Low-frequency variation near common germline susceptibility loci are associated with risk of Ewing sarcoma.

BACKGROUND: Ewing sarcoma (EwS) is a rare, aggressive solid tumor of childhood, adolescence and young adulthood associated with pathognomonic EWSR1-ETS fusion oncoproteins altering transcriptional regulation. Genome-wide association studies (GWAS) have identified 6 common germline susceptibility loci but have not investigated low-frequency inherited variants with minor allele frequencies below 5% due to limited genotyped cases of this rare tumor.
METHODS: We investigated the contribution of rare and low-frequency variation to EwS susceptibility in the largest EwS genome-wide association study to date (733 EwS cases and 1,346 unaffected controls of European ancestry).
RESULTS: We identified two low-frequency variants, rs112837127 and rs2296730, on chromosome 20 that were associated with EwS risk (OR = 0.186 and 2.038, respectively; P-value < 5×10-8) and located near previously reported common susceptibility loci. After adjusting for the most associated common variant at the locus, only rs112837127 remained a statistically significant independent signal (OR = 0.200, P-value = 5.84×10-8).
CONCLUSIONS: These findings suggest rare variation residing on common haplotypes are important contributors to EwS risk. IMPACT: Motivate future targeted sequencing studies for a comprehensive evaluation of low-frequency and rare variation around common EwS susceptibility loci.

Background

Ewing sarcoma (EwS) is a rare bone or soft tissue tumor predominantly occurring in the second decade of life [1]. The specific cells of origin leading to EwS tumors are unknown, with current evidence indicating EwS likely arises from mesoderm- or neural crest-derived mesenchymal stem cells [2,3]. The overall age-adjusted incidence of EwS is 0.128 per 100,000 population with individuals of European ancestry at a 9-fold risk relative to African Americans and Asian/Pacific Islanders (0.155 in White, 0.017 in Asians/Pacific islanders, and 0.017 in African Americans) [4]. The reported disparity in EwS incidence by ancestry suggests the importance of germline susceptibility to EwS risk.

A defining feature of EwS tumors is the somatically acquired translocation between EWSR1 (22q12) and a member of the ETS transcription factor family, most commonly FLI1 (11q24) (85% of cases) [5-7]. The resulting fusion oncoprotein produces aberrant and strong transcriptional regulators that bind to GGAA microsatellites and ETS-like motifs, which are thereby converted into potent enhancers, to promote cellular transformation by deregulating key target genes in cell cycle control, migration and apoptosis pathways [7-12]. Aside from recurrent EWSR1-ETS fusions, most EwS tumors display remarkably low somatic mutation rates [1,13-16].

The presence of EwS EWSR1-ETS fusions provides a molecularly distinct phenotype for genomic characterization, despite small case sample sizes. Previous genome-wide association studies (GWAS) have identified 6 common genetic susceptibility loci associated with EwS risk (1p36.22, 6p25.1, 10q21, 15q15, 20p11.22 and 20p11.23) [17]. The number of identified susceptibility loci are notable given small samples, suggesting a homogenous phenotype as defined by the fusion oncoprotein may aid in identifying germline associations. Effect estimates for variants at these loci exhibit elevated odds ratios (OR > 1.7), which is high for cancer GWAS and striking in light of the rarity of EwS in familial cancer predisposition syndromes [18]. Most EwS susceptibility loci reside near GGAA microsatellites and may disrupt local binding of EWSR1-ETS fusion oncoproteins to these microsatellites, suggesting germline-somatic interactions could be important for EwS susceptibility. As a proof-of-concept such germline-somatic interaction has been demonstrated for the chr10 EwS susceptibility gene EGR2 [11].

Despite recent efforts to characterize the genetic architecture of EwS, thus far, no study has investigated the contribution of low-frequency variants (minor allele frequencies (MAF) < 0.05) to EwS risk. The high locus-to-case discovery ratio of previous EwS GWAS and large effect sizes of common EwS susceptibility loci led our group to revisit whether current series of EwS cases would be sufficient to detect associations between rare or low-frequency variants and EwS risk. We systematically scanned across the genome for well-imputed, low-frequency variants associated with EwS susceptibility in the largest collection of genotyped EwS cases to date (733 EwS cases and 1,346 controls) [17].

Materials and methods

Study populations

The study population for the current association analysis has been described previously [17]. In brief, EwS cases were obtained from five sources: a study published by Postel-Vinay et al. [19], the Institut Curie, the Childhood Cancer Survivor Study (CCSS), the Center for Cancer Research (CCR) at the National Cancer Institute (NCI), and the NCI Bone Disease and Injury Study [20]. Ancestry of these EwS cases was estimated using SNPWEIGHTS based on SNPs found to be suitable for inferring population structure [21]. EwS cases with less than 80% European ancestry were excluded resulting in a combined set of 733 EWS cases. A total of 1,346 principal-component-matched, cancer-free controls were selected from the NCI Prostate Lung Colorectal and Ovarian Cancer Screening trial [22], American Cancer Society Cancer Prevention Study II [23], and the Spanish Bladder Cancer Study [24] for the final analysis and included with controls previously used by Postel-Vinay et al [19]. Each study participant provided informed consent, and approval to conduct this research was granted by the Institution Review Board of Institut Curie, National Cancer Institute, as well as 26 participating institutions for CCSS.

Genotyping and quality control

For the Postel-Vinay study, DNA from tumor tissue, blood, and bone marrow was isolated using proteinase K lysis followed by phenol chloroform extraction. Genomic DNA was genotyped by 610 Quadevl arrays (Illumina). For CCSS samples, blood DNA was isolated using the Gentra PureGene Blood kit (QIAGEN) and saliva DNA was extracted using the Oragene kit (DNA Genotek). Whole genome amplification (WGA) was performed for samples without sufficient DNA. For CCSS samples, genotyping was performed at the NCI Cancer Genomics Research Laboratory (CGR) on the Infinium Human Omni5Exome array (Illumina). The remainder of NCI and Institut Curie samples were genotyped by CGR using the OmniExpress-24 v1.1 array (Illumina).

All genotyping was performed according to standard manufacturer protocols. In brief, WGA was performed on 400 ng DNA, and the amplified DNA was fragmented, precipitated, resuspended, and hybridized to the designated arrays. Single-base extension of probes using captured DNA as template was subsequently carried out with fluorophore-conjugated nucleotides. Arrays were then scanned by iScan (Illumina) and SNPs called by GenomeStudio (Illumina). Our downstream quality control included filtering out samples with abnormal heterozygosity rate, sex discordance, <95% completion rates, and unexpected relatedness (IBD > 10%).

Genotype imputation was performed in three sets: (1) the Postel-Vinay study, (2) the CCSS EwS cases and matched controls, and (3) all remaining NCI and Institut Curie samples. All samples were pre-phased using SHAPEIT [25] and imputed using IMPUTE2 [26]. The 1,000 Genomes Phase 3 was used as the reference [27] resulting in 16,367,531 SNPs. Among these SNPs, 10,216,839 were low-frequency variants with MAF < 0.05.

PCR validation of genotypes

Imputed genotypes for the three EwS-associated low-frequency or rare variants (rs78119607, rs112837127, rs2296730) were validated by allele-specific TaqMan assay (Thermo Fisher Scientific) at CGR following standard manufacturer protocols. The 325 samples used for validation were selected based on imputed genotype, study, and amount of available DNA.

Statistical analysis

For each variant, we report an estimate of the odds ratio (OR), 95% confidence interval (CI), and P-value (pMH) using a Mantel-Haenszel Test where subjects are stratified by study (e.g. CCSS, Postel-Vinay, etc.), and, when stated, the genotype at linked neighboring variant(s). Because we focused on less common variants, we used a dominant model (i.e., genotype defined as presence versus absence of rare variant) and an exact, conditional test (mantelhaen.test(exact = T)) [28,29]. We used pMH < 5 × 10−8 to define initial GWAS significance and pMH < 0.05/1684 = 1.09×10−5 for conditional tests, where 1,684 is the number of SNPs with MAF < 0.05 and R2 > 0.004 with one of 6 previously identified SNPs. Potential interaction between low frequency SNPs and common SNPs were examined by logistic regression models with case-control status as outcome, low frequency and common SNPs as well as an interaction term between them as predictors. All statistical tests were two-sided and performed in R v.3.6.2 [28]. We did not investigate associations with significant variants and clinical data as limited clinical data were available for the participating EwS cases.

Results

Our analysis identified evidence for associations of three putative low frequency (MAF < 0.05) imputed variants associated with EwS risk, which we advanced to validation studies described below. The variants were located at 1q23.3, 20p11.23, and 20p11.22 (Table 1, Fig 1 and S1 Fig) and tagged by rs78119607, rs112837127, and rs2296730, respectively. The MAF among controls of European ancestry ranged from 0.001 for rs78119607 to 0.046 for rs2296730 with minor allele effect sizes ranging from 0.18 to 16.64 (Table 2). The odds ratio for the minor A allele of rs112837127 suggested a potentially protective effect (OR = 0.18) indicating that in some instances low-frequency variation could reduce susceptibility to EwS.

Table 1

Genome-wide significant associations (P-value < 5×10−8) for identified low-frequency and rare variants with EwS susceptibility using a dominant model stratified by study.

Region	Coordinate	Variant	Alleles		Minor Allele Counts (Frequency)		MH P-value
			Major	Minor	Controls N = 1,346	EwS Cases N = 733
1q23.3	163530987	rs78119607	G	A	4 (0.001)	31 (0.021)	2.38×10−11
20p11.23	21063508	rs112837127	G	A	87 (0.032)	9 (0.006)	6.90×10−9
20p11.22	21367741	rs2296730	A	G	123 (0.046)	133 (0.091)	4.92×10−8

Fig 1

Manhattan plots of analyses for all variants (A) and low-frequency and rare variants (MAF < 0.05) (B). Plotted p-values are for allelic tests by chromosome.

Table 2

Estimated odds ratio (OR) for EwS rare variants adjusting for different model covariates.

		Wald method (unadjusted)		Mantel-Haenszel (study)		Mantel-Haenszel (study and variant1)
Rare SNP	Common SNP	OR (95% CI)	Fisher’s P-value	OR (95% CI)	P-value	OR (95% CI)	P-value
rs112837127	rs6106336	0.19 (0.10 to 0.39)	1.64×10−8	0.18 (0.08 to 0.37)	6.90×10−9	0.20 (0.09 to 0.40)	5.84×10−8
rs2296730	rs6106336, rs6047482	2.04 (1.58 to 2.69)	9.78×10−9	2.11 (1.60 to 2.77)	4.92×10−8	1.61 (1.16 to 2.24)	3.50×10−3

Models use a dominant allele coding for minor alleles with each individual as the analysis unit.

1Adjustment for contributing study and nearby common SNP(s).

To validate the imputed genotypes of the three associated low-frequency and rare variants, we first examined the imputation quality score (S1 Table) and distribution of alleles (S2 Table) across three studies populations, and we did not observe significant heterogeneity among the study populations. To further confirm the findings, an allele-specific TaqMan assay was designed for the three variants and carried out in a subset of 325 samples from the EwS GWAS with available remaining DNA. As shown in S2 Fig, we were able to replicate the imputed genotypes for rs112837127 and rs2296730 with 98.46% and 100% concordance rate. The imputed genotype for rs78119607 did not replicate as no minor alleles were called by the TaqMan assay, suggesting poor imputation of this variant using the 1000 Genomes Project reference set despite imputation scores of over 0.43 (S1 Table).

The two validated low frequency variants, rs112837127 and rs2296730, associated with EwS on chromosome 20 are in proximity to two previously identified EwS common susceptibility variants, rs6106336 and rs6047482. The identified low-frequency variants were tested for linkage disequilibrium (LD) with the common variants in 1000 Genomes Project European populations using the LDmatrix tool in LDlink (Fig 2) [30,31]. rs112837127 did not display evidence for LD with either the nearby common variant (R2EUR rs6106336 = 0.005, R2EUR rs6047482 = 0.023) or the other low-frequency variant (R2EUR rs2296730 = 0.003). However, rs2296730 displayed evidence for moderate levels of LD with the common rs6106336 variant (R2EUR = 0.311), but not the common rs6047482 variant (R2EUR = 0.006). Estimates of D′, a measure of allelic transmission, suggest the two associated low-frequency variants (rs112837127 and rs2296730) are transmitted on haplotypes of the common rs6106336 variant (S3 Fig), with the minor A allele of rs112837127 being transmitted with the major T allele of rs6106336 (D′EUR = 1.0) and the minor G allele of rs2296730 being transmitted with the minor G allele of the rs6106336 (D′EUR = 0.772).

Fig 2

Patterns of Linkage Disequilibrium (LD) for rare, low-frequency and common variants associated with EwS at the chromosome 20p11.22–23 susceptibility locus.

R2 values are in shades of red while D’ values are in shades of blue, with darker values indicating higher degree of LD. All LD measures were estimated in LDlink using 1,000 Genomes Project European populations as the reference panel.

To further test if the two low-frequency variants tagged independent EwS association signals, odds ratios and P-values for the association with EwS were calculated with and without conditioning on the neighboring common variants. Conditional analyses indicated that rs112837127 was statistically associated with EwS (OR = 0.20, 95%CI = 0.09–0.40, P-value = 5.84×10−8; Table 2) independent from neighboring common variants. As in the R2 analyses, the low-frequency rs22966730 variant demonstrated evidence for a correlation with the common rs6106336 variant as observed in the attenuated odds ratio estimate and increase in p-value in the conditional analysis (OR = 1.61, 95%CI = 1.16–2.24, P-value = 3.50×10−3; Table 2). Finally, we examined potential interaction between rs2296730 and rs6106336 (p = 0.568), rs2296730 and rs6047482 (p = 0.319), as well as rs6106336 and rs112837127 (p = 0.538) and found no significant evidence for SNP-SNP interactions.

Discussion

We report an analysis of well-imputed low-frequency variants based on common genotyped variants in a large EwS case series to investigate the contribution of low-frequency variants to the underlying genetic architecture of EwS susceptibility. We found evidence for associations of two low-frequency variants (rs112837127 and rs22966730) with EwS risk, and one of the variants, rs112837127, demonstrated an association independent of a nearby common germline susceptibility variant. Our findings suggest that in addition to common germline susceptibility variants, low-frequency variants are important for genetic susceptibility to EwS. Germline variants associated with lower cancer risk are less commonly reported, but not unheard of. Previously, three SNPs located near base excision repair genes were found to be negatively associated with Wilms tumor risk [32]. SNPs in the vitamin D receptor gene have also been linked to decreased risk in prostate cancer in African American men [33] and rs1866074 near the thymine DNA glycosylase gene were reported to be correlated with lower colorectal cancer risk [34]. The minor allele of rs112837127 is most prevalent in British and Finnish populations where the allele frequency could be > 5% while no African or east Asian population carries this allele [35]. This SNP is located in a long terminal repeat region 2.7 Kb upstream of a non-coding RNA, LINC00237, which has been found to drive self-renewal of tumor initiating cells by binding and promoting stability of β-catenin [36]. Interestingly, the activation of Wnt/β-catenin pathway has been shown to antagonize transcription activities of EWS/ETS fusion gene in Ewing sarcoma cells [37]. Whether the minor allele of rs112837127 tags a haplotype with modified LINC00237 expression remains to be investigated.

As EwS is a rare sarcoma of young people, it is not unexpected that low-frequency variation contributes to EwS susceptibility. Although EwS may be an exceptional case of a rare, well-defined malignancy with high associated odds ratios, our study suggests that efforts to examine low-frequency and rare germline associations in existing samples of rare cancer sets could be fruitful, even despite limited sample sizes. Additionally, our study provides an example in which common germline susceptibility loci discovered by GWAS may harbor synthetic associations with rare and low-frequency variants [28]. These synthetic associations may be of particular importance for EwS susceptibility as it is plausible common, low-frequency and rare variation at GGAA microsatellites may interact to impact binding of EWSR1-FLI1 fusion oncoproteins and alter regulation of downstream genes in core EwS regulatory pathways. In the case of EwS, common variant associations may highlight important EwS germline susceptibility regions where low-frequency and rare variation have important roles altering EwS risk. A limitation of our study is the lack of validation in an independent cohort as well as a lack of regional EwS sequencing of the relevant region to identify potential causal variants which can be functionally examined through in vitro experiments. Another limitation is the absence of clinical and demographic data which limited our ability to describe possible associations with the variants identified. As EwS is a rare tumor, few large case series exist for genomic investigation. Larger study populations will be essential for further confirmation of this new association. As future germline association studies investigate the genetic architecture of EwS, improved efforts to systematically interrogate low-frequency variant associations through a variety of sequencing and statistical methods are essential for accelerating understanding of the underlying genetic architecture of EwS susceptibility.

LDassoc regional association plots for identified rare and low-frequency variant associations with EwS susceptibility.

Plots are for rs78119607 (A), rs112837127 (B), and rs2296730 (C).

(DOCX)

Click here for additional data file.

Validation results of EwS associated rare and low-frequency variants by TaqMan assays.

(DOCX)

Click here for additional data file.

Linkage disequilibrium between the common variant rs6106336 and the two identified low frequency variants (A) rs112837127 and (B) rs2296730 using LDpair and all European 1,000 Genomes populations as a reference.

(DOCX)

Click here for additional data file.

Imputation quality scores for each associated low-frequency or rare variant by EwS imputation set.

(DOCX)

Click here for additional data file.

Distribution of alleles across three EwS study populations.

(DOCX)

Click here for additional data file.

15 May 2020

PONE-D-20-05330

Low-frequency variation near common germline susceptibility loci are associated with risk of Ewing sarcoma

PLOS ONE

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Reviewer #1: This paper presents genome-wide association studies of Ewing sarcoma. The authors identified two low-frequency variants, rs112837127 and rs2296730, on chromosome 20 that were associated with EwS risk (OR = 0.186 and 2.038, respectively; P-value < 5×10 -8). The work is meaningful to the cancer therapy, but there are some modifications.

1) Can the authors provide clinical indexes for the studies?

2) A statistical test should be made between genotypes and clinical indexes.

3) The manuscript lacks discussion section. Results section and discussion section should be separated.

4) A cox risk regression model should be made based on current result.

Reviewer #2: The author utilized imputed genotype data from several studies and identified two rare variants that may be associated with EwS. However, the analysis is not solid enough to support the conclusion and additional work is required to verify the findings.

1. The author used data from five sources and combine them together for the imputation and association test. However, the author didn’t mention how he/she combine the data together and how did he/she deal with the batch effects caused by different source of data.

2. The type I error is not estimated in the paper, which is necessary, especially for merging data from different sources.

3. It is also necessary to clarity the cutoff value that the author used to control the imputation quality.

Reviewer #3: Lin and colleagues present a GWAS in Ewing sarcoma identifying two rare variants within previously identified risk loci that modulate risk for this disease. The manuscript is well written and all methodologies used are appropriate, and these results represent an important contribution to our understanding of genetic susceptibility to this rare and deadly cancer.

I have a few minor comments and questions

1. Please note what program was used for statistical analysis

2. Please include a supplementary table showing basic demographic information for individuals included in this analysis, particularly the sex and age distributions as controls were obtained from separate source.

3. It would be helpful to include the total number of SNPs in the dataset after imputation.

4. While it is likely difficult to assess due to the small sample size, did you identify any variation in the allele frequencies of these rare SNPs by age? For example, were those diagnosed at younger ages more likely to be carriers of these SNPs?

5. Did you evaluate whether there was any interaction between these rare SNPs and previously identified common SNPs at these loci?

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29 Jun 2020

Reviewer #1:

This paper presents genome-wide association studies of Ewing sarcoma. The authors identified two low-frequency variants, rs112837127 and rs2296730, on chromosome 20 that were associated with EwS risk (OR = 0.186 and 2.038, respectively; P-value < 5×10 -8). The work is meaningful to the cancer therapy, but there are some modifications.

We thank Reviewer #1 for their time reviewing our manuscript. We agree that our manuscript is meaningful to Ewing sarcoma research and have addressed their comments with the below response and modifications.

1) Can the authors provide clinical indexes for the studies?

We agree with Reviewer #1 that it would be of interest to investigate clinical indices associated with Ewing sarcoma with these two reported variants (rs112837127 and rs2296730) to better understand potential clinical relationships. However, as Ewing sarcoma is a rare malignancy we had to collect cases from across many different recruitment centers and as such have very heterogeneous and limited clinical information for these participating Ewing sarcoma cases. Any effort to collect additional clinical information would require substantial new effort and likely would still result in high amounts of missing clinical information. Even with complete clinical information, association analyses would likely not be very fruitful as our case series of this rare tumor is limited in size and investigating clinical associations with rare variants can be challenging due to limited statistical power to detect associations.

To address this comment we have added the following text to the Methods section:

“We did not investigate associations with significant variants and clinical data as limited clinical data was available for the participating EwS cases.”

As well as the following text to the Discussion section:

“Another limitation is the absence of clinical and demographic data which limited our ability to describe possible associations with the variants identified.”

2) A statistical test should be made between genotypes and clinical indexes.

The main focus of our investigation is on susceptibility of EwS and as such we did not perform analyses on clinical indices. In general, genotype-based tests for low-frequency variants (<5% minor allele frequency) are challenging to perform as Ewing sarcoma is a rare tumor for which to amass a large sample size and a small fraction of individuals are expected to be homozygous for the rare allele (<0.25%). Please see above response for question 1 for additional details on the challenge of collecting clinical data. We agree that future such studies that address the relationship between germline susceptibility variants and clinical indices are needed, but at this time resources do not exist to establish such a study and perform these analyses.

To address this comment, we have added the following text to the Methods section:

“We did not investigate associations with significant variants and clinical data as limited clinical data was available for the participating EwS cases.”

As well as the following text to the Discussion section:

“Another limitation is the absence of clinical and demographic data which limited our ability to describe possible associations with the variants identified.”

3) The manuscript lacks discussion section. Results section and discussion section should be separated.

We thank Reviewer #1’s suggestion and have accordingly revised the manuscript to include separate Results and Discussion sections in our manuscript.

4) A cox risk regression model should be made based on current result.

We are unclear what Reviewer #1 is suggesting as this is a case-control study and we have no available time-to-event data for which to run a Cox model.

Reviewer #2:

The author utilized imputed genotype data from several studies and identified two rare variants that may be associated with EwS. However, the analysis is not solid enough to support the conclusion and additional work is required to verify the findings.

We thank Reviewer #2 for their time reviewing our manuscript and are appreciative of their thoughtful comments. Please see the below text for our detailed responses to their comments.

1. The author used data from five sources and combine them together for the imputation and association test. However, the author didn’t mention how he/she combine the data together and how did he/she deal with the batch effects caused by different source of data.

We agree with Reviewer #2 that it is of paramount importance to account for potential study or batch effects in our investigation, particularly in the analysis of our study. To ensure uniform imputation of our study, we only used high-quality genotypes as input for the imputation process and used the same 1000 Genomes Project reference panel for each study. We then employed a Mantel-Haenzel test which adjusts for difference among studies by estimating effects within each study strata and then combined the stratified results together into a combined estimate. This is a robust approach to account for potential batch differences by study. We have described the approach in the text of the Methods section:

“For each variant, we report an estimate of the odds ratio (OR), 95% confidence interval (CI), and P-value (pMH) using a Mantel-Haenszel Test where subjects are stratified by study (e.g. CCSS, Postel-Vinay, etc.)”

2. The type I error is not estimated in the paper, which is necessary, especially for merging data from different sources.

We agree with Reviewer #2 that adjusting for multiple comparisons in our analysis is important. As such, we have used a conservative Bonferroni-based cutoff of genome-wide significance defined as a p-value less than 5×10-8. This is an industry-based standard p-value threshold estimated on genome-wide LD patterns to ensure low false positive rates from genome-wide association studies. The merging of data from different sources in our analysis does not impact the type I error as the same number of variants are still being investigated, it simply boosts our power to better investigate whether each variant is associated as information from more cases and controls is available. Furthermore, the resulting independent associations of low-frequency variants rs112837127 and rs2296730 near the known Ewing sarcoma chromosome 20 susceptibility locus adds to the evidence that this region is important for Ewing sarcoma susceptibility and suggests that common and low-frequency germline variation interact in this region to impact Ewing sarcoma risk.

We have described the p-value significance threshold in our manuscript as follows:

“We used pMH < 5 × 10-8 to define initial GWAS significance and pMH < 0.05/1684=1.09×10-5 for conditional tests, where 1,684 is the number of SNPs with MAF < 0.05 and R2 > 0.004 with one of 6 previously identified SNPs.”

3. It is also necessary to clarity the cutoff value that the author used to control the imputation quality.

We did not filter SNPs by imputation quality scores, but we did examine the quality score for our candidate SNPs as shown in table S1 and described in the following text in results:

“To validate the imputed genotypes of the three associated low-frequency and rare variants, we first examined the imputation quality score (Supplementary Table 1) and distribution of alleles (Supplementary Table 2) across three studies populations, and we did not observe significant heterogeneity among the study populations.”

Reviewer #3:

Lin and colleagues present a GWAS in Ewing sarcoma identifying two rare variants within previously identified risk loci that modulate risk for this disease. The manuscript is well written and all methodologies used are appropriate, and these results represent an important contribution to our understanding of genetic susceptibility to this rare and deadly cancer.

We thank Reviewer #3 for their careful review of our manuscript and are pleased they found our manuscript to represent an important contribution to our understanding of Ewing sarcoma genetic susceptibility.

I have a few minor comments and questions

1. Please note what program was used for statistical analysis

The program we used is described as follows in the revised methods section:

“All statistical tests were two-sided and performed in R v.3.6.2 (28)”

2. Please include a supplementary table showing basic demographic information for individuals included in this analysis, particularly the sex and age distributions as controls were obtained from separate source.

We understand the interest in knowing additional demographic information from our study participants. Unfortunately, as Ewing sarcoma is a rare tumor it required the recruitment of participants across many years from multiple study centers that provided varying amounts of information on cases. As such, considerable effort would be needed to individually recover information from each participant that likely would not be fruitful in producing a complete dataset. As the main focus of our analysis is on genetic susceptibility to Ewing sarcoma, we feel the omission of this information does not significantly impact the results of our association analysis and goes beyond the original intended scope of our research question. Please also refer to our response for comment 1 from Reviewer #1.

3. It would be helpful to include the total number of SNPs in the dataset after imputation.

We appreciate Reviewer #3’s suggestion and have added the total number of SNPs after imputation to our manuscript. The following text in the Methods section details the SNPs included in our analysis:

“Genotype imputation was performed in three sets: (1) the Postel-Vinay study, (2) the CCSS EwS cases and matched controls, and (3) all remaining NCI and Institut Curie samples. All samples were pre-phased using SHAPEIT (25) and imputed using IMPUTE2 (26). The 1,000 Genomes Phase 3 was used as the reference (27) resulting in 16,367,531 SNPs. Among these SNPs, 10,216,839 were low-frequency variants with MAF < 0.05.”

4. While it is likely difficult to assess due to the small sample size, did you identify any variation in the allele frequencies of these rare SNPs by age? For example, were those diagnosed at younger ages more likely to be carriers of these SNPs?

As mentioned above, we did not have age at diagnosis for most cases so were unable to run this analysis. We agree with Reviewer #3 that even if we had this data this question would likely be difficult to assess due to the small sample size.

5. Did you evaluate whether there was any interaction between these rare SNPs and previously identified common SNPs at these loci?

We thank Reviewer #3’s insight and performed logistic regression using Ewing sarcoma diagnosis as outcome, and the SNPs of interest as predictors to test potential interaction between rs2296730 and rs6106336 (p=0.568), rs2296730 and rs6047482 (p=0.319), as well as rs6106336 and rs112837127 (p = 0.538). None of these combinations reached statistical significance suggesting no interaction between these SNPs and surrounding common SNPs associated with Ewing sarcoma. These results as well as methods has been described in the revised manuscript:

“Potential interaction between low frequency SNPs and common SNPs were examined by logistic regression models with case-control status as outcome, low frequency and common SNPs as well as an interaction term between them as predictors.” -- Methods

“Finally, we examined potential interaction between rs2296730 and rs6106336 (p=0.568), rs2296730 and rs6047482 (p=0.319), as well as rs6106336 and rs112837127 (p = 0.538) and found no significant evidence for SNP-SNP interactions.” -- Results

Submitted filename: Response to reviewers.docx

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4 Aug 2020

Low-frequency variation near common germline susceptibility loci are associated with risk of Ewing sarcoma

PONE-D-20-05330R1

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25 Aug 2020

PONE-D-20-05330R1

Low-frequency variation near common germline susceptibility loci are associated with risk of Ewing sarcoma

Dear Dr. Machiela:

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