Comparing Four Genome-Wide Association Study (GWAS) Programs with Varied Input Data Quantity

Many software packages have been developed for Genome-Wide Association Studies (GWAS) based on various statistical models. One key factor influencing the statistical reliability of GWAS is the amount of input data used. In this paper, we investigate how input data quantity influences output of four widely used GWAS programs, PLINK, TASSEL, GAPIT, and FaST-LMM, in the context of plant genomes and phenotypes. Both synthetic and real data are used. Evaluation is based on p- and q-values of output SNPs, and Kendall rank correlation between output SNP lists. Results show that for the same GWAS program, different Arabidopsis thaliana datasets demonstrate similar trends of rank correlation with varied input quantity, but differentiate on the numbers of SNPs passing a given p- or q-value threshold. We also show that variations in numbers of replicates influence the p-values of SNPs, but do not strongly affect the rank correlation.

IL28B single nucleotide polymorphisms in the treatment of hepatitis C

Introduction: As the most serious form of skin cancer, melanoma is a considerable public health burden. Genome wide association studies (GWAS) have successfully identified several loci important to melanoma susceptibility. Some of these loci have been associated with other cancers as well, similar to the pleiotropy previously demonstrated in the 8q24 and TERT regions. Such pleiotropy can highlight important regions and may help inform variant functionality. However, the potential pleiotropic effects of gene variants identified for other cancers have not been fully explored for melanoma. This study evaluates whether GWAS hits for melanoma, as well as hits for non-melanoma cancers, are associated with melanoma risk. Methods: We included 918 white melanoma cases and 4495 white controls in the Women's Health Initiative, which were genotyped as part of the Population Architecture using Genomics and Epidemiology (PAGE) study. We evaluated 8 single nucleotide polymorphisms (SNPs) previously shown to be associated with melanoma in GWAS as well as 175 SNPs previously shown to be associated with other cancers. These findings were mostly based on GWAS in Caucasian populations. We used logistic regression to evaluate the association between each SNP and melanoma risk, using an age-adjusted additive genetic model. Results: Of the 8 melanoma SNPs evaluated, 7 were statistically significantly associated with melanoma risk (p<0.05). These SNPs were rs1393350 (p=0.001), rs16891982 (p=3.34e-05), rs258322 (p=1.53e-9), rs4785763 (p=3.29e-4), rs7023329 (p=2.20e-5), rs910873 (p=2.38e-4), and rs4636294 (p=5.82e-5). For each of these significant SNPs, the association was in the same direction of risk as previously reported. The SNP that did not replicate was rs2284063 (p=0.38). Our analysis for pleotropic effects of other cancer GWAS hits did not yield any significant associations after adjustment for multiple comparisons (p=2.87e-4). The most significant SNP was rs710521 (p=0.006), previously associated with urinary bladder cancer. Conclusions: This preliminary analysis demonstrates that nearly all previously discovered melanoma SNPs replicated. Our analysis did not discover any non-melanoma cancer SNPs that are also associated with melanoma. The next step in this analysis will be to incorporate data from the other three PAGE studies: the Multiethnic Cohort (MEC), Epidemiologic Architecture of Genes Linked to Environment (EAGLE), and the CALiCo Consortium (see pagestudy.org). This will substantially improve the power of our pleiotropy analyses. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 868. doi:10.1158/1538-7445.AM2011-868

SNPpattern: A Genetic Tool to Derive Haplotype Blocks and Measure Genomic Diversity in Populations Using SNP Genotypes

BackgroundThe apparent effect of a single nucleotide polymorphism (SNP) on phenotype depends on the linkage disequilibrium (LD) between the SNP and a quantitative trait locus (QTL). However, the phase of LD between a SNP and a QTL may differ between Bos indicus and Bos taurus because they diverged at least one hundred thousand years ago. Here, we test the hypothesis that the apparent effect of a SNP on a quantitative trait depends on whether the SNP allele is inherited from a Bos taurus or Bos indicus ancestor.MethodsPhenotype data on one or more traits and SNP genotype data for 10 181 cattle from Bos taurus, Bos indicus and composite breeds were used. All animals had genotypes for 729 068 SNPs (real or imputed). Chromosome segments were classified as originating from B. indicus or B. taurus on the basis of the haplotype of SNP alleles they contained. Consequently, SNP alleles were classified according to their sub-species origin. Three models were used for the association study: (1) conventional GWAS (genome-wide association study), fitting a single SNP effect regardless of subspecies origin, (2) interaction GWAS, fitting an interaction between SNP and subspecies-origin, and (3) best variable GWAS, fitting the most significant combination of SNP and sub-species origin.ResultsFitting an interaction between SNP and subspecies origin resulted in more significant SNPs (i.e. more power) than a conventional GWAS. Thus, the effect of a SNP depends on the subspecies that the allele originates from. Also, most QTL segregated in only one subspecies, suggesting that many mutations that affect the traits studied occurred after divergence of the subspecies or the mutation became fixed or was lost in one of the subspecies.ConclusionsThe results imply that GWAS and genomic selection could gain power by distinguishing SNP alleles based on their subspecies origin, and that only few QTL segregate in both B. indicus and B. taurus cattle. Thus, the QTL that segregate in current populations likely resulted from mutations that occurred in one of the subspecies and can have both positive and negative effects on the traits. There was no evidence that selection has increased the frequency of alleles that increase body weight.

Background: We have developed a new breast cancer risk assessment model (OncoVue) that integrates information from 22 single nucleotide polymorphisms (SNPs) and 5 personal risk factors. It has been shown to effectively stratify risk in three independent validation populations. Genome wide association studies (GWAS) have identified seven candidate SNPs independently associated with breast cancer risk. Using estimates of relative risks and allele frequencies derived from these studies, a theoretical examination of these SNPs has concluded that they have the potential to modestly improve clinical risk estimation. We have examined both the OncoVue model and these GWAS SNPs in the same case-control population to determine their utility in risk estimation.Materials and Methods: A randomly selected subset of participants (376 cases and 982 controls) that had enrolled in a larger case-control study conducted in six distinct geographic regions of the United States were examined. DNAs were genotyped for the 22 SNP variants in OncoVue and combined with personal factors to calculate the risk scores for the individual participants. DNAs were also genotyped for the following GWAS SNPs: rs2981582 (FGFR2), rs3817198 (LSP1), rs889312 (MAP3K), rs4415084 (MRPS30), rs13281615 (POU5F1P1), rs13387042 (TNP1), and rs3803662 (TOX3, formerly TNRCR9). Association of individual GWAS SNP genotypes with breast cancer risk was evaluated by calculating odds ratios (ORs). Assuming independent contribution of each SNP to risk, a combined GWAS risk score was calculated using a multiplicative model. Positive likelihood ratios (PLRs) were calculated using a risk threshold of 1.5-fold the control population mean risk to evaluate the proportion of individuals placed at elevated risk by OncoVue compared to the combined GWAS risk scores.Results: OncoVue exhibited significant ability to stratify risk at the 1.5-fold mean with a PLR of 2.2 and at a risk level of 2.5-fold mean the PLR increased to 5.0. In analyses of individual GWAS SNPs, statistically significant associations with breast cancer were identified for homozygous carriers of the rare allele for the FGFR2, MAP3K and TOX3 genes. However, the PLR for the combined GWAS risk scores at 1.5-fold mean was 1.0 indicating no ability to discriminate risk in this study population.Conclusions: In this case-control population OncoVue effectively stratified risk by accurately assigning elevated risk to breast cancer cases. In the same population, a combined risk score produced from seven GWAS SNPs did not effectively stratify risk. These results indicate that the OncoVue model has clinical utility for identifying elevated risk women who might benefit from additional screening and prevention. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3177.

Introduction Chronic lymphocytic leukemia (CLL) is a B-cell malignancy that is known to have a familial component to disease risk. Although 31 loci have been found to be associated with CLL risk, the functional variant(s) driving these associations is mostly unknown. Here we set out to identify rare, highly-penetrant, cosegregating, susceptibility variants within the known GWAS discovered loci using whole exome sequencing (WES) data in CLL families from the Mayo Clinic family study of B-cell malignancies. Methods We performed WES on germ line DNA of 93 CLL families with two or more members with CLL, using Agilent capture kits and Illumina HiSeq2000. Bioinformatics analyses leveraged the following software packages: Novoalign, Picard, The Genome Analysis Toolkit (GATK), and the Biological Reference Repository (bioR). Quality control filters were implemented; subjects with mis-specified relationships were removed, as were variants with <75% call rate, <8X coverage, and those identified as sequencing artifacts. Each GWAS locus was defined by +/- 1Mb of the top GWAS hit within the locus. Linkage disequilibrium (LD) was calculated among the single nucleotide variants (SNVs) located within each locus. Potentially functional SNVs were identified based on: a) uncommon in public databases (< 5%), b) cosegregating in at least two CLL families, c) being highly conserved and in coding regions, and d) functional prediction status of deleterious (SIFT Score), damaging (PolyPhen Score), and a moderate, or high variant impact (SNP Effect). Results In our 93 CLL families, we sequenced 443 individuals: 160 with CLL, 73 with monoclonal B cell lymphocytosis (MBL), and 210 relatives that were not diagnosed with CLL or MBL at the time of sequencing. Median age of CLL diagnosis was 59 years (range 34-87), and 56% were male. Among the MBL individuals and relatives, the median age at recruitment was 55 years (range 18-93), and 40% were male. A total of 317,666 SNVs passed our sequencing quality control filters of which 10,731 were within +/- 1 Mb of known GWAS hits from 31 loci. Of these SNVs, 91% were in coding regions, 18% were reported to have high or moderate impact, 6% were estimated to be damaging and 6% were predicted to be deleterious. From these SNVs, we identified 76 putatively functional variants distributed across 25 GWAS loci that were cosegregating in the individuals with CLL or MBL in multiple CLL pedigrees. These SNVs were all located in coding regions with high or moderate impact and were predicted to be damaging and deleterious. Of these 76 variants, 56 had a frequency of <0.005 in 1000 Genomes’ European population while the remaining 20 had a frequency of 1%. Conclusions Through WES, we identified a number of rare, penetrant and potentially predisposing SNVs located within 25 of the 31 CLL GWAS-discovered loci. These segregating variants provide a list for future validation and functional studies. Citation Format: Sara Beiggi, Daniel R. O'Brien, Sara J. Achenbach, Kari G. Chaffee, Timothy G. Call, Neil E. Kay, Tait D. Shanafelt, Julie Cunningham, James R. Cerhan, Celine M. Vachon, Susan L. Slager. Cosegregating variants in chronic lymphocytic leukemia (CLL) families that are located in loci discovered by genome wide association studies (GWAS). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2764. doi:10.1158/1538-7445.AM2015-2764

For decades, the heritable contribution of cancer has been the subject of intense study, first pursued in families with multiple affected individuals and more recently investigated in unrelated population-based studies. For example, family studies have yielded important genes for breast cancer (BRCA1 and BRCA2), colon cancer (MSH2 and MLH1), and families with a spectrum of less common cancers (TP53). The pursuit of candidate genes yielded only a handful of conclusive associations, such as NAT2 and GSTM1 in bladder cancer or alcohol dehydrogenase genes (ADH1B and ADH7) in aerodigestive cancers. New genotyping technologies together with a comprehensive map of human haplotypes (International HapMap Project) have enabled investigators to scan across the genome in a sufficiently large set of cases and controls without a set of prior hypotheses in search of common susceptibility alleles with low effect sizes. Genome-wide association studies (GWAS) have emerged as an important tool for discovering regions of the genome using hundreds of thousands of common single nucleotide polymorphism (SNP) markers. This ‘agnostic’ approach has conclusively discovered more than 125 regions for nearly two dozen types of cancer. At least eight regions have been associated with more than one distinct cancer type. For instance, the region flanking the MYC oncogene on 8q24 harbors at least five independent loci associated with prostate cancer as well as loci associated with cancers of the breast, colon, bladder, ovaries, and chronic lymphocytic leukemia. On 5p15.33, there are at least seven cancers, namely, basal cell carcinoma, bladder, brain, lung (adenocarcinoma), melanoma, pancreas, and testicular that map to the TERT-CLPTM1L locus. This locus is notable for the telomerase gene (TERT) in which rare mutations have been associated with dyskeratosis congenital (an inherited bone marrow failure syndrome), idiopathic pulmonary fibrosis, acute myelogenous leukemia, and chronic lymphocytic leukemia. The number of variants identified by GWAS per cancer varies greatly, suggesting different underlying genetic architectures, namely genetic contributions to common and uncommon cancers. Of the 125 regions associated with cancer risk, none of the regions have been conclusively associated with prognosis. In prostate cancer, there are at least 35 distinct loci harboring common susceptibility alleles identified by GWAS yet not a single one clearly distinguishes between aggressive and nonaggressive disease. This observation suggests that there could be regions that distinctly contribute to cancer risk and cancer outcomes (e.g., aggressive or metastatic disease). For lung cancer, a disease strongly driven by exposure to tobacco products, so far, only three or four regions have been conclusively established. Of these regions, the signal on chromosome 15 could be related to smoking. For nearly all common SNP markers (namely those with a minor allele frequency of greater than 5%), the estimated effect sizes are below 1.5. A notable exception is for testicular cancer in which variants on chromosome 12 near the KITLG gene have estimated effect sizes of greater than 2.5 for heterozygotes; this is not surprising since the hereditary component of testicular cancer is very strong. Overall, each region confers a small contribution to the risk for cancer, which suggests that it is daunting to consider any single SNP as a clinical test. GWAS regions are undergoing fine mapping to nominate the optimal candidates for functional studies designed to explain the direct association. Nearly all GWAS signals have mapped to noncoding regions, underscoring the contribution of regulatory mechanisms in neighboring genes (e.g., FGFR2 and breast cancer, MSMB/NCOA4 and prostate cancer). The plausibility of the underlying association can form the foundation for developing new strategies to intervene or diagnose cancer at an earlier stage by providing new insights into biological mechanisms that become targets. The application of GWAS to therapeutic outcomes and toxicities is beginning to discover new regions worthy of follow-up studies. However, for pharmacogenomics to eventually have an impact in the clinic, there will have to be a transition from discovery to characterization of the regions that ultimately yields targets for therapy and chemoprevention. Since we are still early in the discovery of common genetic variants associated with risk for specific cancers, it is not surprising that the utility of applying common SNPs to disease prediction is premature. Common genetic variants represent only a proportion of genetic variants that contribute to disease risk; uncommon, rare and copy number variants will undoubtedly contribute to risk in both familial and unrelated settings, and their relative contributions are expected to vary by cancer site. The addition of common SNP markers to classical epidemiological risk factors for breast cancer has minimally shifted the receiver operator curve (ROC) from 50% to approximately 60%. For prostate cancer, a disease in which epidemiological studies have identified age, ethnic background, and family history as the only conclusive risk factors, it is not surprising that more than 30 common SNPs shift the ROC curve to roughly 65%. Discovery of additional genetic variants should improve the risk prediction but each will be limited by the overall heredity of the cancer. New approaches towards applying common genetic variants could be beneficial in the reclassification of individuals based on genetic risk profiles or alternatively, the identification of subsets of individuals at high risk who might undergo a diagnostic or therapeutic intervention. In conclusion, the fruits of GWAS have important implications for investigating the etiology of different cancers, particularly as it relates to gene-environment and gene-gene interactions. The complex genomic architecture of disease susceptibility will require further discovery of uncommon and rare variants using next-generation sequence technologies. It will be critical to assess the applicability of genetic tests in specific clinical settings, such as when to perform screening tests with calculable risks (e.g., biopsies or chemoprevention) before incorporating SNPs into clinical practice. To incorporate the fruits of current genomic observations, new studies will need to be designed to validate the utility of known genetic variants in assessing risk for cancer as well as its outcomes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr PL01-02. doi:10.1158/1538-7445.AM2011-PL01-02

Introduction: Bladder Cancer (BC) is the 6th most common cancer and the 9th leading cause of cancer death among men worldwide. Tobacco smoking is the main risk factor for BC. Accumulating evidence has found that genetic variants are also associated with the risk of BC. More insights into the biological mechanism of the disease have been gained through candidate gene-environment interaction studies, with the most known examples of the interactions between cigarette smoking and NAT2 and GSTM1 gene variants. Our objective was to perform a genome-wide association case-only study using the French national prospective COBLAnCE cohort (COhort to study BLAdder CancEr), while focusing on three important smoking behavior characteristics. Materials and Methods: The COBLAnCE cohort comprises 1,800 BC patients included within 14 centers in France between 2012 and 2018. Peripheral blood samples and complete history of tobacco smoking were collected at enrollment and genotyping was performed using the Illumina Global Screening Array with a Multi-Disease drop-in panel. After the quality control and imputation, 9,719,614 Single Nucleotide Polymorphisms (SNPs) were investigated. Genotyping data of 1,674, 1,283 and 1,342 patients were analyzed for smoking status, average tobacco consumption and age at smoking initiation, respectively. Genome-wide association study (GWAS) was conducted using logistic (for smoking status) and linear (for age at smoking initiation and average tobacco consumption, both log-transformed) regression models with each of the imputed SNPs as the independent variable adjusting for gender, age and first ten genetic principal components. Results: For smoking status, none of the SNPs reached the GWAS significant threshold of 5e-08, but results are suggesting tentative loci at 1p31.3 (rs114073636: OR [95%CI] = 0.31 [0.20-0.47], p = 8.68e-08; rs116571608: OR = 0.31 [0.20-0.47], p = 8.87e-08) and 4q22.1 (rs542541627: OR = 1.69 [1.39-2.06], p = 1.81e-07; rs1533294: OR = 1.63 [1.34-1.98], p = 1.01e-06). Analyses of age at smoking initiation revealed three significant SNPs: rs531756449, rs77186197 and rs78947799 with positive interaction associations (β (SE) = 0.3 (0.05), p = 8.26e-09; β = 0.29 (0.05), p = 3.74e-09; β = 0.31 (0.06), p = 3.97e-08, respectively). Only one SNP at 11q24.1, reached the significant threshold for the average tobacco consumption analyses: rs2714069 (β = 0.63 (0.11), p = 1.35e-08). The locus 16p13.3 seems to have protective variants for the interaction with average tobacco consumption: rs113683380 (β = -0.66 (0.12), p = 6.44e-08) and rs113590624 (β = -0.64 (0.12), p = 2.74e-07). Conclusion: Our study suggests new candidate loci interacting with smoking behavior for the risk of BC. Based on a case-only approach, our results need to be validated in a case-control or cohort study. A large-scale meta-analysis GWAS would be needed to identify SNPs with small Minor Allele Frequencies. Citation Format: Maryam Karimi, Sebastian Mendez-Pineda, Hélène Blanché, Anne Boland, Jean-François Deleuze, Xiang-Yu Meng, Karine Groussard, Thierry Lebret, Julia Bonastre, Yves Allory, François Radvanyi, Simone Benhamou, Stefan Michiels. Genome-wide interaction study of smoking and bladder cancer risk: results from the COBLAnCE cohort. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5224.

We examine improvements to the linear mixed model (LMM) that better correct for population structure and family relatedness in genome-wide association studies (GWAS). LMMs rely on the estimation of a genetic similarity matrix (GSM), which encodes the pairwise similarity between every two individuals in a cohort. These similarities are estimated from single nucleotide polymorphisms (SNPs) or other genetic variants. Traditionally, all available SNPs are used to estimate the GSM. In empirical studies across a wide range of synthetic and real data, we find that modifications to this approach improve GWAS performance as measured by type I error control and power. Specifically, when only population structure is present, a GSM constructed from SNPs that well predict the phenotype in combination with principal components as covariates controls type I error and yields more power than the traditional LMM. In any setting, with or without population structure or family relatedness, a GSM consisting of a mixture of two component GSMs, one constructed from all SNPs and another constructed from SNPs that well predict the phenotype again controls type I error and yields more power than the traditional LMM. Software implementing these improvements and the experimental comparisons are available at http://microsoft.com/science.

Hundreds of thousands of single nucleotide polymorphisms (SNPs) are available for genome-wide association (GWA) studies nowadays. The epistatic interactions of SNPs are believed to be very important in determining individual susceptibility to complex diseases. However, existing methods for SNP interaction discovery either suffer from high computation complexity or perform poorly when marginal effects of disease loci are weak or absent. Hence, it is desirable to develop an effective method to search epistatic interactions in genome-wide scale. We propose a new method SNPHarvester to detect SNP-SNP interactions in GWA studies. SNPHarvester creates multiple paths in which the visited SNP groups tend to be statistically associated with diseases, and then harvests those significant SNP groups which pass the statistical tests. It greatly reduces the number of SNPs. Consequently, existing tools can be directly used to detect epistatic interactions. By using a wide range of simulated data and a real genome-wide data, we demonstrate that SNPHarvester outperforms its recent competitor significantly and is promising for practical disease prognosis. http://bioinformatics.ust.hk/SNPHarvester.html.

The missing heritability has been a major problem in the analysis of best linear unbiased prediction (BLUP). We introduced the traditional genome-wide association study (GWAS) into the BLUP to improve the heritability estimation. We analyzed eight pork quality traits of the Berkshire breeds using GWAS and BLUP. GWAS detects the putative quantitative trait loci regions given traits. The single nucleotide polymorphisms (SNPs) were obtained using GWAS results with p value <0.01. BLUP analyzed with significant SNPs was much more accurate than that with total genotyped SNPs in terms of narrow-sense heritability. It implies that genomic estimated breeding values (GEBVs) of pork quality traits can be calculated by BLUP via GWAS. The GWAS model was the linear regression using PLINK and BLUP model was the G-BLUP and SNP-GBLUP. The SNP-GBLUP uses SNP-SNP relationship matrix. The BLUP analysis using preprocessing of GWAS can be one of the possible alternatives of solving the missing heritability problem and it can provide alternative BLUP method which can find more accurate GEBVs.

Gene-guided therapy for catheter-ablation of atrial fibrillation: are we there yet?

Unlike tumors that develop because of somatic genetic alterations such as frequent coding mutations or chromosomal deletions, amplifications or translocations, some tumors are addicted to expression of certain genes for their sustained proliferation and survival - for example, LIM domain only 1 (LMO1) expression in neuroblastoma (NB). Numerous genome-wide association studies (GWAS) have identified significant associations between germline single-nucleotide polymorphisms (SNPs), many in the non-coding genome, and cancer. A robust genome-wide association has been reported previously between an intronic LMO1 SNP and NB susceptibility. By further sequencing and epigenetic fine mapping of the SNP locus, it was demonstrated that the causal SNP is part of a transcription factor binding site within an enhancer element, regulates expression of LMO1 and in turn increases the tumorigenic potential. To identify additional examples of regulatory SNPs as cancer drivers, we overlaid published genome-wide significant cancer associations with active chromatin marks from Encyclopedia of DNA Elements and searched for SNPs that resided within gene regulatory elements. To map these SNPs to candidate genes and determine direction of effect, we co-localized GWAS signals with expression quantitative trait (eQTL) signals from the Genotype-Tissue Expression (GTEx) Consortium database. Lastly, we checked for gene amplification and/or overexpression of the mapped genes in the Cancer Genome Atlas (TCGA) data to identify a set of target genes that not only exhibit significant cancer association in GWAS, but also have evidence for epigenetic regulation and propensity for amplification and/or overexpression in tumors. We identified more than 25 novel cancer-target pairs with strong germline, regulatory and somatic evidence. A look up through synthetic lethality screen data available in-house suggested that several of these targets are self-lethal, further underscoring their importance for cancer cell proliferation and survival. Additional in vitro experiments are being planned to further validate the targets. Citation Format: Diptee A. Kulkarni, Karl Guo, Junping Jing, Mugdha Khaladkar, Kijoung Song, Coco Dong, David Cooper, Benjamin Schwartz. Identification of novel cancer target genes by combining data from the cancer genome-wide association studies (GWAS), regulatory DNA elements and The Cancer Genome Atlas (TCGA) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 236.

Combined Donor and Recipient Non-HLA Genotypes Show Evidence of Genome Wide Association with Transplant Related Mortality (TRM) after HLA-Matched Unrelated Donor Blood and Marrow Transplantation (URD-BMT) (DISCOVeRY-BMT study)