Smoking and selected DNA repair gene polymorphisms in controls: Systematic review and meta-analysis

Abstract

Background

When the case-only study design is used to estimate statistical interaction between genetic (G) and environmental (E) exposures, G and E must be independent in the underlying population, or the case-only estimate of interaction (COR) will be biased. Few studies have examined the occurrence of G-E association in published control group data.

Methods

To examine the assumption of G-E independence in empirical data, we conducted a systematic review and meta-analysis of G-E associations in controls for frequently investigated DNA repair genes (XRCC1 Arg399Gln, Arg194Trp, or Arg280His, XPD Lys751Gln, and Asp312Asn, and XRCC3 Thr241Met) and smoking (ever/never smoking, current/not current smoker, smoking duration, smoking intensity and pack-years).

Results

Across the 55 included studies, SNP-smoking associations in controls (OR_z) were not reliably at the null value of 1.0 for any SNP-smoking combinations. Two G-E combinations were too heterogeneous for summary estimates: XRCC1 399 and ever-never smoking (N=21), and XPD 751 and pack-years (N=12). OR_z ranges for these combinations were: [OR_z (95% confidence interval (CI)] 0.7 (0.4, 1.2) – 1.9 (1.2, 2.8) and 0.8 (0.5, 1.3) – 2.3 (0.8, 6.1), respectively). Estimates for studies considered homogeneous (Cochran’s Q p-value <0.10) varied 2- to 5-fold. No study characteristics were identified that could explain heterogeneity.

Conclusions

We recommend the independence assumption be evaluated in the population underlying any potential case-only study, rather than in a proxy control group(s) or pooled controls.

Impact

These results suggest that G-E association in controls may be population-specific. Increased access to control data would improve evaluation of the independence assumption.

Introduction

The case-only study design as proposed by Prentice et. al (1) and promoted by Piegorsch et. al. (2) has been increasingly used to estimate the magnitude of statistical interaction between two measured exposures with respect to a given outcome, most commonly a genetic and an environmental exposure. This method requires only cases, no controls or defined cohort. Provided the relevant exposures are independent in the underlying source population, the case-only study can estimate a specific form of statistical interaction, but not main effects of the two exposures.

There are potential advantages to the case-only method in several settings. Estimation of the interaction parameter from case-only analyses is more efficient than for a traditional case-control study (i.e. fewer cases are required for similar precision of estimate) and with no need for controls, there are fewer participants overall (3). Not using controls may mitigate selection biases due to, for example, differential recruiting success between cases and controls, or differential recall of environmental exposures by case-control status. Invasive procedures that are part of cases’ diagnosis or treatment often cannot be done ethically in healthy volunteers, especially vulnerable groups such as pediatric populations (4). But these advantages come at a cost. A case-only study only estimates interaction on a multiplicative scale (deviation of the rate ratio for those having both the genetic and environmental exposures from the product of rate ratios for those with either the genetic or the environmental exposure, but not both). It cannot estimate the independent effect of either exposure, or interaction on the additive scale. This limits its use to situations in which the independent effects of the two exposures are not of interest, nor are synergism or antagonism of the exposures (5–6). Control-selection bias is the only validity threat the case-only design avoids, in comparison with the case-control design. Consequently, case-only studies have been proposed by several investigators as an initial screening method to identify candidate gene-environment or gene-gene interactions (7–9).

However, the increase in precision and avoidance of control-selection bias in the case-only method requires a major assumption: that the two exposures are independent in the source population (Z=1) (1–2). Although the constancy of rate ratios between different strata of exposure in the underlying source population is the true parameter of interest, control groups from case-control studies are frequently used to estimate Z using OR_z. Data simulations have demonstrated that even when violations of the independence assumption are of small magnitude they can strongly bias the case-only interaction parameter (7). Chance can also play a role. Since the expectation that OR_z=1 when Z=1 is a large sample asymptotic approximation, as sample size decreases, OR_z will deviate from the null with increasing frequency through random error alone (7). Further, when control-group gene-environment (G-E) associations are of similar magnitude but opposite in direction to the interaction effect, a case-only study may not detect interaction effects, a Type II error (7, 10).

However, published control group data on the associations of interest for G×E interaction research are limited. Therefore, we undertook a systematic review and meta-analysis of selected DNA repair gene polymorphisms in XRCC1, XPD and XRCC3 and smoking behavior in control groups, using OR_z to estimate Z. The purpose in estimating OR_z was to determine the degree of bias in the COR, relative to the interaction estimate from a case-control analysis, assuming no control-selection bias. Heterogeneity was explored using stratified analysis and meta-regression of study characteristics. The primary aim of this project was to evaluate the independence assumption for selected SNPs and smoking behavior. This will enable investigators considering a stand-alone case-only study of gene-environment interaction with these exposures to evaluate the independence assumption more rigorously, potentially identifying situations in which case-only estimates may be more or less valid.

Methods

Data Abstraction

PubMed, ISI Web of Science and the CDC Genomics and Disease Prevention databases were searched up to March 6, 2007 for peer-reviewed literature likely to contain non-case data on the joint distribution of any of the polymorphisms of interest. Polymorphisms of interest were non-synonymous single nucleotide changes (SNPs) in XRCC1[Arg399Gln (rs25487), Arg194Trp (rs1799782), and Arg280His (rs25489)], XPD [Asp312Asn (rs1799793) and Lys751Gln (rs13181)], and XRCC3 [Thr241Met (rs861539)] (11–15).

Non-case groups were defined as any group not selected on disease status (e.g. cohorts, convenience samples and control groups from case-control studies). For simplification non-case groups will be referred to as controls throughout this article. There were no language restrictions on searches. A list of keywords for PubMed and the ISI Web of Science was developed in consultation with an information specialist from UNC Health Science Library to ensure that searches would be as inclusive as possible. Keywords for smoking were “smoking”, “tobacco”, “tobacco smoke”, “tobacco smoke pollution”, and “smoker”. The SNPs were searched by combining “polymorphism” and “polymorphism, genetic” with the SNP-specific keywords “XRCC1”, “XPD”, "xeroderma pigmentosum group d protein", “ERCC2” and “XRCC3”. ISI Web of Science keywords were “smok*” and “tobacco,” and “XRCC1”, “XPD”, “ERCC2” and “XRCC3”. GDPInfo was searched by limiting by factor menu terms to “smoking behavior”, “smoking (tobacco) passive”, “smoking (tobacco) bidi”, “smoking (tobacco)”, “smoking (tobacco) maternal”, “tobacco”, “indoor air pollution”, “nicotine (nasal spray)”, and “nicotine (transdermal)”, and gene menu terms to “XRCC1”, “XPD”, “ERCC2” and “XRCC3”.

Inclusion criteria were deliberately broad. To be included, an article had to contain original control group data on the joint distribution of any genotype of interest and any aspect of tobacco smoking behavior. Abstracts were excluded.

Abstracts were screened for controls with relevant genotype and smoking data. SNP designations considered equivalent are shown in Table 1. If an abstract passed the initial screening, the full paper was reviewed for data appropriate for construction of a 2×2 table for genotype-smoking association in controls (OR_z). If a genotype-smoking OR_z could be calculated, the following data were abstracted: SNP, genotype categories (3 level additive, dominant and/or recessive models), smoking status and dose categories [ever/never, current/not current, smoker/non-smoker, ever/former/current, pack-years (PY), duration and/or intensity], and cell counts for all genotype and smoking categories.

Table 1.

SNP designations for data abstraction

	XRCC1	XRCC1	XRCC1	XPD (ERCC2)	XPD (ERCC2)	XRCC3
SNP designations	Arg399Gln	Arg194Trp	Arg280His	Lys751Gln	Asp312Asn	Thr241Met
	G28152A	C26304T	G27466A	A35931C	G23591A	C18067T
	exon 10	exon 6	exon 9	exon 23	exon 10	exon 7
	rs25487	rs1799782	rs25489	rs13181	rs1799793	rs861539
	R399Q	R194W	R280H	K751Q	D312N	T241M
				ERCC2_18880_A>C	ERCC2_6540_G>A
Minor allele	399 Gln	14 Trp	280 His	751 Gln	312 Asn	241 Met
MAF range	0.13–0.39	0.05–0.37	0.03–0.09	0.07–0.45	0.06–0.44	0.02–0.43

MAF=minor allele frequency

The following study characteristics were also abstracted: year of publication, study design (case-control, cohort, cross-sectional, convenience, other), source of control group (for case-control: population, hospital, friends and non-blood related family, convenience, community, neighborhood, other; for cohorts: population, occupational, convenience, other), type of clinic that hospital- or clinic-based control groups were from (disease clinics, checkup clinics), study outcome (cancer [type], non-cancer disease, non-disease), full study/control group size (N), country, percent male participants, ethnicity, Hardy Weinberg equilibrium p-value, full study/control group size (N), and minor allele frequency (MAF). An estimate of central tendency for participants’ age in years (“average age”) was derived for each study using, in order of preference: median, mean, weighted average across study categories, midpoint of range. One non-English language article could not be evaluated.

Selection of Study Comparisons

No study population contributed to any analysis more than once, maintaining independence of observations. Analyses focused on associations with genotype categorized using a dominant model (i.e. homozygotes of the most common allele were the referent group, compared to heterozygotes plus homozygotes of the minor allele) due to the small number of studies that provided sufficient information to assess recessive or additive models. The minor allele did not vary across studies for any of the included SNPs. Smoking status was categorized as (1) ever/never (referent), and (2) current/not current (referent). Smoking amount was analyzed as (1) pack-years [PY, highest vs. lowest non-zero category (referent)], (2) duration [years, longest vs. shortest non-zero category (referent)] and (3) smoking intensity [cigarettes/day, heaviest vs. lightest non-zero category (referent)].

Studies that did not provide sufficient data to include ‘passive only’ smoking in the never smoking group were excluded. For analyses of current/not current smoking, never+former smokers and “non-smokers” (unless identified as never smokers) were considered not current smokers. Pack-years of smoking (PY = pack-years = number of packs smoked per day multiplied by years smoked; 20 cigarettes=1 pack) were collected as categorical variables with different cutpoints. We analyzed PY as relative categories [heaviest vs. lightest smokers regardless of PY cutpoints] and absolute categories [high PY (all categories with a cutpoint above a specified minimum) vs. low PY (all categories with a cutpoint below a specified maximum)]. The minimum and maximum cutpoints varied by SNP but were all chosen to maximize the number of included studies while keeping the range of cutpoints small enough that no study would have >1 cutpoint between the minimum and maximum cutpoints. Similar to PY, smoking intensity was categorized by relative (heaviest vs. lightest smokers regardless of cigarette/day cutpoints) and absolute (>= 20 vs. <20 cigarettes/day) measures. The smoking duration cutpoints were 20 and 40 years, inclusive, for all SNPs.

Statistical analyses

Crude ORs and 95% confidence limits were calculated from cell counts (Stata 9.2, using metan STB-44: sbe24). Funnel plot asymmetry, an indicator of possible publication bias (16), was considered suggestive of study characteristics associated with variance and Z. When data were sufficient (N_studies>=5), asymmetry was formally assessed using Begg and Mazumdar’s test (17) and Egger’s test (18) at α=0.10. Cochran’s Q two-sided homogeneity p-values (α=0.10 due to low power of the test) were used to assess overall heterogeneity in odds ratios (19).

Study characteristic analyses

Key study characteristics hypothesized to influence variation in the strength of control group SNP-smoking associations across studies were assessed using stratified meta-analysis and random-effects meta-regression, with the among-study variance estimated by restricted maximum likelihood (20). Stratified meta-analysis produces a summary OR_z estimate for each stratum of a study characteristic. Meta-regression provides a formal comparison of the stratified estimates in the form of an estimated ratio of odds ratios.

Study characteristics were selected a priori. They included (1) study design (case-control, cohort, or convenience; patient-based control groups, healthy control groups), (2) continent, (3) ethnicity, (4) Hardy-Weinberg equilibrium p-value, (5) average age, (6) gender (% male), (7) study outcome (lung cancer, other cancer, non-cancer disease, non-disease), (8) minor allele frequency and (9) smoking prevalence. Study design was examined for all SNP-smoking combinations; additional study characteristics were examined for XRCC1 399, XPD 751 and XRCC3 241. Stratified random-effects meta-analyses were used when the overall SNP-smoking association had a Cochran’s Q p-value α<0.10, otherwise fixed effects meta-analysis was used, regardless of the homogeneity p-values of individual strata. To reduce the possibility of results being confounded by ethnicity (population stratification) in overall analyses and when examining the study characteristics likely to vary strongly by ethnicity (Hardy Weinberg equilibrium p-values and minor allele frequency) studies were stratified by ethnicity and treated as separate studies if possible. Sample size was not formally examined as a study characteristic because that would have essentially reproduced funnel plot analyses of variance. Variance (or precision) is the more important measure here, and sample size is not the sole determinant of precision. However, because the independence assumption is a large sample approximation, a sensitivity analysis was conducted in which large (N>=1000) studies were examined separately. Stata 9.2 was used for all analyses. Results for study characteristics were assessed for consistency across smoking categories and across SNPs.

Results

Eligible studies

The literature searches identified 228 articles for evaluation. Of these, 55 articles were eligible for inclusion. The primary reason for exclusion was that an article did not present the genotype-smoking distribution in controls (N=98, 57% of exclusions). Exclusion reasons for the remainder included: review article or abstract only (13%), did not assess any relevant SNPs (9%), and did not have any non-cases (10%). Finally, of the 55 studies eligible for inclusion, five were not included in final summary estimates because no data were presented using the dominant genetic model (21–22), there was no measure of adult smoking behavior (23), former smokers were excluded (24), or never smokers were included in lowest PY category (25). Fifty articles representing 46 distinct study populations were included in the final meta-analyses (brief study descriptions in Supplementary Table 1). The number of study populations included for each polymorphism was: XRCC1 Arg399Gln (N=32), XRCC1 Arg194Trp (N=16), XRCC1 Arg280His (N=8), XPD Lys751Gln (N=16), XPD Asp312Asn (N=9), and XRCC3 Arg241Gln (N=13). Thirty-seven studies presented the control distribution of genotype and ever/never smoking, 16 for current/ not current smoking and 14 for PY. Far fewer presented duration (N=4) and/or intensity (N=4). Case-control studies predominated with 12 population-based (26–37) and 23 hospital-based (38–60), four studies nested within cohorts (61–64) and two other case-control studies (65–66). Most control groups were from cancer case-control studies (N=39), one was from a case-control study of rheumatoid arthritis. Nine cohort or convenience sample studies examined non-cancer outcomes, predominantly measures of DNA damage (67–75).

Association between DNA repair gene variants and smoking behavior

Across SNPs there was more variation in ORs assessing control-only G-E associations (OR_zs) for measures of smoking amount (PY, duration, intensity) than for measures of smoking status (ever-never, current-not current) (Table 2). Ten of 11 summary estimates of smoking status fell between 0.9–1.1. Summary estimates for smoking amounts were distributed more broadly, with only five of 10 summary estimates between 0.9–1.1; the most extreme measures were found for duration and intensity. Although only two of 18 genotype-smoking groups were too heterogeneous for a fixed effects summary estimate, based on Cochran’s Q at alpha=0.10, nearly all groups had study estimates above and below the null. Individual study OR_z (95% CI) are presented in Figure 1 and Supplementary Tables 3–8.

Table 2.

DNA repair gene variation and smoking summary estimates

	Ever-never			Current-Not current			Pack-years			Intensity			Duration
	N	Q p- value	ORz (95% CI)*	N	Q p- value	ORz (95% CI)†	N	Q p-value	ORz (95% CI)‡	N	Q p- value	ORz (95% CI)§	N	Q p-value	ORz (95% CI)□
Gene and SNP
XRCC1
Arg399Gln**	21	0.01	--- †††	11	0.40	1.0 (0.9, 1.1)	9	0.30	1.2 (1.0, 1.5)	4	0.49	1.5 (1.2, 1.9)	2	0.03	--- †††
Arg194Trp††	12	0.62	1.0 (0.9, 1.1)	6	0.68	1.1 (0.9, 1.3)	2	0.73	1.1 (0.7, 1.6)	2	0.89	1.1 (0.8, 1.6)	3	0.47	0.7 (0.5, 0.9)
Arg280His‡‡	5	0.47	1.0 (0.8, 1.2)	4	0.51	0.7 (0.5, 1.1)	3	0.32	1.0 (0.6, 1.5)	1	--	0.9 (0.5, 1.8)	1	--	1.2 (0.6, 2.3)
XPD
Lys751Gln§§	12	0.46	0.9 (0.8, 1.1)	6	0.25	1.1 (0.9, 1.3)	7	0.02	--- †††	0			0
Asp312Asn□□	9	0.79	1.1 (1.0, 1.2)	1	---	1.1 (0.7, 1.9)	4	0.11	1.1 (0.8, 1.5)	0			0
XRCC3
Thr241Met***	9	0.52	1.0 (0.9, 1.2)	7	0.73	0.9 (0.8, 1.1)	4	0.67	0.8 (0.6, 1.2)	0			0

Abbreviations: CI=Confidence interval, na=not applicable, PY=pack-years, OR_z=control-only genotype-smoking odds ratio, Q=Cochran’s test of heterogeneity, N=number of studies, G+=gene is positive for any variant allele G−=negative for variant allele (referent), E+=positive for smoking measures, E−=negative for smoking measure (referent), N=number of studies, SNP=single nucleotide polymorphism, Arg=Arginine, Gln=Glutamine, Trp=Tryptophan, His=Histidine, Met=methionine, Asp=Aspartic acid, Asn=Asparagine

^*Fixed effects summary estimates for G+/G− vs. E+/E−. G− is homozygous for the common allele (ref), G+ is the genotype with 1 or 2 variant alleles, E− (ref) is never smoker and E+ is ever smoking

^†Fixed effects summary estimates for G+/G− vs. E+/E−. G− is homozygous for the common allele (ref), G+ is the genotype with 1 or 2 variant alleles, E− (ref) is not current smoker and E+ is current smoking

^‡Fixed effects summary estimates for G+/G− vs. E+/E−. G− is homozygous for the common allele (ref), G+ is the genotype with 1 or 2 variant alleles, E− (ref) is lowest non-zero PY category and E+ is highest category of PY

^§Fixed effects summary estimates for G+/G− vs. E+/E−. G− is homozygous for the common allele (ref), G+ is the genotype with 1 or 2 variant alleles, E− (ref) is lowest non-zero category of intensity (cig/day) and E+ is highest category of smoking intensity.

^□Fixed effects summary estimates for G+/G− vs. E+/E−. G− is homozygous for the common allele (ref), G+ is the genotype with 1 or 2 variant alleles, E− (ref) is lowest non-zero category of duration(yrs) and E+ is highest category of smoking duration.

^**Arg/Arg v. any Gln

^††Arg/Arg v. any Trp

^‡‡Arg/Arg v. any His

^§§Lys/lys v. any Gln

^□□Asp/Asp v. any Asn

^***Thr/Thr v. any Met

^†††Studies too heterogeneous for fixed effects summary estimate

Figure 1.

Weighted Forest Plot for XRCC1 399 and ever-never smoking

For XRCC1 399 any Gln and ever-never smoking, OR_zs ranged from 0.7 (95% CI: 0.3, 1.7) (49) to 1.9 (95% CI: 1.0, 3.7) (34) (See Figure 1); three other measures of smoking behavior were homogeneous enough for a summary estimate of OR_z: current smoker/not current (N=11), PY (N=9) and intensity (N=4) (Table 2). Higher PY and heavier smoking intensity, but not current vs. not current smoking, were associated with XRCC1 Arg399Gln (any Gln) [OR (95%CI): 1.2 (1.0, 1.5) and 1.5(1.2, 1.9), respectively]. For XRCC1 194 and 280, having the variant allele was associated with smoking duration [XRCC1 194: 0.7 (0.5, 0.9), XRCC1 280: 1.2 (0.6, 2.3)] and current smoking [XRCC1 280: 1.2 (0.6, 2.3)] though the number of studies was small. For the two XPD SNPs (751, 312) there was considerable variation in the association between XPD 751 variant allele and higher PY. Study estimates ranged from 1.4 (0.8, 2.6) (41) to 0.5 (0.3, 1.0) (53) (Table 2). Higher PY were associated with the variant allele for XRCC3 241 although the number of studies was small (N=4).

Sensitivity analyses

Among the studies that were assessed for current-not current smoking, a subset could also be assessed for never, former or current smoking (Supplementary Table 2). No consistent pattern emerged for comparisons of never smoking with former or current smoking. Absolute measures of PY, intensity and duration were calculated and compared to relative measures for consistency. Genotype-PY estimates for absolute cutpoints were comparable to estimates using relative categories although strata were sparse (Supplementary Table 2). Additionally, when studies with only smokers were dropped and never smoking was used as the reference category, results were essentially the same for relative and absolute measures of PY.

Genotype-smoking association between XRCC1 Arg399Gln and smoking intensity (cigarettes/day) could be estimated in four studies. There was an association between XRCC1 399 any Gln and greater smoking intensity, which was consistent across methods of smoking intensity categorization. Results did not change appreciably when studies without smoking amount were excluded from ever-never analyses, indicating that articles that presented dose were not driving estimates of smoking status. Results did not change appreciably when studies without smoking amount were excluded from ever-never analyses, indicating that articles that presented dose were not driving estimates of smoking status (data not shown).

There were six large (N>=1000) study populations, four each with data for XRCC1 399 & 194 ever-never smoking. OR_zs for XRCC1 399 ever-never smoking showed evidence of heterogeneity [range of OR_z(95% CI): 1.0(0.8, 1.2) – 1.6(1.2, 2.0)] (Supplementary Table 3)(32, 35, 42, 60). However, OR_zs for XRCC1 194 were consistently null across studies (Supplementary Table 4)(32, 37, 42, 61). In the three large study populations with the relevant measures of smoking,(32, 42, 60) the magnitude of OR_z was consistently different for status and amount for XRCC1 399 ever-never smoking; the one study population with smoking status and amount for XRCC1 194 had OR_zs at the null.(42) Data were too sparse for further evaluation across large studies.

Funnel plot asymmetry

There was no evidence of funnel plot asymmetry for overall genotype-smoking associations (data not shown). In formal testing, the majority of p-values (75%) were >=0.3; the lowest p-value was p=0.14.

Study characteristics

Stratified associations and univariate meta-regression were evaluated across SNPs and smoking categories on the basis of consistency and direction. Study design was examined for all six SNPs for ever/never, current/not current smoking and PY. For smoking status, genotype-smoking associations for XRCC1 399 and 194 and XPD 751 and 312 were generally stronger for population-based case-control studies than for hospital-based or patient-based control groups, although the magnitude of the differences was small; the range of RORs was 0.7 to 0.9 for hospital/patient-based compared to population-based controls (referent) (Table 3). However, for smoking amount as measured by PY (2 evaluable SNPS, XRCC1 399 and XPD 751) the hospital-based/patient-based control groups showed stronger genotype-smoking associations than population-based control groups (range of RORs: 1.2–1.5). When examining PY, for all SNPs, the genotype-smoking association for population-based control groups was below the null. The remaining study characteristics were examined only for XRCC1 399, XPD 751 and XRCC3 241 (Tables 4, 5, and 6 respectively) due to sparse data for the other SNPs.

Table 3.

Genotype-smoking associations stratified by study design

XRCC1
		Arg399Gln			Arg194Trp			Arg280His
	N	ORz (95% CI)	Ratio of ORs (95%CI) †	N	ORz (95% CI) *	Ratio of ORs (95%CI) †	N	ORz (95% CI)*	Ratio of ORs (95%CI) †
Ever-never smoking
Case-based categories
Case-control
Population-based	8	1.1 (0.9, 1.5)	Ref	4	1.0 (0.9, 1.3)	Ref	2	0.9 (0.7, 1.3)	Ref
Hospital-based	9	1.0 (0.9, 1.2)	0.9 (0.7, 1.2)	5	0.9 (0.8, 1.1)	0.9 (0.7, 1.2)	2	1.1 (0.8, 1.4)	1.2 (0.8, 1.8)
Other	1	0.9 (0.6, 1.3)	0.8 (0.4, 1.3)	2	1.1 (0.9, 1.5)	1.1 (0.8, 1.5)	0
Other	3	1.0 (0.8, 1.4)	0.9 (0.6, 1.4)	1	0.7 (0.3, 1.4)	0.7 (0.3, 1.4)	1	0.5 (0.2, 1.1)	0.5 (0.2, 1.3)
Non-case-based categories
Case-control
Population controls	8	1.1 (0.9, 1.5)	Ref	4	1.0 (0.9, 1.3)	Ref	2	0.9 (0.7, 1.3)	Ref
Patient controls ‡	6	1.1 (0.9, 1.2)	0.9 (0.7, 1.3)	5	0.9 (0.8, 1.1)	0.9 (0.7, 1.2)	2	1.1 (0.8, 1.4)	1.2 (0.8, 1.8)
Non-patient controls§	4	0.9 (0.8, 1.1)	0.8 (0.6, 1.1)	2	1.1 (0.9, 1.5)	1.1 (0.8, 1.5)	0
Other	3	1.0 (0.8, 1.4)	0.9 (0.6, 1.4)	1	0.7 (0.3, 1.4)	0.7 (0.3, 1.4)	1	0.5 (0.2, 1.1)	0.5 (0.2, 1.3)

XRCC1
	Arg399Gln			Arg194Trp			Arg280His
	N	ORz (95% CI)	Ratio of ORs (95%CI) †	N	ORz (95% CI) *	Ratio of ORs (95%CI) †	N	ORz (95% CI) *	Ratio of ORs (95%CI) †
Current-not current smoking
Case-based categories
Case-control
Population-based	1	1.2 (1.0, 1.5)	Ref	1	1.1 (0.8, 1.6)	NA	1	0.9 (0.6, 1.4)	NA
Hospital-based	3	0.9 (0.7, 1.2)	0.7 (0.5, 1.0)	2	0.9 (0.7, 1.3)		0
Unknown	1	1.6 (0.7, 3.7)	1.3 (0.5, 3.2)	1	0.9 (0.4, 2.2)		1	1.1 (0.2, 5.8)
Other	6	0.8 (0.6, 1.1)	0.7 (0.5, 1.0)	2	1.5 (0.8, 2.7)		2	0.5 (0.2, 1.0)
Non-case-based categories
Case-control
Population controls	1	1.2 (1.0, 1.5)	Ref	1	1.1 (0.8, 1.6)	NA	1	0.9 (0.6, 1.4)	NA
Patient controls ‡	2	0.8 (0.6, 1.2)	0.7 (0.5, 1.0)	1	1.1 (0.6, 2.1)		0
Non-patient controls §	1	1.0 (0.7, 1.5)	0.8 (0.5, 1.3)	1	0.8 (0.6, 1.2)		0
Unknown	1	1.6 (0.7, 3.7)	1.3 (0.5, 3.2)	1	0.9 (0.4, 2.2)		1	1.1 (0.2, 5.8)
Other	6	0.8 (0.6, 1.1)	0.7 (0.5, 1.0)	2	1.5 (0.8, 2.7)		2	0.5 (0.2, 1.0)

XRCC1
	Arg399Gln			Arg194Trp			Arg280His
	N	ORz (95% CI) *	Ratio of ORs (95%CI) †	N	ORz (95% CI) *	Ratio of ORs (95%CI) †	N	ORz (95% CI) *	Ratio of ORs (95%CI) †
Pack-years
Case-based categories
Case-control
Population-based	2	0.9 (0.6, 1.4)	Ref	0		NA	1	0.5 (0.2, 1.5)	NA
Hospital-based	7	1.3 (1.1, 1.6)	1.5 (0.9, 2.4)	2	1.1 (0.7, 1.6)		2	1.1 (0.7, 1.8)
Non-case-based categories
Case-contrl
Population controls	2	0.9 (0.6, 1.4)	Ref	0		NA	1	0.5 (0.2, 1.5)	NA
Patient controls ‡	4	1.4 (1.1, 1.7)	1.5 (0.9, 2.5)	2	1.1 (0.7, 1.6)		2	1.1 (0.7, 1.8)
Non-patient controls §	3	1.3 (0.9, 1.8)	1.4 (0.8, 2.5)	0			0

	XPD						XRCC3
	Lys751Gln			Asp312Asn			Thr241Met
	N	ORz (95% CI) *	Ratio of ORs (95%CI) †	N	ORz (95% CI)*	Ratio of ORs (95%CI) †	N	ORz (95% CI) *	Ratio of ORs (95%CI) †
Ever-never smoking
Case-based categories
Case-control
Population-based	4	1.0 (0.8, 1.1)	Ref	2	1.3 (1.0, 1.8)	Ref	2	1.0 (0.8, 1.3)	Ref
Hospital-based	7	0.9 (0.8, 1.1)	0.9 (0.7, 1.2)	7	1.0 (0.9, 1.2)	0.8 (0.6, 1.1)	4	1.0 (0.7, 1.3)	1.0 (0.6, 1.4)
Other	0			0			0
Other	1	1.0 (0.6, 1.6)	1.0 (0.6, 1.7)	0			3	1.2 (0.9, 1.6)	1.2 (0.8, 1.8)
Non-case-based categories
Case-control
Population controls	4	1.0 (0.8, 1.1)	Ref	2	1.3 (1.0, 1.8)	Ref	2	1.0 (0.8, 1.3)	Ref
Patient controls ‡	3	0.8 (0.6, 1.1)	0.8 (0.5, 1.2)	3	1.1 (0.9, 1.3)	0.8 (0.6, 1.1)	4	1.0 (0.7, 1.3)	1.0 (0.6, 1.4)
Non-patient controls §	4	1.0 (0.8, 1.2)	1.0 (0.7, 1.4)	4	1.0 (0.8, 1.2)	0.7 (0.5, 1.1)	0
Other	1	1.0 (0.6, 1.6)	1.0 (0.5, 1.7)	0			3	1.2 (0.9, 1.6)	1.2 (0.8, 1.8)

	XPD						XRCC3
	Lys751Gln			Asp312Asn			Thr241Met
	N	ORz (95% CI) *	Ratio of ORs (95%CI) †	N	ORz (95% CI)*	Ratio of ORs (95%CI) †	N	ORz (95% CI) *	Ratio of ORs (95%CI) †
Current-not current smoking
Case-based categories
Case-control
Population-based	1	1.0 (0.7, 1.3)	NA	0			1	1.0 (0.7, 1.5)	Ref
Hospital-based	1	0.7 (0.5, 1.2)		1	1.1 (0.7, 1.9)	NA	2	0.9 (0.6, 1.2)	0.8 (0.5, 1.4)
Other	0			0			1	2.0 (0.6, 6.6)	2.0 (0.6, 6.8)
Other	4	1.4 (1.0, 1.8)		0			3	0.9 (0.6, 1.2)	0.8 (0.5, 1.4)
Non-case-based categories
Case-control
Population controls	1	1.0 (0.7, 1.3)	NA	0			1	1.0 (0.7, 1.5)	Ref
Patient controls ‡	1	0.7 (0.5, 1.2)		1	1.1 (0.7, 1.9)	NA	2	0.9 (0.6, 1.2)	0.8 (0.5, 1.4)
Non-patient controls §	0			0			1	2.0 (0.6, 6.6)	2.0 (0.6, 6.8)
Other	4	1.4 (1.0, 1.8)		0			3	0.9 (0.6, 1.2)	0.8 (0.5, 1.4)

	XPD						XRCC3
	Lys751Gln			Asp312Asn			Thr241Met
	N	ORz (95% CI) *	Ratio of ORs (95%CI) †	N	ORz (95% CI)*	Ratio of ORs (95%CI) †	N	ORz (95% CI) *	Ratio of ORs (95%CI) †
Pack-years
Case-based categories
Case-control
Population-based	2	0.8 (0.5, 1.3)	Ref	0			1	0.8 (0.4, 1.4)	NA
Hospital-based	5	1.3 (0.8, 2.1)	1.5 (0.7, 3.5)	4	1.1 (0.8, 1.5)	NA	3	0.8 (0.5, 1.3)
Other	0			0			0
Non-case-based categories
Case-control
Population controls	2	0.8 (0.5, 1.3)	Ref	0			1	0.8 (0.4, 1.4)	NA
Patient controls ‡	2	1.0 (0.6, 1.6)	1.2 (0.5, 2.6)	1	0.9 (0.5, 1.4)	NA	2	0.8 (0.5, 1.3)
Non-patient controls §	3	1.6 (0.8, 3.1)	2.0 (0.9, 4.3)	3	1.3 (0.9, 1.8)		1	1.1 (0.4, 2.7)

Abbreviations: CI=Confidence interval, HWE = Hardy Weinberg equilibrium, MAF=minor allele frequency, na=not applicable, OR_z=control-only genotype-smoking odds ratio, PY=pack-years, N=number of studies, Ref=referent, Q=Cochran’s test of homogeneity, Arg=Arginine, Gln=Glutamine, Trp=Tryptophan, His=Histidine, Met=methionine, Asp=Aspartic acid, Asn=Asparagine

^*Unadjusted OR (95% CI): Genotype contrast for all SNPs is A/A (ref) vs. any a, where A is the more common allele and a is the less common; random effects for XRCC1 Arg399Gln ever-never & XPD Lys751Gln, fixed effects for others

^†Ratio of Odds Ratios: Meta-regression used to compare odds ratios in given study design stratum to the odds ratio in the designated reference stratum

^‡Patient controls: controls are persons attending a hospital or disease clinic for treatment or diagnosis, does not include patients at wellness or check-up clinics

^§Non-patient controls: Case-control study participants who are not patients (i.e. not treated at hospital or disease clinic); they may be friend and family controls, cohort members in a nested case-control etc.; also excludes population-based controls

Table 4.

XRCC1 Arg399Gln and Smoking: Overall and by study characteristics

	Ever-never *				Current-Not current †				PY ‡,§
	N	Q p- value	ORz (95% CI)	Ratio of ORs (95% CI) □	N	Q p-value	ORz (95% CI)	Ratio of ORs (95% CI)	N	Q p- value	ORz (95% CI)	Ratio of ORs (95% CI)
Overall
Not stratified by ethnicity w/in study	21	0.01			11	0.4			9	0.3
Stratified by ethnicity within study **	23	0.02			12	0.3			na
By Study Characteristic
Continent
North America	7	0.01	1.1 (0.9, 1.3)	Ref	2	0.6	1.2 (1.0, 1.5)	Ref	2	0.1	1.2 (0.8, 1.7)	1.1 (0.7, 1.7)
Europe	11	0.2	1.1 (0.9, 1.3)	1.0 (0.8, 1.3)	5	0.5	0.8 (0.6, 1.0)	0.7 (0.5, 0.9)	5	0.8	1.1 (0.9, 1.4)	Ref
Asia	3	0.1	1.1 (0.7, 1.9)	1.0 (0.7, 1.6)	4	0.7	1.0 (0.8, 1.3)	0.8 (0.6, 1.1)	2	0.6	1.9 (1.2, 2.9)	1.7 (1.0, 2.7)
Ethnicity/nationality **
Single-ethnicity studies **,††	14	0.3	1.0 (0.9, 1.2)	Ref	4	0.1	1.0 (0.8, 1.2)	Ref	5	0.4	1.1 (0.9, 1.5)	Ref
Multi-ethnic studies ††	2	0.01	1.2 (0.7, 2.1)	1.2 (0.9, 1.7)	1	na	0.9 (0.2, 3.4)	0.9 (0.2, 3.5)	0
Unknown ethnicity	7	0.1	1.1 (0.9, 1.3)	1.1 (0.8, 1.4)	7	0.6	0.9 (0.7, 1.1)	0.9 (0.6, 1.3)	4	0.2	1.3 (1.1, 1.7)	1.2 (0.8, 1.8)
White >=99% ‡‡	10	0.6	1.0 (0.9, 1.1)	Ref	2	0.2	0.9 (0.6, 1.2)	na	5	0.4	1.1 (0.9, 1.5)	na
African American >=99%	2	0.03	1.1 (0.5, 2.5)	1.1 (0.7, 1.8)	1	na	2.1 (1.1, 3.9)		0
Han >=99%	2	0.2	1.4 (0.8, 2.5)	1.5 (0.8, 2.5)	1	na	1.0 (0.7, 1.4)		0
Multi-ethnic studies	2	0.01	1.2 (0.7, 2.1)	1.3 (0.9, 1.8)	1	na	0.9 (0.2, 3.4)		0
HWE p-value **
Single-ethnicity (continuous)	13	0.2	1.0 (0.9, 1.2)	0.8 (0.5, 1.3)	4	0.1	1.0 (0.8, 1.2)	1.3 (0.2, 7.2)	5	0.4	1.1 (0.9, 1.5)	16.9 (0.4, 713)
p <0.05	0
p< 0.10	1	---	0.9 (0.6, 1.3)	na	0				0
p>=0.10	21	0.02	1.1 (1.0, 1.2)		12				9
Age
Age non-missing	20	0.01	1.1 (1.0, 1.2)	1.0 (1.0, 1.0)	10	0.3	1.0 (0.9, 1.1)	1.0 (1.0, 1.0)	9	0.3	1.2 (1.0, 1.5)	1.0 (1.0, 1.1)
<= 47.9y §§	1	---	0.8 (0.4, 1.6)	na	5	0.3	0.9 (0.7, 1.1)	Ref	0			na
> 47.9 y	19	0.01	1.1 (1.0, 1.2)		5	0.4	1.1 (0.9, 1.3)	1.2 (0.9, 1.7)	9	0.3	1.2 (1.0, 1.5)
<=59y **	8	0.1	1.1 (0.9, 1.3)	Ref	8	0.6	0.9 (0.7, 1.1)	Ref	3	0.1	1.1 (0.8, 1.6)	Ref
>59y	12	0.02	1.1 (0.9, 1.3)	1.0 (0.8, 1.3)	2	0.2	1.1 (0.9, 1.4)	1.3 (1.0, 1.7)	6	0.4	1.3 (1.0, 1.6)	1.2 (0.8, 1.8)
At or below median	12	0.1	1 (0.9, 1.2)	Ref	5	0.3	0.9 (0.7, 1.1)	Ref	5	0.4	1.2 (1.0, 1.5)	Ref
Above median	8	0.02	1.2 (0.9, 1.4)	1.1 (0.9, 1.4)	5	0.4	1.1 (0.9, 1.3)	1.2 (0.9, 1.7)	4	0.2	1.3 (1.0, 1.8)	1.1 (0.7, 1.6)
Gender
Percent male (mixed gender only)	13	0.1	1.0 (0.9, 1.2)	1.4 (0.6, 3.5)	4	0.7	1.0 (0.8, 1.3)	0.9 (0.1, 8)	6	0.1	1.3 (1.0, 1.5)	1 (0.1, 11.4)
All female	5	0.01	1.1 (0.8, 1.4)	Ref	2	0.3	1.2 (0.9, 1.5)	Ref	1	--	0.9 (0.4, 1.8)	Ref
Mixed gender	13	0.1	1.0 (0.9, 1.2)	1.0 (0.7, 1.3)	4	0.7	1.0 (0.8, 1.3)	0.9 (0.6, 1.2)	6	0.1	1.3 (1.0, 1.5)	1.4 (0.7, 2.9)
All male	3	0.7	1.2 (0.9, 1.6)	1.1 (0.7, 1.7)	4	0.7	0.7 (0.6, 1.0)	0.6 (0.4, 0.9)	2	0.7	1.3 (0.8, 2.2)	1.5 (0.6, 3.6)
Study outcome
Lung cancer	6	0.4	1.0 (0.9, 1.1)	Ref	1	---	0.9 (0.6, 1.3)	Ref	6	0.2	1.3 (1.1, 1.6)	Ref
Other cancer	12	0.02	1.2 (1.0, 1.4)	1.2 (1.0, 1.6)	3	0.3	1.1 (0.9, 1.3)	1.2 (0.8, 2)	3	0.7	1.0 (0.7, 1.5)	0.8 (0.5, 1.2)
Non-cancer disease	0				1	---	1.6 (0.6, 3.7)	1.8 (0.7, 4.7)	0
Non-disease	3	0.7	1.0 (0.8, 1.4)	1.1 (0.7, 1.6)	6	0.6	0.8 (0.6, 1.1)	0.9 (0.6, 1.5)	0
Lung cancer	6	0.4	1.0 (0.9, 1.1)	Ref	1	---	0.9 (0.6, 1.3)	na	6	0.2	1.3 (1.1, 1.6)	Ref
All other	15	0.04	1.2 (1.0, 1.3)	1.2 (1.0, 1.5)	10	0.3	1 (0.9, 1.2)		3	0.7	1 (0.7, 1.5)	0.8 (0.5, 1.2)
MAF †
MAF (cutpoints assigned by tertiles across SNP)
0.10–0.27	6	0.1	1.1 (0.7, 1.5)	Ref	4	0.1	1.1 (0.9, 1.4)	Ref	2	0.6	1.9 (1.2, 2.9)	Ref
>0.27–0.36	10	0.01	1.1 (1.0, 1.3)	1.1 (0.8, 1.5)	4	0.4	0.9 (0.6, 1.4)	0.8 (0.5, 1.4)	7	0.5	1.1 (0.9, 1.4)	0.6 (0.4, 1.0)
>0.36–0.50	6	0.9	1 (0.8, 1.2)	1 (0.7, 1.4)	4	0.7	0.8 (0.7, 1.1)	0.8 (0.5, 1.1)	0
MAF-assigned ethnicity
White	15	0.3	1.1 (0.9, 1.2)	Ref	6	0.5	0.9 (0.7, 1.1)	Ref	7	0.5	1.1 (0.9, 1.4)	Ref
African American ‡‡‡	3	0.1	1 (0.5, 1.8)	0.9 (0.6, 1.5)	1		2.1 (1.1, 3.9)	2.4 (1.2, 4.7)	0
Han	3	0.1	1.1 (0.7, 1.9)	1.0 (0.7, 1.5)	4	0.7	1.0 0.8, 1.3)	1.1 (0.8, 1.6)	2	0.6	1.9 (1.2, 2.9)	1.6 (1.0, 2.6)
Multi-ethnic studies	2	0.01	1.2 (0.7, 2.1)	1.2 (0.8, 1.7)	1	--	0.9 0.2, 3.4)	1 (0.3, 4.1)	0
Smoking prevalence §§§
Continuous	21	0.01		1.7 (0.6, 4.7)	11	0.4	1.0 (0.9, 1.1)	0.2 (0.1, 0.7)	9	0.3	1.2 (1.0, 1.5)	0.7 (0.2, 2.6)
>0–0.507	5	0.2	1 (0.8, 1.2)	Ref	9	0.5	1.0 (0.9, 1.2)	Ref	6	0.4	1.3 (1.1, 1.6)	Ref
>0.507–1	16	0.01	1.1 (1.0, 1.3)	1.1 (0.9, 1.5)	2	0.8	0.7 (0.5, 1.1)	0.7 (0.5, 1.1)	3	0.3	0.9 (0.6, 1.4)	0.7 (0.4, 1.1)

Abbreviations: CI=Confidence interval, na=not applicable, HWE = Hardy Weinberg equilibrium PY=pack-years, OR_z=control-only genotype-smoking odds ratio (bolded), N=number of studies, Ref=referent, Q=Cochran’s test of homogeneity, SNP=single nucleotide polymorphism, MAF=minor allele frequency, Arg=Arginine, Gln=Glutamine, Trp=Tryptophan, His=Histidine, Met=methionine, Asp=Aspartic acid, Asn=Asparagine

^*OR=Unadjusted odds ratio for XRCC1 Arg399Gln: XRCC1 Arg/Arg (ref) vs. any Gln, never moking (ref) vs. ever smoking, random effects estimates

^†OR=Unadjusted odds ratio for XRCC1 Arg399Gln: XRCC1 Arg/Arg (ref) vs. any Gln, not current smoker (ref) vs. current smoker, fixed effects estimates

^‡OR=Unadjusted odds ratio for XRCC1 Arg399Gln: XRCC1 Arg/Arg (ref) vs. any Gln, lightest smokers (ref) vs. heaviest smokers [lightest excludes never smokers], fixed effects

^§PY contrast is between lightest non-zero category of pack-years (ref) vs. heaviest category of PY

^□Ratio of Odds Ratios: Compares odds ratio in given study characteristic stratum to the odds ratio in the designated reference stratum for that study characteristic by meta-regression

^**Studies that can be stratified by ethnicity are included as separate single-ethnicity studies; studies w 99%–100% of 1 ethnicity are classified as single-ethnicity

^††Only includes studies with explicitly stated ethnic makeup

^‡‡White = Caucasian, white or non-Hispanic white; African American = African American or black; Han = Han, Han Chinese or ethnic Chinese; Japan, Korea and China may include ethnic minorities.

^§§Median of studies included in XRCC1 399 current/not current smoker analyses.

^□□Categories based on thirds from studies included in XRCC1 399 PY analyses (<=59y, >59y–63y, >63y)

^***Median of all studies w age info (all SNPs) w age info [range:23.6–69y, mean: 56.51y SD: 9.84y]

^†††Median proportion male in all studies (all SNPs, all smoking exposures): 0.69

^‡‡‡For 399 ever-never 1 study from Hungary is included using MAF as proxy for ethnicity

^§§§Smoking prevalence is contrast-specific (defined as "ever", "current" or "heavier PY" as appropriate)

Table 5.

XPD Lys751Gln and Smoking: Overall and by study characteristics

	Ever-never *				Current-Not current †				PY ‡,§
	N	Q p- value	ORz (95% CI)	Ratio of ORs (95% CI) □	N	Q p- value	ORz (95% CI)	Ratio of ORs (95% CI)	N	Q p-value	ORz (95% CI)	Ratio of ORs (95% CI)
Overall
	12	0.5	0.9 (0.8, 1.1)		6	0.2	1.1 (0.9, 1.3)		7	0.019	1.2 (1.0, 1.5)
By Study Characteristic
Continent
North America	4	0.4	0.9 (0.8, 1)	Ref	1	---	0.9 (0.7, 1.3)	Ref	3	0.006	1.5 (0.7, 3.2)	Ref
Europe	6	0.3	1.0 (0.8, 1.3)	1.2 (0.9, 1.5)	5	0.2	1.2 (0.9, 1.5)	1.2 (0.8, 1.8)	3	0.503	1.0 (0.7, 1.4)	0.7 (0.3, 1.5)
Asia	2	0.5	1.0 (0.6, 1.7)	1.1 (0.7, 1.9)	0				1	na	0.8 (0.3, 1.7)	0.5 (0.1, 1.9)
Ethnicity/nationality
Single-ethnicity studies **	6	0.3	1.0 (0.8, 1.2)	Ref	1	---	1.2 (0.8, 1.8)	na	5	0.097	1.4 (0.9, 2.1)	na
Multi-ethnic studies ††	2	0.6	0.8 (0.7, 1.0)	0.8 (0.6, 1.1)	1	---	0.9 (0.7, 1.3)		1	---	0.8 (0.5, 1.3)
Unknown ethnicity	4	0.7	1.0 (0.8, 1.3)	1.1 (0.8, 1.4)	4	0.1	1.1 (0.8, 1.5)		1	---	0.8 (0.4, 1.4)
White >=99% ‡‡	4	0.2	1.0 (0.8, 1.2)	Ref	1	---	1.2 (0.8, 1.8)	na	4	0.181	1.6 (1.0, 2.4)	na
African American >=99%	0			1.0 (0.6, 1.8)	0				0
Han >=99%	2	0.5	1.0 (0.6, 1.7)	0.8 (0.6, 1.1)	0				1	---	0.8 (0.3, 1.7)
HWE p-value
Continuous (single ethnicity)	6	0.3	1.0 (0.8, 1.2)	0.8 (0.5, 1.2)	1	---	1.2 (0.8, 1.8)	na	5	0.097	1.4 (0.9, 2.1)	2.2 (0, 93.7)
HWE p <0.05	1	---	1.1 (0.8, 1.7)	Ref	2	0.6	1.3 (0.9, 1.8)	Ref	0			na
HWE p <0.10	2	0.9	1.1 (0.8, 1.5)	Ref	2	0.6	1.3 (0.9, 1.8)	Ref	1	---	0.8 (0.4, 1.4)	na
HWE p >=0.10	10	0.4	0.9 (0.8, 1.0)	0.8 (0.6, 1.1)	4	0.1	1.0 (0.8, 1.3)	0.8 (0.5, 1.2)	6	0.022	1.2 (0.8, 1.9)
Age
Age non-missing (continuous)	12	0.5	0.9 (0.8, 1.1)	1.0 (1.0, 1.0)	6	0.2	1.1 (0.9, 1.3)	1.0 (1.0, 1.0)	7	0.019	1.1 (0.8, 1.7)	1.0 (0.9, 1.1)
<= 47.9y §§	0			na	3	0.5	1.4 (1.0, 1.9)	Ref	0			na
> 47.9 y	12	0.5	0.9 (0.8, 1.1)	0 (0, 0)	3	0.4	0.9 (0.7, 1.2)	0.7 (0.5, 1.0)	7	0.019	1.1 (0.8, 1.7)
<=59y ***	7	0.6	0.9 (0.8, 1.0)	Ref	6	0.2	1.1 (0.9, 1.3)	na	4	0.054	1.4 (0.8, 2.4)	Ref
>59y	5	0.2	1.0 (0.8, 1.2)	1.1 (0.9, 1.4)	0				3	0.362	0.9 (0.6, 1.2)	0.6 (0.3, 1.2)
At or below median	6	0.5	0.9 (0.8, 1.1)	Ref	3	0.5	1.4 (1.0, 1.9)	Ref	4	0.054	1.4 (0.8, 2.4)	Ref
Above median	6	0.3	1.0 (0.8, 1.1)	1.1 (0.9, 1.4)	3	0.4	0.9 (0.7, 1.2)	0.7 (0.5, 1.0)	3	0.362	0.9 (0.6, 1.2)	0.6 (0.3, 1.2)
Gender
Percent male, mixed gender only	8	0.9	1.0 (0.9, 1.2)	0.8 (0.3, 2.4)	2	0.3	1.5 (1.0, 2.3)	na	5	0.006	1.2 (0.7, 2)	0 (0, 0.1)
All female	2	0.1	0.9 (0.7, 1.1)	Ref	1	---	0.9 (0.7, 1.3)	Ref	1	na	0.9 (0.4, 1.9)	na
Mixed gender	8	0.9	1.0 (0.9, 1.2)	1.1 (0.9, 1.5)	2	0.3	1.5 (1.0, 2.3)	1.6 (0.9, 2.7)	5	0.006	1.2 (0.7, 2)
All male	2	0.2	0.7 (0.5, 1.1)	0.8 (0.5, 1.3)	3	0.2	1.0 (0.8, 1.4)	1.1 (0.7, 1.7)	1	na	1.3 (0.7, 2.5)
Study outcome
Lung cancer	3	0.5	1.0 (0.8, 1.2)	Ref	0				3	0.080	1.6 (0.8, 3.1)	Ref
Other cancer	8	0.2	0.9 (0.8, 1.1)	0.9 (0.7, 1.2)	2	0.4	0.9 (0.7, 1.2)	Ref	4	0.565	0.9 (0.7, 1.2)	0.5 (0.3, 0.9)
Non-cancer disease	0				0				0
Non-disease	1	---	1.0 (0.6, 1.6)	1.0 (0.6, 1.7)	4	0.7	1.3 (1.0, 1.8)	1.5 (1.0, 2.2)	0
Lung cancer	3	0.5	1.0 (0.8, 1.2)	Ref	0			na	3	0.080	1.6 (0.8, 3.1)	Ref
All other	9	0.3	0.9 (0.8, 1.1)	0.9 (0.7, 1.2)	6	0.2	1.1 (0.9, 1.3)		4	0.565	0.9 (0.7, 1.2)	0.5 (0.3, 0.9)
MAF
MAF non-missing	12	0.5	0.9 (0.8, 1.1)	1.3 (0.3, 6.2)	6	0.2	1.1 (0.9, 1.3)	0 (0, 3772.6)	7			1.2 (0, 45.7)
MAF (cutpoints assigned by median across SNP)
0.01–0.37	7	0.5	0.9 (0.8, 1.0)	Ref	2	0.3	1.0 (0.7, 1.3)	Ref	5	0.016	1.3 (0.8, 2.1)	Ref
>0.37–0.50	5	0.7	1.1 (0.9, 1.4)	1.2 (1.0, 1.6)	4	0.2	1.1 (0.9, 1.5)	1.1 (0.7, 1.8)	2	0.768	0.8 (0.5, 1.3)	0.7 (0.3, 1.5)
MAF-assigned ethnicity
White	8	0.4	1.0 (0.9, 1.2)	Ref	5	0.2	1.2 (0.9, 1.5)	na	5	0.052	1.3 (0.9, 2.1)	na
African American	0				0				0
Han	2	0.5	1.0 (0.6, 1.7)	1.0 (0.6, 1.7)	0			na	1	---	0.8 (0.3, 1.7)	na
Multi-ethnic studies	2	0.6	0.8 (0.7, 1.0)	0.8 (0.6, 1.0)	1	---	0.9 (0.7, 1.3)		1	---	0.8 (0.5, 1.3)
Smoking prevalence ‡‡‡
Continuous	12	0.5	0.9 (0.8, 1.1)	0.3 (0.1, 1.1)	6	0.2	1.1 (0.9, 1.3)	2 (0.5, 8.6)	7			0.3 (0, 7.8)
>0–0.507	3	0.9	1.2 (0.9, 1.6)	Ref	5	0.2	1.1 (0.9, 1.3)	na	5	0.010	1.0 (0.6, 1.6)	Ref
>0.507–1	9	0.5	0.9 (0.8, 1.0)	0.7 (0.5, 1.0)	1	---	1.2 (0.8, 1.8)		2	0.359	1.5 (0.9, 2.6)	1.6 (0.7, 3.9)

Abbreviations: CI=Confidence interval, na=not applicable, HWE = Hardy Weinberg equilibrium, PY=pack-years, OR_z=control-only genotype-smoking odds ratio,), N=number of studies, Ref=referent, Q=Cochran’s test of homogeneity, SNP=single nucleotide polymorphism, Arg=Arginine, Gln=Glutamine, Trp=Tryptophan, His=Histidine, Met=methionine, Asp=Aspartic acid, Asn=Asparagine

^*OR=Unadjusted odds ratio for XPD Lys751Gln: Lys/Lys (ref) vs. any Gln, never smoking (ref) vs. ever smoking, fixed effects

^†OR=Unadjusted odds ratio for XPD Lys751Gln: Lys/Lys (ref) vs. any Gln, not current smoker (ref) vs. current smoker, fixed effects

^‡OR=Unadjusted odds ratio for XPD Lys751Gln: Lys/Lys (ref) vs. any Gln, lightest non-zero smokers (ref) vs. heaviest smokers; stratified random effects

^§PY contrast is between lightest non-zero category of pack-years (ref) vs. heaviest category of PY

^□Ratio of Odds Ratios: Compares OR in given study characteristic stratum to the OR in the designated reference stratum for that study characteristic by meta-regression

^**Studies w 99%–100% of 1 ethnicity are classified as single-ethnicity

^††Only includes studies with explicitly stated ethnic makeup

^‡‡White = Caucasian, white or non-Hispanic white; African American = African American or black; Han = Han, Han Chinese or ethnic Chinese

^§§Median of studies included in XRCC1 751 current/not current smoker analyses.

^□□Categories based on thirds from studies included in XRCC1 399 PY analyses (<=59y, >59y–63y, >63y)

^***Median of all studies w age info (all SNPs) w age info [range:23.6–69y, mean: 56.7y SD: 9.8y]

^†††Median proportion male in all studies (all SNPs, all smoking exposures): 0.69

^‡‡‡Smoking prevalence is contrast-specific (defined as "ever", "current" or "heavier PY" as appropriate)

Table 6.

XRCC3 Thr241Met and Smoking: Overall and by study characteristics

	Ever-never *				Current-Not current †				PY ‡,§
	N	Q p- value	ORz (95% CI)	Ratio of ORs (95% CI) □	N	Q p- value	ORz (95% CI)	Ratio of ORs (95% CI) □	N	Q p- value	ORz (95% CI)	Ratio of ORs (95% CI) □
Overall
	9	0.5	1.0 (0.9, 1.2)		7	0.7	0.9 (0.8, 1.1)		4	0.7	0.8 (0.6, 1.2)
By Study Characteristic
Continent
North America	2	0.1	0.9 (0.6, 1.3)	Ref	1	---	0.8 (0.5, 1.2)	na	2	0.3	0.7 (0.4, 1.3)	Ref
Europe	7	0.7	1.1 (0.9, 1.3)	1.1 (0.8, 1.7)	5	0.9	0.9 (0.7, 1.1)		2	0.6	0.9 (0.6, 1.4)	1.2 (0.6, 2.5)
Asia	0				1	---	2.0 (0.6, 6.6)		0
Ethnicity/nationality
Single-ethnicity studies **	3	0.7	0.8 (0.6, 1.1)	Ref	3	0.3	0.8 (0.6, 1.1)	Ref	2	0.9	1.0 (0.6, 1.7)	na
Multi-ethnic studies ††	2	0.2	1.0 (0.7, 1.3)	1.2 (0.8, 1.9)	1	---	1.0 (0.7, 1.5)	1.2 (0.7, 2)	1	---	0.6 (0.3, 1.2)
Unknown ethnicity	4	0.9	1.2 (1.0, 1.5)	1.5 (1.0, 2.2)	3	0.7	0.9 (0.7, 1.3)	1.1 (0.7, 1.7)	1	---	0.8 (0.4, 1.4)
White >=99% ‡‡	3	0.7	0.8 (0.6, 1.1)	Ref	2	0.9	0.8 (0.6, 1.1)	Ref	2	0.9	1.0 (0.6, 1.7)	na
Han >=99%	0				1	---	2.0 (0.6, 6.6)		0
HWE p-value
Continuous (single ethnicity)	3	0.7	0.8 (0.6, 1.1)	1.0 (0.3, 3.4)	3	0.3	0.8 (0.6, 1.1)	0.4 (0.1, 1.6)	2	0.9	1.0 (0.6, 1.7)
HWE p <0.05	0				1	---	2.0 (0.6, 6.6)	na	0			na
HWE p <0.10	0			na	1	---	2.0 (0.6, 6.6)	na	0			na
HWE p >=0.10	9	0.5	1.0 (0.9, 1.2)		6	0.9	0.9 (0.7, 1.1)		4	0.7	0.8 (0.6, 1.2)
Age
Age non-missing	9	0.5	1.0 (0.9, 1.2)	1.0 (1.0, 1.0)	7	0.7	0.9 (0.8, 1.1)	0.9 (0.8, 1.1)	4	0.7	0.8 (0.6, 1.2)	0.9 (0.8, 1.1)
<= 47.9y §§	1	---		na	2	0.6	0.8 (0.6, 1.1)	Ref	0			na
> 47.9 y	8	0.4	1.0 (0.9, 1.2)		5	0.6	1.0 (0.8, 1.2)	1.2 (0.8, 1.9)	4	0.7	0.8 (0.6, 1.2)
<=59y ***	5	0.3	1 (0.8, 1.2)	Ref	6	0.9	0.9 (0.7, 1.1)	na	1	---	1.1 (0.4, 2.7)	na
>59y	4	0.7	1.1 (0.9, 1.5)	1.2 (0.8, 1.6)	1	---	2.0 (0.6, 6.6)		3	0.5	0.8 (0.5, 1.1)
At or below median	5	0.3	1.0 (0.8, 1.2)	Ref	4	0.8	0.8 (0.7, 1.1)	Ref	3	0.8	0.9 (0.6, 1.4)	na
Above median	4	0.7	1.1 (0.9, 1.5)	1.2 (0.8, 1.6)	3	0.5	1.0 (0.8, 1.4)	1.2 (0.8, 1.8)	1	---	0.6 (0.3, 1.2)
Gender
Percent male, mixed gender only	6	0.3	1 (0.9, 1.3)	1.1 (0.1, 12.6)	4	0.5	1.0 (0.8, 1.3)	0.1 (0, 3.4)
All female	0				0				0
Mixed gender	6	0.3	1.0 (0.9, 1.3)	Ref	4	0.5	1.0 (0.8, 1.3)	Ref	3	0.6	0.8 (0.5, 1.1)	na
All male	3	0.8	1.0 (0.8, 1.4)	1.0 (0.7, 1.4)	3	0.8	0.8 (0.6, 1.1)	0.9 (0.6, 1.3)	1	---	1.0 (0.5, 1.9)
Study outcome
Lung cancer	0				0				1	---	1.1 (0.4, 2.7)	na
Other cancer	5	0.4	1 (0.8, 1.2)	Ref	4	0.5	1.0 (0.7, 1.2)	Ref	3	0.5	0.8 (0.5, 1.1)
Non-cancer disease	0				0				0
Non-disease	4	0.7	1.2 (0.9, 1.6)	1.2 (0.8, 1.7)	3	0.7	0.9 (0.6, 1.2)	0.9 (0.6, 1.3)	0
Lung cancer	0				0			na	1	---	1.1 (0.4, 2.7)	na
All other	9	0.5	1.0 (0.9, 1.2)	na	6	0.7	0.9 (0.8, 1.1)		3	0.5	0.8 (0.5, 1.1)
MAF
MAF non-missing	9	0.5	1.0 (0.9, 1.2)	0.9 (0, 235.8)	7	0.7	0.9 (0.8, 1.1)	0.2 (0, 4.6)	4	0.7	0.8 (0.6, 1.2)	0 (0, 45050)
MAF (cutpoints assigned by median across SNP)
0.01–0.37	5	0.3	1.0 (0.8, 1.3)	Ref	2	0.1	0.9 (0.6, 1.4)	Ref	4	0.7	0.8 (0.6, 1.2)	na
>0.37–0.50	4	0.5	1.0 (0.8, 1.3)	1.0 (0.7, 1.4)	5	0.9	0.9 (0.7, 1.1)	1.0 (0.6, 1.7)	0
MAF-assigned ethnicity
White	7	0.5	1.1 (0.9, 1.3)	Ref	5	0.9	0.9 (0.7, 1.1)	na	3	0.8	0.9 (0.6, 1.4)	na
Han	0				1	---	2.0 (0.6, 6.6)		0
Multi-ethnic studies	2	0.2	1.0 (0.7, 1.3)	0.9 (0.7, 1.3)	1	---	1.0 (0.7, 1.5)		1	---	0.6 (0.3, 1.2)
Smoking prevalence 13
Continuous	9	0.5	1.0 (0.9, 1.2)	0.1 (0, 1.8)	7	0.7	0.9 (0.8, 1.1)	0.5 (0.1, 3.1)	4	0.7	0.8 (0.6, 1.2)	2.4 (0.1, 56.6)
>0–0.507	0			na	6	0.7	0.9 (0.8, 1.2)	na	2	0.5	0.7 (0.4, 1.1)	na
>0.507–1	9	0.5	1.0 (0.9, 1.2)		1	---	0.8 (0.6, 1.2)		2	0.9	1.0 (0.6, 1.7)

Abbreviations: CI=Confidence interval, na=not applicable, HWE = Hardy Weinberg equilibrium, PY=pack-years, OR_z=control-only genotype-smoking odds ratio,), N=number of studies, Ref=referent, Q=Cochran’s test of homogeneity, SNP=single nucleotide polymorphism, Arg=Arginine, Gln=Glutamine, Trp=Tryptophan, His=Histidine, Met=methionine, Asp=Aspartic acid, Asn=Asparagine

^*OR=Unadjusted odds ratio for XRCC3 Thr241Met: Thr/Thr (ref) vs. any Met, never smoking (ref) vs. ever smoking, fixed effects

^†OR=Unadjusted odds ratio for XRCC3 Thr241Met: Thr/Thr (ref) vs. any Met, not current smoker (ref) vs. current smoker, fixed effects

^‡OR=Unadjusted odds ratio for XRCC3 Thr241Met: Thr/Thr (ref) vs. any Met, lightest non-zero smokers (ref) vs. heaviest smokers; stratified random effects

^§PY contrast is between lightest non-zero category of pack-years (ref) vs. heaviest category of PY

^□Ratio of Odds Ratios: Compares odds ratio in given study characteristic stratum to the odds ratio in the designated reference stratum for that study characteristic by meta-regression

^**Studies w 99%–100% of 1 ethnicity are classified as single-ethnicity

^††Only includes studies with explicitly stated ethnic makeup

^‡‡White = Caucasian, white or non-Hispanic white; African American = African American or black; Han = Han, Han Chinese or ethnic Chinese

^§§Median of studies included in XRCC1 751 current/not current smoker analyses.

^□□Categories based on thirds from studies included in XRCC1 399 PY analyses (<=59y, >59y–63y, >63y)

^***Median of all studies w age info (all SNPs) w age info [range:23.6–69y, mean: 56.7y SD: 9.8y]

^†††Median proportion male in all studies (all SNPs, all smoking exposures): 0.69

^‡‡‡Smoking prevalence is contrast-specific (defined as "ever", "current" or "heavier PY" as appropriate)

For PY, lung cancer studies were above the null for all three SNPs. When compared to studies of other cancers the genotype-smoking association was stronger for lung cancer studies (referent) compared to other cancer studies [ROR= 0.8(0.5, 1.2) and 0.5(0.3, 0.9) for XRCC1 399 and XPD 751, respectively]. All studies with PY were cancer studies. Older average age of study participants weakly but consistently showed stronger associations between ever smoking and variant allele for XRCC1 399, XPD 751 and XRCC3 241 than did younger age. For XRCC1 399 only, this was evident across all three smoking categories. Also, for XRCC1 399 current-not current smokers and PY only, studies with lower minor allele frequencies (N=3) showed stronger associations (~2.0) than those with higher MAF. These three studies had only African-American or Asian participants. No strong and/or consistent patterns emerged for other study characteristics examined.

Discussion

This systematic review and meta-analysis of DNA repair genotypes and smoking behavior in control data was conducted with the goal of examining the independence assumption of case-only studies of gene-environment interaction. There was considerable variation in estimates of Z for XRCC1 399 ever-never smoking and XPD 751 PY of smoking. Point estimates of OR_z varied as much as 5-fold, even when studies were homogeneous enough for a summary estimate. Summary estimates for individual SNPs varied across smoking categorizations, with larger magnitudes of association generally found for measures of smoking dose (PY, intensity, duration) than for smoking status (ever-never, current-not current). There was a weak association between XRCC1 399 and higher smoking dose (PY, intensity). No study characteristics examined strongly predicted the magnitude of association although study outcome (lung cancer vs. other cancer for PY), study design (population-based vs. hospital/patient-based), and age warrant further investigation.

Although the validity of case-only estimates rests on the independence assumption (2, 65), literature on independence assumption verification is limited. Data simulations have demonstrated that small violations of the independence assumption can strongly bias the case-only interaction parameter (7). Even an OR_z of 1.2 biased the COR by nearly 30%. Further, when Z ≠ 1 in population subgroups, the COR for those subgroups will be biased as well.

However, little empirical work has been conducted to quantitatively assess the magnitude of control-only associations (OR_z) between DNA repair gene variations and smoking. A population-based study (N=339) of Japanese males assessed association between ‘habitual smoking’ (ever/never) and a panel of 153 SNPs in 40 candidate genes, including the DNA repair genes OGG1 and NUDT1(MTH1) (66). Association was found between smoking and 3 of 4 of the SNPs in OGG1 (0.4–0.6, borderline statistical significance).

Smoking amount (PY and/or intensity) may be causally associated with variation in XRCC1 399, or with a polymorphism in linkage disequilibrium with XRCC1 399. There is evidence that the XRCC1 399 and XPD 751 variants are functional (67–69). Different aspects of smoking behavior (smoking initiation, smoking cessation, intensity etc.) operate through multiple overlapping pathways (70) therefore would not be expected to be identically affected by DNA repair variation. This is supported by the differing results for smoking status and amount for several SNPs (XRCC1 399, XRCC1 280, XPD 751, XRCC3 241). There is some evidence that variation in DNA repair activity may affect neurological and/or respiratory outcomes, which could in turn affect smoking behavior (71–75). If the variants are functional, or linked to functional variants, heterogeneity could be due to gene-environment interaction in specific populations.

There are also several possible non-causal explanations for these finding. Although publication bias is a concern with meta-analyses, visual inspection of funnel plots and formal tests of asymmetry argue against this. Spurious results for XRCC1 399 and smoking amount could be caused by selection bias in a subsample of studies. Just over half of the studies with smoking amount information for XRCC1 399 were lung cancer studies (8 of 14) and lung cancer studies had on average higher OR_zs than other cancer studies for all PY analyses. The connection between smoking and lung cancer is well known, possibly leading to more variation in response rates or recall by smoking history and/or family history of cancer, but the direction of possible bias is unpredictable. The OR_z for the one XRCC1 399 study that explicitly excluded participants with smoking-related diseases was essentially the same as the summary estimate (42).

Population stratification could have contributed to the heterogeneity in XRCC1 399 ever-never and XPD 751 PY estimates since the variant alleles are found at different frequencies in different ethnic groups within the same study, and smoking behavior may also differ by ethnicity. Although this cannot be rigorously assessed without individual level data, there were no clear patterns in OR_z for any SNP for study-level ethnicity, either by stated ethnicity, when stratified by single-ethnicity vs. multi-ethnicity studies, or when MAF was used as a crude proxy to assign ethnicity for studies with unknown ethnic makeup. Finally, chance could play a role, particularly given the large number of associations examined and sparse data for many analyses. However, in the four studies with large sample sizes (N>=1000) for XRCC1 399 ever-never smoking, OR_zs ranged from 1.0 (0.8, 1.2) to 1.6 (1.2, 2.0), while OR_z was essentially null across the four large studies that examined XPD 194 ever-never smoking [OR_zs=0.9, 1.0, 1.1 and 1.1]. Further, the magnitudes of OR_z differed across smoking status and amount for XRCC1 399 (3 populations) but not for XRCC1 194 (1 population). This large-sample sensitivity analysis is consistent with the overall interpretation of OR_z’s population-specificity for each SNP, rather than chance alone driving the heterogeneity among studies.

Implications for stand-alone case-only studies

Z is a measure of the magnitude of bias in the COR. If Z=1, the case-only estimate of interaction is not biased by genotype-environment association in the underlying population (65). Commonly, this assumption is assessed in control data from a small number of outside studies, using significance testing. Significance testing alone is not sufficient for assessment of potential bias (76). Rarely is Z estimated and/or adjusted for, analogous to other forms of bias such as confounding.

Results from this project illustrate some of the pitfalls of this approach. For instance, for XRCC1 399 ever-never smoking, 18 of the 21 included studies have estimates that are not statistically significantly different than the null value of 1.0. Considering any of these in a statistical significance testing framework would lead to the conclusion that the independence assumption was valid; therefore a case-only study estimate of interaction would not be biased, at least from independence assumption violation. However, the range of OR_zs for these 18 studies is 0.7–1.6, many with wide CIs, indicating the substantial range of potential bias of the effect estimate. Given that different conclusions can be drawn from subsets of smoking behavior and that less than half of the studies that collect control genotype and smoking information present it in publications, this ever-never approach seems inappropriate.

In the estimation framework, results from this project demonstrate the difficulty of using ancillary data to assess the independence assumption. Even when the Cochran’s Q p-value is high, such as for XRCC1 399 current-not current smoking (p=0.4), point estimates of OR_z can vary as much as 5-fold [2.1(1.1, 3.9) for African Americans (27) to 0.4(0.1, 1.2) (77)]. Without further information that certain study characteristics might be influential, there is no good way to decide which of the available ancillary control groups might best represent the underlying (unmeasured) population for a proposed case-only study. Further, it is necessary to do a broad literature search to even to be aware of the possible values of OR_z and range of bias in the COR. Additionally, since both summary estimates and individual study estimates vary across smoking categories, it is important that the independence assumption be evaluated for all smoking categories that will be used in the case-only analyses. For investigations of smoking amount, it will be difficult for many SNPs to locate enough published control group data to even assess the possible range of the magnitude of bias.

This study has several strengths. Using a comprehensive search strategy in collaboration with information specialists increased power to detect and investigate heterogeneity between studies. Sample size was large for smoking status analyses and relatively large for XRCC1 399 and XPD 751 PY analyses. There were sufficient data for many studies to compare OR_z for smoking status and amount within studies, and by smoking category across multiple SNPs. However, of the searched studies that collected the appropriate information only about 1/3 presented it such that it could be abstracted for meta-analysis, limiting sample size, especially for measures of smoking amount.

Only unadjusted odds ratios could be calculated so study estimates may have been confounded. Although some study characteristics could be determined accurately from articles, others were more likely to be misclassified. In particular, average age of study participants was difficult to determine. However, the fact that age was not a central study feature for any of the studies makes it likely that misclassification is non-differential with respect to smoking and genotype. Several potentially informative study characteristics could not be examined because too few articles presented the relevant information using the same metric. In particular, response rates, which may vary by smoking behavior and family/personal history of cancer (78–81), and control group exclusion criteria, were presented very differently. Only two of the 12 articles with multi-ethnic study populations presented data stratified by ethnicity, complicating interpretation of HWE p-value, ethnicity and MAF as study characteristics. Few studies presented enough control group information to examine multiple measures of smoking in the same study population.

This systematic review of control-group associations between smoking and widely studied polymorphisms in DNA repair genes was conducted to accomplish several objectives. The overarching goal was to enable investigators to make more effective use of ancillary data to evaluate the independence assumption prior to launching a stand-alone case-only study. Results from this study suggest that the independence assumption is frequently violated and caution is warranted before proceeding with any case-only interaction analysis. At a minimum, the independence assumption should be more rigorously evaluated than is often done. For a case-only analysis of a case-control study, separate OR_zs should be calculated for each anticipated COR in the relevant subgroup before proceeding. Evaluation of the independence assumption for a proposed stand-alone case-only study should include, whenever possible, results from studies similar to the current study, relevant literature reviews, and a thorough search for individual studies with control or cohort data to ascertain at least the range of OR_zs, both overall and in relevant subgroups. Finally, it serves as a reminder that in the traditional case-control study, interaction is a contrast between control-only association and case-only association, and interaction can be driven by unanticipated associations in controls.

Evaluation of the independence assumption for case-only interaction studies would be greatly improved with more transparency and finer detail in published articles. This could perhaps be accomplished by expanding supplementary online tables to include selected joint genotype-smoking distributions in non-case groups. If it could reliably be shown that Z=1 across individual studies, better use could be made of data pooling from control groups and cohorts for selected SNPs and exposures, especially where individual level data on potential confounders can be provided. However, despite the current emphasis on pooling controls, our results indicate that investigators should not proceed with case-only studies without rigorously evaluating the independence assumption in individual studies.