Open Access

Hardy-Weinberg analysis of a large set of published association studies reveals genotyping error and a deficit of heterozygotes across multiple loci

Human Genomics20083:36

DOI: 10.1186/1479-7364-3-1-36

Received: 7 April 2008

Accepted: 7 April 2008

Published: 1 September 2008

Abstract

In genetic association studies, deviation from Hardy-Weinberg equilibrium (HWD) can be due to recent admixture or selection at a locus, but is most commonly due to genotyping errors. In addition to its utility for identifying potential genotyping errors in individual studies, here we report that HWD can be useful in detecting the presence, magnitude and direction of genotyping error across multiple studies. If there is a consistent genotyping error at a given locus, larger studies, in general, will show more evidence for HWD than small studies. As a result, for loci prone to genotyping errors, there will be a correlation between HWD and the study sample size. By contrast, in the absence of consistent genotyping errors, there will be a chance distribution of p-values among studies without correlation with sample size. We calculated the evidence for HWD at 17 separate polymorphic loci investigated in 325 published genetic association studies. In the full set of studies, there was a significant correlation between HWD and locus-standardised sample size (p = 0.001). For 14/17 of the individual loci, there was a positive correlation between extent of HWD and sample size, with the evidence for two loci (5-HTTLPR and CTSD) rising to the level of statistical significance. Among single nucleotide polymorphisms (SNPs), 15/23 studies that deviated significantly from Hardy-Weinberg equilibrium (HWE) did so because of a deficit of hetero-zygotes. The inbreeding coefficient (F(is)) is a measure of the degree and direction of deviation from HWE. Among studies investigating SNPs, there was a significant correlation between F(is) and HWD (R = 0.191; p = 0.002), indicating that the greater the deviation from HWE, the greater the deficit of heterozygotes. By contrast, for repeat variants, only one in five studies that deviated significantly from HWE showed a deficit of heterozygotes and there was no significant correlation between F(is) and HWD. These results indicate the presence of HWD across multiple loci, with the magnitude of the deviation varying substantially from locus to locus. For SNPs, HWD tends to be due to a deficit of heterozygotes, indicating that allelic dropout may be the most prevalent genotyping error.

Keywords

meta-analysis polymorphism variant deviation

Introduction

Genotyping errors are an important and increasingly recognised problem in modern genetics [1]. Traditional family-based genetic studies allow for straightforward identification of genotyping errors through a familial Mendelian inheritance check. Over the past decade, however, there has been increasing interest in case-control association studies, a type of study in which investigators generally compare a group of subjects having a particular disease with another group not having the disease, to identify a genotypic difference between the groups. Unfortunately, these association studies do not allow for simple inheritance checks to identify errors and, as a result, we have limited insight into the prevalence and nature of genotyping errors in published association studies.

Hardy-Weinberg law states that if conditions of population equilibrium are met (random mating and negligible mutation, migration, stratification, genetic drift and selection), then genotype frequencies should fit a predictable binomial distribution calculable from the allele frequencies. Significant deviation from the predicted distribution has been used as a marker for genotyping error.[2] Previous work has estimated that the control sample genotype distribution violates Hardy-Weinberg equilibrium (HWE) in approximately 10 per cent of published association studies [35]. Furthermore, exclusion of studies that violate HWE alters the results of a substantial fraction of gene association meta-analyses [6].

The inbreeding coefficient (F(is)) can be used as a measure of the degree and direction of deviation from HWE (HWD). Positive F(is) values indicate an excess of homozygotes and negative F(is) values indicate a deficit of homozygotes. Salanti and colleagues [4] found that with a moderate level of HWD (F(is) = 0.10), only 7 per cent of association studies had at least 80 per cent power to find significant evidence for violation of HWE. Because of this low level of power, focusing on statistically significant violation of HWE in individual association studies substantially limits the insight that we can gain into potential genotyping errors from HWE analysis [7]. A complementary approach that bypasses the problem of limited power in individual studies is the analysis of HWD patterns across a set of studies. As originally demonstrated by Weir,[8] if a locus is prone to genotyping error, the evidence for HWD will increase with increasing sample size. By contrast, if there is no substantial genotyping error, or if the error is random, there will be no relationship between HWD and sample size. By examining a set of studies at a given locus, we can learn about the level of genotyping error present at that locus. Furthermore, by looking at the evidence across multiple loci, we can gain insight into the level and nature of genotyping error in association studies in general.

Here, we investigate: (1) the relationship between sample size and HWD across well-studied loci, and (2) the direction of deviation in a set of association studies compiled from previous meta-analyses.

Materials and methods

Studies

Genetic loci for analysis were identified through published meta-analyses. Meta-analyses were identified through PubMed at the National Library of Medicine, limiting the search to meta-analyses published between 2001 and 2005 and using the search terms: (1) association genetic; (2) association polymorphism; (3) association variant. These results were supplemented by a database of meta-analyses compiled by Ioannidis and colleagues [9, 10]. Loci were subsequently chosen using the criteria: (1) biallelic markers; (2) at least ten independent studies; and (3) sample size data for all three genotype groups included in the publication. For each included study, we recorded the control group sample size for the three genotype groups (Supplementary Table 1).
Table 1

Relationship between sample size and Hardy-Weinberg exact test p-values for individual loci

Variant

Variant

No. of studies

Correlation (p-value)

PON192

SNP

39

-0.120 (0.467)

GPIIIa

SNP

33

-0.050 (0.783)

5-HTTLPR

Repeat

31

-0.444(0.014)

L-myc-ECORI

Repeat

28

-0.296(0.126)

MTHFR677

SNP

23

-0.201(0.358)

VDR

SNP

17

-0.140(0.593)

CTSD

SNP

16

-0.582(0.018)

DRD2

SNP

21

-0.191(0.407)

Neurod1

SNP

14

-0.433(0.122)

TPH

SNP

13

0.297(0.324)

COLIAI

SNP

13

0.188(0.538)

ADDI

SNP

12

-0.275(0.387)

SRD5A2

SNP

12

-0.326(0.301)

BSMI

SNP

11

-0.188(0.503)

IL-I

SNP

11

-0.520(0.101)

CYPI7

SNP

10

-0.175(0.628)

Analyses

The most straightforward way to assess HWD in a set of studies investigating a given locus is to pool the genotype cell counts from each of the relevant studies and assess HWD among the three pooled genotype groups. All of these studies investigated population samples with different ethnicities, however, and consequently different allele frequencies. As a result, simply combining data from different studies would find substantial HWD due to lack of heterozygotes, even in the absence of geno-typing error.

We took an alternative approach to assessing HWD among a set of studies investigating a given locus. For each locus, we determined the correlation between the HWD exact test p-value of each study and study sample size. The stronger the correlation, the stronger the evidence for HWD at that locus. Given that many included studies had small homozygote minor allele cell counts (fewer than five subjects), and that the chi square test is an unreliable test of HWD in the presence of small cell counts, an exact test was used to determine the strength of evidence for HWD [11].

In addition to investigating the correlation between HWD and sample size among studies investigating each individual locus, we also wanted to explore the strength and significance of this correlation across all studies, regardless of locus. A straightforward assessment of correlation between sample size and HWD, however, would be confounded by statistical artefact. Specifically, the mean sample size varies substantially across loci. Because the level of HWD varies substantially across loci (as demonstrated by our initial analyses), a correlation between sample size and HWD p-value among the set of all studies could merely represent that loci with larger mean sample sizes have greater HWD. In order to control for this potential confound, we calculated a standardised sample size for each study, such that each locus had a mean sample size = 50 and sample size standard deviation = 10. Subsequently, we calculated the strength and significance of the correlation between this locus-standardised sample size and HWD p-value for the set of all studies. The raw sample size for each study was converted to a T-score so that each locus had an overall mean standardised sample size of 50 ± 10. Subsequently, the correlation between standardised sample size and exact test p-value was calculated for the set of all studies.

Inbreeding coefficient was calculated using the following formula:
F ( is ) = P ( AA ) / P ( A ) + P ( aa ) / P ( a ) 1

where p = frequency; A = major allele; a = minor allele; AA = homozygous major allele; aa = homozygous minor allele. All analyses were carried out in SPSS 12.0 (SPSS Inc., Chicago, IL, USA).

Results

In total, 325 studies, investigating 17 loci, fit the criteria for analysis. Twenty-eight studies (9 per cent) showed significant HWD. This proportion is in line with the results of previous studies [35]. The number of studies per locus ranged from ten (CYP1) to 39 (PON1 Q192R). The average sample size per locus ranged from 71 (DRD2) to 1,020 (ADD1) (Figure 1).
https://static-content.springer.com/image/art%3A10.1186%2F1479-7364-3-1-36/MediaObjects/40246_2008_Article_217_Fig1_HTML.jpg
Figure 1

Hardy-Weinberg disequilibrium (HWD) p -value vs sample size across 325 studies.

Among individual loci, 14/17 variants showed a negative correlation between sample size and HWD p- value, indicating that the majority of studied variants show evidence of consistent genotyping error. Overall, the correlations ranged from R = 0.29 (TPH) to R = -0.59 (CTSD) and was significant for two loci (CTSD and 5-HTTLPR) (Table 1). Among the set of all 325 studies, 23 studies had a homozygote minor allele cell count = 0. The strength and significance of correlations were not substantially changed with the exclusion of these studies (data not shown).

The 325 studies investigated 15 single nucleotide polymorphism (SNP) loci (267 studies) and two repeat polymorphism loci (58 studies). The percentage of individual studies that significantly deviated from HWE was the same (9 per cent) for both the SNP and repeat polymorphism categories. Similarly, the standardised sample size-HWD correlation was statistically significant for both SNP (p = 0.018) and repeat polymorphism (p = 0.004) groups. Of the 28 studies that showed significant deviation from HWE, 23 studies were SNP studies and five were repeat polymorphism studies. Fifteen out of 23 HWE-violating SNP studies showed a deficit of heterozygotes, while only one in five HWE-violating repeat polymorphism studies showed a deficit of heterozygotes. In addition, for SNP studies, there was a significant correlation between F(is) and HWD p-value (R = 0.190; p = 0.002), while repeat polymorphisms showed no evidence of correlation (R = 0.03). In the set of all 325 studies, there was a significant correlation between standardised sample size and HWD (R = 0.18; p = 0.001) (Figure 2).
https://static-content.springer.com/image/art%3A10.1186%2F1479-7364-3-1-36/MediaObjects/40246_2008_Article_217_Fig2_HTML.jpg
Figure 2

Mean F(is) statistic stratified by variant type.

To gain insight into the reliability of the results found among controls, and to help to differentiate between selection and genotyping error as the primary cause of HWD, we investigated the correlation between F(is) among cases (F(cases)) and controls (F(controls)) for each individual study. If the HWD among control subjects is due to selection, then we would expect the genotype that is deficient among controls to be overrepresented among cases, and thus F(is) among control and case studies would show a negative correlation. By contrast, if the HWD among control subjects is due to genotyping error, then we would expect the genotype that is deficient among controls also to be deficient among cases, and thus the inbreeding coefficients would show a positive correlation. Lastly, if the HWD among controls were due purely to chance, then we would expect no correlation whatsoever between F(is) statistics.

Looking across 12 loci and 221 studies for which we had data for both cases and controls, we found a significant positive correlation between F (controls) and F (cases) (r = 0.174; p = 0.01). Further, the correlation was in the positive direction for 11/12 loci. These findings indicate that for any given study, the direction and magnitude of HWD among cases is similar to the direction of magnitude of HWD among controls. This result is consistent with genotyping error rather than selection as the primary source of HWD, and provides further evidence that these findings are not due purely to chance.

Discussion

The primary finding of this analysis was the identification of HWD across a large subset of published association studies investigating both SNP and repeat variants. Although deviation was present at most loci, the degree of deviation varied substantially across loci. At least among SNP studies, the predominant cause of this deviation was a deficit of heterozygotes.

In addition to genotyping error, other factors can contribute to HWD. For example, strong selection against a specific genotype can skew the genotypic distribution of a population. In fact, HWD among cases has been used as a test for genotype phenotype association,[12, 13] and Wittke-Thompson and colleagues [14] have demonstrated a pattern of expected deviation among cases and, under some conditions, controls for various disease models. Our finding that the HWD among cases has a strong tendency to be in the same direction as the deviation found among controls is contrary to the expected result under the selection model, however.

Population stratification is another factor that can contribute to HWD. To eliminate the possibility of ethnic differences between studies causing stratification and HWD in our study, we did not pool the three genotype counts for all studies investigating a given locus and calculate a HWD p-value from this pooled sample. Instead, for each locus, we determined the correlation between the HWD exact test p-value and study sample size. Thus, any effect of stratification in our study is not due to allele frequency differences between studies investigating the same locus. Although population stratification within individual studies may contribute to HWD in our study, there are multiple considerations that are likely to mitigate its effect. First, most studies included in our analysis utilise samples that are ethnically homogeneous. Secondly, a significant proportion of the studies formally tested and rejected the presence of population stratification in their sample. Thirdly, the consistent direction of deviation across studies and the different patterns of deviation found between SNP and repeat variants are more consistent with genotyping error than stratification as a primary cause of HWD. We cannot however, definitively exclude stratification as a contributing cause of HWD among these studies.

Previous studies investigating the nature and consequences of genotyping error based on simulations or experimental samples specifically designed to assess genotyping error have proposed allelic dropout as one of the most frequent causes of gen-otypic error [2, 15, 16]. Intuitively, it is clear that heterozygotes, which get half a dose of each allele compared with homozygotes, may be more often missed or misclassified. In fact, even in the most sophisticated high-throughput algorithms, heterozygotes have a lower call rate than homozygotes [17]. Our investigation of a large set of published studies is consistent with this prediction. Further, our findings are consistent with the hypothesis that genotyping error is not stochastic, but more common at certain loci [1821]. These findings raise concerns about the level and widespread nature of genotyping errors in genetic association studies and the conclusions drawn from those studies. In light of this finding, the approach employed here could be useful to identify loci most prone to error. For example, Yonan and colleagues [22] recently used HWD to identify genotyping errors at the 5hydroxytryptamine transporter 5-HTTLPR variant and developed an alternate assay less prone to error.

We propose that future genetic association meta-analyses examine the correlation between sample size and HWE to determine the level of genotyping error among included studies. Further, we believe that the method and points that this analysis highlight can be of utility to investigators performing individual association studies. First, this result should caution investigators against dismissing the possibility of genotyping error merely because their sample does not show significant deviation from HWE. Instead, investigators should further examine the magnitude and direction of deviation. For instance, a large F(is) statistic in the same direction among cases and controls raises the concern for genotyping error, and should prompt investigators to perform genotyping quality checks.
Supplementary Table

Included association studies stratified by locus

Study

locus

std N

a1/a1

a1/a2

a2/a2

N

p-value

Brummett

5-HTTLPR

47.62162

33

91

78

202

0.4612

Comings

5-HTTLPR

47.72973

58

95

51

204

0.3294

Du

5-HTTLPR

46.75676

40

86

60

186

0.3763

Ebstein

5-HTTLPR

43.24324

32

66

23

121

0.3611

Flory

5-HTTLPR

48.86486

37

112

76

225

0.7835

Greenberg

5-HTTLPR

58.16216

66

217

114

397

0.0328

Gusatavsson

5-HTTLPR

46.16216

35

83

57

175

0.6461

Gusatavsson

5-HTTLPR

43.45946

22

66

37

125

0.4725

Hamer

5-HTTLPR

70.97297

108

336

190

634

0.053

Herbst

5-HTTLPR

59.67568

79

198

148

425

0.3712

Hu

5-HTTLPR

77.72973

135

390

234

759

0.2373

Jorm

5-HTTLPR

77.72973

155

350

254

759

0.0896

Katsuragi

5-HTTLPR

42.16216

66

31

4

101

1

Kumakiri-TCI

5-HTTLPR

44.48649

85

48

11

144

0.26

Lang

5-HTTLPR

49.02703

41

102

85

228

0.2748

Lesch

5-HTTLR

52.05405

52

141

91

284

0.9039

Lesch

5-HTTLPR

48.64865

43

106

72

221

0.7841

Mazzanti

5-HTTLPR

48.32432

41

106

68

215

1

Melke

5-HTTLPR

46.97297

35

84

71

190

0.2915

Murakami

5-HTTLPR

46.91892

124

55

10

189

0.2523

Nakamura

5-HTTLPR

46.75676

128

55

3

186

0.4221

Osher-TPQ

5-HTTLPR

44.7027

39

73

36

148

0.8703

Ricketts

5-HTTLPR

38.7027

10

14

13

37

0.185

Samachowiec

5-HTTLPR

43.51351

18

67

41

126

0.356

Schmidt

5-HTTLPR

39.78378

12

29

16

57

1

Sen

5-HTTLPR

59.13514

83

183

149

415

0.0557

Stoltenberg

5-HTTLPR

41.35135

17

45

24

86

0.6704

Strobel

5-HTTLPR

43.35135

22

67

34

123

0.3619

Tsai

5-HTTLPR

47.08108

100

71

21

192

0.1629

Umekage

5-HTTLPR

49.89189

161

70

13

244

0.156

O'Donnell

ACE DI

54.48314

492

845

313

1650

0.1486

O'Donnell

ACE DI

53.34439

437

719

288

1444

0.8315

Agerholm-Larsen

ACE DI

89.81205

2113

4006

1922

8041

0.7849

Barley

ACE DI

46.52294

55

109

46

210

0.678

Benetos

ACE DI

46.06965

47

56

25

128

0.2764

Berge

ACE DI

46.13599

34

77

29

140

0.3092

Busjahn

ACE DI

46.13046

33

79

27

139

0.1272

Cambien

ACE DI

49.41404

200

390

143

733

0.0632

Castellano

ACE DI

46.40685

76

90

23

189

0.7523

Celermajer

ACE DI

46.37922

49

89

46

184

0.6599

Friedl

ACE DI

45.72692

16

37

13

66

0.4583

Kauma

ACE DI

48.20896

148

264

103

515

0.4783

Kiema

ACE DI

46.64456

75

115

42

232

0.8941

Kiema

ACE DI

46.65561

54

127

53

234

0.239

Ludwig

ACE DI

47.58983

117

206

80

403

0.6152

Mattu

ACE DI

52.1393

442

556

228

1226

0.025

Puija

ACE DI

46.09176

46

70

16

132

0.203

Rigat

ACE DI

45.80431

29

37

14

80

0.8164

Tiret

ACE DI

46.44555

60

103

33

196

0.3825

Busch

ADD1

48.02101

405

76

0

481

0.0608

Clark

ADD1

46.77722

162

80

14

256

0.347

Ju

ADD1

49.49696

166

357

225

748

0.3028

Manunta

ADD1

45.95909

80

26

2

108

1

Morrison

ADD1

56.05307

1227

643

64

1934

0.0747

Mulatero

ADD1

46.28524

117

43

7

167

0.2699

Narita

ADD1

46.88778

56

150

70

276

0.1494

Nicod

ADD1

46.79934

167

83

10

260

1

Persu

ADD1

46.41791

121

63

7

191

0.8258

Ranade

ADD1

51.2272

296

530

235

1061

0.95

Shioji

ADD1

67.08184

241

560

305

06

0.428

Yamagishi

ADD1

60.96739

599

365

859

2823

0.967

berg

bsm1

41.67598

2

9

8

49

0.504

boschitsch

bsm1

48.04469

36

67

60

63

0.0539

garnero

bsm1

53.906

38

34

96

268

0.523

gennari

bsm1

61.84358

7

29

20

40

0.087

gomez

bsm1

47.93296

27

72

62

6

0.5075

hansen

bsm1

50.11173

46

98

56

200

0.7787

jorgensen

bsm1

69.60894

77

276

96

549

0.209

kiel

bsm1

45.2514

22

7

74

113

22E-10

kroger

bsm1

40.22346

2

4

7

23

0.3787

langdahl

bsm1

43.40782

25

34

2

80

0.848

marc

bsm1

44.63687

9

59

24

02

0.634

mcclure

bsm1

44.69274

8

43

52

03

1

melhus

bsm1

43.18436

7

35

34

76

0.7943

riggs

bsm1

44.02235

5

36

40

9

0.765

vandevyer

bsm1

71.78771

107

306

75

588

0.2098

aerssens

COLIA1

50.90116

151

73

5

239

0.295

alvarez

COLIA1

44.65116

2

3

0

24

1

de vernejoul

COLIA1

47.93605

85

5

1

37

0.0267

efstathiodou

COLIA1

47.18023

73

29

9

111

0.043

heegaard

COLIA1

47.18023

82

27

2

111

1

hustmyer

COLIA1

46.22093

58

6

4

78

0.079

keen

COLIA1

47.73256

85

40

5

130

1

langdahl

COLIA1

48.13953

94

48

2

44

0.664

liden

COLIA1

45.90116

44

20

3

67

0.698

mcguigan

COLIA1

46.51163

70

7

1

88

1

roux

COLIA1

47.06395

8

24

2

07

1

uitterlinden

COLIA1

82.87791

905

392

42

339

1

weichetova

COLIA1

47.61628

94

30

2

126

1

bagnoli

CTSD

42.01754

1

26

99

126

1

bertram

CTSD

46.92982

1

29

152

182

1

bhojak

CTSD

58.68421

0

56

260

316

0.151

crawford

CTSD

41.49123

0

20

100

120

1

crawford

CTSD

40.78947

2

28

82

112

1

emahazion

CTSD

44.03509

3

27

119

149

0.3899

ingegni

CTSD

41.49123

1

21

98

120

1

mateo

CTSD

61.31579

8

54

284

346

0.0143

matsui

CTSD

72.98246

1

7

471

479

0.0372

mcilroy

CTSD

47.36842

1

16

170

187

0.3491

menzer

CTSD

57.4564

1

33

268

302

1

papassotiropoulos

CTSD

61.75439

0

47

304

351

0.3847

papassotiropoulos

CTSD

47.10526

0

18

166

184

1

prince

CTSD

46.22807

0

22

152

174

1

styczynska

CTSD

39.73684

0

9

91

100

1

chang

CYP17

45.82569

26

79

77

182

0.4248

gsur

CYP17

43.25688

12

67

47

126

0.1219

habuchi

CYP17

52.75229

69

157

107

333

0.4371

haiman

CYP17

73.34862

127

350

305

782

0.1312

kittles

CYP17

42.56881

10

46

55

111

1

latil

CYP17

44.63303

24

84

48

156

0.2511

lunn

CYP17

44.77064

18

73

68

159

0.8621

stanford

CYP17

61.46789

79

256

188

523

0.6477

wadelius

CYP17

44.81651

26

88

46

160

0.1979

yamada

CYP17

46.65138

29

120

51

200

0.004

amadeo

drd2

43.48837

0

7

36

43

1

Anghelescu

drd2

56.27907

3

32

63

98

1

Bau

drd2

60

6

36

72

114

0.5764

blum

drd2

39.06977

0

4

20

24

1

blum

drd2

40.69767

0

6

25

31

1

bolos

drd2

63.02326

8

30

89

127

0.034

comings

drd2

58.60465

0

24

84

108

0.3553

cook

drd2

38.3953

0

6

4

20

1

geijer

drd2

52.32558

5

24

52

8

0.3226

gelernter

drd2

49.30233

3

2

44

68

0.7138

goldman

drd2

4.86047

2

11

23

36

0.6232

heinz

drd2

59.76744

4

35

74

113

1

Hietala

drd2

45.11628

0

11

39

50

1

lawford

drd2

44.18605

3

11

32

46

0.1562

neiswanger

drd2

40.4652

0

4

26

30

1

noble

drd2

46.97674

3

4

4

58

0.3437

Ovchiunikov

drd2

51.16279

4

23

49

76

0.494

parsian

drd2

39.30233

0

3

22

25

1

Pastorelli

drd2

48.37209

2

3

49

64

0.2895

Samochoweic

drd2

78.3953

5

5

36

92

1

suarez

drd2

53.95349

2

23

63

88

1

abbate

gpIIIa

43.2963

3

9

5

73

0.4229

aleksic

gpIIIa

60.74074

0

4

403

544

0.000039

anderson

gpIIIa

50.848

9

65

202

276

0.2337

anderson

gpIIIa

46.88889

6

42

22

170

0.3835

ardissino

gpIIIa

48

4

33

63

200

0.324

boncler

gpIIIa

43.55556

0

9

6

80

0.5896

bottiger

gpIIIa

53.18519

9

84

247

340

0.5261

carter

gpIIIa

44.81481

0

28

86

114

0.2131

carter

gpIIIa

48.59259

3

57

156

216

0.5836

carter

gpIIIa

43.92593

2

24

64

90

1

corral

gpIIIa

44.33333

0

35

66

101

0.038

durante-mangoni

gpIIIa

43.22222

0

9

52

71

0.3451

garcia

gpIIIa

44.2963

1

12

87

100

0.3864

gardemann

gpIIIa

84.7037

31

297

863

1191

0.3654

grand maison

gpIIIa

44.2963

1

23

76

100

1

hermann

gpIIIa

47.11111

4

43

129

176

0.7646

hermann

gpIIIa

59.96296

10

143

370

523

0.5047

hooper

gpIIIa

47.44444

2

39

144

185

1

joven

gpIIIa

49.85185

3

85

66

250

0.0483

kekomaki

gpIIIa

42.22222

2

7

35

44

0.1123

kekomaki

gpIIIa

43.62963

1

17

64

82

1

laule

gpIIIa

76.59259

20

254

698

972

0.7073

mamotte

gpIIIa

61.7037

12

136

422

570

0.7302

marian

gpIIIa

46.66667

7

38

119

64

0.135

moshfegh

gpIIIa

43.88889

6

14

69

89

0.0023

osborn

gpIIIa

46.77778

8

27

32

67

0.0015

pastinen

gpIIIa

46.18519

2

26

123

151

0.6399

ridker

gpIIIa

66.66667

22

164

518

704

0.0513

samani

gpIIIa

49.2963

5

97

133

235

0.0086

scaglione

gpIIIa

44.22222

1

27

70

98

0.6863

senti

gpIIIa

45.62963

3

28

105

136

0.4363

weiss

gpIIIa

43.11111

1

12

55

68

0.525

zotz

gpIIIa

43.96296

0

23

68

91

0.3467

Combarros

IL-1

52.10145

195

104

7

306

0.408

Du

IL-1

43.76812

126

62

3

191

0.2122

Green

IL-1

66.37681

221

27

65

503

0.3238

Grimaldi

IL-1

54.2029

142

63

30

335

0.109

Hedley

IL-1

55.36232

153

68

30

35

0.113

Ki

IL-1

36.66667

72

21

0

93

0.5969

Minster

IL-1

46.73913

115

99

18

232

0.75

Nicoll

IL-1

42.02899

82

74

11

167

0.3481

Pirskanen

IL-1

67.10145

248

209

56

513

0.2582

Rebeck

IL-1

43.47826

97

74

16

187

0.7202

Tsai

IL-1

42.24638

147

22

1

170

0.5822

chenevix-Trench

LmycECOR1

57.46667

46

72

43

161

0.2068

chernitsa

LmycECOR1

46.26667

18

38

21

77

1

crossen

LmycECOR1

49.33333

43

43

14

100

0.5194

dlugosz

LmycECOR1

44.66667

11

38

16

65

0.2145

dolcetti

LmycECOR1

46.4

24

35

19

78

0.3718

ejarque

LmycECOR1

50.66667

40

45

25

110

0.0825

fernandez

LmycECOR1

49.46667

30

49

22

101

0.842

ge

LmycECOR1

39.46667

6

12

8

26

0.7061

hseih

LmycECOR1

47.73333

22

39

27

88

0.2921

isbir

LmycECOR1

47.06667

39

29

15

83

0.0323

isbir

LmycECOR1

42.8

23

26

2

51

0.1768

ishizaki

LmycECOR1

49.33333

17

63

20

100

0.0157

kato

LmycECOR1

49.06667

17

61

20

98

0.0254

kondratieva

LmycECOR1

49.6

28

52

22

102

1

kuminoto

LmycECOR1

68.13333

59

134

48

241

0.0934

murakami

LmycECOR1

79.6

69

183

75

327

0.0358

saranath

LmycECOR1

49.46667

30

49

22

101

0.842

shibuta

LmycECOR1

50.26667

34

55

18

107

0.6938

shibuta

LmycECOR1

50.26667

34

55

18

107

0.6938

shih

LmycECOR1

53.33333

43

54

33

130

0.0767

taylor

LmycECOR1

46.13333

22

31

23

76

0.1118

tefre

LmycECOR1

53.2

35

59

35

129

0.3782

togo

LmycECOR1

76.8

85

143

78

306

0.2544

weston

LmycECOR1

43.33333

10

22

23

55

0.2616

weston

LmycECOR1

40.8

11

17

8

36

0.7464

weston

LmycECOR1

37.73333

2

4

7

13

0.5079

yaylim

LmycECOR1

40.93333

14

16

7

37

0.5121

young

LmycECOR1

42.4

16

29

3

48

0.0606

Adams

MTHFR C677T

47.57246

29

97

96

222

0.557

brugada

MTHFR C677T

45.14493

12

73

70

155

0.2683

Brulhart

MTHFR C677T

56.05072

73

195

188

456

0.0715

Christensen

MTHFR C677T

43.91304

13

61

47

121

0.4287

de Franchis

MTHFR C677T

48.87681

39

129

90

258

0.6041

Deloughery

MTHFR C677T

61.12319

94

262

240

596

0.117

Gallagher

MTHFR C677T

43.33333

7

45

53

105

0.6343

Izumi

MTHFR C677T

46.81159

25

102

74

201

0.2965

Kluijtmans

MTHFR C677T

43.55072

6

42

63

111

1

Kluijtmans

MTHFR C677T

84.81884

106

527

617

1250

0.6841

Ma

MTHFR C677T

50.03623

39

116

135

290

0.0868

malinow

MTHFR C677T

43.22464

8

45

49

102

0.8129

markus

MTHFR C677T

45.36232

22

63

76

161

0.1545

morita

MTHFR C677T

67.71739

79

361

338

778

0.2587

Narang

MTHFR C677T

41.34058

5

19

26

50

0.7298

salden

MTHFR C677T

45.47101

18

75

71

164

0.8626

Schmitz

MTHFR C677T

46.34058

27

90

71

188

1

Schwartz

MTHFR C677T

51.77536

43

141

154

338

0.2251

tosetto

MTHFR C677T

44.23913

17

71

42

130

0.1486

van bockxmeer

MTHFR C677T

44.71014

15

58

70

143

0.5591

Verhoef

MTHFR C677T

43.15217

7

48

45

100

0.3479

verhoef

MTHFR C677T

57.64493

72

200

228

500

0.013

Wilcken

MTHFR C677T

47.68116

24

113

88

225

0.1929

Awata

Neurod1

71.75824

1

55

327

383

0.7094

Cinek

Neurod1

61.42857

42

130

117

289

0.5308

Dupont

Neurod1

42.1978

18

53

43

114

0.8444

Dupont

Neurod1

42.1978

18

53

43

114

0.8444

Hansen

Neurod1

58.35165

48

108

105

261

0.0374

Iwata

Neurod1

48.79121

0

17

157

174

1

Jackson

Neurod1

64.3956

2

73

241

316

0.1963

Kanatsuka

Neurod1

49.12088

0

22

155

177

1

Malecki

Neurod1

44.94505

14

75

50

139

0.1004

Malecki

Neurod1

48.46154

25

68

78

171

0.1277

Mockizuki

Neurod1

42.96703

0

12

109

121

1

Owerback

Neurod1

38.46154

10

36

34

80

1

Yamada

Neurod1

43.07692

4

33

85

122

0.7447

Ye

Neurod1

43.2967

0

3

111

124

1

antikainen

PON1 Q192R

45.24735

87

75

7

169

0.0753

aubo

PON1 Q192R

47.73852

154

23

33

30

0.2833

aynacioglu

PON1 Q192R

44.11661

11

43

5

05

0.652

ayub

PON1 Q192R

43.14488

32

5

3

50

0.4242

cascorbi

PON1 Q192R

59.62898

521

39

7

983

0.872

chen

PON1 Q192R

49.52297

208

66

37

411

0.634

ferre

PON1 Q192R

46.06007

106

93

6

25

0.692

gardemann

PON1 Q192R

51.71378

279

26

40

535

0.94

hasselwander

PON1 Q192R

49.11661

179

78

3

388

0.1905

heijman

PON1 Q192R

52.93286

291

263

50

604

0.4386

hermann

PON1 Q192R

54.64664

362

265

74

70

0.08

hong

PON1 Q192R

45.63604

75

84

32

191

0.3597

imai

PON1 Q192R

49.87633

59

82

90

431

0.1672

ko

PON1 Q192R

46.11307

30

96

92

28

0.5562

lawlor

PON1 Q192R

91.4841

1430

1115

24

2786

0.2662

letellier

PON1 Q192R

43.9576

55

38

3

96

0.3843

leus

PON1 Q192R

44.27562

56

48

0

114

1

liu

PON1 Q192R

44.52297

25

74

29

128

0.1104

mackness

PON1 Q192R

47.24382

156

99

27

282

0.0698

odawara

PON1 Q192R

44.41696

25

53

44

22

0.2648

ombres

PON1 Q192R

45.86572

06

84

4

204

0.7264

osei-hyiaman

PON1 Q192R

46.34276

8

44

6

23

0.1172

pati

PON1 Q192R

43.67491

60

2

8

80

0.000

pfohl

PON1 Q192R

45.26502

73

77

20

170

1

rice

PON1 Q192R

52.98587

312

24

54

607

0.4298

robertson

PON1 Q192R

85.08834

37

90

97

2424

0.0263

ruiz

PON1 Q192R

46.90813

40

110

3

263

0.1968

salonen

PON1 Q192R

44.18728

59

43

7

09

1

sangera

PON1 Q192R

46.57244

4

23

80

244

0.6933

sangera

PON1 Q192R

45.17668

77

66

22

65

0.299

sen-banerjee

PON1 Q192R

51.41343

279

226

13

518

0.000013

senti

PON1 Q192R

49.25795

193

65

38

396

0.7234

serrato

PON1 Q192R

46.62544

120

99

28

247

0.3007

suehiro

PON1 Q192R

46.71378

34

24

94

252

0.5929

tuban

PON1 Q192R

47.57951

136

43

22

30

0.0794

wang

PON1 Q192R

50.65371

193

230

52

475

0.1919

watzinger

PON1 Q192R

46.85512

147

96

7

260

0.8684

yamada

PON1 Q192R

62.89753

523

56

29

68

0.9473

zama

PON1 Q192R

44.29329

17

6

37

115

0.4408

Febbo

SRD5A2

73.11111

78

330

39

799

0.5038

Hsing

SRD5A2

51.06667

105

36

62

303

0.159

Latil

SRD5A2

44.53333

8

64

84

56

0.4069

Lunn

SRD5A2

44.17778

13

58

77

148

0.6865

Lunn

SRD5A2

37.95556

1

5

2

8

1

Margiotti

SRD5A2

42.75556

9

40

67

116

0.4555

Nam

SRD5A2

44.8

21

69

72

62

0.488

Pearce

SRD5A2

64.26667

76

263

26

600

0.4703

Pearce

SRD5A2

50.22222

43

56

85

284

0.058

Pearce

SRD5A2

55.86667

21

159

23

411

0.4226

Soderstrom

SRD5A2

44.66667

16

66

77

159

0.728

Yamada

SRD5A2

46.62222

50

97

56

203

0.5742

abbar

TPH

58.38095

30

33

118

28

0.5079

bellivier

TPH

40.57143

11

45

38

94

0.8226

du

TPH

39.61905

13

52

9

84

0.047

furlong

TPH

73.2381

67

208

62

437

1

geijer

TPH

40.95238

13

47

38

98

1

kunugi

TPH

51.52381

55

05

49

209

1

ono

TPH

44.19048

26

7

35

32

0.3875

paik

TPH

54.09524

66

116

54

236

0.896

rujescu

TPH

62.66667

40

55

3

326

0.635

souery

TPH

47.52381

27

74

66

67

0.46

tsai

TPH

50.66667

33

113

54

200

0.0624

turecki

TPH

43.90476

18

7

40

129

0.1507

zaisman

TPH

42.28571

34

54

24

112

0.8488

Blazer

VDR Taq1

50.06579

35

74

59

68

0.2079

Blazer

VDR Taq1

39.93421

3

2

9

4

0.026

Correa-Cerro

VDR Taq1

45.26316

11

52

32

95

0.1957

Furuya

VDR Taq1

42.96053

1

8

4

60

1

Gsur

VDR Taq1

51.51316

22

87

8

90

1

Habuchi

VDR Taq1

61.18421

3

8

253

337

0.3282

Hamasaki

VDR Taq1

47.76316

8

34

9

33

0.0823

Kibel

VDR Taq1

41.31579

7

5

3

35

0.4978

Kibel

VDR Taq1

39.40789

1

3

2

6

1

Luscombe

VDR Taq1

49.14474

30

67

57

154

0.2436

Ma

VDR Taq1

77.76316

86

299

204

589

0.1706

Medeiros

VDR Taq1

52.56579

4

92

73

206

0.2529

Suzuki

VDR Taq1

45.92105

2

20

83

05

0.684

Tayeb

VDR Taq1

63.94737

62

8

36

379

0.95

Taylor

VDR Taq1

49.67105

36

73

53

62

0.2677

Taylor

VDR Taq1

39.53947

1

6

1

8

0.4779

Watanabe

VDR Taq1

52.30263

6

36

60

202

0.042

Declarations

Acknowledgments

The authors are very grateful to Pratima Naik for her contribution to this study and to Scott Stoltenberg and Laura Scott for advice and helpful discussion. They also thank the reviewers for their thorough and helpful comments, which helped them significantly to improve the manuscript.

Authors’ Affiliations

(1)
Molecular & Behavioral Neuroscience Institute, University of Michigan
(2)
Departments of Psychiatry and an Genetics, University of Michigan

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