Establishing a valid approach for estimating familial risk of cancer explained by common genetic variants

Abstract

We critically examined existing approaches for the estimation of the excess familial risk of cancer which can be attributed to identified common genetic risk variants and propose an alternative, more straightforward approach for calculating this proportion using well-established epidemiological methodology. We applied the underlying equations of the traditional approaches and the new epidemiological approach for colorectal cancer (CRC) in a large population-based case-control study in Germany with 4447 cases and 3480 controls, who were recruited from 2003–2016 and for whom interview, medical and genomic data were available. Having a family history of CRC (FH) was associated with a 1.77-fold risk increase in our study population (95% CI 1.52–2.07). Traditional approaches yielded estimates of the FH-associated risk explained by 97 common genetics variants from 9.6% to 23.1%, depending on various assumptions. Our alternative approach resulted in smaller and more consistent estimates of this proportion, ranging from 5.4% to 14.3%. Commonly employed methods may lead to strongly divergent and possibly exaggerated estimates of excess familial risk of cancer explained by associated known common genetic variants. Our results suggest that familial risk and risk associated with known common genetic variants might reflect two complementary major sources of risk.

Introduction

In the era of genome wide association studies (GWAS), many single nucleotide polymorphisms (SNPs) have been found to be associated with a higher risk for various types of cancers^1–3. With increasing sample size of the GWAS consortia, the power to detect common variants with small effects has rapidly increased, and discovery of several SNPs at once is now the rule rather than the exception (e.g.^{4, 5}).

Many of the SNPs discovery studies included estimates of how much of the familial risk of cancer can be attributed to previously known and the newly discovered genetic variant(s). They mostly employed an equation first proposed by Cox et al. in 2007, or slight modifications thereof^6–9 which includes two major components:

1. the relative risk attributable to a given SNP, commonly denoted as $\lambda * = \frac{p{\left(p{r}_2+q{r}_1\right)}^2+q{\left(p{r}_1+q\right)}^2}{{\left(p^2{r}_2+2pq{r}_1+q^2\right)}^2}$, where p is the population frequency of the minor allele, q = 1-p, and r₁ and r₂ are the relative risks for heterozygotes and rare homozygotes relative to common homozygotes, and

2. the overall familial relative risk estimated from epidemiological studies, commonly denoted as λ_o. The share of the familial risk attributable to the SNP is then obtained as log λ * / λ_o. If applied to multiple independent SNPs in a multiplicative model, the numerator consists of the sum of the log(λ*) across SNPs.

Although this approach seems straightforward, there are a number of issues that deserve critical discussion.

• First, the relative risk estimates typically come from the large GWAS that detected the SNPs, and will - if winner’s curse is not appropriately addressed - typically be lower in independent samples, which may lead to substantial overestimation of the proportion of FH risk explained by the SNPs.

• Second, if the contribution of multiple SNPs is not completely independent but these SNPs are in some linkage disequilibrium, the numerator of the equation may be substantially overestimated. On the other hand, if too restrictive “inclusion criteria” are employed that would consider only totally independent SNPs, the complementary information which correlated SNPs may still convey would be lost, leading to potential underestimation of the numerator.

• Third, the estimator for relative risk associated with FH in the denominator is commonly taken from pooled estimates from epidemiological studies and may differ from the relative risk in the populations used to derive the genetic risks.

• Fourth, the implicit partitioning of the excess risk of family history into some proportion that is explained by known genetic variants and some proportion that is explained by yet to be identified genetic factors neglects the fact that a substantial proportion of familial aggregation may be due to other reasons, such as familial aggregation of environmental or lifestyle risk factors.

• Fifth, carriers of common, low penetrance risk alleles are not restricted to persons with a FH. In fact, given the limited proportion of persons with a FH, the common, low penetrance risk alleles occur more often in persons without FH. These risk alleles are hence not restricted to familial risk, but in fact convey risk independent of FH.

In this article, we propose an alternative, straightforward “epidemiological approach”, using well-established epidemiological methodology that is unaffected by these concerns. We will use colorectal cancer (CRC), the third most common cancer globally¹⁰, whose heritability was estimated to be 35%¹¹, as an example to demonstrate our approach. Next to others, family history (FH) has been identified as a major CRC risk factor¹². In the past decade, more and more SNPs associated with CRC risk have been discovered by GWAS (e.g.^13–16). Risk increases associated with single SNPs are mostly very small, with odds ratios (OR) for the risk alleles ranging between 1 and 1.1, but polygenetic scores based on multiple SNPs were shown to be highly predictive of CRC risk^17–20.

Methods

Alternative epidemiological approach

In the proposed approach both genomic data and familial risk are derived from the same dataset. The underlying concept is graphically depicted in Supplementary Figure 1. The concept reflects the causal role of both genetic and environmental factors for CRC risk. The association of FH with CRC risk reflects clustering of both types of factors within families. Based on this model, we propose to estimate the proportion of the CRC risk that is associated with having a FH (which is most commonly defined as FH in a first-degree relative) that can be explained by common genetic variants by

$$\mathit{\text{Prop}}\left(\mathit{\text{SNPs}}\right)=\left(1-\frac{R{R}_a-1}{R{R}_b-1}\right)*100,$$

Where RR_a is the relative risk (RR) for FH which is adjusted for common genetic variants, RR_b is the RR for FH which is not adjusted for common genetic variants and both RRs are adjusted for environmental factors affecting CRC risk. Should the genetic variants not explain any of the FH-associated risk, RR_a would equal RR_b, in which case Prop(SNPs) would be 0. Should the genetic variants completely (100%) explain the excess CRC risk for a FH beyond the excess risk explained by environmental factors, RR_a would equal 1. In practice, one would expect RR_a to be between 1.0 and RR_b and Prop(SNPs) to be between 0 and 1 (100%). Analogous calculations could be made using the log(RRs) rather than the RRs of both unadjusted and adjusted FH risk estimates (results are presented in the supplement). In case-control studies, RRs are commonly approximated by odds ratios (ORs).

Study population

Data for the current analyses were taken from the DACHS study (Darmkrebs: Chancen der Verhütung durch Screening), which has been described in detail elsewhere^{21, 22}. Briefly, DACHS is an ongoing population-based case-control study in southwest Germany. Patients with a first diagnosis of CRC aged at least 30 years are eligible for participation. Recruitment is conducted by all 22 hospitals in the study area which offer first line treatment to patients with CRC. Controls are randomly selected from population registries using frequency matching with respect to sex, age and county of residence. This analysis was based on 4447 cases and 3480 controls who were recruited from 2003 to 2016 and for whom genomic data were available. The ethics committees of the Medical Faculty at the University of Heidelberg and the Medical Chambers of Baden-Württemberg and Rhineland-Palatinate approved the study. Written informed consent was obtained from all participants.

Data collection

Standardized in-person interviews were conducted with both cases (typically during their hospital stay) and controls (at their homes) by trained interviewers. Detailed information about the participants’ family history and a variety of other risk and preventive factors was collected, and blood or buccal samples were taken. All CRC cases were histologically confirmed.

Genotyping

DNA was extracted from blood samples (in 99.1% of participants) or from buccal cells (in 0.9% of participants) using conventional methods. Details about genotyping and imputation are provided in Supplementary Table 1.

Identification and selection of SNPs for the genetic risk score

A literature review was conducted to find SNPs that were found to be associated with a higher risk for persons of European descent as reported in detail elsewhere²⁰. Of 105 identified SNPs (Supplementary Table 2), 97 could be reliably measured or imputed. For six SNPs, the risk allele in our sample was not the same as reported in the respective discovery study (rs1957636, rs72647484, rs7259371, rs2696839, rs11884596, rs2516420). The correlation between the SNPs measured with the correlation coefficient from the ‘genetics’ package in R is depicted in Supplementary Figure 2.

Statistical analyses

A genetic risk score (GRS) was derived with various approaches. It was both calculated unweighted, as mere sum of risk alleles, or as weighted sum of risk alleles, with weights equal to the log of the per-risk-allele-OR as reported in the discovery study. We furthermore applied various linkage disequilibrium (LD) thresholds (no LD, D’≥0.95, D’≥0.5, D’≥0.1, or max. one SNP per locus) for inclusion of SNPs in the GRS (only including the most significant SNP of all SNPs in LD in logistic regression models within our sample), which resulted in different numbers of included SNPs (97, 90, 80, 71 and 59, respectively). Additionally, both continuous GRS and categorized GRS were analyzed, with GRS categories defined by percentiles of the GRS distribution in controls: 0–10, 10–20, 20–40, 40–60, 60–80, 80–90, 90–100.

Proportions of FH risk explained by common genetic variants were estimated according to four traditional approaches, using the formulas given by the respective paper, for four different ORs for FH, for five different inclusion criteria for common genetic variants under observation and both with relative risk estimates and risk allele frequencies for the genetic variants from the discovery studies and with relative risk estimates and risk allele frequencies from the DACHS study.

Next, we employed the newly suggested epidemiological approach and estimated the proportion of FH risk explained by common genetic variants by $\mathit{\text{Prop}}\left(\mathit{\text{SNPs}}\right)=\left(1-\frac{O{R}_a-1}{O{R}_b-1}\right)*100$, where both ORs were estimated from multiple logistic regression models as outlined above. Different methods of handling the genetic information (GRS vs. separate variables) and, again, different LD thresholds were used for calculating the proportion of explained familial risk. Confidence intervals were computed using bootstrapping methods (n=1000).

Data availability

Data will be made available upon reasonable request.

Results

Table 1 shows some main characteristics of the study population used for this analysis. Sex and age were equally distributed among cases and controls, reflecting matching. FH in a first-degree relative was more common in cases than controls (13.7% vs. 10.0%, p-value <.0001), and a higher proportion of cases had a higher GRS compared to controls (p-value <.0001). Figure 1 depicts the distribution of the GRS among cases and controls with relation to FH. A major difference in the distribution of the GRS was seen between cases and controls, but not between participants with and without FH, neither in cases nor in controls.

Table 1.

Selected characteristics of the study population, imputed data

Characteristic	Group	Cases	Controls	p-valuea
		n = 4447	n = 3480
Sex	Female	1749 (39.3)	1344 (38.6)	0.5206
	Male	2698 (60.7)	2136 (61.4)
Age	<50	220 (5.0)	146 (4.2)	0.6458b
	50–59	681 (15.3)	521 (15.0)
	60–69	1333 (30.0)	1080 (31.0)
	70–79	1519 (34.2)	1165 (33.5)
	≥80	694 (15.6)	568 (16.3)
Personal history of colonoscopy		1127 (25.4)	2027 (58.3)	<.0001
Family history	No FH	3487 (78.4)	2896 (83.2)	<.0001
	SDR onlyc	352 (7.9)	236 (6.8)
	FDRd	608 (13.7)	348 (10.0)
Genetic risk scoree	Very low	274 (6.2)	348 (10.0)	<.0001
	Low	335 (7.5)	348 (10.0)
	Low-medium	739 (16.6)	696 (20.0)
	Medium	880 (19.8)	696 (20.0)
	Medium-high	961 (21.6)	696 (20.0)
	High	611 (13.7)	348 (10.0)
	Very high	647 (14.6)	348 (10.0)
Cancer stage	I	1035 (23.3)	-	-
	II	1343 (30.2)	-
	III	1423 (32.0)	-
	IV	647 (14.5)	-

Note: values are expressed as n (%)

Abbreviations: FDR, first-degree relative; FH, family history; SDR, second-degree relative

^aTest for differences between cases and controls

^bP-value for t-test with continuous variable

^cFH in any SDR only

^dFH in at least one FDR

^eClassification of GRS: Very low, ≤10^th percentile; Low, 10^th-20^th percentile; Low-medium, 20^th-40^th percentile; Medium, 40^th-60^th percentile; Medium-high, 60^th-80^th percentile; High, 80^th-90^th percentile; Very high, >90^th percentile; GRS generated with weighted risk alleles (weights equaling the beta-coefficient as found in the respective discovery study) of SNPs not in LD (cut-off 0.95)

Figure 1. Scatter plot and box plot showing the distribition of GRS.

The bottom and top of the box indicate the 25^th and 75^th percentile, with the middle line in the box representing the median. The p-value was estimated using Mann-Whitney U test.

Abbreviations: FDR, 1^st-degree relative; FH, family history; GRS, genetic risk score

Traditional estimates of the proportion of FH-associated risk that can be statistically explained by common genetic variants are shown in Table 2. The estimated proportions ranged widely, depending on the combination of the employed criteria (LD, assumed OR for FH, estimation approach). Estimates of the proportion explained by the genetic variants were generally much lower when they were based on the relative risk estimates and risk allele frequencies from the DACHS study rather than the discovery studies, but substantial variation was observed even within both groups of estimates, ranging from 9.6 to 23.1% and from 14.2 to 42.5%, respectively.

Table 2.

Estimated proportion (%) of family history risk explained by common genetic variants according to traditional approaches

		Estimated proportion of explained family history risk [%] based on risk estimates for SNPs from
Assumed risk increase by family history (odds ratio)	Approach	Discovery study					DACHS studya
		All SNPsb (NSNPs=97)	Excluding SNPs in LD			Max. 1 SNP per locus	All SNPsb (NSNPs=97)	Excluding SNPs in LD			Max. 1 SNP per locus
			D’≥0.95 (NSNPs=90)	D’≥0.5 (NSNPs=80)	D’≥0.1 (NSNPs=71)	(NSNPs=59)		D’≥0.95 (NSNPs=90)	D’≥0.5 (NSNPs=80)	D’≥0.1 (NSNPs=71)	(NSNPs=59)
2.4	Cox 2007 [6]	25.1	21.2	18.4	15.7	14.2	15.0	13.9	11.9	11.2	10.5
	Pharoah 2008 [7]	29.9	25.5	22.0	18.8	17.0	15.8	14.5	12.4	11.7	10.9
	Zheng 2013 [8]	27.8	24.0	20.7	17.6	16.0	15.0	13.9	11.9	11.2	10.5
	Jenkins 2016 [9]	26.4	22.5	19.5	16.7	15.1	14.0	12.8	11.0	10.3	9.6
2.2	Cox 2007 [6]	27.8	23.5	20.3	17.5	15.7	16.7	15.4	13.2	12.5	11.7
	Pharoah 2008 [7]	31.2	26.6	23.0	19.7	17.8	16.5	15.1	13.0	12.2	11.4
	Zheng 2013 [8]	30.9	26.6	23.0	19.6	17.8	16.7	15.4	13.2	12.4	11.7
	Jenkins 2016 [9]	29.3	25.0	21.6	18.5	16.7	15.5	14.2	12.2	11.5	10.7
2.0	Cox 2007 [6]	31.6	26.7	23.2	19.9	17.9	19.0	17.5	15.1	14.2	13.3
	Pharoah 2008 [7]	32.7	27.9	24.1	20.6	18.7	17.3	15.9	13.6	12.8	12.0
	Zheng 2013 [8]	35.1	30.3	26.2	22.3	20.3	19.0	17.5	15.1	14.2	13.3
	Jenkins 2016 [9]	33.3	28.5	24.6	21.0	19.0	17.7	16.2	13.9	13.0	12.2
1.77c	Cox 2007 [6]	38.4	32.5	28.1	24.1	21.7	23.1	21.3	18.3	17.2	16.2
	Pharoah 2008 [7]	34.8	29.7	25.7	21.9	19.8	18.4	16.9	14.5	13.6	12.7
	Zheng 2013 [8]	42.7	36.8	31.7	27.0	24.6	23.1	21.2	18.3	17.2	16.2
	Jenkins 2016 [9]	40.5	34.6	29.9	25.5	23.1	21.5	19.7	16.9	15.8	14.8

^aEstimates based on odds ratios and allele frequencies as observed in the DACHS study

^bIn the DACHS study, 97 out of 105 SNPs could be analyzed (see Supplementary Table 2)

^cFH estimate obtained in the DACHS study

In the DACHS study, the OR for FH derived from a model adjusted for the matching variables sex and age and a number of environmental factors was 1.77 (95% confidence interval (CI) 1.52 to 2.07), and this OR estimate was only slightly altered by additional adjustment for the GRS, regardless of how restrictive inclusion of SNPs was with respect to LD (Table 3). The proportion of FH associated risk explained by common genetic variants could be quantified as between 6.4% (95% CI: 1.5 to 12.3%) and 8.9% (95% CI: 4.0 to 14.8%) by the epidemiological approach. Very similar results were obtained in sensitivity analyses using the log(ORs) instead of the ORs (Supplementary Table 3).

Table 3.

Association between family history and the risk for colorectal cancer with and without adjustment for various genetic risk scores

FH of CRC1	No FH	FH in a FDR	% of explained familial risk2
Cases, n (%)	3487 (85.2)	608 (14.8)	-
Controls, n (%)	2896 (89.3)	348 (10.7)	-
Model 1, OR (95% CI)a	Ref.	1.77 (1.52 to 2.07)	-
Model 2, OR (95% CI)b	Ref.	1.70 (1.46 to 1.99)	8.94 (4.02–14.81)
Model 3, OR (95% CI)c	Ref.	1.70 (1.46 to 1.99)	8.94 (3.16–15.45)
Model 4, OR (95% CI)d	Ref.	1.71 (1.46 to 2.00)	7.71 (2.18–13.91)
Model 5, OR (95% CI)e	Ref.	1.71 (1.47 to 2.00)	7.38 (2.52–13.33)
Model 6, OR (95% CI)f	Ref.	1.72 (1.47 to 2.01)	6.42 (1.50–12.27)

Abbreviations: CI, confidence interval; CRC, colorectal cancer; FDR, first-degree relative; FH, family history; OR, odds ratio; Ref., reference

¹Participants with a FH of CRC in a second-degree relative were excluded for this analysis (n=588)

²95% confidence intervals for explained familial risk was calculated using bootstrapping methods (n=1000)

^aRegression model 1 adjusted for sex, age, education, smoking, hormone replacement therapy among women, BMI and previous colonoscopy

Regression models 2–6 adjusted for same variables as model 1 plus additionally adjusted for genetic risk score:

^bcontinuous, weighted, no LD threshold

^ccontinuous, weighted, LD threshold D’≥0.95

^dcontinuous, weighted, LD threshold D’≥0.5

^econtinuous, weighted, LD threshold D’≥0.1

^fcontinuous, weighted, max. 1 SNP per locus

In the application of our alternative approach, we considered a number of design options, such as (1) adjustment for all common genetic variants as single variables or (2) adjustment for common genetic variants through one variable, i.e. a GRS. We calculated the FH-associated risk proportion explained by common genetic variants with both approaches, and furthermore with a large variety of GRS model options and they all consistently yielded estimates in a low range between 5 and 14% (Table 4, Supplementary Table 3).

Table 4.

Proportions of explained familial risk (and 95% confidence intervals) with epidemiological approach, using different adjustment possibilities

Adjustment for common genetic variants …			Linkage disequilibrium
			- (NSNPs=97)		D’≥0.95 (NSNPs=90)		D’≥0.5 (NSNPs=80)		D’≥0.1 (NSNPs=71)		max. 1 SNP per locus (NSNPs=59)
included as separate variables			13.8	2.2–23.2	14.3	3.3–23.2	13.8	3.3–22.2	13.2	3.3–21.3	10.2	1.3–18.3
included as a GRS…	categorizeda	unweighted	10.6	4.8–16.9	10.9	4.8–17.0	8.9	3.4–15.2	7.3	1.9–13.8	6.3	0.8–12.5
		Weightedb	8.6	3.9–14.5	8.9	3.6–14.9	8.1	2.8–14.7	7.2	1.3–12.2	5.4	0.8–11.4
	continuous	unweighted	11.6	6.1–18.7	11.0	5.0–18.5	9.3	3.6–16.2	8.3	2.8–15.1	7.2	1.6–14.1
		weightedb	8.9	4.0–14.8	8.9	3.2–15.5	7.7	2.2–13.9	7.4	2.5–13.3	6.4	1.5–12.3

Please note: 95% confidence intervals of explained familial risk was calculated using bootstrapping methods (n=1000)

Abbreviations: GRS, genetic risk score; OR, odds ratio

^aCategories defined by percentiles of distribution in controls: 0–10, 10–20, 20–40, 40–60, 60–80, 80–90, 90–100

^bWeights equal to log(OR) from discovery study

Discussion

Our analyses support suggestions from theoretical considerations that traditional approaches commonly employed in GWAS might overestimate the proportions of familial risk that can be explained by common genetic variants, due to the underlying assumptions. Furthermore, estimates obtained by these approaches strongly varied even with relatively minor variations in the inclusion criteria of SNPs. By contrast, our proposed epidemiological approach yielded much lower, but consistent and robust estimates of the familial proportion explained by known common genetic variants.

Traditional approaches of calculating the explained proportion of familial risk by common genetic variants have been widely employed in the GWAS literature (e.g.^{3, 5, 15, 23–26}). Manuscripts reporting on the newly identified susceptibility loci commonly estimate the incremental and total proportion of familial risk explained by the respective SNPs. For example, so far published estimates for the familial risk for CRC allegedly explained by common genetic variants ranged from ~6% for ten SNPs³, to ~8% for 20 SNPs under observation¹⁵, to ~12% for 76 SNPs⁵, to ~22%⁶ for 45 SNPs in a simulation study. Most studies took some LD measure into account^{5, 6}, while others simply added up contributions of SNPs without LD-pruning (e.g.¹⁵). However, as shown by our analyses, such estimates may be quite sensitive to the underlying assumptions. In our example, the estimates obtained by traditional approaches had a wide range. In particular, the following patterns were observed:

First, all estimates were substantially higher when the relative risk estimates and the risk allele frequencies of the discovery study rather than those of the DACHS study were employed, which might to some degree reflect the well-known phenomenon of “winner’s curse”²⁷. The obtained relative risk estimates and the risk allele frequencies are subject to variation within populations, which might contribute to differing explained risk proportions.

Second, all of the traditional estimates were strongly dependent on the inclusion criteria of SNPs, being approximately 50% higher for the least restrictive approach (including all 97 SNPs) compared to the most restrictive approach (59 SNPs). Using a less restrictive cutoff might lead to overestimation of the explained familial risk by ignoring the redundancy of the risk information conveyed by SNPs in high linkage disequilibrium.

Third, the traditional estimates strongly depend on the assumed risk increase by family history, a direct consequence of the underlying equations, and may be biased if the risk associated with family history in the analyzed study differs from the assumed familial risk.

Although our suggested alternative approach is less susceptible to violations of these assumptions, possible disadvantages of the “epidemiological approach” also have to be kept in mind. Foremost, it requires availability of epidemiological data, especially information on FH of CRC and possible confounding risk factors. For our approach the risk estimate of having a FH of CRC needs to be obtained from the same dataset from which the genetic data is analyzed. Many large genomic datasets might lack this kind of information, but could nevertheless conduct our proposed approach in subsets for which FH information is available. Furthermore, some of the considerations that are mentioned above might also apply for our approach, such as the issue of winner’s curse if the cohort under investigation was included in deriving the GRS without appropriate correction for overoptimism or the appropriate threshold of the linkage disequilibrium. However, estimates of the familial risk explained by known genetic variants were found to be much less affected by choice of the threshold for the linkage disequilibrium with the proposed epidemiological approach than with the traditional approaches (range for epidemiological approach 5.4–14.3% vs. range for traditional approaches 9.6–23.1%).

It has to be kept in mind that our analysis, like most previous analyses that estimated explained familial risk, was restricted to common variants. In principle, however, analogous approaches could also be applied in analyses additionally encompassing rare, highly penetrant germline mutations.

A specific strength of our study was the availability and use of comprehensive genetic and environmental data from the DACHS study, one of the largest case-control studies on CRC, with an unselected population-based multi-center recruitment.

In summary, we suggest an alternative, straightforward approach based on standard epidemiological methodology to estimate the proportion of FH-associated risk of cancer that is attributable to common genetic risk variants. Application of this approach in a large population-based case-control study on CRC supports suggestions that this proportion may be substantially smaller than previously assumed and highly dependent on SNP pruning methods, the assumed risk for having a FH of CRC and the number of identified SNPs. On the other hand, relevant prevalences of those variants in both people with and without FH imply that, for the example of CRC, polygenetic risk scores based on meanwhile identified common genetic variants explain quite a substantial proportion of overall risk far beyond FH in the total population. Rather than reflecting a major subcomponent of familial risk, common genetic variants appear to reflect substantial complementary risk. Vice versa, rather than exclusively or even primarily reflecting genetic factors, familial aggregation of risks may also reflect to a large extent other components, such as shared environmental or lifestyle factors²⁸. While the alternative epidemiological approach was illustrated using CRC as an example, it should be equally applicable to other cancer outcomes, and, in fact, any other disease outcome.

Novelty and Impact:

• Traditional approaches are prone to overestimate the proportion of familial risk of cancer that can be explained by meanwhile identified common genetic variants for various reasons.

• We offer an alternative approach that is not affected by such overestimation and illustrate our point using data from a large case-control study on colorectal cancer.

• The suggested easy-to-implement alternative approach may help to prevent major overestimation of the proportion of familial risk explained by common genetic variants.

Abbreviations:

CRC

Colorectal cancer

CI

confidence interval

FH

family history

GRS

genetic risk score

GWAS

genome-wide association study

OR

odds ratio

SNP

single nucleotide polymorphism