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. 2014 Feb 6;94(2):233-45.
doi: 10.1016/j.ajhg.2014.01.010.

Whole-exome Sequencing Identifies Rare and Low-Frequency Coding Variants Associated With LDL Cholesterol

Leslie A Lange  1 Youna Hu  2 He Zhang  3 Chenyi Xue  4 Ellen M Schmidt  4 Zheng-Zheng Tang  5 Chris Bizon  6 Ethan M Lange  7 Joshua D Smith  8 Emily H Turner  8 Goo Jun  2 Hyun Min Kang  2 Gina Peloso  9 Paul Auer  10 Kuo-Ping Li  2 Jason Flannick  11 Ji Zhang  3 Christian Fuchsberger  2 Kyle Gaulton  12 Cecilia Lindgren  12 Adam Locke  2 Alisa Manning  13 Xueling Sim  2 Manuel A Rivas  12 Oddgeir L Holmen  14 Omri Gottesman  15 Yingchang Lu  16 Douglas Ruderfer  17 Eli A Stahl  17 Qing Duan  1 Yun Li  18 Peter Durda  19 Shuo Jiao  20 Aaron Isaacs  21 Albert Hofman  22 Joshua C Bis  23 Adolfo Correa  24 Michael E Griswold  24 Johanna Jakobsdottir  25 Albert V Smith  26 Pamela J Schreiner  27 Mary F Feitosa  28 Qunyuan Zhang  28 Jennifer E Huffman  29 Jacy Crosby  30 Christina L Wassel  31 Ron Do  9 Nora Franceschini  32 Lisa W Martin  33 Jennifer G Robinson  34 Themistocles L Assimes  35 David R Crosslin  36 Elisabeth A Rosenthal  37 Michael Tsai  27 Mark J Rieder  8 Deborah N Farlow  38 Aaron R Folsom  27 Thomas Lumley  39 Ervin R Fox  24 Christopher S Carlson  20 Ulrike Peters  20 Rebecca D Jackson  40 Cornelia M van Duijn  21 André G Uitterlinden  41 Daniel Levy  42 Jerome I Rotter  43 Herman A Taylor  44 Vilmundur Gudnason Jr  26 David S Siscovick  45 Myriam Fornage  46 Ingrid B Borecki  28 Caroline Hayward  29 Igor Rudan  47 Y Eugene Chen  3 Erwin P Bottinger  15 Ruth J F Loos  16 Pål Sætrom  48 Kristian Hveem  14 Michael Boehnke  2 Leif Groop  49 Mark McCarthy  50 Thomas Meitinger  51 Christie M Ballantyne  52 Stacey B Gabriel  53 Christopher J O'Donnell  54 Wendy S Post  55 Kari E North  32 Alexander P Reiner  56 Eric Boerwinkle  30 Bruce M Psaty  57 David Altshuler  58 Sekar Kathiresan  59 Dan-Yu Lin  5 Gail P Jarvik  60 L Adrienne Cupples  61 Charles Kooperberg  20 James G Wilson  62 Deborah A Nickerson  8 Goncalo R Abecasis  2 Stephen S Rich  63 Russell P Tracy  64 Cristen J Willer  65 NHLBI Grand Opportunity Exome Sequencing Project
Collaborators, Affiliations
Free PMC article

Whole-exome Sequencing Identifies Rare and Low-Frequency Coding Variants Associated With LDL Cholesterol

Leslie A Lange et al. Am J Hum Genet. .
Free PMC article

Abstract

Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or <2(nd) percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments.

Figures

Figure 1
Figure 1
Effect Sizes Observed for Gene-Based Burden Tests Relative to GWAS Variants Effect sizes are shown in SD units for genes identified by burden tests of nonsynonymous (NS) and splice variants (blue), LoF variants only (red), and GWAS variants (black) from Willer et al. For genes, the burden effect sizes and burden frequencies are plotted. For GWAS variants, the observed effect sizes and MAFs are plotted. The burden frequencies for the gene-based tests (red and black) were observed in this study, whereas the GWAS-variant frequencies are plotted as reported in Willer et al. The alpha level was set to 5 × 10−7 to reflect the significance threshold used for gene-based burden tests.
Figure 2
Figure 2
LDL-C Values for Individuals with Different Types of Genetic Variants in Four LDL-C-Associated Genes A representation of LDL-C values for each individual with a rare variant in (A) PCSK9, (B) LDLR, (C) APOB, or (D) PNPLA5. The left side of the figure shows LDL-C levels per individual with a rare allele in the gene. On the right, bean plots indicate the mean (black line) and distribution (bean shape) of LDL-C values for individuals with a LoF variant, a missense variant, or no rare variant. (A) Individuals classified on the basis of variants in PCSK9 (MAF < 5% is considered rare). (B) Individuals classified by carrier status of variants in LDLR (MAF < 0.1% is considered rare on the basis of the most significant burden test for this gene). (C) Individuals classified by genetic status at APOB (MAF < 5% is considered rare). (D) Individuals with a rare variant in PNPLA5 (nonsynonymous or splice with MAF < 0.1%) are shown.

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