Identification of low frequency variants associated with ... · Identification of low frequency...

Identification of low frequency variants associated with

gout and serum uric acid levels

Patrick Sulem1*, Daniel F. Gudbjartsson1*, G. Bragi Walters1*, Hafdis T. Helgadottir1, Agnar Helgason1, Sigurjon A. Gudjonsson1, Carlo Zanon1, Soren Besenbacher1, Gyda Bjornsdottir1, Olafur T. Magnusson1 , Gisli Magnusson1 , Eirikur Hjartarson1, Jona Saemundsdottir1, Arnaldur Gylfason1, Adalbjorg Jonasdottir1, Hilma Holm1, Ari Karason1, Thorunn Rafnar1, Hreinn Stefansson1, Ole A. Andreassen2, Jesper H. Pedersen3, Allan I. Pack4, Marieke C.H. de Visser5, Lambertus A. Kiemeney5,6,7, Arni J Geirsson8, Gudmundur I. Eyjolfsson9, Isleifur Olafsson10, Augustine Kong1, Gisli Masson1, Helgi Jonsson8,11, Unnur Thorsteinsdottir1,11, Ingileif Jonsdottir1,11,12 & Kari Stefansson1,11 1deCODE genetics, Sturlugata 8, 101 Reykjavik, Iceland 2Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway. 3Department of Cardiothoracic Surgery, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark 4Center for Sleep and Circardian Neurobiology, Division of Sleep Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 5Department of Epidemiology, Biostatistics & HTA, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, the Netherlands. 6Comprehensive Cancer Center IKO, 6501 BG Nijmegen, the Netherlands. 7Department of Urology, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, the Netherlands

8Landspitali, The National University Hospital of Iceland, Department of Medicine, Reykjavik, Iceland 9Icelandic Medical Center (Laeknasetrid) Laboratory in Mjodd (RAM), 10Landspitali, The National University Hospital of Iceland, Department of Clinical Biochemistry, Reykjavik, Iceland, 11University of Iceland, Faculty of Medicine, Reykjavik, Iceland 12Landspitali, The National University Hospital of Iceland, Department of Immunology, Reykjavik, Iceland. * Authors with an equal contribution

Nature Genetics: doi:10.1038/ng.972

Supplementary Information Contents: Supplementary Note: Supplementary Note Supplementary Table 1: Association with gout of genome-‐wide significant markers at

the 19q13 locus Supplementary Table 2: Association with gout of genome-‐wide significant markers at

the ABCG2 locus Supplementary Table 3: Novel and previously reported sequence variants associating

with serum uric acid levels and gout Supplementary Table 4: Sex-‐stratified uric acid levels association for novel and

previously reported sequence variants associating with gout and serum uric acid levels

Supplementary Table 5: Sex-‐stratified gout association for novel and previously reported sequence variants associating with gout and serum uric acid levels

Supplementary Table 6: Association of uric acid sequence variants with age at visit to the clinician

Supplementary Figure 1: The sequencing depth of the 457 whole-‐genome sequenced individuals

Supplementary Figure 2: Quantile-‐quantile plot of the SNPs in the genome-‐wide association scan for gout

Supplementary Figure 3: Quantile-‐quantile plot of the SNPs in the genome-‐wide association scan for uric acid levels


Supplementary Note

Whole Genome Sequencing

SNPs were identified through the Icelandic whole genomic sequencing project. A total of 457

Icelanders were selected for sequencing based on having various neoplasic, cardiovascular and

psychiatric conditions. All of the individuals were sequenced to a depth of at least 10X. Based on this

data, 15,957,390 SNPs were imputed based on this set of individuals.

Sample preparation. Paired-‐end libraries for sequencing were prepared according to the

manufacturer's instructions (Illumina). In short, approximately 5 μg of genomic DNA, isolated from

frozen blood samples, was fragmented to a mean target size of 300 bp using a Covaris E210

instrument. The resulting fragmented DNA was end repaired using T4 and Klenow polymerases and

T4 polynucleotide kinase with 10 mM dNTP followed by addition of an 'A' base at the ends using

Klenow exo fragment (3′ to 5′-‐exo minus) and dATP (1 mM). Sequencing adaptors containing 'T'

overhangs were ligated to the DNA products followed by agarose (2%) gel electrophoresis. Fragments

of about 400 bp were isolated from the gels (QIAGEN Gel Extraction Kit), and the adaptor-‐modified

DNA fragments were PCR enriched for ten cycles using Phusion DNA polymerase (Finnzymes Oy) and

PCR primers PE 1.0 and PE 2.0 (Illumina). Enriched libraries were further purified using agarose (2%)

gel electrophoresis as described above. The quality and concentration of the libraries were assessed

with the Agilent 2100 Bioanalyzer using the DNA 1000 LabChip (Agilent). Barcoded libraries were

stored at −20 °C. All steps in the workflow were monitored using an in-‐house laboratory information

management system with barcode tracking of all samples and reagents.

DNA sequencing. Template DNA fragments were hybridized to the surface of flow cells (Illumina PE

flowcell, v4) and amplified to form clusters using the Illumina cBot. In brief, DNA (8–10 pM) was

denatured, followed by hybridization to grafted adaptors on the flowcell. Isothermal bridge

amplification using Phusion polymerase was then followed by linearization of the bridged DNA,

denaturation, blocking of 3 ends and hybridization of the sequencing primer. Sequencing-‐by-‐

synthesis was performed on Illumina GAIIx instruments equipped with paired-‐end modules. Paired-‐

end libraries were sequenced using 2 × 101 cycles of incorporation and imaging with Illumina

sequencing kits, v4. Each library or sample was initially run on a single lane for validation followed by

further sequencing of ≥4 lanes with targeted cluster densities of 250–300 k/mm2. Imaging and

analysis of the data was performed using the SCS 2.6 and RTA 1.6 software packages from Illumina,

respectively. Real-‐time analysis involved conversion of image data to base-‐calling in real-‐time.


Alignment. For each lane in the DNA sequencing output, the resulting qseq files were converted into

fastq files using an in-‐house script. All output from sequencing was converted, and the Illumina

quality filtering flag was retained in the output. The fastq files were then aligned against Build 36 of

the human reference sequence using bwa version 0.5.7 (ref. 1).

BAM file generation. SAM file output from the alignment was converted into BAM format using

samtools version 0.1.8 (ref. 2), and an in-‐house script was used to carry the Illumina quality filter flag

over to the BAM file. The BAM files for each sample were then merged into a single BAM file using

samtools. Finally, Picard version 1.17 (see http://picard.sourceforge.net/) was used to mark

duplicates in the resulting sample BAM files.

SNP calling and genotyping in whole-‐genome sequencing

A two-‐step approach was applied. The first step was to detect SNPs by identifying sequence positions

where at least one individual could be determined to be different from the reference sequence with

confidence (quality threshold of 20) based on the SNP calling feature of the pileup tool samtools2.

SNPs that always differed heterozygous or homozygous from the reference were removed. The

second step was to use the pileup tool to genotype the SNPs at the positions that were flagged as

polymorphic. Because sequencing depth varies and hence the certainty of genotype calls also varies,

genotype likelihoods rather than deterministic calls were calculated (see below). Of the 2.5 million

SNPs reported in the HapMap2 CEU samples, 96.3% were observed in the whole-‐genome sequencing

data. Of the 6.9 million SNPs reported in the 1000 Genomes Project data, 89.4% were observed in the

whole-‐genome sequencing data.

Long range phasing

Long range phasing of all chip-‐genotyped individuals was performed with methods described

previously3,4. In brief, phasing is achieved using an iterative algorithm which phases a single proband

at a time given the available phasing information about everyone else that shares a long haplotype

identically by state with the proband. Given the large fraction of the Icelandic population that has

been chip-‐typed, accurate long range phasing is available genome-‐wide for all chip-‐typed Icelanders.

Genotype imputation

We imputed the SNPs identified and genotyped through sequencing into all Icelanders who had been

phased with long range phasing using the same model as used by IMPUTE5. The genotype data from

sequencing can be ambiguous due to low sequencing coverage. In order to phase the sequencing


genotypes, an iterative algorithm was applied for each SNP with alleles 0 and 1. We let H be the long

range phased haplotypes of the sequenced individuals and applied the following algorithm:

1. For each haplotype h in H, use the Hidden Markov Model of IMPUTE to calculate for every

other k in H, the likelihood, denoted γh,k, of h having the same ancestral source as k at the

SNP.

2. For every h in H, initialize the parameter , which specifies how likely the one allele of the

SNP is to occur on the background of h from the genotype likelihoods obtained from

sequencing. The genotype likelihood Lg is the probability of the observed sequencing data at

the SNP for a given individual assuming g is the true genotype at the SNP. If L0, L1 and L2 are

the likelihoods of the genotypes 0, 1 and 2 in the individual that carries h, then set

.

3. For every pair of haplotypes h and k in H that are carried by the same individual, use the

other haplotypes in H to predict the genotype of the SNP on the backgrounds of h and k:

and . Combining these predictions with the genotype

likelihoods from sequencing gives un-‐normalized updated phased genotype probabilities:

, , and

. Now use these values to update θh and θk to

and .

4. Repeat step 3 when the maximum difference between iterations is greater than a

convergence threshold ε. We used ε=10−7.

Given the long range phased haplotypes and the allele of the SNP on a new haplotype h not in H,

is imputed as .

The above algorithm can easily be extended to handle simple family structures such as parent-‐

offspring pairs and triads by letting the P distribution run over all founder haplotypes in the family

structure. The algorithm also extends trivially to the X-‐chromosome. If source genotype data are only

ambiguous in phase, such as chip genotype data, then the algorithm is still applied, but all but one of


the Ls will be 0. In some instances, the reference set was intentionally enriched for carriers of the

minor allele of a rare SNP in order to improve imputation accuracy. In this case, expected allele

counts will be biased toward the minor allele of the SNP. Call the enrichment of the minor allele E

and let be the expected minor allele count calculated from the naïve imputation method, and let

be the unbiased expected allele count, then and hence .

This adjustment was applied to all imputations based on enriched imputations sets. We note that if

is 0 or 1, then will also be 0 or 1, respectively.

Genotype imputation information

The informativeness of genotype imputation was estimated by the ratio of the variance of imputed

expected allele counts and the variance of the actual allele counts:

where is the allele count. was estimated by the observed variance

of the imputed expected counts and was estimated by , where is the allele

frequency.

In silico genotyping

In addition to imputing sequence variants from the whole genome sequencing effort into chip

genotyped individuals, we also performed a second imputation step where genotypes were imputed

into relatives of chip genotyped individuals, creating in silico genotypes. The inputs into the second

imputation step are the fully phased (in particular every allele has been assigned a parent of origin)

imputed and chip type genotypes of the available chip typed individuals. The algorithm used to

perform the second imputation step consists of:

1. For each ungenotyped individual (the proband), find all chip genotyped individuals within two

meiosis of the individual. The six possible types of two meiosis relatives of the proband are

(ignoring more complicated relationships due to pedigree loops): Parents, full and half

siblings, grandparents, children and grandchildren. If all pedigree paths from the proband to

a genotyped relative go through other genotyped relatives, then that relative is excluded.

E.g. if a parent of the proband is genotyped, then the proband’s grandparents through that


parent are excluded. If the number of meiosis in the pedigree around the proband exceeds a

threshold (we used 12), then relatives are removed from the pedigree until the number of

meiosis falls below 12, in order to reduce computational complexity.

2. At every point in the genome, calculate the probability for each genotyped relative sharing

with the proband based on the autosomal SNPs used for phasing. A multipoint algorithm

based on the hidden Markov model Lander-‐Green multipoint linkage algorithm using fast

Fourier transforms is used to calculate these sharing probabilities6,7. First single point sharing

probabilities are calculated by dividing the genome into 0.5cM bins and using the haplotypes

over these bins as alleles. Haplotypes that are the same, except at most at a single SNP, are

treated as identical. When the haplotypes in the pedigree are incompatible over a bin, then a

uniform probability distribution was used for that bin. The most common causes for such

incompatibilities are recombinations within the pedigree, phasing errors and genotyping

errors. Note that since the input genotypes are fully phased, the single point information is

substantially more informative than for unphased genotyped, in particular one haplotype of

the parent of a genotyped child is always known. The single point distributions are then

convolved using the multipoint algorithm to obtain multipoint sharing probabilities at the

center of each bin. Genetic distances were obtained from the most recent version of the

deCODE genetic map4.

3. Based on the sharing probabilities at the center of each bin, all the SNPs from the whole

genome sequencing are imputed into the proband. To impute the genotype of the paternal

allele of a SNP located at , flanked by bins with centers at and . Starting with

the left bin, going through all possible sharing patterns , let be the set of haplotypes of

genotyped individuals that share identically by descent within the pedigree with the

proband’s paternal haplotype given the sharing pattern and be the probability of

at the left bin – this is the output from step 2 above – and let be the expected allele count

of the SNP for haplotype . Then is the expected allele count of the paternal

haplotype of the proband given and an overall estimate of the allele count given the

sharing distribution at the left bin is obtained from . If is empty then no

relative shares with the proband’s paternal haplotype given and thus there is no

information about the allele count. We therefore store the probability that some genotyped

relative shared the proband’s paternal haplotype, and an expected allele


count, conditional on the proband’s paternal haplotype being shared by at least one

genotyped relative: . In the same way calculate and .

Linear interpolation is then used to get an estimates at the SNP from the two flanking bins:

If is an estimate of the population frequency of the SNP then is an

estimate of the allele count for the proband’s paternal haplotype. Similarly, an expected

allele count can be obtained for the proband’s maternal haplotype.

Case control association testing

Logistic regression was used to test for association between SNPs and disease, treating disease status

as the response and expected genotype counts from imputation or allele counts from direct

genotyping as covariates. Testing was performed using the likelihood ratio statistic. The conditional

analysis of the chromosome 1 centromere and 19q13 loci was performed by adding the strongest

SNP at each locus as a covariate while testing every SNP in the region for association with gout. When

testing for association based on the in silico genotypes, controls were matched to cases based on the

informativeness of the imputed genotypes, such that for each case controls of matching

informativeness where chosen. Failing to match cases and controls will lead to a highly inflated

genomic control factor, and in some cases may lead to spurious false positive findings. The

informativeness of each of the imputation of each one of an individual’s haplotypes was estimated by

taking the average of

over all SNPs imputed for the individual, where is the expected allele count for the haplotype at

the SNP and is the population frequency of the SNP. Note that and

. The mean informativeness values cluster into groups corresponding to the

most common pedigree configurations used in the imputation, such as imputing from parent into

child or from child into parent. Based on this clustering of imputation informativeness we divided the

haplotypes of individuals into seven groups of varying informativeness, which created 27 groups of


individuals of similar imputation informativeness; 7 groups of individuals with both haplotypes having

similar informativeness, 21 groups of indivdiuals with the two haplotypes having different

informativeness, minus the one group of individuals with neither haplotype being imputed well.

Within each group we calculate the ratio of the number of controls and the number of cases, and

choose the largest integer that was less than this ratio in all the groups. For example, if in one

group there are 10.3 times as many controls as cases and if in all other groups this ratio was greater,

then we would set and within each group randomly select ten times as many controls as

there are cases. For gout we used .

Quantitative trait association testing

A generalized form of linear regression was used to test for association of UA with SNPs. Let be

the vector of quantitative measurements, and let be the vector of expected allele counts for the

SNP being tested. We assume the quantitative measurements follow a normal distribution with a

mean that depends linearly on the expected allele at the SNP and a variance covariance matrix

proportional to the kinship matrix:

where

is based on the kinship between individuals as estimated from the Icelandic genealogical database

( ) and and estimate of the heritability of the trait ( ). It is not computationally feasible to use this

full model and we therefore split the individuals with in silico genotypes and UA measurements into

smaller clusters. Here we chose to restrict the cluster size to at most 300 individuals.

The maximum likelihood estimates for the parameters , , and involve inverting the kinship

matrix. If there are individuals in the cluster, then this inversion requires calculations, but

since these calculations only need to be performed once the computational cost of doing a GWAS will

only be calculations; the cost of calculating the maximum likelihood estimates if the kinship

matrix has already been inverted.

Effective sample size estimation

In order to estimate the effective sample size of the case control and quantitative trait association

analyses, we compared the variances of the logistic and generalized linear regression parameter

estimates based on the in silico genotypes to their one step imputation counterparts. For the

quantitative trait association analysis, assume that a single step imputation (SNPs are imputed, but in


silico genotypes are not used) association analysis with subjects leads on average to an estimate

of the regression parameter with variance and that the corresponding in silico genotype

association analysis leads to an estimate of the regression parameter with variance , then

assuming that variance goes down linearly with sample size we estimate the effective sample size in

the in silico genotype association analysis as . For the case control association analysis,

the number of controls is much greater than the number cases and we use the same formula to

estimate the effective number of cases, with the -‐s representing the number of cases and the -‐s

representing the variances of the logistic regression coefficient.

References

1. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754-1760 (2009).

2. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078-2079 (2009).

3. Kong, A. et al. Detection of sharing by descent, long-range phasing and haplotype imputation. Nat. Genet. 40, 1068-1075 (2008).

4. Kong, A. et al. Fine-scale recombination rate differences between sexes, populations and individuals. Nature 467, 1099-1103 (2010).

5. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906-913 (2007).

6. Lander, E.S. & Green, P. Construction of multilocus genetic linkage maps in humans. Proc. Natl. Acad. Sci. USA 84, 2363-2367 (1987).

7. Kruglyak, L. & Lander, E.S. Faster multipoint linkage analysis using Fourier transforms. J. Comput. Biol. 5, 1-7 (1998).


Supplementary Table 1 – Association with gout of genome-‐wide significant markers at the 19q13 locus (top marker is bolded)

P-‐value Allelic Odds Ratio Allelic

Frequency in Cases

Allelic Frequency in Controls

Info Coding allele Other allele

Marker Chr 19-‐Build 36 position

Gene (if aa

change)

Amino acid

change

1.7×10-‐8 2.815 0.0251 0.0116 0.89 T G chr19:54326383 54,326,383 -‐ -‐ 4.6×10-‐8 2.509 0.0288 0.0144 0.87 T C chr19:54400812 54,400,812 -‐ -‐ 1.7×10-‐10 3.101 0.0273 0.0116 0.88 C A chr19:54505919 54,505,919 -‐ -‐ 1.5×10-‐16 3.122 0.0442 0.0183 0.89 G C chr19:54660818 54,660,818 ALDH16A1 P476R 2.7×10-‐8 1.660 0.0946 0.0658 0.92 G C rs1064257 54,685,347 -‐ -‐ 2.2×10-‐8 2.113 0.0506 0.0314 0.76 C G rs62128084 54,706,613 -‐ -‐ 3.2×10-‐11 3.080 0.0293 0.0124 0.87 C G chr19:54788061 54,788,061 -‐ -‐ 1.3×10-‐11 3.205 0.0288 0.0119 0.87 G A chr19:54812310 54,812,310 -‐ -‐ 4.7×10-‐11 2.203 0.0566 0.0317 0.90 C T rs62128132 54,909,767 -‐ -‐ 1.8×10-‐9 2.941 0.0259 0.0114 0.88 G A chr19:54991872 54,991,872 -‐ -‐ 2.0×10-‐9 2.932 0.0259 0.0113 0.88 A G chr19:55018776 55,018,776 -‐ -‐ 1.8×10-‐9 2.939 0.0259 0.0113 0.89 T C chr19:55068782 55,068,782 -‐ -‐ 1.9×10-‐9 2.938 0.0259 0.0113 0.89 G T chr19:55071043 55,071,043 -‐ -‐ 1.9×10-‐9 2.938 0.0259 0.0113 0.89 C G chr19:55071103 55,071,103 -‐ -‐ 1.1×10-‐9 3.141 0.0218 0.0078 0.99 C T chr19:55268031 55,268,031 -‐ -‐ 1.1×10-‐9 3.136 0.0219 0.0079 0.98 C G chr19:55270330 55,270,330 -‐ -‐ 5.2×10-‐10 3.162 0.0225 0.0081 0.96 C T chr19:55483086 55,483,086 -‐ -‐ 1.8×10-‐10 3.240 0.0228 0.008 0.98 T C chr19:55576372 55,576,372 -‐ -‐ 1.9×10-‐8 2.611 0.027 0.0125 0.94 G A chr19:55602702 55,602,702 -‐ -‐

The tests for association are based on an effective sample size of 968 gout cases and over 40,000 controls.


Supplementary Table 2 – Association with gout of genome-‐wide significant markers at the ABCG2 locus (top marker is bolded)

P-‐value Allelic Odds

Ratio

Allelic Frequency in

Cases

Allelic Frequency in Controls

Info Coding allele

Other allele

Marker Chr 4-‐Build 36 position

Gene (if aa

change)

Amino acid

change

1.2×10-‐9 1.35 0.479 0.419 0.97 T C rs2725261 89,255,377 -‐ -‐

5.3×10-‐12 1.65 0.147 0.102 0.99 G A rs1481012 89,258,106 -‐ -‐

2.9×10-‐12 1.66 0.145 0.100 1.00 C G rs45499402 89,262,658 -‐ -‐

3.0×10-‐12 1.66 0.145 0.100 1.00 C G chr4:89263204 89,263,204 -‐ -‐

3.0×10-‐12 1.66 0.145 0.100 1.00 T A chr4:89263336 89,263,336 -‐ -‐

3.0×10-‐12 1.66 0.145 0.100 1.00 A G rs75544042 89,264,355 -‐ -‐

3.0×10-‐12 1.66 0.145 0.100 1.00 T C chr4:89265226 89,265,226 -‐ -‐

2.8×10-‐12 1.66 0.145 0.100 1.00 A C rs74904971 89,269,050 -‐ -‐

2.8×10-‐12 1.66 0.145 0.100 1.00 T G rs2231142 89,271,347 ABCG2 Q141K

2.8×10-‐12 1.66 0.145 0.100 1.00 G A rs4148155 89,273,691 -‐ -‐

2.3×10-‐8 1.31 0.518 0.462 1.00 A C rs2622620 89,282,875 -‐ -‐

1.9×10-‐9 1.34 0.562 0.502 0.99 A C rs2622627 89,284,377 -‐ -‐

2.0×10-‐9 1.34 0.561 0.501 0.99 C A rs2725249 89,284,892 -‐ -‐

1.6×10-‐9 1.35 0.565 0.505 0.99 A C rs2622626 89,285,739 -‐ -‐

4.6×10-‐8 1.36 0.752 0.703 0.98 A C rs2725248 89,287,031 -‐ -‐

4.5×10-‐10 1.36 0.513 0.451 0.99 A G rs2725247 89,287,281 -‐ -‐

2.8×10-‐10 1.36 0.511 0.448 0.99 T G rs17731799 89,287,479 -‐ -‐

4.7×10-‐10 1.36 0.513 0.451 0.99 A G rs2725246 89,287,522 -‐ -‐

2.7×10-‐8 1.37 0.751 0.701 0.98 C T rs2622625 89,287,761 -‐ -‐

4.7×10-‐10 1.36 0.513 0.451 0.99 A G rs2725245 89,287,762 -‐ -‐

1.8×10-‐9 1.35 0.564 0.504 0.99 C T rs2725244 89,287,785 -‐ -‐

4.7×10-‐10 1.36 0.512 0.449 0.99 C T rs2622624 89,288,430 -‐ -‐

1.9×10-‐9 1.34 0.564 0.504 0.99 A T rs2725242 89,288,551 -‐ -‐

2.6×10-‐9 1.34 0.565 0.506 0.99 C T chr4:89293003 89,293,003 -‐ -‐

4.0×10-‐9 1.34 0.559 0.500 0.99 T C rs13109944 89,293,429 -‐ -‐

7.1×10-‐10 1.35 0.508 0.447 0.99 G A rs28856119 89,293,627 -‐ -‐

4.4×10-‐10 1.36 0.493 0.432 0.98 A G chr4:89293691 89,293,691 -‐ -‐

1.7×10-‐9 1.35 0.491 0.431 0.97 T C chr4:89293698 89,293,698 -‐ -‐

2.1×10-‐9 1.35 0.490 0.431 0.97 T C chr4:89293703 89,293,703 -‐ -‐

2.3×10-‐9 1.34 0.490 0.431 0.97 G T chr4:89293711 89,293,711 -‐ -‐

2.1×10-‐9 1.35 0.495 0.436 0.98 C T chr4:89293717 89,293,717 -‐ -‐

2.1×10-‐9 1.34 0.498 0.438 0.98 C G chr4:89293718 89,293,718 -‐ -‐

7.1×10-‐10 1.35 0.508 0.447 0.99 G C rs34633905 89,293,795 -‐ -‐

1.2×10-‐9 1.35 0.505 0.444 1.00 A C rs2725239 89,294,647 -‐ -‐

1.3×10-‐9 1.35 0.505 0.444 1.00 G C rs2622603 89,296,505 -‐ -‐

1.1×10-‐9 1.35 0.504 0.443 0.99 T C rs2622605 89,298,410 -‐ -‐

1.5×10-‐9 1.34 0.504 0.444 1.00 C T rs2622605 89,298,410 -‐ -‐

1.3×10-‐9 1.35 0.504 0.444 1.00 C T rs3114020 89,302,690 -‐ -‐

7.5×10-‐10 1.35 0.504 0.442 1.00 A G rs2725226 89,304,355 -‐ -‐

7.6×10-‐10 1.35 0.506 0.444 0.99 T A rs2622608 89,305,768 -‐ -‐

6.9×10-‐10 1.35 0.505 0.443 0.99 C A rs2622609 89,307,499 -‐ -‐

The tests for association are based on an effective sample size of 968 gout cases and over 40,000 controls.


Supplementary Table 3 – Novel and previously reported sequence variants associating with serum uric acid levels and gout

Allele Uric Acid Gout SNP Chr Pos Effect/other Freq Info Effect (95% CI)a P OR (95% CI) P Novel SNP associations chr1_142697422 1 142,697,422 C/T 0.986 0.55 0.48 (0.36, 0.60) 4.5×10-‐16 1.92 (1.01, 3.63) 0.046 chr1_144539240 1 144,539,240 A/G 0.987 0.65 0.41 (0.30, 0.52) 2.5×10-‐13 2.06 (1.11, 3.82) 0.023 c.1580C>G 19 54,660,818 G/C 0.019 0.89 0.36 (0.29, 0.44) 4.5×10-‐21 3.12 (2.38, 4.17) 1.5×10-‐16 Replication of previously reported SNP associations rs1967017 1 144,435,002 T/C 0.449 0.93 0.03 (0.01, 0.05) 0.0016 1.09 (0.98, 1.19) 0.12 rs12129861 1 144,437,046 G/A 0.500 0.90 0.04 (0.01, 0.06) 0.0012 1.04 (0.94, 1.15) 0.46 rs780094 2 27,594,741 T/C 0.340 1.00 0.04 (0.02, 0.06) 0.00071 1.19 (1.08, 1.32) 0.00092 rs780093 2 27,596,107 T/C 0.342 1.00 0.04 (0.02, 0.06) 0.00082 1.18 (1.06, 1.30) 0.0012 rs734553 4 9,532,102 T/G 0.790 1.00 0.24 (0.22, 0.27) 1.0×10-‐80 1.39 (1.23, 1.59) 2.4×10-‐7 rs13129697 4 9,536,065 T/G 0.767 1.00 0.23 (0.21, 0.26) 1.6×10-‐79 1.32 (1.18, 1.49) 5.1×10-‐6 rs2199936 4 89,264,355 A/G 0.101 1.00 0.16 (0.12, 0.19) 1.9×10-‐20 1.66 (1.44, 1.91) 3.0×10-‐12 rs2231142 4 89,271,347 T/G 0.101 1.00 0.16 (0.12, 0.19) 2.3×10-‐20 1.67 (1.43, 1.92) 2.8×10-‐12 rs675209 6 7,047,083 T/C 0.260 1.00 0.04 (0.01, 0.06) 0.0022 1.03 (0.92, 1.15) 0.62 rs742132 6 25,715,550 A/G 0.716 1.00 0.01 (-‐0.01, 0.04) 0.25 1.09 (0.98, 1.21) 0.12 rs1165196 6 25,921,129 A/G 0.492 1.00 0.05 (0.03, 0.07) 2.8×10-‐6 1.10 (1.00, 1.21) 0.059 rs1183201 6 25,931,423 T/A 0.479 1.00 0.05 (0.03, 0.07) 6.4×10-‐6 1.06 (0.96, 1.18) 0.22 rs12356193 10 61,083,359 A/G 0.849 1.00 0.04 (0.01, 0.07) 0.0062 1.05 (0.92, 1.21) 0.46 rs17300741 11 64,088,038 A/G 0.499 1.00 0.04 (0.02, 0.06) 1.9×10-‐5 1.06 (0.96, 1.16) 0.27 rs2078267 11 64,090,690 C/T 0.502 1.00 0.05 (0.03, 0.07) 1.1×10-‐5 1.07 (0.97, 1.17) 0.18 rs505802 11 64,113,648 C/T 0.280 1.00 0.03 (0.01, 0.05) 0.0076 1.03 (0.93, 1.15) 0.57 rs1106766 12 56,095,723 C/T 0.689 0.98 0.04 (0.02, 0.06) 0.00084 1.06 (0.95, 1.17) 0.30 The tests for association are based on an effective sample size of 15,506 individuals with uric acid measurements and 968 gout cases and over 40,000 controls. Previously reported SNPs are from Yang et al. and Kolz et al. Publications. aEffects on uric acid levels are in standard deviations.


Supplementary Table 4 – Sex-‐stratified uric acid levels association for novel and previously reported sequence variants associating with gout and serum uric acid levels

Allele Male Female

SNP Chr Pos Effect/other Freq Info Effect (95% CI) P Effect (95% CI) P Pdiff

chr1_142697422 1 142697422 C/T 0.986 0.55 0.58 (0.40, 0.76) 2.4×10-‐10 0.41 (0.26, 0.55) 2.7×10-‐8 0.14 rs1967017 1 144435002 T/C 0.449 0.93 0.04 (0.01, 0.07) 0.0086 0.03 (0.00, 0.05) 0.043 0.47 rs12129861 1 144437046 G/A 0.5 0.9 0.04 (0.01, 0.08) 0.0064 0.03 (0.00, 0.06) 0.035 0.46 rs780094 2 27594741 T/C 0.34 1 0.03 (0.00, 0.07) 0.041 0.04 (0.01, 0.07) 0.0036 0.78 rs780093 2 27596107 T/C 0.342 1 0.03 (0.00, 0.06) 0.053 0.04 (0.01, 0.07) 0.0033 0.70 rs734553 4 9532102 T/G 0.79 1 0.19 (0.15, 0.23) 5.1×10-‐23 0.28 (0.25, 0.31) 2.1×10-‐70 0.00031

rs13129697 4 9536065 T/G 0.767 1 0.18 (0.15, 0.22) 1.2×10-‐22 0.27 (0.24, 0.30) 1.3×10-‐69 0.00026 rs2199936 4 89264355 A/G 0.101 1 0.20 (0.15, 0.25) 7.0×10-‐15 0.13 (0.09, 0.17) 3.3×10-‐9 0.036 rs2231142 4 89271347 T/G 0.101 1 0.20 (0.15, 0.25) 6.5×10-‐15 0.13 (0.08, 0.17) 4.1×10-‐9 0.034 rs675209 6 7047083 T/C 0.26 1 0.06 (0.02, 0.09) 0.001 0.02 (-‐0.01, 0.05) 0.25 0.074 rs742132 6 25715550 A/G 0.716 1 0.01 (-‐0.03, 0.04) 0.66 0.02 (-‐0.01, 0.05) 0.19 0.62 rs1165196 6 25921129 A/G 0.492 1 0.05 (0.01, 0.08) 0.0038 0.05 (0.02, 0.08) 0.00013 0.82 rs1183201 6 25931423 T/A 0.479 1 0.04 (0.01, 0.07) 0.012 0.05 (0.03, 0.08) 0.0001 0.59 rs12356193 10 61083359 A/G 0.849 1 0.05 (0.01, 0.10) 0.018 0.03 (0.00, 0.07) 0.069 0.50 rs17300741 11 64088038 A/G 0.499 1 0.05 (0.01, 0.08) 0.0037 0.04 (0.02, 0.07) 0.0007 0.95 rs2078267 11 64090690 C/T 0.502 1 0.05 (0.02, 0.08) 0.003 0.05 (0.02, 0.07) 0.00048 0.96 rs505802 11 64113648 C/T 0.28 1 0.03 (0.00, 0.07) 0.062 0.03 (0.00, 0.06) 0.048 0.87 rs1106766 12 56095723 C/T 0.689 0.98 0.04 (0.01, 0.08) 0.0097 0.03 (0.00, 0.06) 0.023 0.59 c.1580C>G 19 54660818 G/C 0.019 0.89 0.35 (0.24, 0.46) 2.9×10-‐10 0.38 (0.28, 0.48) 4.9×10-‐14 0.71


Supplementary Table 5 – Sex-‐stratified gout association for novel and previously reported sequence variants associating with gout and serum uric acid levels

Allele Male Female

SNP Chr Pos Effect/other Freq Info OR (95% CI) P OR (95% CI) P Pdiff

chr1_142697422 1 142697422 C/T 0.986 0.55 5.62 (2.01, 15.70) 0.00098 0.88 (0.40, 1.94) 0.75 0.0049 rs1967017 1 144435002 T/C 0.449 0.93 1.09 (0.95, 1.23) 0.21 1.08 (0.92, 1.25) 0.37 0.92 rs12129861 1 144437046 G/A 0.5 0.9 1.06 (0.93, 1.22) 0.32 0.99 (0.85, 1.15) 0.89 0.45 rs780094 2 27594741 T/C 0.34 1 1.19 (1.05, 1.35) 0.006 1.16 (1.00, 1.35) 0.057 0.82 rs780093 2 27596107 T/C 0.342 1 1.19 (1.04, 1.35) 0.0084 1.16 (1.00, 1.35) 0.058 0.82 rs734553 4 9532102 T/G 0.79 1 1.33 (1.14, 1.56) 0.00034 1.47 (1.22, 1.79) 7.6×10-‐5 0.44

rs13129697 4 9536065 T/G 0.767 1 1.30 (1.11, 1.52) 0.00075 1.35 (1.14, 1.61) 0.001 0.74 rs2199936 4 89264355 A/G 0.101 1 1.79 (1.49, 2.13) 1.8×10-‐10 1.44 (1.16, 1.80) 0.0011 0.13 rs2231142 4 89271347 T/G 0.101 1 1.79 (1.49, 2.13) 1.7×10-‐10 1.45 (1.15, 1.79) 0.0011 0.15 rs675209 6 7047083 T/C 0.26 1 0.95 (0.82, 1.10) 0.47 1.15 (0.98, 1.35) 0.088 0.076 rs742132 6 25715550 A/G 0.716 1 1.07 (0.94, 1.23) 0.3 1.09 (0.93, 1.29) 0.3 0.86 rs1165196 6 25921129 A/G 0.492 1 1.08 (0.96, 1.22) 0.22 1.12 (0.97, 1.30) 0.13 0.71 rs1183201 6 25931423 T/A 0.479 1 1.03 (0.92, 1.18) 0.58 1.10 (0.95, 1.28) 0.19 0.48 rs12356193 10 61083359 A/G 0.849 1 1.13 (0.95, 1.35) 0.17 0.97 (0.79, 1.19) 0.76 0.25 rs17300741 11 64088038 A/G 0.499 1 1.04 (0.92, 1.18) 0.51 1.09 (0.94, 1.26) 0.24 0.62 rs2078267 11 64090690 C/T 0.502 1 1.05 (0.93, 1.19) 0.45 1.11 (0.96, 1.28) 0.16 0.57 rs505802 11 64113648 C/T 0.28 1 1.02 (0.89, 1.17) 0.75 1.04 (0.88, 1.22) 0.64 0.85 rs1106766 12 56095723 C/T 0.689 0.98 1.07 (0.93, 1.22) 0.33 1.04 (0.89, 1.22) 0.61 0.78 c.1580C>G 19 54660818 G/C 0.019 0.89 3.85 (2.86, 5.56) 1.7×10-‐16 2.04 (1.28, 3.23) 0.0024 0.027


Supplementary Table 6 – Association of uric acid sequence variants with age at visit to the clinician

Allele

SNP Chr Pos Effect/other Effect (95% CI) P

chr1_142697422 1 142697422 C/T -‐14.94 (-‐30.00, 0.11) 0.052 rs1967017 1 144435002 T/C -‐0.87 (-‐2.71, 0.97) 0.35 rs12129861 1 144437046 G/A -‐0.80 (-‐2.66, 1.07) 0.40 rs780094 2 27594741 T/C -‐0.23 (-‐1.98, 1.52) 0.80 rs780093 2 27596107 T/C -‐0.23 (-‐1.98, 1.53) 0.80 rs734553 4 9532102 T/G 0.24 (-‐2.15, 2.64) 0.84

rs13129697 4 9536065 T/G -‐0.35 (-‐2.60, 1.90) 0.76 rs2199936 4 89264355 A/G -‐3.41 (-‐5.85, -‐0.97) 0.0061 rs2231142 4 89271347 T/G -‐3.41 (-‐5.85, -‐0.97) 0.0062 rs675209 6 7047083 T/C 1.10 (-‐0.95, 3.14) 0.29 rs742132 6 25715550 A/G 0.83 (-‐1.14, 2.81) 0.41 rs1165196 6 25921129 A/G -‐0.77 (-‐2.51, 0.97) 0.38 rs1183201 6 25931423 T/A -‐0.72 (-‐2.48, 1.04) 0.42 rs12356193 10 61083359 A/G 0.56 (-‐1.97, 3.09) 0.67 rs17300741 11 64088038 A/G 0.08 (-‐1.62, 1.78) 0.93 rs2078267 11 64090690 C/T 0.20 (-‐1.50, 1.90) 0.82 rs505802 11 64113648 C/T -‐0.25 (-‐2.11, 1.60) 0.79 rs1106766 12 56095723 C/T 0.55 (-‐1.42, 2.51) 0.58 c.1580C>G 19 54660818 G/C -‐7.62 (-‐12.36, -‐2.89) 0.0016

Effect is expressed in years.


Supplementary Figure 1

The sequencing depth of the 457 whole-genome sequenced individuals.



Quantile-quantile plot of the 15,957,390 SNPs in the genome-wide association scan for gout. The blue ‘x’s represent the P values scaled down by the genomic control inflation factor of 1.10. The diagonal red line represents where the dots are expected to fall under the null hypothesis of no association. The horizontal green line represents P = 5 × 10-8.



Quantile-quantile plot of the 15,957,390 SNPs in the genome-wide association scan for uric acid levels. The blue ‘x’s represent the P values scaled down by the genomic control inflation factor of 1.18. The diagonal red line represents where the dots are expected to fall under the null hypothesis of no association. The horizontal green line represents P = 5 × 10-8.


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