Discovery and Refinement of Loci Associated with Lipid Levels

© 2013 Nature America, Inc. All rights reserved. * from GWAS of other CAD risk factors and related traits and (50,459SNPs) GWAS SNPs),previous (14,886 our from loci promising senting 1 Table Supplementary array Metabochip the with genotyped 94,595 arrays (GWAS) study tion associa including genome-wide with ancestry, genotyped studies 23 from European individuals of subjects examined We New RESULTS CAD. for factors risk into genome. the in These results provide elements direction for biological and therapeutic research functional other and genes of SNPs those with lipid-associated of locations the compared and relationships loci uncover between to analyses pathway out carried levels, sion expres mRNA with association for SNPs Welipid-associated tested do not include genes implicated in lipid biology by previous literature. at lipid levels with associated loci 157 identify analyses Our individuals. non-European- ancestry 7,898 and individuals European-ancestry 188,577 levels lipid blood in changes cate pathways and targets therapeutic that enable important clinically variance trait of total for ~10–12% accounting lipids, blood with associated 95 loci families in dyslipidemia of forms delian focused on genetic variation can proceed through large-scale association analyses organisms model in studies ment comple can and disease heart of prevention the and management cholesterol for therapies new for targets identify can levels lipid of (CAD) disease artery for coronary factors risk modifiable are lipids heritable, Blood lipids of coronary We genotyping associated lipids, are Levels Consortium Lipids Global Genetics Discovery and refinement of loci associated with lipid levels Nature Ge Nature Received 12 October 2012; accepted 13 September 2013; published online 6 October 2013; Full Full lists of authors and affiliations appear at the end of the paper.

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© 2013 Nature America, Inc. All rights reserved. tion of nuclear hormone receptors, which have an important role in in role important an have which receptors, hormone nuclear of tion UGT1A1 and others) among pathways, transport lipid by loci and X the retinoic to associated receptor activation previously bolic process and bile acid biosynthesis pathways), AKR1C4 pathways), process metabolic and steroid by cholesterol the loci ated pathway), activity lipase triglyceride the in genes by loci associated previously to (connected ( loci identified at 1 in gene least 20 included of pathways our newly These pathways. enriched 71 identified MAGENTA loci, 157 the Across loci. ciated asso among pathways of biological over-representation the evaluate We using MAGENTA a analysis, gene set enrichment performed Pathway on 8.7 data, sets, SNP control in average observed in (25 publications more or 3 by implicated mate manual curation of the results, text-mining we on focused genes To approxi (LD). disequilibrium linkage in proxies of gene number and nearest the to distance frequency, allele for matched SNPs of permutations 100 using search literature text-mining our repeated we results, of review manual and search automated after candidates build on overlapping knowledge, they are not truly independent. implicated loci ( nuclear receptor activation (RXR) pathway, which includesalso genes ple, ( literature-identifiedcandidates triglyceride and HDL cholesterol levels. by associating variants near tissuesperipheral to have an unexpected role in the targeting gested ofthat lipidsvascular endothelial growth factors latter,of studiesthe recent morerecent, suchas candidatesconnectionwhose tolipid levels is as (such lines cell human models (suchas mouse in documented extensively been have metabolism lipid to connections whose genes highlighted search This levels. role of a nearby gene in regulating blood lipid 30 loci, we found no literature support for the ( (52%) loci 62 the of 32 incandidate strong 1 least at identified we curation, manual After keywords. relevant of context the in aliases for occurrences of these gene names and their segment. appropriate the in shown are traits more or two with association show that Loci below. listed are names locus and name, trait the after parentheses in listed is trait one only with associated primarily loci of number The traits. lipid multiple with association show that loci of number the illustrates Venn diagram The traits. lipid different 1 Figure s e l c i t r A in genes of regulation transcriptional the through metabolism lipid SupplementaryTable 4 APOB To estimate the probability of finding finding of probability the estimate To Multiple types of evidence supported several VLDLR , ,

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© 2013 Nature America, Inc. All rights reserved. ANXA9 Locus T were we inconclusive, variants now of found convincing evidence coding of studies association previous where loci some In Coding that didates were by not supported literature or pathway analyses. TMEM176A and and the literature same candidate identified ( review analysis eQTL three, all In genes. candidate prioritized also searches CPS1 ANGPTL1 HDGF PIGV Locus T Nature Ge Nature SETD2 ATG7 EHBP1 LOC84931 INSIG2 FAM13A C4orf52 GSK3B STAB1 RBM5 FN1 SNX13 DAGLB RSPO3 ADH5 ACAD11 CMTM6 MOGAT2 KAT5 OR4C46 MARCH8 TMEM176A IKZF1 SNP, rs9930333(within1Mbofrs1121980, a with twoormoretraitsatgenome-widesignificance,thetraitcorrespondingtostrongest Chr., chromosome;A1,minorallele;A2,majorTG,triglycerides;TC,totalcholesterol.Effectsizesaregivenwithrespecttotheallele(A1)ins.d.Forlociassociated HAS1 FTO ZBTB42 MIR148A CSNK1G3 BRCA2 SOX17 SPTLC3 APOH MTMR3 SNX5 a with twoormoretraitsatgenome-widesignificance, thetraitcorrespondingtostrongest Chr., chromosome;A1,minorallele;A2,major TG,triglycerides;TC,totalcholesterol.Effectsizesaregivenwithrespecttothe minorallele(A1)ins.d.Forlociassociated SNP, rs13076253(within1 Mbofrs17404153, The secondarytraitwasmoststronglyassociatedwithadifferentSNP:rs719726(within1Mbofrs1936800, The secondarytraitwas moststronglyassociatedwithadifferentSNP, rs17526895 (within1Mbofrs10490626, able able 2 able 1 - PXK NR0B2 - - PRXCA PMVK - CERS2 - -

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DGAT2 ALOX5 variation New New loci primarily associated with HD New New loci primarily associated with ; ; P n = 0.05). For the remaining four loci (near (near loci four remaining For the 0.05). = and etics rs267733 Marker name rs2710642 rs2030746 rs10490626 rs1250229 rs17404153 rs7640978 rs4722551 rs4530754 rs4942486 rs10102164 rs364585 rs1801689 rs5763662 rs2328223 rs12748152 Marker name rs2290547 rs2606736 rs1047891 rs4650994 rs12145743 rs3822072 rs10019888 rs6805251 rs13326165 rs2013208 rs4142995 rs702485 rs1936800 rs2602836 rs499974 rs12801636 rs11246602 rs970548 rs17173637 rs4917014 rs17695224 rs1121980 rs4983559 GPR146

ADVANCE ONLINE PUBLICATION ONLINE ADVANCE ), analysis of expression levels identified can Chr. 13 17 20 20 22 Chr. 11 11 11 10 19 16 14 1 2 2 2 3 2 3 7 5 8 2 1 1 1 4 3 3 3 3 3 4 7 7 7 7 6 4 position (Mb) position (Mb) r 2 150.96 121.31 118.84 216.30 132.16 122.86 =0.99). 211.54 178.52 156.70 119.56 127.44 100.01 150.53 105.28 r hg19 63.15 32.53 32.95 55.42 25.99 64.21 17.85 12.96 30.38 2 27.14 47.06 11.40 89.74 26.06 52.53 50.13 50.31 17.92 75.46 65.39 51.51 46.01 52.32 53.81 hg19 =0.00). 6.45

L c The secondarytraitwas moststronglyassociatedwiththedifferent SNPrs4719841(within1Mbofrs4722551, D HDL HDL HDL HDL, LDL,TG HDL HDL HDL HDL HDL HDL HDL HDL HDL HDL HDL HDL, TG HDL HDL HDL HDL HDL, TC HDL HDL, TG HDL LDL LDL LDL, TC LDL LDL, TC LDL, HDL LDL, TC LDL, TC LDL LDL, TC LDL, TG LDL LDL LDL LDL Associated Associated L L trait(s) cholesterol levels obtained from joint GWA trait(s) cholesterol levels obtained from joint GWA DAGLB

a c a b , TC RBM5 b

association, association, 0.16 0.08 0.35 0.40 0.09 0.27 0.46 0.14 0.21 0.20 0.04 0.48 0.21 0.38 0.04 MAF , , 0.09 0.20 0.39 0.33 0.49 0.34 0.46 0.18 0.39 0.21 0.50 0.38 0.45 0.49 0.44 0.19 0.23 0.15 0.26 0.12 0.32 0.26 0.43 0.40 MAF , , SPTLC3 ADH5 Minor/major Minor/major allele - , G/A A/G G/A G/A A/G A/G T/G C/A C/A T/C T/C T/C T/C C/T T/C allele

A/G G/A G/A A/G G/A A/G A/G A/G A/G A/G G/A G/T T/G G/T A/C A/C C/A T/C C/T T/C T/C C/T C/T C/T other complex traits complex other to that for is similar variation and coding loci associated lap between over ~30% This on distance). no restriction was there when loci (18 ( LD strong in and of kb 100 within variant nonsynonymous 1 least at was there 1 × 10 p.Cys325Gly; (APOH rs1801689 was variant associated achievable strongly sizes sample large the in example, For the collaboration. through of benefits the demonstrating P P valueislistedfirst. valueislistedfirst. 0.039, 0.023,0.029 −0.051, 0.050,0.037 −0.051, −0.042 −0.039, −0.038 −0.028, −0.023 −0.034, −0.028 −11 0.021, 0.020 0.032, 0.030 0.020, −0.020 −0.020, 0.021 Effect ofA1 0.026, 0.025 Effect ofA1 for LDL cholesterol levels). Overall, at 15 for Overall, of LDL levels). the 62 cholesterol new loci, r −0.033 −0.024 −0.024 −0.025 2 0.024 0.103 0.077 =0.74). 0.03 −0.027 −0.027 −0.030 −0.025 −0.036 −0.026 −0.026 −0.029 0.025 0.021 0.020 0.020 0.029 0.025 0.024 0.019 0.024 0.034 0.022 0.020 r r 2 2 =0.98). > 0.8) with the index SNP ( SNP index the with 0.8) > b The secondarytraitwasmoststronglyassociatedwithadifferent 9 b S S The secondarytraitwas moststronglyassociatedwithadifferent . Unexpectedly, in the 11 loci where a candidate a where . candidate in 11 the loci Unexpectedly, and Metabochip meta-analysis and Metabochip meta-analysis Joint 173, 178,187 Joint 187, 173,178 173, 184 173, 187 172, 186 173, 187 172, 187 173, 187 n 187, 168 187, 187 186, 155 165 173 173 172 111 171 172 163 (×1,000) n 187 181 187 129 182 187 186 187 170 187 165 187 187 187 187 187 176 184 185 184 (×1,000) 6 ×10 5 ×10 9 ×10 2 ×10 3 ×10 2 ×10 1 ×10 4 ×10 4 ×10 2 ×10 4 ×10 4 ×10 1 ×10 1 ×10 6 ×10 1 ×10 5 ×10 9 ×10 7 ×10 2 ×10 5 ×10 1 ×10 9 ×10 9 ×10 4 ×10 9 ×10 6 ×10 3 ×10 5 ×10 4 ×10 3 ×10 2 ×10 2 ×10 2 ×10 1 ×10 1 ×10 2 ×10 7 ×10 1 ×10 Supplementary Table 7) Table Supplementary APOH −9 −9 −9 −12 −8 −8 −9 −11 −14 −12 −11 −10 −11 −8 −9 , 4×10 , 2×10 , 5×10 −9 −8 −15 −8 −11 −12 −9 −8 −10 −8 −8 −12 −12 −10 −8 −12 −8 −8 −10 −10 −8 −9 −8 −13 , 6×10 , 2×10 , 5×10 , 9×10 Joint , 3×10 Joint s e l c i t r A , 3×10 , 3×10 , 8×10 locus P −8 −9 −8 P value −9 −9 −11 −11 r value 2 −8 2 =0.10). −12 −8 −9 2 , 7.0×10 , our most most our , , 1×10 P −9 = −9 -

© 2013 Nature America, Inc. All rights reserved. for enrichment (and, when present, enrichment was weaker). et al. Supplementary Table 9 with twoormoretraits atgenome-widesignificance,thetrait correspondingtothestrongest Chr., chromosome;A1,minorallele;A2,major TG,triglycerides;TC,totalcholesterol.Effectsizesaregivenwithrespecttothe minorallele(A1)ins.d.Forlociassociated PEPD INSR MPP3 PDXDC1 AKR1C4 MET VEGFA LRPAP1 Locus T enrichment; (3.7-fold activity’ enhancer ‘strong with 10 of the 15 functional chromatin states by defined Ernst 8 in enriched were SNPs lipid-associated cells, HepG2 In Methods). tance to the nearest gene and number of SNPs in LD ( lists of permuted SNPs, matched for minor allele frequency (MAF), dis humanTo hepatocytes. wegenerated significance, 100,000 determine of HepG2commonlymodel elementsin a tional cells, identified used func annotated SNPsoverlappedexperimentally associated whether data (ENCODE) Elements DNA of Encyclopedia cells liver in regulation gene in changes with associated be might variants lipid-associated that evidence is there and synthesis, prioritized candidate genes. The liver is an important hub of lipid bio withoutremain loci identified newly 62 the of 18 efforts, Despiteour transcription Overlap interactions. protein-protein or ways of than for and path analyses eQTL studies ation was less significant literature agreement and between review examination of vari coding thus, times); of 3.8 by chance overlap expected the to compared 0.03 ( of 7 times coincided methods these from examination candidates the variation, by and review literature by suggested was a traits atgenome-widesignificance,thetraitcorrespondingtostrongest Chr., chromosome;A1,minorallele;A2,majorTC,totalcholesterol.Effectsizesaregiven withrespecttotheminorallele(A1)ins.d.Forlociassociatedtwoormore PPARA TOM1 DLG4 PHC1 PHLDB1 VIM VLDLR GPR146 HBS1L KCNK17 PXK UGT1A1 FAM117B ABCB11 ASAP3 Locus T s e l c i t r A a At onelocus,thesecondarytraitwasmoststronglyassociatedwithadifferentSNP, rs4253776(within1Mbofrs4253772, At onelocus,secondary traitsweremoststronglyassociatedwitha differentSNP, rs6818397(within1Mbofrs6831256, able able 4 able 3

−5 - CUBN ; , no more than three functional chromatin states showed evidence - Supplementary Table 8 A2ML1

New New loci primarily associated with total cholesterol levels obtained from joint GWA New New loci primarily associated with triglyceride levels obtained from joint GWA

between Marker name rs731839 rs7248104 rs8077889 rs3198697 rs1832007 rs38855 rs998584 rs6831256

in rs10904908 rs3780181 rs1997243 rs9376090 rs2758886 rs13315871 rs11563251 rs11694172 rs2287623 rs1077514 Marker name rs4253772 rs138777 rs314253 rs4883201 rs11603023

liver

association ). In the other eight cell types examined by Ernst Chr. 19 19 17 16 10 7 6 4 ). The strongest enrichment was in regions hg19 position Chr. 22 22 17 12 11 10

116.36 signals 9 7 6 6 3 2 2 2 1 33.90 41.88 15.13 43.76 (Mb) 7.22 5.25 3.47 hg19 position

and 118.49 135.41 234.68 203.53 169.83 46.63 35.71 17.26 39.25 58.38 23.77 (Mb) TG, HDL TG TG TG TG TG TG, HDL TG, TC 7.09 9.08 2.64 1.08 Associated

regulators trait(s) a , LDL 2 r 3 2 a , we evaluated evaluated we , Associated et al. P > 0.8) (Online TC, LDL TC TC, LDL TC TC TC TC, LDL TC TC TC TC TC, LDL TC TC TC trait(s) = 2 × 10 × 2 = 0.35 0.42 0.22 0.43 0.18 0.47 0.49 0.42

MAF of 2 P 4

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a . Using . P coding coding Minor/major < 1 × −25 P allele G/A A/G G/A G/A G/A C/A A/C T/C = 0.11 0.36 0.37 0.12 0.42 0.43 0.08 0.16 0.28 0.30 0.10 0.12 0.25 0.41 0.15 MAF - - - - - ;

fine-mapping analysis for 65 loci: 60 selected for fine mapping on on mapping fine for selected 60 loci: 65 for analysis fine-mapping GWAS substan by often identified those from were size effect and frequency in association different tially strongest the with variants that found loci cholesterol–associated LDL of mapping five fine Previous Initial ( expression gene or variation coding pathways, of analyses or review literature by gested Table 9 func a overlapped that SNP tional mark1 least at contained loci new 62 our ( HNF4A 10 × 2 associated loci and functional marks in HepG2 cells in more detail detail more in cells HepG2 in marks functional and loci associated tion of regulatory elements), elements), regulatory of tion isola (formaldehyde-assisted (FAIRE chromatin open of indicators (trimethylation of histone H3 at lysine 36 (H3K36me3), histone H3 at lysine 4 (H3K4me2), (H3K4me3), 4 lysine at H3 histone (H3K9ac), 9 lysine (H3K27ac), 27 lysine at H3 histone of (acetylation regions regulatory active with associated marks histone included loci lipid-associated with overlap significant ( P upeetr Tbe 9 Table Supplementary valueislistedfirst. e rcee t ivsiae h oelp ewe lipid- between overlap the investigate to proceeded We 0.026, 0.025,0.022 Minor/major 0.022, −0.022 0.029, −0.026

allele 1 −4 Effect ofA1 fine A/G G/A G/A G/A G/A A/G A/G G/A G/A , including all but 3 of the loci where no candidates were sug no candidates but where 3 all of loci the , including T/C C/T T/C C/T T/C C/T 0 . Metabochip genotypes enabled us to carry out an initial initial an out carry to us enabled genotypes Metabochip . −0.022 −0.020 −0.033 −0.019 ) and regions that interact with the transcription factors factors transcription the with interact that regions and ) 0.025 P = 6 × 10 × 6 =

2 mapping 4 and/or chromatin state a S −0.023, −0.024 −0.044, −0.044 DVANCE ONLINE PUBLICATION ONLINE DVANCE and Metabochip meta-analysis 0.032, 0.031 0.037, 0.034 r Effect ofA1 2 =0.18). r 2 −0.035 −0.025 −0.036 Supplementary Table 10 Supplementary −10 0.021 0.022 0.025 0.033 0.023 0.028 0.027 −0.03 =0.95). P S

= 3 × 10 × 3 = of and Metabochip meta-analysis ) and CEBP/B ( CEBP/B and )

P Joint 65 177, 187,173 = 3 × 10 × 3 = ). Notable regulatory elements showing showing elements regulatory Notable ).

176, 185 175, 184 lipid-associated N P 176 176 176 178 178 (×1,000) = 5 × 10 × 5 = −22 Joint P ), promoters (trimethylation of of (trimethylation promoters ), −20 = 8 × 10 2 185, 171 184, 170 186, 172 187, 173 P 3 highlighted in = 2 × 10 × 2 = n ; acetylation of histone H3 at H3 histone of acetylation ; 185 187 187 187 183 187 187 187 187 184 184 (×1,000) P = 1 × 10 × 1 = −9 3 ×10 5 ×10 1 ×10 2 ×10 2 ×10 2 ×10 3 ×10 2 × 10 ; DNase I sensitivity, sensitivity, I DNase ; −12 ).

loci −15 ), transcribed ), regions transcribed −12 −9 −10 −8 −8 −12 −8 −15

, 3×10 Joint Nature Ge Nature , 1 × 10 , 2×10 1 ×10 5 ×10 3 ×10 2 ×10 1 ×10 3 ×10 7 ×10 3 ×10 3 ×10 3 ×10 4 ×10 1 ×10 2 ×10 4 ×10 6 ×10 ; dimethylation of of dimethylation ; −5 ). Overall, 56 of of 56 Overall, ). Supplementary Supplementary Joint P P value −8 −8 −10 −9 −8 −11 −10 −10 −9 −8 −8 −9 −9 −12 −9 = 4 × 10 −9 −10 −11 , 3×10 , 5×10 P , 3×10 , 2×10 value , 2 × 10 n etics −14 −8 −8 P −10 −9 = −8 ), - - - -

© 2013 Nature America, Inc. All rights reserved. Thus, although the large changes observed by Sanna the large observed changes Thus, although (GWAS with ant GWAS variant with MAF ( Table 11 ( GWAS signal the from different clearly was signal fine-mapping the where loci eight identified mapping fine samples, to ationalized from the GWAS index SNP in terms of frequency and effect size (oper and was weak) (ii) was different relatively in the signal where regions genome- wide significant evidence for reached association (to avoid(i) chance it fluctuations whether evaluated and variant Metabochip traits. other with association of because study previous our of basis the Nature Ge Nature ( 0.05. > MAF had all loci fine-mapped in association strongest for in Except encoded variant p.Arg46Leu the typical. not are they unique, means no by are mapping P 10 mapping f P near from Sanna results with P 10 near mapped CAD with Of the two SNPs the new loci, association the strongest showing loci). overlapping CAD (eight with than each) loci overlapping (nine for the new greater loci, we overlap observed with BMI, SBP and DBP levels (17 loci; loci; (18 T2D for 10 DBP (29 loci; a 4.1-fold excess for BMI (32 loci; excess 5.1-fold for loci; CAD (40 significant nominally ( SNPs associated nominally ( DBP; and (SBP n (BMI; index 2 (T2D; type diabetes ( in CAD locus of each studies for genetic SNPs associated strongly most the evaluated we traits, related in here identified loci 157 the of role the Toevaluate cardiovascular Association studies functional APOA5 variant was present. For ancestry–specific African ( African of between and samples LD in in mapping fine differences enabled where populations loci three ancestry, European included loci ( loci five in variant GWAS origi nal the from distinct clearly SNPs associated identified analyses ancestry-specific sample sizes, small comparatively ancestry. Despite = 0.37, 0.37, = n n 7,6 individuals) 77,167 = = 3 × 10 = 1 × 10 × 1 = = 3 × 10 × 3 = = 46,186 non-diabetic individuals) non-diabetic 46,186 = 323, at sa ( Asian East 3,263), = −9 −5 −6 For each of these loci, we identified the most strongly associated associated strongly most the identified we loci, these of each For We also attempted fine mapping in samples with African African with samples in mapping fine attempted also We ), ), a 2.5-fold excess for SBP (20 loci; ) and and ) ; fine-mapping fine-mapping ; LDLR LRP4 , results are consistent with those of other fine-mapping and and fine-mapping other of those with consistent are results , f ). ). The two largest differences were at the loci near P f = 0.12, 0.12, = f = 0.03, 0.03, = −44 = 2 × 10 × 2 = CMTM6 = 0.24, 0.24, = ) or East Asian ( Asian East or ) −26 (GWAS n −5 ; ; fine-mapping variant etics n ; fine-mapping fine-mapping ; of P ), ), APOE P r = 123,865 individuals) 123,865 = = 1 × 10 2

IGF2R = 3 × 10

n lipid-related < 0.8 with the GWAS index SNP). In the European European the In SNP). GWAS index the with 0.8 < traits P = 69,395 individuals) 69,395 = P P 7 P

= 2 × 10 × 2 = , (rs7640978: (rs7640978: = 0.001) and a 2.2-fold excess for fasting glucose glucose fasting for excess 2.2-fold a and 0.001) = = 2 × 10 × 2 = −13 25 RBM5 = 1 × 10 × 1 = f f , consistent with the analyses in individuals of individuals in analyses the with consistent , ADVANCE ONLINE PUBLICATION ONLINE ADVANCE = 0.07, 0.07, = = 0.17, 0.17, = , 2 n ), ), 6 (GWAS = cases) 8,130 including 47,117, −9 . NPC1L1 −3 et et al. ), ), a 3.4-fold excess for WHR (27 loci; 3 n (rs2013208: (rs2013208: ) ) ( n 1 f ) ) = 0.24, = 114,590, including 37,653 cases) 37,653 including 114,590, = , systolic and diastolic blood pressure pressure blood diastolic and systolic , 171 ad ot Ain ( Asian South and 1,771) = −6 −136 APOA5 P Supplementary TableSupplementary 12 −12 1 P f < 0.05) with all these traits, including a including < traits, with all these 0.05) P

; fine-mapping fine-mapping ; 0 = 0.24, 0.24, = = 6 × 10 × 6 = loci . Large differences were also observed observed were also . differences Large = 8 × 10 × 8 = 4 f ), ), ) and and ) P = 0.16, 0.16, = and 5 nominated for fine mapping mapping fine for nominated 5 and (GWAS P LDL upeetr Tbe 11 Table Supplementary ST3GAL4 = 1 × 10

f with = 0.07, PCSK9 P ) ancestry and and ancestry ) = 1 × 10 × 1 = = 9 × 10 APOE 3 3 P P P 0 −11 3 3 and waist-hip ratio (WHR; (WHR; ratio waist-hip and

= 1 × 10 = 2 × 10 × 2 = HDL P . We observed an excess of of excess an observed We . −14 metabolic 2 f and fasting glucose levels levels glucose fasting and = 7 × 10 × 7 = = 0.27, 0.27, = ), ), −11 , the variants showing the the showing variants the , P (GWAS (GWAS variant ; fine-mapping fine-mapping ; = 9 × 10 × 9 = = 3 × 10 MED1 ), ), a 3.7-fold excesses for −8 −24 f = 0.11, 0.11, = , , ; fine-mapping ; vari fine-mapping P −4 CAD P CETP −10 ), ), a 2.3-fold excess (GWAS −9 = 2 × 10 × 2 = CETP

Supplementary Supplementary

f et et al. and −651 = 0.26, 0.26, = −12 ) and and ) ; fine-mapping fine-mapping ; ). ). Interestingly, = 4 × 10 × 4 = P P 2 = 2 × 10 = 6 × 10 × 6 = 9 , , , ,

), ), consistent , body , mass body PCSK9 1 SORT1 P n , where an an where , 0 CAD = 4,901) 4,901) = after fine fine after COBLL1 f ). These These ). f f −5 = 0.35, 0.35, = = 0.37, 0.37, = = 0.20, 0.20, = P SORT1 P ; fine- ; = 2 × 2 = = 7 × 7 = −4 = 1 × 27 (top −19 and ). −9 , 2 8 ). ). ), ), - - - ,

SNPs; SNPs; in (62.9% levels SNPs; 1,847 cholesterol HDL for effect of direction the in ant P ( variants independent For loci. significant genome-wide 157 the outside samples non-overlapping of analyses cance, we compared the directions of effect in GWAS and Metabochip signifi genome-wide reaching yet not loci for evidence Toevaluate significance Evidence HDL cholesterol levels appear to be causally related to CAD risk CAD to related causally be to appear levels cholesterol HDL not but levels triglyceride and cholesterol LDL increased that show to loci triglyceride-associated of examination detailed and analysis analysis. In a companion manuscript in this issue, we use multivariate relationship between lipid level and CAD effects requires multivariate ( fractions lipid multiple affect variants most whereas high HDL cholesterol levels are associated with reduced risk reduced with are levels HDL associated high cholesterol whereas risk of CAD,and with increased are LDL levels cholesterol associated cholesterol total show that high consistently studies Epidemiological Association CAD as such traits, other with association secondary mediate on might lipid levels effect traits metabolic other for signals association weaker generated phenotype another or BMI T2D and DBP). In such some as cases, DBP); and near SLC39A8 near DBP); and SBP T2D, CAD, levels, triglyceride with (associated near variants included These many traits. with associated was locus a single where (92/154; BMI for adjusted (104/149; risk the at as locus, in signal association primary the to according categorized was locus each of effect of direction the disease outcomes; metabolic related cardiovascular or of risk increased with cholesterol–decreasing associated HDL was allele the or allele triglyceride-increasing (Pearson’s P (Pearson’s cholesterol LDL with correlated was SNPs) 8 for available not were results (CAD loci this lipid-associated of on 149 CAD In effect the analysis, risk. CAD on impact predict could levels lipid with ciation asso whether test to regression linear used we risk, CAD to related LDL ( significant with remained cholesterol association the only loci), (14 levels cholesterol HDL or where loci (12 levels cholesterol LDL with associated uniquely loci on focused we at risk CAD increased with associated were loci) (64 levels HDL cholesterol with association strongest the showing at loci levels cholesterol 2 × 10 total or ( CAD loci) of risk increased with (30 associated were loci) (38 levels triglyceride loci), levels (31 cholesterol LDL with association strongest the showing loci equivocal more are levels and CAD is clear, whereas the results for HDL cholesterol levels of CAD < 0.1 in the GWAS-only analysis, a significant excess was concord was excess a GWAS-onlysignificant < in the 0.1 analysis, = 0.02) effect sizes but not with HDL cholesterol effect sizes sizes effect cholesterol HDL with not but sizes effect 0.02) = We tested whether the LDL cholesterol–, total cholesterol– or or cholesterol– total cholesterol–, LDL the whether tested We To better explore how lipid with individual associations levels were −16 P 3 1 10 × 1 < 5 and 0.006, respectively). Conversely, trait-decreasing alleles alleles Conversely, and respectively). trait-decreasing 0.006, . . In genetic studies, the connection between LDL cholesterol (associated with HDL cholesterol levels, BMI, SBP and and SBP BMI, levels, cholesterol HDL with (associated

for r r Tables 1 = 0.74; 0.74; = = –9 × 10 × –9 =

P of

FTO < 1 × 10 × 1 < additional P P MIR581

= 1 × 10 × 1 = lipid > 0.05 for other lipids), triglyceride levels (6 loci) loci) (6 levels triglyceride lipids), other for 0.05 > , consistent with previous reports previous with consistent , −16 7 P – 36– . = 7 × 10 × 7 = 4

traits . . We CAD with increased association observed −4 ), triglyceride levels (59.1% of 1,783 SNPs; SNPs; 1,783 of (59.1% levels triglyceride ), 3 3 4 8 (associated with HDL cholesterol levels, BMI, −16 . In other cases, such as as such cases, other In . ; ; . In our data, trait-increasing alleles at the the at alleles trait-increasing data, our In . −6 P

loci ), LDL cholesterol levels (58.6% of 1,730 1,730 of (58.6% levels cholesterol LDL ), ), SBP (96/155; (96/155; SBP ), = 0.99; 0.99; =

P with = 0.019). There were many instances instances many were There 0.019). = −6

not ) and triglyceride (Pearson’s triglyceride and )

CAD P

= 0.03). = Supplementary Fig. 6 Fig. Supplementary yet FTO

reaching P , , a with strong association r = 2.7 × 10 × 2.7 = 2 < 0.1) with association association with 0.1) < Fig. Fig.

genome-wide s e l c i t r A SORT1 1 ), dissecting the the dissecting ), 3 P 4 = 0.02. When When = 0.02. ; near near ; P −3 = 2 × 10 × 2 = ) and WHR WHR and ) , a primary primary a , ). Because Because ). r VEGFA = 0.46; 0.46; =

3 −12 9 . - - - ,

© 2013 Nature America, Inc. All rights reserved. gate levels of different lipid particles, each with potentially distinct distinct potentially with each particles, lipid different of levels gate cholesterol, LDL aggre levels summarize cholesterol and triglyceride to related but HDL are CAD risk. not levels causally HDL cholesterol levels and triglyceride cholesterol LDL both that hypothesis the with shows that our data are investigation consistent multivariate detailed CAD, but HDL cholesterol levels did not. In a companion paper, more with association predicted all levels and triglyceride LDL cholesterol on effects that found we analyses, DBP. univariate and In SBP BMI, signals. additional of locations the refine help may panels, large reference very imputation of from including emerging, variants viduals of European ancestry. A indi more fine-mapping detailed exercise, 100,000 nearly of examination the through possible was ping map fine instances, remaining the in whereas, ancestry, Asian East or African of individuals thousand few a of analysis the by enabled was mapping fine instances, 12 these of 3 In instances. 12 in signal GWAS-identified previous the from region a in signal strongest the we fine- were able to loci, separate our of 65 lipid-associated mapping analysis In variants. diverse causal for of search the individuals focus help and can ancestry samples large of analysis genetic how illustrate results fine-mapping Our variants. causal) potentially (and disease. heart of risk reduce and levels lesterol that, one day, of inhibition GPR146 may pharmaceutical cho modify to it speculate so is tempting target, tor,pharmaceutical an attractive GPR146 humans. of in inhibitors studies for incentive added an cholesterol—providing gene, human this of homolog the near variants that show we Here levels. lipid blood fies 20090036394 disclosing that, in the mouse, Application knockout Patent of US consider possibilities, Toillustrate findings. Note preted digest of genes by highlighted our study in the truly that understanding causality will be very challenging. Wesuggests include an inter result This disagreed. sometimes prioritization Table 2 in (summarized studies round the of first functional of focus the be might loci new 62 the of 44 in genes 70 that suggests variants protein-altering and levels expression mRNA of regulation studies. Prioritizing on the basis of literature review, pathway analysis, a for daunting loci—providing challenge future associated functional documented. been not has regulation such in role of analysis or whose genes only review include remainder the databases, literature pathway curated by supported is levels lipid of blood regulation the in role whose genes include loci new 62 the of 38 100 kb within of Whereas the SNP showing the strongest association. genes protein-coding include here identified loci the of one but All ( 157 to loci lipid-associated of number total the bringing loci, new 62 and levels lipid blood between association and informatic analyses human Herestudies. genetic we demonstrate organisms, model of studies from gained can be understanding This disease. and metabolic for cardiovascular modify that pathways lipid blood levels in humans the facilitate of will and design new therapies genes the of understanding Molecular DISCUSSION studies. future in 10 P s e l c i t r A of loci our We association the risk. for CAD evaluated consequences < 1 × 10 −16 Lipid-associated loci were strongly associated with CAD, T2D, T2D, CAD, with associated strongly were loci Lipid-associated associated strongly many includes typically locus associated Each kb 100 of are within 1 In there genes 240 of total, our 62 lipid- new . . Clearly, a range of to approaches be will needed follow up these ), ), suggesting that there are many additional loci to be discovered ). Although we found significant overlap, different sources of sources overlap, different we significant found ). Although −16 ) ) and total cholesterol levels (61.0% of 1,904 SNPs; GPR146 GPR146 , are associated with the levels of total total of levels the with associated are , encodes a G protein–coupled recep protein–coupled G a encodes in vitro in Tables1 experiments, bio experiments, Supplementary Supplementary Supplementary Supplementary – Gpr146 4 and and Fig. Fig. P modi < 1 × 1 ). ). ------

( our hope that the next round of genetic studies will build on these these on build will studies genetic of round next the that hope our is It biology. lipid into insights and leads new generate encode they identi proteins loci diverse and the contain they of genes many candidate the fied, number large The conducted. yet levels lipid blood risk). CAD and levels cholesterol HDL plasma between association clear show (which studies logical epidemio and risk) CAD and variants cholesterol–associated HDL between connection clear no show (which studies genetic of results the reconcile that variants cholesterol–associated HDL of groupings tion of these subphenotypes in larger samples could lead to functional levels overall, cholesterol were with associated neither of genetic dissec Detailed these. HDL with association strongest the show which ( levels sphingomyelin with ( levels malogen cholesterol– HDL among impact loci, associated variants near example, different For markedly subphenotypes. a these on have can variants terol–associated ancestry European of samples 5 from individuals 4,034 in cells, in membranes lipid of components are which sphingolipids, with and ( Study Framingham Heart the from individuals 2,900 in subfractions lipid with (deCODE) (deCODE) K. Stefansson and U.T.; (DIAGEN) P.E.H.S. and S.R.B.; (DILGOM) S.R.; (CLHNS) A.B.F., K.L.M. and L.S.A.; P.V.;(CoLaus) (CROATIA-Vis) C.H. and I.R.; (BLSA) L.F.; (BRIGHT) P.B.M.; N.S.; (CARDIOGRAM) (CHS) B.M.P. and J.I.R.; andD.P.S.; B58C-T1DGC) (B58C-Metabochip) C.M.L., C. Power and M.I.M.; (AMC-PAS) P.D. and G.K.H.; (Amish GLGC) (ARIC) A.R.S.; E.B.; (B58C-WTCCC T.L.A.; (AGES Reykjavik study) T.B.H. and V.G.; (AIDHS/SDS) D.K.S.; managementDesign, and coordination of contributing cohorts: analysis, results, interpretation and presentation of results. the manuscript. All analysis and writing group authors extensively the discussed E.I. and K.L.M. led the biological interpretation of results. C.J.W. and wroteG.R.A. supplementary material. C.J.W. led the analysis and bioinformatics efforts. bioinformatics analyses. E.M.S. and S. Sengupta prepared the tables, figures and S. Sengupta, G.M.P., M.L.B., J.C., S.G., A.G. and S. Kanoni performed C.J.W. E.M.S. and(Lead). S. Sengupta performed meta-analysis, and E.M.S., S. Kathiresan, K.L.M., E.I., G.M.P., M.S.S., S.R., E.M.S., S. S.S.R., Sengupta and Writing and analysis group: Supplementary Note our study. acknowledgment Detailed of funding sources is provided in the We thank the especially more than 196,000 volunteers who participated in online version o Note: Any Supplementary Information and Source Data files are available in the the pa the of in version available are references associated any and Methods M mpg/dap edu/boehnke/snippe to go e set, result full the for browse To CAD. treatments improved blood of identify help genetics eventually, the and, into levels lipid research continued facilitate will they URLs. CAD. for treatments improved and insights mechanistic of variants other clear and impact functional to accelerate the translation of alleles these leads into loss-of-function rare examine to gies and technolo imputation genotyping sequencing, new using results, AU A Supplementary Table 14 Table Supplementary du/csg/abecasis/public/lipids2013 c ethods All All authors contributed to the research and reviewed the manuscript. In summary, we report the largest genetic association study of of study association genetic largest the report we summary, In know T H O Summary results for our studies are available. We hope that that We hope available. are studies our for results Summary R R C ple/dapple.ph l ed ONT f f the pape g Supplementary Fig. 7 Fig. Supplementary ments RIBU a P pe DVANCE ONLINE PUBLICATION ONLINE DVANCE < 1 × 10 . r . T r . I r ONS / p ; DAPPLE, DAPPLE, ; G.R.A., M. L.A.C., P.D.,G.R.A., Boehnke, P.W.F., . −40 ). The results suggest that HDL choles HDL that suggest results The ). P LIPC ), variants near near variants ), <1 × 10 × <1 were strongly with associated plas / http://www.broadi and and . Snipper, . Snipper, −5 ), and variants near near variants and ), Supplementary Table 13 Table Supplementary ht ABCA1 tp://www.sph.umich. http://csg.sph.umic

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© 2013 Nature America, Inc. All rights reserved. (B58C-Metabochip) (B58C-Metabochip) C.M.L., E.H. and T.F.; and (B58C-WTCCC B58C-T1DGC) (AMC-PAS)R.S.; S. Kanoni; (Amish GLGC) J.R.O. and M.E.M.; (ARIC) K.A.V.; analysis fromPrimary contributing cohorts: E.I.; (ULSAM) E.I.; (WGHS) P.M.R.; and (Whitehall II) M. Kumari. W.H.-H.S.; (THISEAS) G.D. and M.D.; (Tromsø) T.W.; U.d.F. (TWINGENE) and Twin E.I. and Registry) N.L.P.; (TAICHI) H.-Y.C., C.A.H., Y.-J.H., E.K., S.-Y.L. and U.d.F. and B.G.; (SEYCHELLES) M. Burnier, M. Bochud and P. Bovet; (Swedish E.I. and L.L.; (PROMIS) D.F.F.; (Rotterdam Study) A. Hofman; (SCARFSHEEP) (NSPHS) U.G.; (ORCADES) S.H.W.; (PARC) Y.-D.I.C. and (PIVUS) R.M.K.; A.-L.H., A. A.R., Pouta(NFBC1986) and M.-R.J.; (NSPHS and FRISCII) Å.J.; GPC GWAS) (MRC R.N.N.; National ofSurvey Health and Development) D.K.; H.S.; L.C.G.; (METSIM) (MDC) A. Stančáková; (MORGAM) G.C.; (MRC/UVRI (KORAF4) A. Döring; L.J.v.P.;(LifeLines) (LOLIPOP) J.S.K. and J.C.C.; (LURIC) C.A.M. and F.B.; (IMPROVE) U.d.F., A. Hamsten and E.T.; (KORAF3) C.M.; G.W.; (Go-DARTS) A.S.F.D., A.D.M., C.N.A.P. and L.A.D.; (GxE/Spanish Town) K. Heikkilä; (GenomEUTwin-GENMETS) A.J.; (GenomEUTwin-NLDTWIN) (GenomEUTwin-FINRISK) V.S.; (GenomEUTwin-FINTWIN) J. Kaprio and (Framingham) S. Kathiresan and J.M.O.; (GenomEUTwin: MZGWA) J.B.W.; H.M.D.H. and P.J.K.; (FBPP) A.C., andR.S.C. S.C.H.; (FINCAVAS) T.V.M.N.; Study) A. Hofman; (ERF) C.M.v.D.; (ESS (Erasmus Stroke Study)) E.G.V.d.H., (Estonian Genome Center of the University of Tartu)) K.F.; (ERF and Rotterdam (EGCUT (Estonian Genome Center of the University of Tartu)) A.M.; (EGCUT K. (DPS) Silander; J. Lindström; (DR’s EXTRA) P. Komulainen; (EAS) J.L.B.; P.V.;(CoLaus) (deCODE) G.I.E., H.H. and I.O.; (DIAGEN) G.M.; (DILGOM) (B58C-Metabochip) C. Power and E.H.; (BRIGHT) P.B.M.; (CHS) B.M.P.; (Amish GLGC) andA.R.S. B.D.M.; andD.P.S.; (B58C-WTCCC B58C-T1DGC) Reykjavik study) T.B.H. and V.G.; (AIDHS/SDS) L.F.B.; (AMC-PAS) J.J.P.K.; Phenotype definition of contributing cohorts: P.M.R.; and (Whitehall II) A. Hingorani, C.L., M. Kumari and M. Kivimaki. A. Hamsten(TWINGENE) and E.I.; (ULSAM) E.I.; (WGHS) D.I.C., S.M. and and N.L.P.; (TAICHI) D. Absher, T.L.A., E.K., T.Q. and L.L.W.; (THISEAS) P.D.; B.G. and R.J.S.; (SEYCHELLES) F.M. and G.B.E.; (Swedish Twin E.I. Registry) K. Stirrups; (Rotterdam Study) A.G.U. and F.R.; (SardiNIA) (SCARFSHEEP) R.N.; H.C.; (PARC) M.O.G., M.R.J. and J.I.R.; (PIVUS) E.I. and L.L.; (PROMIS) P.D. and A.-L.H., M.-R.J., M.M., P.E. and S.V.; (NSPHS and FRISCII) Å.J.; (ORCADES) National ofSurvey Health and Development) A.W., D.K. and K.K.O.; (NFBC1986) (MORGAM) L.T. and P. Brambilla; (MRC/UVRI GPC GWAS) M.S.S.; (MRC J.S.K. and J.C.C.; (LURIC) B.F.V.M.E.K.; (MDC) and R.D.; (MICROS) A.A.H.; Hamsten; (KORAF3) H.G. and T.I.; (KORAF4) N.K.; C.W.;(LifeLines) (LOLIPOP) L.L.B.; (GLACIER) I.B.; (Go-DARTS) C.J.G., C.N.A.P. and M.I.M.; (IMPROVE) A. (FIN-D2D 27) A.J.S.; (FINCAVAS) T.L.; (Framingham) J.M.O.; (FUSION stage 2) (Erasmus Stroke Study)) C.M.v.D.; (FBPP) A.C. and G.B.E.; M.S.S.; (FENLAND) Tartu)) T.E.; (EPIC) P.D.; (EPIC_N_SUBCOH GWAS) I.B.; (ERF) C.M.v.D.; (ESS T.A.L.; (EAS) J.F.W.; (EGCUT (Estonian Genome Center of the University of (CHS) J.I.R.; (DIAGEN) N.N. and G.M.; (DILGOM) A. Palotie; (DR’s EXTRA) W.L.M.;T1DGC) (B58C-Metabochip) M.I.M.; (BLSA) D.H.; (BRIGHT) P.B.M.; L.F.B. and M.L.G.; (AMC-PAS) P.D. andand G.K.H.; (B58C-WTCCC B58C- Genotyping of contributing cohorts: (Whitehall II) A. Hingorani and M. Kivimaki. G.D. and P.D.; (Tromsø) U.d.F. I.N.; (TWINGENE) and E.I.; (ULSAM) E.I.; and N.L.P.; (TAICHI) T.L.A., Y.-D.I.C., C.A.H., T.Q., J.I.R. and W.H.-H.S.; (THISEAS) M. Bochud and P. Bovet; (SUVIMAX) P.M.; (Swedish Twin E.I. and Registry) (SCARFSHEEP) A. G.R.A.; Hamsten and U.d.F.; (SEYCHELLES) M. Burnier, P.D. and D. (RotterdamSaleheen; Study) A. Hofman and A.G.U.; (SardiNIA) and J.I.R.; (PennCath) D.J.R. and M.P.R.; (PIVUS) E.I. and L.L.; (PROMIS) J.D., M.-R.J.; (NFBC1986) (NSPHS) U.G.; (ORCADES) H.C.; (PARC) Y.-D.I.C., R.M.K. J. and Seeley E.H.Y.; (MRC National of Survey Health and Development) D.K.; (MORGAM) D. and Arveiler J.F.; (MRC/UVRI GPC GWAS) P. Kaleebu, G.A., (MEDSTAR) M.S.B., S.E.E.; (METSIM) J. Kuusisto and M.L.; (MICROS) P.P.P.; J.S.K. and J.C.C.; (LURIC) B.O.B. and W.M.; L.C.G. and(MDC) S. Kathiresan; S.E.H.; (InCHIANTI) S.B.; (KORAF4) C.G.; B.H.R.W.;(LifeLines) (LOLIPOP) J.N.H. and (HUNT2) K. R.S.C.; Hveem; (IMPROVE) U.d.F., A. Hamsten, E.T. and (Go-DARTS) A.D.M. and C.N.A.P.; (GxE/Spanish Town) B.O.T., C.A.M., F.B., K.O.K., V.S., J. Kaprio, A.J., D.I.B., N.L.P. and T.D.S.; (GLACIER) P.W.F., G.H.; (FUSION stage 2) F.S.C., J.T. and J. Saramies; (GenomEUTwin) J.B.W., N.G.M., (FRISCII) A. an Nature Ge Nature D.P.S.; (BLSA) T.T.; (BRIGHT) T.J.; (CLHNS) Y.W.; J.S.B.;(CoLaus) (deCODE) n etics

ADVANCE ONLINE PUBLICATION ONLINE ADVANCE (ADVANCE) D. Absher; (AIDHS/SDS) (ADVANCE) L.L.W.; (AIDHS/SDS) (ADVANCE) C.I.; (AGES

version of t The authors declare competing interests:financial details are available in the E.I. and S.G.; (WGHS) D.I.C.; and (Whitehall II) S. Shah. S. Kanoni; (Tromsø) A.U.J.; A.G. and (TWINGENE) E.I.; (ULSAM) C. Song, (TAICHI) D. Absher, T.L.A., H.-Y.C., M.O.G., C.A.H., T.Q. and L.L.W.; (THISEAS) and M. Bochud; (SUVIMAX) T.J.; (Swedish Twin C. Song andRegistry) E.I.; C. Sidore, J.L.B.-G. and S. Sanna; (SCARFSHEEP) R.J.S.; (SEYCHELLES) G.B.E. E.I.; (PROMIS) J.D., D.F.F. and K. Stirrups; (Rotterdam Study) A.I.; (SardiNIA) I.S. and S.K.S.; (NSPHS and FRISCII) Å.J.; (PARC) X.L.; (PIVUS) C. Song and of Health and Development) A.W. and J. Luan; M. Kaakinen, (NFBC1986) (METSIM) A.U.J.; (MRC/UVRI GPC GWAS) (MRC R.N.N.; National Survey I.M.N.; (LOLIPOP) W.Z.; (LURIC) B.F.V.;M.E.K.; (MDC) P.F.(MDC) and R.D.; R.J.S.; (InCHIANTI) T.T.; (KORAF3) M.M.-N.; (KORAF4) A.-K.P.; (LifeLines) C.N.A.P. and L.A.D.; (GxE/Spanish Town) C.D.P.; (HUNT) A.U.J.; (IMPROVE) M.M.; (GLACIER) UK-TWINS) D. Shungin; (GLACIER) P.W.F.; (Go-DARTS) I.S.; GENMETS) (GenomEUTwin-SWETWIN) P.K.E.M.; (GenomEUTwin- J. Kettunen; (GenomEUTwin-FINTWIN) K. Heikkilä; (GenomEUTwin- (FRISCII and NSPHS) Å.J.; (FUSION stage 2) T.M.T.; (GenomEUTwin-FINRISK) (FIN-D2D 27) A.U.J.; (FINCAVAS) L.-P.L.; (Framingham) L.A.C. and G.M.P.; G.B.E.; T.P.(FENLAND) and C. Pomilla; GWAS)(FENLAND J.H.Z. and J. Luan; A. Demirkan; (Family Heart Study (FHS)) I.B.B. and M.F.F.; (FBPP) A.C. and (ESS (Erasmus Stroke Study)) C.M.v.D. and E.G.V.d.H.; (EUROSPAN) GWAS) E.H.Y. and C.L.; (EPIC_N_SUBCOH GWAS) N.W.; (ERF) A.I.; E.M., K.F. and T.E.; (ELY) D.G.; (EPIC) K. Stirrups and D.G.; (EPIC_N_OBSET EXTRA) A.U.J.; (EGCUT (Estonian Genome Center of the University of Tartu)) G.T.; (DIAGEN) A.U.J.; (DILGOM) M.P.; (EAS) R.M.F.; (DPS) A.U.J.; (DR’s 20. 19. 18. 17. 16. 15. 14. 13. 12. 11. 10. 9. 8. 7. 6. 5. 4. 3. 2. 1. reprints/index.htm at online available is information permissions and Reprints C OM

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© 2013 Nature America, Inc. All rights reserved. Health Health and Welfare, Helsinki, Finland. Oulu, Finland. Dentistry, Queen Mary University of London, London, UK. Institute for Health Research (NIHR) Biomedical Cardiovascular Research Unit, William Harvey Research Institute, Barts and The London School of Medicine and Sweden. 32 of Epidemiology for Child Health, University College London Institute of Child Health, London, UK. Department of Public Health, University of Helsinki, Helsinki, Finland. Sciences, University of Edinburgh, Edinburgh, UK. School of Medicine, St. Louis, Missouri, USA. the University of Tartu, Tartu, Estonia. Genomics, Institute McKusick-Nathans of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. Medical School. Dundee, UK. 19 Research Institutes, Zhunan, Taiwan. Lausanne, Lausanne, Switzerland. USA. Massachusetts, Chapel Hill, North Carolina, USA. UK. Uppsala, Sweden. Medical and Population Genetics, Broad Institute, Cambridge, USA. Massachusetts, Public Health, Boston, USA. Massachusetts, for Statistical Genetics, Department of University Biostatistics, of Michigan, Ann Arbor, Michigan, USA. and University Bioinformatics, of Michigan, Ann Arbor, Michigan, USA. 1 K S Paul Colin John B Cornelia Bamidele Alan R Veikko Jackie F Price Patricia B A Colin Cecilia Johanna M M Chao Agnes Hsiung Nature Ge Nature Helmholtz Helmholtz Zentrum München, Neuherberg, Germany. Nutrition, Harokopio University, Athens, Greece. Health Research Institute, Hyattsville, Maryland, USA. Institute, US National Institutes of Health, Bethesda, Maryland, USA. Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA. Cambridge, UK. Research Unit on AIDS, Entebbe, Uganda. Biostatistics, School of Public Health, Imperial College London, London, UK. 68 USA. Pennsylvania, Philadelphia, Medicine, USA. Pennsylvania, Philadelphia, Institutes of Health, Baltimore, Maryland, USA. Institutet, Stockholm, Sweden. Scienze Biomediche, Universita di Sassari, Sassari, Italy. Odontology, Umeå University, Umeå, Sweden. of Clinical Sciences, Genetic & Molecular Epidemiology Unit, Lund University Diabetes Center, Scania University Hospital, Malmö, Sweden. Los Angeles, California, USA. Ricerche, Monserrato, Italy. 53 Groningen, University Medical Center Groningen, Groningen, The Netherlands. Medical Informatics, Biometry and Epidemiology, Ludwig Maximilians University of Munich, Munich, Germany. Neuherberg, Germany. Diabetes and Nutrition, University of Maryland, School of Medicine, Baltimore, Maryland, USA. Finland. 44 Sinai Medical Center, Los Angeles, California, USA. Public Health, Social and Preventive Medicine, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany. Department Department of Internal Medicine, Division of Medicine, Cardiovascular University of Michigan, Ann Arbor, Michigan, USA. tephen Department Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden. Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands. HudsonAlpha HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA. Division of Genetics, Program in Genomics, Children’s Hospital, Boston, USA. Massachusetts, Department of Clinical Chemistry, Fimlab Tampere,Laboratories, Finland. aren L arjo-Riitta arjo-Riitta Järvelin ika ika 11 Department Department of Medical Epidemiology and Karolinska Biostatistics, Institutet, Stockholm, Sweden. W K 46 N 34 W Franks S ivimaki Department Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK. M S Genome Genome Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK. A Palmer M M S huldiner alomaa K n M hitfield Lindgren Rich O 38 M etics uusisto c ohlke 74 van K Institute Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.

T unroe 9 NIHR NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK. enzie Science Science for Life Laboratory, Uppsala University, Uppsala, Sweden. 27 ayo 57 15

204 56 , , Bruce 49 D 184 12 Service Service of Medical Genetics, Lausanne University Hospital, Lausanne, Switzerland. ADVANCE ONLINE PUBLICATION ONLINE ADVANCE , , , Jaspal 172 Department Department of Medicine I, University Hospital Grosshadern, Ludwig Maximilians University of Munich, Munich, Germany. 59 146 47 , , 167 20 uijn , 34 208 174 M , , , 55 , 202 26 188 , , Bruce H R , , Unnur , , D 155 , 56 , , 21 Center Center for Genetics, Neurobehavioral The Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 35 ichael ichael Boehnke E 36 , , , , Pierre , , 62 K harambir Genetic Genetic Epidemiology Group, Department of Epidemiology and Public Health, University College London, London, UK. Cardiology, Department of Specialities of Medicine, Geneva University Hospital, Geneva, Switzerland. 18 , , E , , Agneta lena lena N , , Inger , , M , Center Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden. S 67 13 irsten 37 17 rik rik Ingelsson S , icholas icholas G 31 amuli Ripatti S Human Human Genetics Center, University of Texas Health Science Center, School of Public Health, Houston, Texas, USA. Division Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, USA. Massachusetts, Psaty teve teve Division Division of Preventive Medicine and Health Services Research, Institute of Population Health Sciences, National Health , 83 18

, , Peter Vollenweider 24 K 40 Genetic Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands. T , Institute Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia. Department Department of Internal Medicine II–Cardiology, University of Ulm Medical Centre, Ulm, Germany. ooner 114 T remoli M 73 O N E horsteinsdottir 66 , University University of Cambridge Metabolic Research Institute Laboratories, of Metabolic Science, Addenbrooke’s Hospital, 6 179 Humphries 160 eneton S jølstad

Center Center for Human Genetic Research, General Massachusetts Hospital, Boston, USA. Massachusetts, 59 26 K W Department Department of Systems Pharmacology and Translational University Therapeutics, of Pennsylvania School of Medicine, K iegbahn 64 Department Department of Public Health and Primary Care, Unit of Medicine, Umeå University, Umeå, Sweden. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. 81 yvik

, , , Antti Jula 71 olffenbuttel S 180 M deCODE Genetics/Amgen, deCODE Reykjavik, Genetics/Amgen, Iceland. Institute Institute of Epidemiology I, Helmholtz Zentrum München, Neuherberg, Germany. 28 anghera 191 43 , 143 artin 4 Department Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. 83 , , 8 MRC MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Hills Road, Cambridge, UK. , 79 , 208 168 10 T Department Department of Epidemiology and MRC Biostatistics, Health Protection Agency (HPA) Centre for Environment , , Jaakko 175 131 9 , Research Research Centre on Public Health, University of Milan, Milano-Bicocca, Italy. , 61 165 homas Quertermous 36 26 , 38 , , Panos , 8 Atherosclerosis Research Atherosclerosis Unit, Department of Medicine Solna, Karolinska University Hospital, Karolinska , , Andres , , Biocenter Biocenter Oulu, University of Oulu, Oulu, Finland. 169 172 , , 208 , N 33 , , Peter J 39

156 75 , , ancy L ancy Pedersen , , , , , L Adrienne Cupples 171 & Gonçalo R & Abecasis Gonçalo 161 M 64 W , , Jouko , , 128 197 T , S arkku arkku Laakso , , 189 infried infried , , uomilehto 77 teven teven C Hunt T D 69 30 , M 3 198 , Lars , Lars Department Department of Experimental Medicine, University of Milan, Milano-Bicocca, Italy. Department Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA. im im M MRC Unit for Lifelong Health and Ageing, London, UK. , , Centre Centre for Paediatric Epidemiology and Research Council Biostatistics/Medical (MRC) Centre eloukas K ika ika D 71 , , Jose etspalu oudstaal 45 S aniel aniel I Chasman D Ealing Ealing Hospital, Southall, UK. Department Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, 52 aramies K

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Center Center , 164 187 , 183 , , , © 2013 Nature America, Inc. All rights reserved. Cedars-Sinai Cedars-Sinai Medical Center, Los Angeles, California, USA. Institute of Medicine, Environmental Karolinska Institutet, Stockholm, Sweden. and Health, School of Public Health, Imperial College London, London, UK. s e l c i t r A 1 Strasbourg, Strasbourg, Faculty of Medicine, Strasbourg, France. 132 Psychology, VU University, Amsterdam, The Netherlands. The Netherlands. Medical Center Groningen, Groningen, The Netherlands. Finland. Development, International University of East Anglia, Norwich, UK. Glenfield Hospital, Leicester, UK. Research Leicester Biomedical Cardiovascular Research Unit, Glenfield Hospital, Leicester, UK. Aging (NCHA), Leiden, The Netherlands. Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands. USA. Pennsylvania, Philadelphia, USA. Oulu, Finland. Institute, Hinxton, Cambridge, UK. Haartman Institute, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland. Hospital, Helsinki, Finland. Medicine, Tampere, Finland. Germany. Medicine, University of Bristol, Bristol, UK. 103 New York, USA. for Health and Welfare, Helsinki, Finland. Metabolism, Taichung Veterans General Hospital, School of Medicine, National Yang-Ming University, Taipei, Taiwan. Cambridge, Cambridge, UK. Hannover, Germany. & Endocrinology Metabolism, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan. Maryland, USA. Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, UK. Umeå University, Umeå, Sweden. Centre for Diabetes, and Endocrinology Metabolism, University of Oxford, Oxford, UK. University University of the West Indies, Mona, Jamaica. Queensland, Australia. Finland. Network (OPEN), Odense University Hospital, Odense, Denmark. Hospital, Kuopio, Finland. 166 Research Center, Oulu University Hospital, Oulu, Finland. Services, National Institute for Health and Welfare, Helsinki, Finland. Finland. 160 and General Practice, Norwegian University of Science and Technology, Levanger, Norway. Genetics, University of Utah School of Medicine, Salt Lake City, Utah, USA. 156 National Institute on Ageing, Bethesda, Maryland, USA. Kopavogur, Iceland. Clinical Sciences, Lund University, Malmö, Sweden. of Medicine, Rangueil Hospital, Toulouse, France. USA. California, Los Angeles, Los Angeles, California, USA. Endocrinology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, California, USA. Lausanne University Hospital, Lausanne, Switzerland. University of Dresden, Medical Faculty Carl Gustav Carus, Dresden, Germany. Ulm University Medical Centre, Ulm, Germany. and Preventive Medicine (IUMSP), Lausanne University Hospital, Lausanne, Switzerland. Azienda Sanitaria Firenze (ASF), Florence, Italy. K.L.M. ( 207 Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA. Heart Study, Framingham, Massachusetts, USA. on Aging at Tufts University, Boston, Massachusetts, de Estudios Madrid, Avanzados)–Alimentacion, Spain. USA. Epidemiology and Population Genetics, National Center for Madrid, Investigation, Cardiovascular Spain. 198 Finland. Grupo Hospital RD06/0014/0015, La Universitario Paz, Madrid, Spain. Abdulaziz University, Faculty of Medicine, Jeddah, Saudi Arabia. Center, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy. Sciences and Education, St. George’s, University of London, London, UK. Veterans Medical Administration Center, Baltimore, Maryland, USA. Medicine, Division of Endocrine and Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan. Hospital, Finland. Lappeenranta, USA. Pennsylvania, Hospital, Kuopio, Finland. Health Research Institute, Group Health Cooperative, Seattle, Washington, USA. 179 Hospital, Kuopio, Finland. 0

These authors contributed equally to this work. The The Mindich Child Health and Development Institute, The Icahn School of Medicine at Mount Sinai, New York, New York, USA. Children’s Hospital Oakland Research Institute, Oakland, California, USA. Unit of Primary Care, Oulu University Hospital, Oulu, Finland. Genetics, Cardiovascular British Heart Foundation Institute Laboratories, Science, Cardiovascular University College London, London, UK. Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina, USA. Department Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. Health Cardiovascular Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, Washington, USA. 116 147 [email protected] 162 126 196 171 Division Division of Translational Medicine and Human Genetics, Perelman School of Medicine at the University of TranslationalPennsylvania, Research Center, Office Office of Population Studies Foundation, University of San Carlos, Talamban, Cebu City, Philippines. 106 Department Department of Clinical Physiology, University of Tampere School of Medicine, Tampere, Finland. INSERM UMRS 937, Pierre and Marie Curie University, Paris, France. Department Department of Medical Sciences, Uppsala University, Uppsala, Sweden. Research Research Unit, Kuopio University Hospital, Kuopio, Finland. Laboratory of Genetics, National Institute on Aging, Baltimore, Maryland, USA. 115 93 102 129 Cardiovascular Institute, Cardiovascular Perelman School of Medicine at the University of TranslationalPennsylvania, Research Center, Pennsylvania, Philadelphia, Center Center for Biomedicine, European Academy (EURAC), Bozen/Bolzano Bolzano, Italy (Affiliated Institute of the University of Lübeck). The The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York, USA. 184 153 96 Department Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. Department Department of Vascular Medicine, Academic Medical Center, Amsterdam, The Netherlands. 173 Unit Unit of Chronic Disease Epidemiology and Prevention, National Institute for Health and Welfare, Helsinki, Finland. Department Department of Cardiology, Karolinska University Hospital, Stockholm, Sweden. Synlab Synlab Academy, Synlab Services, Mannheim, Germany. 182 177 168 110 98 108 Center Center for Diseases, Non-Communicable Karachi, Pakistan. Department of Neurology, General Central Hospital, Bolzano, Italy. Institute of Regional Health Services Research, University of Southern Denmark, Odense, Denmark. ), E.I. ( Kuopio Kuopio Research Institute of Exercise Medicine, Kuopio, Finland. Department Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland. Department Department of Internal Medicine, Päijät-Häme Central Hospital, Lahti, Finland. 186 90 117 123 113 Department Department of and Clinical Sciences/Obstetrics Gynecology, Oulu University Hospital, Oulu, Finland. Paul Paul Langerhans Institute Dresden, German Center for Diabetes Research (DZD), Dresden, Germany. [email protected] Department Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands. Clinical Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria. Department Department of Statistical Sciences, University College London, London, UK. 120 101 105 Department Department of Clinical Chemistry,Sciences/Clinical University of Oulu, Oulu, Finland. The The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, 175 Institute Institute for Medical Informatics and Biometrics, University of Dresden, Medical Faculty Carl Gustav Carus, Dresden, 139 204 136 INSERM INSERM U872, Centre de Recherche des Cordeliers, Paris, France. 208 149 Lee Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore. Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA. Chemical Chemical Pathology, Department of Pathology, University of the West Indies, Mona, Jamaica. 151 These authors jointly directed this work. Correspondence should be addressed to C.J.W. ( 134 Department Department of Psychiatry, University of California, Los Angeles, Los Angeles, California, USA. 146 143 201 Department Department of Medicine, Helsinki University Hospital, Helsinki, Finland. 202 155 Department Department of Medicine, Stanford University School of Medicine, Stanford, California, USA. 128 Department Department of Preventive Medicine and Epidemiology, Loyola University Medical School, Maywood, Illinois, 131 Imperial Imperial College Healthcare National Health Service (NHS) Trust, London, UK. Nutrition and Genomics Laboratory, Jean Mayer US Department of Agriculture Human Nutrition Research Center 165 Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA. Institute Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan. 87 LifeLines LifeLines Cohort Study, University of Groningen, University Medical Center Groningen, Groningen, Department Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway. National National Heart & Lung Institute, Imperial College London, Hammersmith Hospital, London, UK. Research Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany. 170 194 161 ) or G.R.A. ( 125 189 Institute Institute of Biomedicine/Physiology, University of Eastern Finland, Kuopio Campus, Kuopio, La La Red Temática de Cooperativa Investigación en (RECAVA)Enfermedades Cardiovasculares Department Department of Chronic Disease Prevention, National Institute for Health and Welfare, Turku, 164 Department Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Faculty Faculty of Medicine, University of Iceland, Reykjavík, Iceland. 195 197 Institute Institute of Clinical Medicine, Department of Medicine, University of Oulu and Clinical 191 Institute Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Department Department of Medicine, Lausanne University Hospital, Lausanne, Switzerland. 158 84 119 Department Department of Sciences, Pharmacological University of Milan, Monzino Cardiology [email protected] 167 141 The The Laboratory in Mjodd, Reykjavik, Iceland. 192 Nord-Trøndelag Health Study (HUNT) Research Centre, Department of Public Health Netherlands Netherlands Genomics Initiative Netherlands Consortium (NGI)-sponsored for Healthy 206 86 Department Department of Medicine, University of Eastern Finland and Kuopio University Ministry Ministry of Health, Victoria, Republic of Seychelles. 181 Department Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Centre Centre for Vascular Prevention, Danube University Krems, Krems, Austria. 127 Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA. 174 Department Department of Clinical Physiology and Nuclear Medicine, Kuopio University 172 Department Department of Laboratory Medicine, University of Groningen, University 89 Tropical Metabolism Research Unit, Tropical Medicine Research Institute, Queensland Queensland Institute of Medical Research, Royal Brisbane Hospital, Brisbane, Department Department of Public Health and Clinical Medicine, Nutritional Research, 107 138 183 159 133 Department Department of Clinical Pharmacology, University of Tampere School of Department Department of Internal Medicine, Division of and Endocrinology Diabetes, Department Department of Medicine, University of Philadelphia, Pennsylvania, Kaiser Kaiser Permanente, Division of Research, Oakland, California, USA. Department Department of Epidemiology and Public Health, University of 92 122 99 178 Laboratory Laboratory of National Neurogenetics, Institute on Aging, Bethesda, ). Department Department of Internal Medicine, Division of Endocrine and Department Department of Sciences, Cardiovascular University of Leicester, Department Department of Neurology, University of Lübeck, Lübeck, Germany. 112 154 a 200 188 Genetic Genetic Epidemiology Group, Wellcome Trust Sanger DVANCE ONLINE PUBLICATION ONLINE DVANCE Laboratory Laboratory of Epidemiology, Demography and Biometry, Madrid Madrid Institute for Advanced Studies (Instituto Madrileño Geriatric Geriatric Research and Education Clinical Center, 163 95 148 Hannover Hannover Unified Biobank, Hannover Medical School, Department Department of Mental Health and Substance Abuse 109 Department Department of Cardiology, Toulouse University School 97 114 Division Division of Cardiology, Helsinki University Central Clinical Clinical Gerontology Unit, University of 100 145 176 National National Institute for Health and Welfare, Diabetes Diabetes Prevention Unit, National Institute 111 Department Department of Medicine, University of Department Department of Medicine, Kuopio University Department Department of Medical Genetics, 85 199 Division Division of Epidemiology,Cardiovascular Department Department of Cardiovascular 152 169 121 104 Icelandic Icelandic Heart Association, 118 142 Odense Odense Patient Data Explorative 140 National National Institute for Health School School of Social and Community 190 Department Department of Internal Service Service of Nephrology, Department Department of Medicine III, 144 91 Division Division of Population Health 187 130 185 205 MRC MRC Human Genetics Division Division of Reproductive [email protected] Department Department of Internal Department Department of Biological South South Karelia Central Cardiovascular 157

Nature Ge Nature 137 Cardiovascular Cardiovascular 124 203 150 Institute Institute of Social School School of 135 Framingham Department Department of 94

Geriatric Geriatric Unit, Division Division of 180 193 n 88

), Group Group etics King King Oxford Oxford

© 2013 Nature America, Inc. All rights reserved. able SNPs when estimating the median test statistic and inflation factor factor able and SNPs test inflation the statistic median estimating when cedure with results. initial meta-analysis For GWASpro samples, the we used all avail repeated then and sample each to control genomic applied first we stratification, population due Toto potential test statistics for inflated correct with weights proportional to the square root of the sample size for each sample. of new genome-wide significant loci. Non-European samples were used only only used were samples Non-European loci. significant genome-wide new of discovery the for samples European only We used loci. new other and locus lipid-associated described previously nearest the from away Mb >1 were and a reached they if novel be to considered were Signals samples. Metabochip the with lipids would and not these would be behave as associated null in variants of majority the that GWAS, expecting original the in traits lipid all for >0.50 samples, Metabochip we used a of subset SNPs ( Meta-analysis. MMAP and ( individuals) of number total individuals) the of 1% sample; (1 of number total the of 3% sample; individuals), (1 of GENABEL number total the of 9% samples; (4 Merlin individuals), of of the total number of individuals), EMMAX (9 samples;PLINK 14%using of the(26 performed samples;total 53%were number individual of the totalanalyses each number These of for individuals), SNPTESTvariable. count (4 samples; independent 20% allele the as expected the and variable dependent as the values trait transformed normal inverse the with regression linear using analysis. statistical Primary only). European example, (for group ancestry single a to limited were controls meta-analysis were analyzed separately ( before and cases control status, disease cardiovascular or genomic diabetes on ascertained studies In using adjusted were studies all approaches model using principal-component for structure adjustments population were adjusted for age, age Friedewald the using formula estimated were and individuals) study total of (24% studies ten in measured directly were levels excluded cholesterol LDL were possible. when medication lipid-lowering on be to known Individuals ing. Phenotypes. here). described with analyses by the lipids associated blood were loci these of (5 traits non-lipid with associated loci of mapping fine for selected SNPs 93,308 and GWAS of traits basis non-lipid of the on prioritized SNPs 50,459 included also Metabochip The loci. lipid fied 28,923 additional SNPs were for selected fine mapping of 65 previously identi with and triglycerides and cholesterol with loci most SNPs represent independent These cholesterol. SNPs total for 938 and erides triglyc for SNPs 5,056 cholesterol, LDL for SNPs 5,055 cholesterol, HDL for we used our previous GWAS of individuals ~100,000 Metabochip iSelect the Illumina using phenotypes metabolic and cardiovascular for GWAS previous of Genotyping. approvalobtained ethics individually for data their generation and analyses. Supplementary Table 1 able), GXE ( ( ( GPC MRC/UVRI ancestry, African recent of descent, CLHNS ( ( ancestry, including 2 studies of individuals of South Asian descent, AIDHS/SDS T2D. Another 8 studies consisted of primarily with non-Europeanindividuals studies including and both population-based case-control studies of CAD and ancestry (see of European of individuals primarily consisted studies 37 these, Of studies. 45 studied. Samples ONLINE doi: analyses. fine-mapping of examination and meta-analysis for n n P = 426) from the Caribbean, and FBPP ( = 1,516) and PROMIS ( PROMIS and 1,516) = value of <5 × value 10 10.1038/ng.2797 4 1 in the remaining studies. Trait residuals within each study cohort cohort study each within Traitresiduals studies. remaining the in

METHODS n Supplementary Table 1 Blood lipid levels were typically measured after >8 h of fast of h >8 after measured typically were levels lipid Blood We genotyped 196,710 genetic variants prioritized on the basis basis the on prioritized variants genetic 196,710 We genotyped = 397) and SPT ( Meta-analysis was performed using the Stouffer method Stouffer the using performed was Meta-analysis P n < 0.005 in our original GWAS for HDL cholesterol, LDL LDL cholesterol, HDL for GWAS original our in 0.005 < We for statistics SNPsMetabochip summary from collected 4 = 1,771) and TAI-CHI ( 3 −8 were carried out were in carried 24 (35% of studies study individuals); in the combined GWAS and Metabochip meta-analysis GWAS combined in meta-analysis the and Metabochip and the 2 and sex and were then quantile normalized. Explicit n Individual SNP association tests were performed = 3,385); 2 studies of individuals of East Asian Asian East of individuals of studies 2 3,385); = 8 genotyping array. To design the Metabochip, Metabochip, the design Toarray. genotyping n Supplementary Note = 838) from the United States (more details in Supplementary Supplementary Table 1 and the P n n < 0.0005 for total cholesterol. An An cholesterol. total for 0.0005 < = 1,614; triglyceride results unavail = 7,044); and 5 studies of individuals Supplementary Note n n = 1,687) from Uganda, SEY Uganda, from 1,687) = = that 7,168) had Supplementary Table 1 Supplementary ). Each contributing study 4 to 5,023 SNPsprioritize ). ). All meta-analyses 4 for details), 2 P or mixed- values of λ . . For 44 1 , ). 4 1 5 - - - - - .

Generating permuted sets of non-associated SNPs.sets of permuted Generating non-associated with approach Google. and PubMed using this searches literature traditional supplemented We OMIM. and literature published in (“cholesterol”, terms search “lipids”,selected “HDL”, “LDL” “triglycerides”) or and names gene corresponding the of co-occurrence the for checks then and genes nearby of list a generates first Snipper locus, each For review. manual to subjected then and Snipper with generated was locus each within didates literature. published the of review automated Initial R. in package kinship lme the SNPs using of sets these on residuals lipid We regressed loci. reported newly (iii) and loci; loci published ously previ the from SNPs secondary and lead (i) residual: trait each for models 3 ( cohort Framingham the in loci new by explained variance explained. variance trait of Proportion times. <7 observed Finally, within each study, we excluded variants for which the minor was allele Genomic loci. <1.20. were all and known inspected, were analyses for study-specific for values control findings published with allelic consistent and were statistics effects reported whether evaluated We alleles. of strand assignment same the used analyses all ensure to frequencies allele Weinspected studies. outlier identify to size sample study against plotted were study each of control quality steps. Average from standard errors statistics for association control. Quality each of the samples. We examined the correlation between each of of each each We the the 62 samples. new between the correlation examined for 22 HapMapSNPs in release million ~2.6 for genotypes imputed obtain to described as performed were genotyping and profiling surgical DNA expression and RNA during isolation, collection, tissue from donors; or resection postmortem collected were samples Tissue samples. fat human 960 in subcutaneous human 609 and samples fat omental transcripts human 741 samples, liver 39,280 of levels expression the with association as act might SNPs associated Cis loci. new from genes more or 1 included that pathways on discussion our focus we loci; known previously 95 the as well as loci new 62 the included analysis This loci. lipid-related different across genes necting (DAPPLE) to for examine evidence protein-protein networks interaction con GSEA for performed were permutations 1,000,000 of number genes. total A minimum of the 10,000 gene of set permutations were 5% performed, and of up to cutoff rank a size). using identical enrichment of evaluated We sets gene sampled randomly multiple term to pathway compared given (and a to regard with distributed randomly are cutoff rank given a above ranks score gene all that hypothesis null the test to used is test GSEA and scores, a are modified on of ranked gene the basis association their annotation GO, including PANTHER, resources, Ingenuity and Finally, KEGG. the genes several using gene each to terms Subsequently, pathway attaches analyses. MAGENTA downstream for kept is score lowest the with gene the only genes, to multiple SNP is assigned same the When score. association a gene create to LD) and density marker size, (gene confounders for adjusted SNP significant most The boundaries. transcript of downstream kb 40 or upstream kb 110 within when gene given a SNPs to assigns MAGENTA Briefly, SNPs. first GWASing Metabochip and as implemented in MAGENTA previously annotated pathways, we used gene set enrichment analysis (GSEA), Pathway analyses. list. permuted each for 1 SNP selected 3 same the bins and randomly SNPs within that fell associated with SNPs of and number gene MAF, nearest to the distance statistics: of three on basis the ( SNPs lipid-related non all identified first we lists, To these SNPs. generate permuted of lists generated we loci, our and searches literature between overlap chance We used the Disease Association Protein-Protein Link Evaluator package package Evaluator Link Protein-Protein Association Disease the Weused -expression -expression quantitative trait analysis. locus r 2 To flag potentially erroneous analyses, we outcarried a series 08 Fr ah ne SP w ietfe 50 non-lipid- 500 identified we SNP, index each For 0.8. > To investigate whether lipid-associated variants overlapped P 4 ; (ii) previously published lipid loci plus newly reported reported newly plus loci lipid published previously (ii) ; > 0.10 for any of the four lipid traits) and created bins bins created and traits) lipid four the of any for 0.10 > 1 cis 7 using the meta-analysis of all studies, includ regulators of nearby genes, we examined examined we genes, nearby of regulators P We estimated the increase in trait trait in increase the Weestimated value within this interval is then then is interval this within value To whether lipid- determine P To estimate the expected values below 1 × 10 × 1 below values An initial list of can of list initial An 4 Nature Ge Nature 6 . MACH was used used was MACH . n = 7,132) using using 7,132) = - associated associated n etics −4 . - - - -

© 2013 Nature America, Inc. All rights reserved. be found in in found be Study Heart (Liposcience) Framingham and ( samples (Liposcience), Offspring assay LipoProtein-II the ( samples (WGHS) Study Health Genome subfractions. lipid with Association estimate to distribution overlap empirical the to tion functional domains (for overlapping large loci of number greater or equal an with sets muted a Weestimated above) and evaluated permuted SNP lists for overlap with domains. functional (see SNPs non-associated of sets permuted 100,000 created we marks, tional marks functional described to previously proxies SNPs their and index of the We positions the compared ( or Teslovichin study this in a list index SNPsof variants causal by all potentially selecting at identified loci SNPsassociated that fell in important regulatory domains. We created initially Functional annotation of associated variants. described previously as analyses association performing kb 500 of within SNP the position, SNPs index transcripts and all Nature Ge Nature r 2 > 0.8 from 1000 Genomes Project or HapMap data) with each index SNP. index each with HapMap data) or Project Genomes 1000 from 0.8 > n Supplementary Figure 7 Figure Supplementary 4 etics 8 . Additional information on subfraction measurements can can measurements subfraction on information Additional . P value for each functional domain as the proportion of per of proportion the as domain functional each for value P values). For values). small n = 2,900) were measured with the LipoProtein-I assay assay LipoProtein-I the with measured were 2,900) = et et al. 23 , 2 4 . To assess the expected overlap with func with overlap expected the assess To . 4 . We then selected any variant in strong LD strong in any . variant We selected then P . Log transformations were used for for used were transformations Log . iorti fatos o Women’s for fractions Lipoprotein values, we values, used a normal approxima n = 23,170) were measured using using measured were 23,170) = 4 7 . We attempted to identify lipid- P values. - - -

48. 47. 46. 45. 44. 43. 42. 41. (72.2%). draw blood before h 8 for fasting were 16,730 participants, Western European ancestry) reference and panel Northern of using residents (Utah MACH. CEU 22 Of release HapMap the the from 23,170 imputed WGHS genotypes SNP of WGHS used analysis association principal genetic The and components. sex age, for adjusted were models All traits. non-normalized

hsa, D.I. Chasman, E.E. Schadt, etn, B.J. Keating, of meta-analysis efficient and fast METAL: G.R. Abecasis, & Y. Li, C.J., Willer, R.M.J. Williams, & S.A. Star, L.C., DeVinney, E.A., Suchman, S.A., Stouffer, H.M. Kang, A.L. Price, of concentration the of Estimation D.S. W.T.,Fredrickson, Friedewald, & Levy,R.I. e1000730 (2009). e1000730 ocnrto, n coetrl otn i gnm-ie analysis. genome-wide in content cholesterol and concentration, liver. 3 gene-centric 50 k SNP array for large-scale genomic association studies. scans. association genomewide Life 1949). Army During Adjustment studies. association genome-wide studies. association genome-wide o-est lppoen hlseo i pam, ihu ue f h preparative the of use without plasma, ultracentrifuge. in cholesterol lipoprotein low-density , e3583 (2008). e3583 , PLoS Biol. PLoS t al. et t al. et et al. et t al. et Clin. Chem. Clin. t al. et

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