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1.  Germline Gain-of-Function Mutations in AFF4 Cause a Developmental Syndrome Functionally Linking the Super Elongation Complex and Cohesin 
Nature genetics  2015;47(4):338-344.
Transcriptional elongation is critical for gene expression regulation during embryogenesis. The super elongation complex (SEC) governs this process by mobilizing paused RNA polymerase II (RNAP2). Using exome sequencing, we discovered missense mutations in AFF4, a core component of the SEC in three unrelated probands with a novel syndrome that phenotypically overlaps Cornelia de Lange syndrome (CdLS), that we have named CHOPS syndrome (C for Cognitive impairment and Coarse facies, H for Heart defects, O for Obesity, P for Pulmonary involvement and S for Short stature and Skeletal dysplasia). Transcriptome and chromatin immunoprecipitation sequencing (ChIP-seq) analyses demonstrated similar alterations of genome-wide binding of AFF4, cohesin and RNAP2 between CdLS and CHOPS syndrome. Direct molecular interaction between SEC, cohesin and RNAP2 was demonstrated. This data supports a common molecular pathogenesis for CHOPS syndrome and CdLS caused by disturbance of transcriptional elongation due to alterations in genome-wide binding of AFF4 and cohesin.
PMCID: PMC4380798  PMID: 25730767
2.  A Gaussian copula approach for the analysis of secondary phenotypes in case–control genetic association studies 
Biostatistics (Oxford, England)  2011;13(3):497-508.
In many case–control genetic association studies, a set of correlated secondary phenotypes that may share common genetic factors with disease status are collected. Examination of these secondary phenotypes can yield valuable insights about the disease etiology and supplement the main studies. However, due to unequal sampling probabilities between cases and controls, standard regression analysis that assesses the effect of SNPs (single nucleotide polymorphisms) on secondary phenotypes using cases only, controls only, or combined samples of cases and controls can yield inflated type I error rates when the test SNP is associated with the disease. To solve this issue, we propose a Gaussian copula-based approach that efficiently models the dependence between disease status and secondary phenotypes. Through simulations, we show that our method yields correct type I error rates for the analysis of secondary phenotypes under a wide range of situations. To illustrate the effectiveness of our method in the analysis of real data, we applied our method to a genome-wide association study on high-density lipoprotein cholesterol (HDL-C), where “cases” are defined as individuals with extremely high HDL-C level and “controls” are defined as those with low HDL-C level. We treated 4 quantitative traits with varying degrees of correlation with HDL-C as secondary phenotypes and tested for association with SNPs in LIPG, a gene that is well known to be associated with HDL-C. We show that when the correlation between the primary and secondary phenotypes is >0.2, the P values from case–control combined unadjusted analysis are much more significant than methods that aim to correct for ascertainment bias. Our results suggest that to avoid false-positive associations, it is important to appropriately model secondary phenotypes in case–control genetic association studies.
PMCID: PMC3372941  PMID: 21933777
Case–control studies; Statistical genetics; Statistical methods in Epidemiology
3.  Hepatic sortilin regulates both apolipoprotein B secretion and LDL catabolism 
The Journal of Clinical Investigation  2012;122(8):2807-2816.
Genome-wide association studies (GWAS) have identified a genetic variant at a locus on chromosome 1p13 that is associated with reduced risk of myocardial infarction, reduced plasma levels of LDL cholesterol (LDL-C), and markedly increased expression of the gene sortilin-1 (SORT1) in liver. Sortilin is a lysosomal sorting protein that binds ligands both in the Golgi apparatus and at the plasma membrane and traffics them to the lysosome. We previously reported that increased hepatic sortilin expression in mice reduced plasma LDL-C levels. Here we show that increased hepatic sortilin not only reduced hepatic apolipoprotein B (APOB) secretion, but also increased LDL catabolism, and that both effects were dependent on intact lysosomal targeting. Loss-of-function studies demonstrated that sortilin serves as a bona fide receptor for LDL in vivo in mice. Our data are consistent with a model in which increased hepatic sortilin binds intracellular APOB-containing particles in the Golgi apparatus as well as extracellular LDL at the plasma membrane and traffics them to the lysosome for degradation. We thus provide functional evidence that genetically increased hepatic sortilin expression both reduces hepatic APOB secretion and increases LDL catabolism, providing dual mechanisms for the very strong association between increased hepatic sortilin expression and reduced plasma LDL-C levels in humans.
PMCID: PMC3408750  PMID: 22751103
4.  Dense Genotyping of Candidate Gene Loci Identifies Variants Associated with High-Density Lipoprotein Cholesterol 
Plasma levels of high density lipoprotein cholesterol (HDL-C) are known to be heritable, but only a fraction of the heritability is explained. We used a high density genotyping array containing SNPs from HDL-C candidate genes selected on known biology of HDL-C metabolism, mouse genetic studies, and human genetic association studies. SNP selection was based on tagging-SNPs but also included low-frequency nonsynonymous SNPs.
Methods and Results
Association analysis in a cohort containing extremes of HDL-C (case-control, n=1733) provided a discovery phase, with replication in three additional populations for a total meta-analysis in 7,857 individuals. We replicated the majority of loci identified through genome wide association studies and present on the array (including ABCA1, APOA1/C3/A4/A5, APOB, APOE/C1/C2, CETP, CTCF-PRMT8, FADS1/2/3, GALNT2, LCAT, LILRA3, LIPC, LIPG, LPL, LRP4, SCARB1, TRIB1, ZNF664), and provide evidence suggestive of association in several previously unreported candidate gene loci (including ABCG1, GPR109A/B/81, NFKB1, PON1/2/3/4). There was evidence for multiple, independent association signals in five loci, including association with low frequency nonsynonymous variants.
Genetic loci associated with HDL-C are likely to harbor multiple, independent causative variants, frequently with opposite effects on the HDL-C phenotype. Cohorts composed of extreme individuals may be efficiently used in a case-control discovery of quantitative traits.
PMCID: PMC3319351  PMID: 21303902
lipids; genetic association; HDL cholesterol; cardiovascular diseases
5.  Gene-based interaction analysis by incorporating external linkage disequilibrium information 
Gene–gene interactions have an important role in complex human diseases. Detection of gene–gene interactions has long been a challenge due to their complexity. The standard method aiming at detecting SNP–SNP interactions may be inadequate as it does not model linkage disequilibrium (LD) among SNPs in each gene and may lose power due to a large number of comparisons. To improve power, we propose a principal component (PC)-based framework for gene-based interaction analysis. We analytically derive the optimal weight for both quantitative and binary traits based on pairwise LD information. We then use PCs to summarize the information in each gene and test for interactions between the PCs. We further extend this gene-based interaction analysis procedure to allow the use of imputation dosage scores obtained from a popular imputation software package, MACH, which incorporates multilocus LD information. To evaluate the performance of the gene-based interaction tests, we conducted extensive simulations under various settings. We demonstrate that gene-based interaction tests are more powerful than SNP-based tests when more than two variants interact with each other; moreover, tests that incorporate external LD information are generally more powerful than those that use genotyped markers only. We also apply the proposed gene-based interaction tests to a candidate gene study on high-density lipoprotein. As our method operates at the gene level, it can be applied to a genome-wide association setting and used as a screening tool to detect gene–gene interactions.
PMCID: PMC3025792  PMID: 20924406
gene–gene interaction; linkage disequilibrium; imputation
6.  Mining the LIPG Allelic Spectrum Reveals the Contribution of Rare and Common Regulatory Variants to HDL Cholesterol 
PLoS Genetics  2011;7(12):e1002393.
Genome-wide association studies (GWAS) have successfully identified loci associated with quantitative traits, such as blood lipids. Deep resequencing studies are being utilized to catalogue the allelic spectrum at GWAS loci. The goal of these studies is to identify causative variants and missing heritability, including heritability due to low frequency and rare alleles with large phenotypic impact. Whereas rare variant efforts have primarily focused on nonsynonymous coding variants, we hypothesized that noncoding variants in these loci are also functionally important. Using the HDL-C gene LIPG as an example, we explored the effect of regulatory variants identified through resequencing of subjects at HDL-C extremes on gene expression, protein levels, and phenotype. Resequencing a portion of the LIPG promoter and 5′ UTR in human subjects with extreme HDL-C, we identified several rare variants in individuals from both extremes. Luciferase reporter assays were used to measure the effect of these rare variants on LIPG expression. Variants conferring opposing effects on gene expression were enriched in opposite extremes of the phenotypic distribution. Minor alleles of a common regulatory haplotype and noncoding GWAS SNPs were associated with reduced plasma levels of the LIPG gene product endothelial lipase (EL), consistent with its role in HDL-C catabolism. Additionally, we found that a common nonfunctional coding variant associated with HDL-C (rs2000813) is in linkage disequilibrium with a 5′ UTR variant (rs34474737) that decreases LIPG promoter activity. We attribute the gene regulatory role of rs34474737 to the observed association of the coding variant with plasma EL levels and HDL-C. Taken together, the findings show that both rare and common noncoding regulatory variants are important contributors to the allelic spectrum in complex trait loci.
Author Summary
Genetic association studies have identified genomic regions that affect quantifiable traits such as lipid levels. When a gene and a trait are found to be associated with one another, the gene is often further studied to determine its role in affecting the trait. One approach is to sequence the gene in individuals at the extremes of the trait's distribution with the hope of finding rare mutations that directly contribute to the trait. Until now studies using this approach have focused on genetic variation in the protein coding sequence of these genes and have been largely successful in identifying functionally important mutations. However, other studies have found an abundance of noncoding variation in the genome that may also contribute to the heritability of these traits. Here we seek to determine the contribution of such noncoding mutations to high density lipoprotein cholesterol (HDL-C) levels in humans using the HDL-C candidate gene LIPG as an example. Through a sequencing study in individuals with high and low HDL-C levels, we demonstrate that both rare and common noncoding mutations are influential contributors to the allelic spectrum of such traits and should be further characterized after initial association with the trait.
PMCID: PMC3234219  PMID: 22174694
7.  Pathway-Wide Association Study Implicates Multiple Sterol Transport and Metabolism Genes in HDL Cholesterol Regulation 
Pathway-based association methods have been proposed to be an effective approach in identifying disease genes, when single-marker association tests do not have sufficient power. The analysis of quantitative traits may be benefited from these approaches, by sampling from two extreme tails of the distribution. Here we tested a pathway association approach on a small genome-wide association study (GWAS) on 653 subjects with extremely high high-density lipoprotein cholesterol (HDL-C) levels and 784 subjects with low HDL-C levels. We identified 102 genes in the sterol transport and metabolism pathways that collectively associate with HDL-C levels, and replicated these association signals in an independent GWAS. Interestingly, the pathways include 18 genes implicated in previous GWAS on lipid traits, suggesting that genuine HDL-C genes are highly enriched in these pathways. Additionally, multiple biologically relevant loci in the pathways were not detected by previous GWAS, including genes implicated in previous candidate gene association studies (such as LEPR, APOA2, HDLBP, SOAT2), genes that cause Mendelian forms of lipid disorders (such as DHCR24), and genes expressing dyslipidemia phenotypes in knockout mice (such as SOAT1, PON1). Our study suggests that sampling from two extreme tails of a quantitative trait and examining genetic pathways may yield biological insights from smaller samples than are generally required using single-marker analysis in large-scale GWAS. Our results also implicate that functionally related genes work together to regulate complex quantitative traits, and that future large-scale studies may benefit from pathway-association approaches to identify novel pathways regulating HDL-C levels.
PMCID: PMC3268595  PMID: 22303337
GWAS; lipid; HDL-C; pathway analysis; cholesterol; sterol transport; sterol metabolism; genetic association
8.  Biological, Clinical, and Population Relevance of 95 Loci for Blood Lipids 
Teslovich, Tanya M. | Musunuru, Kiran | Smith, Albert V. | Edmondson, Andrew C. | Stylianou, Ioannis M. | Koseki, Masahiro | Pirruccello, James P. | Ripatti, Samuli | Chasman, Daniel I. | Willer, Cristen J. | Johansen, Christopher T. | Fouchier, Sigrid W. | Isaacs, Aaron | Peloso, Gina M. | Barbalic, Maja | Ricketts, Sally L. | Bis, Joshua C. | Aulchenko, Yurii S. | Thorleifsson, Gudmar | Feitosa, Mary F. | Chambers, John | Orho-Melander, Marju | Melander, Olle | Johnson, Toby | Li, Xiaohui | Guo, Xiuqing | Li, Mingyao | Cho, Yoon Shin | Go, Min Jin | Kim, Young Jin | Lee, Jong-Young | Park, Taesung | Kim, Kyunga | Sim, Xueling | Ong, Rick Twee-Hee | Croteau-Chonka, Damien C. | Lange, Leslie A. | Smith, Joshua D. | Song, Kijoung | Zhao, Jing Hua | Yuan, Xin | Luan, Jian'an | Lamina, Claudia | Ziegler, Andreas | Zhang, Weihua | Zee, Robert Y.L. | Wright, Alan F. | Witteman, Jacqueline C.M. | Wilson, James F. | Willemsen, Gonneke | Wichmann, H-Erich | Whitfield, John B. | Waterworth, Dawn M. | Wareham, Nicholas J. | Waeber, Gérard | Vollenweider, Peter | Voight, Benjamin F. | Vitart, Veronique | Uitterlinden, Andre G. | Uda, Manuela | Tuomilehto, Jaakko | Thompson, John R. | Tanaka, Toshiko | Surakka, Ida | Stringham, Heather M. | Spector, Tim D. | Soranzo, Nicole | Smit, Johannes H. | Sinisalo, Juha | Silander, Kaisa | Sijbrands, Eric J.G. | Scuteri, Angelo | Scott, James | Schlessinger, David | Sanna, Serena | Salomaa, Veikko | Saharinen, Juha | Sabatti, Chiara | Ruokonen, Aimo | Rudan, Igor | Rose, Lynda M. | Roberts, Robert | Rieder, Mark | Psaty, Bruce M. | Pramstaller, Peter P. | Pichler, Irene | Perola, Markus | Penninx, Brenda W.J.H. | Pedersen, Nancy L. | Pattaro, Cristian | Parker, Alex N. | Pare, Guillaume | Oostra, Ben A. | O'Donnell, Christopher J. | Nieminen, Markku S. | Nickerson, Deborah A. | Montgomery, Grant W. | Meitinger, Thomas | McPherson, Ruth | McCarthy, Mark I. | McArdle, Wendy | Masson, David | Martin, Nicholas G. | Marroni, Fabio | Mangino, Massimo | Magnusson, Patrik K.E. | Lucas, Gavin | Luben, Robert | Loos, Ruth J. F. | Lokki, Maisa | Lettre, Guillaume | Langenberg, Claudia | Launer, Lenore J. | Lakatta, Edward G. | Laaksonen, Reijo | Kyvik, Kirsten O. | Kronenberg, Florian | König, Inke R. | Khaw, Kay-Tee | Kaprio, Jaakko | Kaplan, Lee M. | Johansson, Åsa | Jarvelin, Marjo-Riitta | Janssens, A. Cecile J.W. | Ingelsson, Erik | Igl, Wilmar | Hovingh, G. Kees | Hottenga, Jouke-Jan | Hofman, Albert | Hicks, Andrew A. | Hengstenberg, Christian | Heid, Iris M. | Hayward, Caroline | Havulinna, Aki S. | Hastie, Nicholas D. | Harris, Tamara B. | Haritunians, Talin | Hall, Alistair S. | Gyllensten, Ulf | Guiducci, Candace | Groop, Leif C. | Gonzalez, Elena | Gieger, Christian | Freimer, Nelson B. | Ferrucci, Luigi | Erdmann, Jeanette | Elliott, Paul | Ejebe, Kenechi G. | Döring, Angela | Dominiczak, Anna F. | Demissie, Serkalem | Deloukas, Panagiotis | de Geus, Eco J.C. | de Faire, Ulf | Crawford, Gabriel | Collins, Francis S. | Chen, Yii-der I. | Caulfield, Mark J. | Campbell, Harry | Burtt, Noel P. | Bonnycastle, Lori L. | Boomsma, Dorret I. | Boekholdt, S. Matthijs | Bergman, Richard N. | Barroso, Inês | Bandinelli, Stefania | Ballantyne, Christie M. | Assimes, Themistocles L. | Quertermous, Thomas | Altshuler, David | Seielstad, Mark | Wong, Tien Y. | Tai, E-Shyong | Feranil, Alan B. | Kuzawa, Christopher W. | Adair, Linda S. | Taylor, Herman A. | Borecki, Ingrid B. | Gabriel, Stacey B. | Wilson, James G. | Stefansson, Kari | Thorsteinsdottir, Unnur | Gudnason, Vilmundur | Krauss, Ronald M. | Mohlke, Karen L. | Ordovas, Jose M. | Munroe, Patricia B. | Kooner, Jaspal S. | Tall, Alan R. | Hegele, Robert A. | Kastelein, John J.P. | Schadt, Eric E. | Rotter, Jerome I. | Boerwinkle, Eric | Strachan, David P. | Mooser, Vincent | Holm, Hilma | Reilly, Muredach P. | Samani, Nilesh J | Schunkert, Heribert | Cupples, L. Adrienne | Sandhu, Manjinder S. | Ridker, Paul M | Rader, Daniel J. | van Duijn, Cornelia M. | Peltonen, Leena | Abecasis, Gonçalo R. | Boehnke, Michael | Kathiresan, Sekar
Nature  2010;466(7307):707-713.
Serum concentrations of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with serum lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 × 10-8), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (e.g., CYP7A1, NPC1L1, and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and impact lipid traits in three non-European populations (East Asians, South Asians, and African Americans). Our results identify several novel loci associated with serum lipids that are also associated with CAD. Finally, we validated three of the novel genes—GALNT2, PPP1R3B, and TTC39B—with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD.
PMCID: PMC3039276  PMID: 20686565
9.  The T111I variant in the endothelial lipase gene and risk of coronary heart disease in three independent populations 
European Heart Journal  2009;30(13):1584-1589.
Endothelial lipase (LIPG) is implicated in the metabolism of high-density lipoprotein cholesterol (HDL-C). Small studies in selected populations have reported higher HDL-C levels among carriers of the common T111I variant in LIPG, but whether this variant is associated with plasma lipids and risk of coronary heart disease (CHD) in the general population is unclear. The objective of this study was to address the associations of the T111I variant with plasma lipids and risk of CHD in three independent prospective studies of generally healthy men and women.
Methods and results
The T111I variant was genotyped in case–control studies of CHD nested within the Diet, Cancer, and Health study with 998 cases, Nurses’ Health Study with 241 cases, and Health Professionals Follow-up Study with 262 cases. The minor allele frequency in the combined pool of controls was 0.29. The T111I variant was not associated with HDL-C or any other lipid and lipoprotein measures. Compared with wildtype homozygotes, the pooled estimate for risk of CHD was 0.95 (0.85–1.06) per T111I allele.
Our analysis among healthy Caucasian men and women from three independent studies does not support an association between the T111I variant and HDL-C, other plasma lipids, or risk of CHD.
PMCID: PMC2733737  PMID: 19411665
Genetic epidemiology; Endothelial lipase; HDL-cholesterol; CHD-risk
10.  Loss-of-function variants in endothelial lipase are a cause of elevated HDL cholesterol in humans 
The Journal of Clinical Investigation  2009;119(4):1042-1050.
Elevated plasma concentrations of HDL cholesterol (HDL-C) are associated with protection from atherosclerotic cardiovascular disease. Animal models indicate that decreased expression of endothelial lipase (LIPG) is inversely associated with HDL-C levels, and genome-wide association studies have identified LIPG variants as being associated with HDL-C levels in humans. We hypothesized that loss-of-function mutations in LIPG may result in elevated HDL-C and therefore performed deep resequencing of LIPG exons in cases with elevated HDL-C levels and controls with decreased HDL-C levels. We identified a significant excess of nonsynonymous LIPG variants unique to cases with elevated HDL-C. In vitro lipase activity assays demonstrated that these variants significantly decreased endothelial lipase activity. In addition, a meta-analysis across 5 cohorts demonstrated that the low-frequency Asn396Ser variant is significantly associated with increased HDL-C, while the common Thr111Ile variant is not. Functional analysis confirmed that the Asn396Ser variant has significantly decreased lipase activity both in vitro and in vivo, while the Thr111Ile variant has normal lipase activity. Our results establish that loss-of-function mutations in LIPG lead to increased HDL-C levels and support the idea that inhibition of endothelial lipase may be an effective mechanism to raise HDL-C.
PMCID: PMC2662558  PMID: 19287092
11.  Concept, Design and Implementation of a Cardiovascular Gene-Centric 50 K SNP Array for Large-Scale Genomic Association Studies 
PLoS ONE  2008;3(10):e3583.
A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a “cosmopolitan” tagging approach to capture the genetic diversity across ∼2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.
PMCID: PMC2571995  PMID: 18974833

Results 1-11 (11)