University of Groningen

Genetics of human cardiovascular traitsVerweij, Niek

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The st segment and adjacent t wave amplitudes (st-t wave) of the electro-cardiogram are quantitative charac-teristics of cardiac repolarization. re-polarization abnormalities have been linked to ventricular arrhythmias and sudden cardiac death. we performed the first Gwa meta-analysis of st-t wave amplitudes in up to 37,977 in-dividuals identifying 71 robust geno-type-phenotype associations clustered within 28 independent loci. fifty-four

candidate genes were prioritized, in-cluding genes with established roles in cardiac repolarization phase (SCN5A/SCN10A, KCND3, KCNB1, NOS1AP, KCNA7 and HEY2) while others repre-sent unknown molecular processes for the heart. These associations provide insights in the spatiotemporal contri-bution of genetic variation influencing cardiac repolarization and may provide novel leads for future functional fol-low-up.

i n t r o d u c t i o n

duration of cardiac repolarization has been previously studied by genome wide association studies and led to the discovery of 35 associated loci1. How-ever, abnormalities of repolarization are not limited to changes in duration but concern changes in amplitudes as well2. deviations of st-t wave ampli-tudes can be indicative of a variety of cardiac pathologies, including myocar-dial ischemia, ventricular hypertrophy, long Qt syndome, early repolarization, and Brugada syndrome2-7. for clini-cal use, phenotypes are dichotomized based on optimal sensitivity and spec-ificity to predict worse outcome. How-ever, there is no evidence that the un-

derlying biology of st-t wave is truly binary. Therefore, we considered that common variation involved in the bi-ology of quantitative st-t waves traits might provide additional biological in-sights in the (patho)physiological mech-anisms of repolarization. Genome wide association analyses have been proven a powerful and unbiased tool to identify novel mechanisms and pathways. Here we aim to identify vari-ants that are associated with the heart’s repolarization phase during the st-t wave of the ecG to further advance our knowledge on biological factors regulating cardiac repolarization.

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genome Wide discovery analysis and rePlication

we performed genome-wide meta-anal-yses in 15,943 subjects of european de-scent on the st-t wave (table s1 and s2), with up to 2,316,136 imputed au-tosomal snPs. Phenotypes for the st segment amplitudes and t wave ampli-tudes were deducted from leads used in the clinic for diagnosing Brugada syn-drome7 (septal: v1 and v2) and er6-8 (lateral: i, avl, v5 and v6 and inferior: ii, iii and avf). aiming to capture ad-ditional information available on the ecG during cardiac repolarization, we also investigated the other leads (anteri-or: v3, v4 and lead avr), with presumed anatomical meaning during the st-t wave. This resulted in 10 phenotypes representing the st-t wave amplitude phenotypes: 5 st segment amplitudes and 5 t wave amplitudes (figure 1a). across the 10 phenotypes, a total of 1,340 unique snPs passed the threshold of P < 1×10-6, 379 unique snP’s were genome wide significant (P < 6.25×10-9 based on 8 independent phenotypes). The discovery analysis identified 41 distinct genotype-pheno-type associations clustered within 17 independent loci to be genome wide significant. conditional analysis re-vealed a secondary signal (rs9851710 conditioned on rs6801957: β(se) per minor allele =0.13 (0.0204), P = 2.38×10-10) in the scn5a/scn10a lo-

cus to remain genome wide associated with st avr amplitudes, this snP was already identified to be associated with other st-twaves (table s3). There was no evidence for inflation of test statis-tics in the final meta-analysis (table s4) or significant heterogeneity. to increase the robustness of the significant and suggestive (P > 6.25×10-9 and P < 1.0×10-6) associa-tions in our discovery cohort we per-formed replication testing in an addi-tional 22,034 individuals derived from 7 independent cohorts (table s1). all genome wide significance loci in the discovery phase were all replicated. an additional 56 genotype-phenotype associations in 36 loci (20 additional loci) showed suggestive evidence for as-sociation of which 35 became genome wide significant. The 71 significant genotype-phenotype associations after replication (table s3) are located in 28 independent loci (table, fig. 1B, fig-ure s1). some loci were predominantly associated with st segment amplitudes (locus 4, 5, 16, 26, and 28) while others were predominantly associated with t wave amplitudes (locus 6, 18 and 29). The test statistics for every combination of genotype-phenotype association are summarized in table s5.

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fig.2afigure 1.a we conducted genome wide analyses of st-segment am-plitudes at 80 milliseconds after j-point and t-wave (top) amplitudes reflecting temporal patterns in the cardiac cycle during the repolarization phase. in total 12 phenotypes were defined by taking the sum of the st-t wave amplitudes in the lateral (i, avl, v5 and v6), inferior (ii, iii and avf), sep-tal (v1 and v2), anterior (v3, v4) leads and lead avr. These lead groups cover the combination of leads with presumed anatomical meaning of the heart, and those that are used in the clinic for diagnosing Brugada syndrome7 and early repolarization6-8. b Genome wide association analyses of all st-t wave traits identified 71 significant genotype-phenotype associations in 28 genetic loci (2mB). The x-axis represents the chromo-somal position for each snP, which was assigned the lowest P-value across the twelve traits; the y-axis represents the -log10 of the P-value for association. twenty eight loci were significant for st-t wave amplitudes. cfour genes overlapping four loci are directly involved in the cardiac action potential; snPs near SCN5A were associated with st segment and t-top amplitudes whereas the loci con-taining potassium channel coding genes KCND3, KCNA7 and KCNB1 were primarily associated with st segment amplitudes.

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fig.2cfigure 2.asnPs were significantly more enriched in dHss of fe-tal heart tissue (n=12) compared to other tissue and cells (n=337), across the full range of P-values of the discovery meta-analyses (genome wide), suggesting that functionality of regulatory dna elements may underlie some of the as-sociations.b dePict identified 56 significantly enriched gene-sets rel-evant for st-t wave amplitudes. The most significant me-ta-term was “abnormal cardiovascular system physiology” (left), which consist of 8 individual reconstituted gene sets (right).c, dnext, we performed a meta-analysis of the 28 identified st-t wave loci using 1000 Genomes imputed data, for this 1000 Genomes variants needed to be in ld r2>0.1 with the Hapmap sentinel snP. subsequent prioritization of po-tential causal annotations in these loci also suggested that regions of dHs in fetal heart are possibly underlying the as-sociations as well as cardiac transcription factors, conserved regions, (exonic) active- and weak enhancers. while regions that are transcribed, tightly packed (heterochromatin) or function as promoters in the ventricles may be less import-ant for the biological mechanisms of genetic variants that are associated with st-t wave amplitudes. subtle difference are present between the ventricles and fetal heart which could suggest that promoters that overlap potential causal st-t wave snPs may be active in the fetal heart but repressed in the ventricles. Percentages between parentheses indicate the amount of snPs in the 28 loci overlapping with the an-notation. conservation (GerP & 29 mammals), dHs of fetal heart, enhancers of the left ventricle and tbx3 bound regions were used to prioritize potential causal snPs in the 28 loci.

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fig.2d other electrocardiograPhic traits

Previous genome wide associations have studied other ecG indices1,9-13 such as Qrs duration, Pr interval, heart rate and Qt interval. we intersected the previous identified snPs with our st-t wave loci and found overlap (with-in 2mB) in 13 loci. lead snPs from previous and current findings in 4 loci were in low ld (r2 < 0.02), suggesting that 19 of the current loci are novel as-sociations for ecG traits (table s6).

heritaBility estimates

The 28 identified sentinel snPs collec-tively explained between 1.6% (t wave septal) and 5.1% (st segment avr) of the observed phenotypic variance (ta-ble s7). familial heritability estimates in the erasmus rucphen family study (erf) varied between h2=30% (t wav inferior) and h2=42% (st septal) sug-gesting additional genetic variants and mechanisms remain to be discovered (table s8). The variation in proportion of variance associated with covariates varied more widely; between r2=0.09 (st segment inferior) and r2 = 0.36 (st segment septal).

identification of candidate genes and PathWay analyses

we prioritized 54 candidate genes in the 28 st-t wave loci (table). Thir-ty-four genes were prioritized based on the proximity criteria, 5 genes con-tained one or more non-synonymous snPs (table s9), 12 genes by eQtl analyses (table s10), 65 genes based

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on dePict analysis of which 25 were within genome-wide significant loci (at false discovery rate ≤ 5%; table s11) and 3 genes based on literature mining using Grail (table s12). The top 5 keywords retrieved from Grail were “muscle”, “channel”, “transcription”, “heart” and “channels”. The dePict analysis was also applied to subsequently gain more de-tailed insight into the biology underly-ing changes in st-t wave amplitudes of the electrocardiogram. This method identified 56 reconstituted gene sets in 9 independent meta gene sets. The top meta gene set represents various pro-tein complexes likely related to fascia adherens whereas the second top meta gene set (‘abnormal cardiovascular system Physiology’) represents various expected gene sets (figure 2, table s13 and s14).

role of regulatory dna and 1000 genomes imPutation

st-t wave associated snPs were 3 fold enriched in dHss from human fetal heart (figure 2), which was significantly higher compared to 337 other cell types and tissues (P = 0.0004, z-score statis-tics) in line with earlier observations 14,15. we then performed fine mapping of all significant genotype-phenotype associ-ations (table, table s3) in Prevend and lifelines imputed with 1000 Genomes, covering more of the known common variants in humans. we observed that

64 (of 71) associations were assigned to a different lead snP and that 63 (of 71) associations were more significant in the 1000 Genomes imputed data. to facilitate future functional experiments towards the identification of causal variants and their underlying biologi-cal mechanisms, we prioritized poten-tial causal snPs using the probabilistic framework of Paintor. The 5 most significant annotations (conservation scores, dHs of fetal heart, enhancers of the left ventricle and tbx3 bound regions, see figure 2) were used to pri-oritize potential causal snPs in the 28 loci. This yielded 315 snPs in the 99% confidence set and 96 snPs in the 95% confidence set under the assumption of 1 causal snP per locus (table s15). as an illustration of functional follow-up we selected the two snPs (rs34119223 (Pposterior>0.999) intronic of KCND3 and rs4657166 (Pposterior>0.999) intronic of nos1aP ) that were within the 10% credible set of Paintor and assessed differential protein binding by electro-phoretic mobility shift assay’s (emsa) on nuclear extract of adult mouse heart. for rs34119223 we observed a clear dif-ference in nuclear protein binding of the reference allele (cc) compared to the alternative allele (the cc deletion, figure s2). rs34119223 is located in an conserved intronic enhancer region which may be specific for heart, brain and primary cd34+ cells (figure s3).

d i s c u s s i o n

amplitudes of the st segment and t wave are important traits that are as-sociated with abnormal heart rhythm, conduction disturbances and ventricu-lar arrhythmias. in this study we per-formed the first genome wide associa-tion analyses of st-t wave amplitudes of the ecG in up to 37,977 individu-als. traits were defined according to the combination of ecG leads presumed to have anatomical meaning or used in the clinic for diagnosing Brugada syn-drome and early repolarization6-8. st-t wave traits were moderately heritable (h2=30%-42%) while the proportion of variance associated with covariates (gender, bmi and age) varied consid-erably (9%-36%), strongly supporting the genetic investigation of these traits. we identified 28 genome-wide signifi-cant loci for st-t wave amplitudes and by expression Qtl and bioinformatic strategies we prioritized a core set of 54 candidate genes and regions enriched for functions that are relevant to repo-larization of the heart. a recent genome wide asso-ciation study on Brugada revealed the association of two loci that are shared by our st-t wave loci16. one of these signals in the scn5a/scn10a locus is in complete ld with our sentinel snP (rs10428132, r2= 0.96 with rs6801957) and rs9388451 near HEY2, is a st-t wave sentinel snP; suggesting that Brugada syndrome susceptibility loci

share a common genetic background with st-t wave traits. one of the stron-gest genome-wide associated signal for all amplitudes of the st segment was in the KCND3 gene (locus 4). By means of fine mapping we identified an in-tronic cytosine-cytosine deletion to be a promising candidate causal snP at the KCND3 locus that could exert its effect through disruption of a nuclear protein binding site. KCND3 encodes the kv4.3 α-subunit that conducts the cardiac fast transient outward k+ cur-rent (ito,f). This current is prominent in phase 1 of the action potential and contributes to the ‘notch’ of the cardio-myocyte’s action potential2. mutations in KCND3 have been implicated with Brugada syndrome, an arrhythmogenic disorder with increased risk of sudden cardiac death17,18, as well as mutations in SCN5A2 and SCN10A19. Here we associated loci containing potassium channels KCND3 (locus 4), KCNB1 (locus 28) and KCNA7 (locus 26), pre-dominantly with amplitudes of the st segment and not with the t wave, sug-gesting that these potassium channels are specifically activated during the st segment of the repolarization phase and may have clinical relevance6,20,21. fu-ture studies are required to examine the potential role of the other st-t wave snPs for increased arrhythmogenic risk and sudden death through an al-tered cardiac repolarization.

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in addition to HEY2, a tran-scriptional regulator of cardiac elec-trical function in the right ventricular outflow tract16, we identified six addi-tional loci containing genes with strong evidence of being directly involved in cardiac tissue development via tran-scriptional regulatory pathways. locus 5 contains a myocyte enhancer factor–2 (MEF2D) important for cardiac muscle morphogenesis and heart looping22,23. locus 18 and 22 contain two transcrip-tion factors from the soX family, SOX5 and SOX8. The SOX5 is important for a functioning heart and involved in correct wnt signaling 24, this locus was previously associated with Pr interval and resting heart rate but the identi-fied variant in the current study is in low ld (r2 = 0.01), suggesting multiple molecular-genetic mechanisms at this locus influence heart function. tran-scripts of SOX8 are concentrated in the subendothelial mesenchym of the whole outflow tract 25 in which n-ca-derin26 (encoded by CDH2, locus 25), the transcription factor tbx327 (TBX3, locus 19) and wnt428 (specific for the endocardial endothelial cushion devel-opment, part of the wnt pathway) in lo-cus 2 also play essential developmental roles, among other cardiac cell lineag-es. less characterized genes that could underlie cardiac repolarization through altered transcription, or development are: SKI, as mutations (1p36 deletion- and sprintzen-Goldberg syndrome) are characterized by heart defects, brain abnormalities and muscle tone (hypo-tonia) in infancy29; NFIA  (locus 3, a

transcription factor); KLF12 (locus 20, a transcription factor), vGll2 (locus 13, a muscle specifc transcription co-factor) and TNKS (locus 15, activator of the wnt pathway). other interesting loci containing even more compelling cardiac genes are: snPs near TMEM43 (locus 10) in which mutations cause ar-rhythmogenic right ventricular cardio-myopathy and emery-dreifuss muscu-lar dystrophy, and snPs overlapping a sarcomeric gene MYH7B and mir499 (locus 27) which regulates a multitude of cardiac mrna and micrornas and promotes ventricular specification. snPs in GALNT1, a glycosyltransfer-ases that is required for normal heart valve development and cardiac func-tion by regulating the extracellular ma-trix and altering conserved signaling pathways that regulate cell proliferation during heart development 30. five of the 28 st-t wave sen-tinel snPs were in ld with reported associations of Qt-duration1 indicating that st-t wave amplitudes encompass-es additional information of cardiac repolarization. The known functions of the candidate genes and our pathway analyses suggest that cardiac repolar-ization is influenced and regulated by a wide variety of molecular mechanisms, including ion-channels, structural pro-teins and cardiac transcription factors. However, the finding that protein com-plexes related to fascia adherens are most enriched in our pathway analyses hints that some of the biology underly-ing st-t waves is less well captured in well-established pathways and (current-

ly) better represented by data-driven (and not manually curated) pathways.ventricular repolarization is a complex process, to capture this process we chose to apply a selection of composite ecG parameters with the aim to gain more insight into the biological processes un-derlying the st-t wave. Based on our results it is tempting to speculate that some loci highlight spatiotemporal pat-terns. These surface ecG parameters are not highly specific to identify the exact anatomical region of the heart; e.g. the septal leads (v1, v2) may also include aspects of the right ventricular wall activity. However, there is no data available of more precise measurements such as could be derived from more so-phisticated surface ecG equipment or intra-cardiac ecG measurements. The main aim of this study was to elucidate and advance our un-derstanding of cardiac repolarization, not to improve upon clinical diagno-sis or risk stratification, although this should be subject to further investi-gation. There is some previous data on Brugada available, identifying two loci31 (HEY2, and SCN5A-SCN10A)

both highly significant in our study. This suggest it might be of interest to perform look-ups (decrease multiple testing burden) of the st-t wave asso-ciated variants. unfortunately, as previ-ous data on Brugada has not been made publically available and the authors were not willing to perform look-ups or perform a genetic risk score analysis, we are unable to translate our quantitative findings in relation to the dichotomous criterion of the Brugada syndrome in summary, we present a large number of genome-wide significant loci robustly associated with cardiac repolarization parameters, some with compelling biological basis for their as-sociation and a number of loci not pre-viously implicated in cardiac function. The identified loci and selected genes have the potential to aid future studies that are focused on risk stratification or on molecular mechanisms underly-ing cardiac repolarization and diseases of cardiac repolarization; to facilitate these studies we have made our results (including the genome-wide associa-tion) publicly available.

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PhenotyPe modeling

summing correlated variables when expecting that genetic variants are as-sociated to multiple of these variables will increase the power for detection; with genetic variant (snp), and traits (ampitudei), consider the correlation: Corr(Snp, Ampitude1 + Ampitude2) = Corr(Snp, Ampitude1) + Corr(Snp, Ampi-tude2) + 2×Corr(Ampitude1, Ampitude2) > Corr(Snp, Ampitude1) + Corr(Snp, Ampitude2), hence if Corr(Ampitude1, Ampitude2) > 0, the traits ampitudei measure the same latent variable (L). Therefore phenotypes for the st seg-ment amplitudes 80 ms after j-point and t wave amplitudes were defined by taking the sum of the lateral (i, avl, v5 and v6), inferior (ii, iii and avf), sep-tal (v1 and v2), anterior (v3, v4) leads and lead avr. individuals were exclud-ed for bundle branch block or Qrs du-ration >120msec, atrial fibrillation, flut-ter, history of myocardial infarction or electronic pacemaker rhythm and when available, heart failure and ecG alter-ing medication. also participants with extreme measurements (more than ±4sd from mean) were excluded on a per phenotype basis.

statistical analyses to control for multiple testing of the 10 phenotypes while accounting for

the correlation between them, we per-formed an eigenvalue decomposition of the correlation matrix (table s2) of the phenotypes. The variance of the eigen-values (var(λobs) = 2.36) was used to es-timate the effective number of indepen-dent phenotypes tested 31. our findings indicate that studying 10 related st-t wave traits is equivalent to analysis of 8 independent phenotypes. we there-fore adopt αa = 5×10-8/8 = 6.25×10-9 as threshold for declaring genome-wide significance in order to correct for the effective number of independent phe-notypes studied. residuals of st-t wave ampli-tudes were calculated using general lin-ear regression models to adjust for age, gender and body mass index, and stan-dardized to a mean of zero and a stan-dard deviation of one. Gwas analyses in Prevend and lifelines of 2,316,136 genotyped or imputed snPs (table s16) were performed on the standard-ized residuals using an additive genetic model in Plink (v.1.07). test statistics from each cohort were then corrected for their respective genomic control inflation factor to adjust for residual population sub-structure and meta-an-alyzed using the inverse-variance meth-od. snPs with maf<1% (weighted av-erage across cohorts) were removed. for conditional analyses we re-

peated the primary association analysis for each trait whilst conditioning on the trait-specific genome wide significant sentinel snPs by adding the snP gen-otypes as covariates. association results for each study were again combined by inverse variance weighting. variants were considered to be independent if the pair-wise ld (r2) was less than 0.1 and if they were separated by at least 1 mB; this was defined a ‘locus’. replication of the significant genotype-phenotype associations (P< 6.25×10-9) and associations that did not exceed this threshold, but were suggestive (6.25×10-9 < P <1×10-6), was performed in the rotterdam study (i, ii and iii), erf, aric, cHs and yfs and combined using fixed-ef-fects meta-analysis by inverse variance weighting (table s16). The pre-spec-ified statistical significance threshold for heterogeneity by cochran’s Q (Phet) was Phet < 0.0007 to account for multi-ple testing. inverse variance weighting was used to determine meta-P-values for combined discovery and replication data. an association was considered replicated if the direction of effect was concordant with discovery, replication P < 0.01 and meta-P < 6.25×10-9. Heritability estimates were calculated in the erasmus rucphen family study (erf) using the “tdist” function in solar software, includ-ing gender, age and bmi as covariates.

data-driven exPression Prioritized integration for comPlex traits (de-Pict) dePict systematically identifies the most likely causal gene at a given asso-ciated locus, tests gene sets for enrich-ment in associated snPs, and iden-tifies tissues and cell types in which genes from associated loci are highly expressed (see Pers et al. for a detailed description). for this work we ran de-Pict on 140 independently associated loci (association P values < 10-5; Plink parameters, ‘--clump-p1 1e-5 --clump-kb 500 --clump-r2 0.05’) resulting in 103 independent, autosomal dePict loci containing 363 genes (loci overlap-ping with the major histocompatibility complex region are by default excluded in dePict). we have extended the lo-cus definition used in dePict (ld r2 > 0.5) with 100kb at either side of the loci, because several genes that may be important for cardiac repolarization were outside the default dePict locus boundaries (e.g. scn5a, tmem43, vGll2). The gene set enrichment re-sults for this slightly extended locus definition were similar to the results based on the default locus definition used in dePict (see table s17).

identification of candidate genes we prioritized candidate genes based on nearby genes: we considered the nearest gene and any other gene located within 10kb of the sentinel snP. cod-ing variants: for identification of cod-ing variants and ld calculations we used the 1000 Genomes (1000G) Proj-

1 2 2 1 2 3

tags on the 5-end. The labeled  probe was annealed  with equal amount of unlabeled reverse strand primer using standard protocol. dna-protein bind-ing reaction was assayed by mixing 2 to 10μg nuclear extract with 0.2 pm an-nealed oligonucleotides in binding buf-fer (10 mm tris (pH7.5), 50 mm kcl, 1 mm dtt), 2.5% glycerol and 75 ng/ul poly(didc) in a final volume of 15 μl. The mixture was then incubated for 30 min at room temperature, followed by polyacrylamide gel electrophore-sis at 25°c on a 4.5% polyacrylamide gel. fluorescence was visualized with the odyssey infrared imaging system (li-cor Biosciences). competition of labeled probe was carried out by adding 100x excess unlabeled probe.

ect dataset (march 2012 release) in the european populations. we considered genes that harbor non-synonymous snPs in ld with the st-t wave snPs at r2>0.8. Grail analyses: we carried out a literature analysis by employing the Grail text-mining algorithm, a statistical tool that identifies subsets of genes with known functional interrela-tionships based on Pubmed abstracts. we carried out the analysis using the 2006 dataset to avoid confounding by subsequent Gwas discovery. dePict method (see above), and expression Qtl (eQtl) analyses in cis, we search for eQtls (sentinel snPs or snPs in ld, r2>0.8, Hapmap r27) in an eQtl dataset that was compiled from the summary statistics of various studies and tissues (see table s10). we only considered eQtls that were in ld (r2

> 0.8, Hapmap r27) with the sentinel snP and reached a P value cutoff of at least P < 0.05 / (28 loci×17 eQtl stud-ies).

imPutation using 1000 genomes Genome positions from the Prevend and lifelines genotypes were convert-ed from hg18 to hg19 using the ucsc liftover tool. Genome-wide genotype imputation was performed with sHa-Peit (v2) and imPute2 (v2.3.0) us-ing the complete 1000 Genomes v3, march 2012 haplotypes Phase i inte-grated variant set release as reference panel.

functional information

we overlapped snPs with data from

the encode project33 and roadmap epigenomics Program34, conservation across mammals, various cardiac tran-scription factor measured by chiP-seq and contiguous annotations of human fetal heart, left ventricle and right ven-tricle as determined by chromHmm 34 (supplementary note).

Prioritization of Potentially causal variants and enrichment of dna el-ements

for the prioritization of genetic variants for future functional follow-up and in-sight of the underlying dna elements that might be relevant for causal st-t wave amplitude variants, we employed the Paintor (Probabilistic anno-tation integrator) framework. in short, this method allows us to priori-tize genetic variants in each of the 28 significant associated loci by integrat-ing ld structure, strength of associ-ation and annotations of functional dna elements to estimate the proba-bility for each variant to be causal but also investigate which annotations are potentially causal.

electroPhoretic moBility shift assay (emsa)nuclear extracts were prepared from whole adult mouse heart using ne-Per nuclear and cytoplasmic extraction reagents Pierce (rockford, il, usa). oligonucleotides (Biolegio, The neth-erlands) were designed based on the ge-nomic sequence flanking the snPs (see table s18). The forward probes were fluorescent labeled with irdye-700

1 2 4 1 2 5

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4.30

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1LI

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2316

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50e-

1737

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2316

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7359

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2316

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2416

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4.80

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3777

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2416

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8493

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4.80

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2416

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ant

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3777

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n

2416

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sep

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4.10

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3782

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3.00

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2416

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63)/

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90e-

1130

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2416

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1.60

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3071

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2416

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3070

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2416

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4006

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1.20

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3073

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2518

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1rs

8083

566

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07)/

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0.01

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30e-

1037

771

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2518

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1rs

8083

566

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07)/

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70e-

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770

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2518

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1rs

8083

566

st.a

vr

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0.07

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60e-

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33rs

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3003

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90)/

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10e-

1437

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KCN

A7nc, N

TF4

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HRC

d

2720

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22rs

6087

666

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20)/

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10e-

1537

770

TRP

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DEM

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2720

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738

st.a

vr

0.55

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21)/

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06(0

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5.90

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3778

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, PRO

CRe ,M

YH7B

cd, N

CO

Ad

2820

q13.

13rs

1190

7908

st.a

nt0.

83t

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2(0.

01)

8.00

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3775

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FX1(

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U1d

2820

q13.

13rs

6019

750

st.la

t1

c(0

.93)

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13(0

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4.30

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3777

1KC

NB1

n ,STA

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13

4

13

5

2820

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13rs

2202

261

st.in

f-

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93)/

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4.40

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3777

0KC

NB1

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2820

q13.

13rs

2202

261

st.a

vr

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0.93

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4(0.

01)

5.60

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3778

4KC

NB1

n ,STA

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