetic studies using selected samples: application to sequence data

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