Publications by authors named "Stephanie A Bien"

33 Publications

Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations.

Genes (Basel) 2021 Jul 8;12(7). Epub 2021 Jul 8.

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.

Background: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed.

Methods: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including = 229 African American and = 381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African ( = 27,955) and Hispanic/Latino ( = 28,324) ancestry participants.

Results: Our results revealed 24 suggestive signals ( < 1 × 10) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study ( = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN.

Conclusions: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.
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http://dx.doi.org/10.3390/genes12071049DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8307403PMC
July 2021

Multi-ethnic genome-wide association analyses of white blood cell and platelet traits in the Population Architecture using Genomics and Epidemiology (PAGE) study.

BMC Genomics 2021 Jun 9;22(1):432. Epub 2021 Jun 9.

Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Background: Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented.

Results: We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance (P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance (P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations.

Conclusions: Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.
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http://dx.doi.org/10.1186/s12864-021-07745-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191001PMC
June 2021

Genetic architectures of proximal and distal colorectal cancer are partly distinct.

Gut 2021 Jul 25;70(7):1325-1334. Epub 2021 Feb 25.

Cancer Prevention and Control Program, Catalan Institute of Oncology - IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.

Objective: An understanding of the etiologic heterogeneity of colorectal cancer (CRC) is critical for improving precision prevention, including individualized screening recommendations and the discovery of novel drug targets and repurposable drug candidates for chemoprevention. Known differences in molecular characteristics and environmental risk factors among tumors arising in different locations of the colorectum suggest partly distinct mechanisms of carcinogenesis. The extent to which the contribution of inherited genetic risk factors for CRC differs by anatomical subsite of the primary tumor has not been examined.

Design: To identify new anatomical subsite-specific risk loci, we performed genome-wide association study (GWAS) meta-analyses including data of 48 214 CRC cases and 64 159 controls of European ancestry. We characterised effect heterogeneity at CRC risk loci using multinomial modelling.

Results: We identified 13 loci that reached genome-wide significance (p<5×10) and that were not reported by previous GWASs for overall CRC risk. Multiple lines of evidence support candidate genes at several of these loci. We detected substantial heterogeneity between anatomical subsites. Just over half (61) of 109 known and new risk variants showed no evidence for heterogeneity. In contrast, 22 variants showed association with distal CRC (including rectal cancer), but no evidence for association or an attenuated association with proximal CRC. For two loci, there was strong evidence for effects confined to proximal colon cancer.

Conclusion: Genetic architectures of proximal and distal CRC are partly distinct. Studies of risk factors and mechanisms of carcinogenesis, and precision prevention strategies should take into consideration the anatomical subsite of the tumour.
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http://dx.doi.org/10.1136/gutjnl-2020-321534DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223655PMC
July 2021

A general framework for functionally informed set-based analysis: Application to a large-scale colorectal cancer study.

PLoS Genet 2020 08 24;16(8):e1008947. Epub 2020 Aug 24.

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants associated with various phenotypes, but together they explain only a fraction of heritability, suggesting many variants have yet to be discovered. Recently it has been recognized that incorporating functional information of genetic variants can improve power for identifying novel loci. For example, S-PrediXcan and TWAS tested the association of predicted gene expression with phenotypes based on GWAS summary statistics by leveraging the information on genetic regulation of gene expression and found many novel loci. However, as genetic variants may have effects on more than one gene and through different mechanisms, these methods likely only capture part of the total effects of these variants. In this paper, we propose a summary statistics-based mixed effects score test (sMiST) that tests for the total effect of both the effect of the mediator by imputing genetically predicted gene expression, like S-PrediXcan and TWAS, and the direct effects of individual variants. It allows for multiple functional annotations and multiple genetically predicted mediators. It can also perform conditional association analysis while adjusting for other genetic variants (e.g., known loci for the phenotype). Extensive simulation and real data analyses demonstrate that sMiST yields p-values that agree well with those obtained from individual level data but with substantively improved computational speed. Importantly, a broad application of sMiST to GWAS is possible, as only summary statistics of genetic variant associations are required. We apply sMiST to a large-scale GWAS of colorectal cancer using summary statistics from ∼120, 000 study participants and gene expression data from the Genotype-Tissue Expression (GTEx) project. We identify several novel and secondary independent genetic loci.
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http://dx.doi.org/10.1371/journal.pgen.1008947DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7470748PMC
August 2020

Association of Body Mass Index With Colorectal Cancer Risk by Genome-Wide Variants.

J Natl Cancer Inst 2021 01;113(1):38-47

Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel.

Background: Body mass index (BMI) is a complex phenotype that may interact with genetic variants to influence colorectal cancer risk.

Methods: We tested multiplicative statistical interactions between BMI (per 5 kg/m2) and approximately 2.7 million single nucleotide polymorphisms with colorectal cancer risk among 14 059 colorectal cancer case (53.2% women) and 14 416 control (53.8% women) participants. All analyses were stratified by sex a priori. Statistical methods included 2-step (ie, Cocktail method) and single-step (ie, case-control logistic regression and a joint 2-degree of freedom test) procedures. All statistical tests were two-sided.

Results: Each 5 kg/m2 increase in BMI was associated with higher risks of colorectal cancer, less so for women (odds ratio [OR] = 1.14, 95% confidence intervals [CI] = 1.11 to 1.18; P = 9.75 × 10-17) than for men (OR = 1.26, 95% CI = 1.20 to 1.32; P = 2.13 × 10-24). The 2-step Cocktail method identified an interaction for women, but not men, between BMI and a SMAD7 intronic variant at 18q21.1 (rs4939827; Pobserved = .0009; Pthreshold = .005). A joint 2-degree of freedom test was consistent with this finding for women (joint P = 2.43 × 10-10). Each 5 kg/m2 increase in BMI was more strongly associated with colorectal cancer risk for women with the rs4939827-CC genotype (OR = 1.24, 95% CI = 1.16 to 1.32; P = 2.60 × 10-10) than for women with the CT (OR = 1.14, 95% CI = 1.09 to 1.19; P = 1.04 × 10-8) or TT (OR = 1.07, 95% CI = 1.01 to 1.14; P = .02) genotypes.

Conclusion: These results provide novel insights on a potential mechanism through which a SMAD7 variant, previously identified as a susceptibility locus for colorectal cancer, and BMI may influence colorectal cancer risk for women.
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http://dx.doi.org/10.1093/jnci/djaa058DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781451PMC
January 2021

Minority-centric meta-analyses of blood lipid levels identify novel loci in the Population Architecture using Genomics and Epidemiology (PAGE) study.

PLoS Genet 2020 03 30;16(3):e1008684. Epub 2020 Mar 30.

Department of Medicine, University of Washington Medical Center, Seattle, Washington, United States of America.

Lipid levels are important markers for the development of cardio-metabolic diseases. Although hundreds of associated loci have been identified through genetic association studies, the contribution of genetic factors to variation in lipids is not fully understood, particularly in U.S. minority groups. We performed genome-wide association analyses for four lipid traits in over 45,000 ancestrally diverse participants from the Population Architecture using Genomics and Epidemiology (PAGE) Study, followed by a meta-analysis with several European ancestry studies. We identified nine novel lipid loci, five of which showed evidence of replication in independent studies. Furthermore, we discovered one novel gene in a PrediXcan analysis, minority-specific independent signals at eight previously reported loci, and potential functional variants at two known loci through fine-mapping. Systematic examination of known lipid loci revealed smaller effect estimates in African American and Hispanic ancestry populations than those in Europeans, and better performance of polygenic risk scores based on minority-specific effect estimates. Our findings provide new insight into the genetic architecture of lipid traits and highlight the importance of conducting genetic studies in diverse populations in the era of precision medicine.
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http://dx.doi.org/10.1371/journal.pgen.1008684DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145272PMC
March 2020

Ancestry-specific associations identified in genome-wide combined-phenotype study of red blood cell traits emphasize benefits of diversity in genomics.

BMC Genomics 2020 Mar 14;21(1):228. Epub 2020 Mar 14.

University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC, 27599, USA.

Background: Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population.

Results: We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP.

Conclusion: This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.
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http://dx.doi.org/10.1186/s12864-020-6626-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071748PMC
March 2020

Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations.

PLoS Genet 2019 12 23;15(12):e1008500. Epub 2019 Dec 23.

Genomics Platform, Broad Institute, Cambridge, Massachusetts, United States of America.

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.
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http://dx.doi.org/10.1371/journal.pgen.1008500DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953885PMC
December 2019

Cumulative Burden of Colorectal Cancer-Associated Genetic Variants Is More Strongly Associated With Early-Onset vs Late-Onset Cancer.

Gastroenterology 2020 04 19;158(5):1274-1286.e12. Epub 2019 Dec 19.

Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington; Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington.

Background & Aims: Early-onset colorectal cancer (CRC, in persons younger than 50 years old) is increasing in incidence; yet, in the absence of a family history of CRC, this population lacks harmonized recommendations for prevention. We aimed to determine whether a polygenic risk score (PRS) developed from 95 CRC-associated common genetic risk variants was associated with risk for early-onset CRC.

Methods: We studied risk for CRC associated with a weighted PRS in 12,197 participants younger than 50 years old vs 95,865 participants 50 years or older. PRS was calculated based on single nucleotide polymorphisms associated with CRC in a large-scale genome-wide association study as of January 2019. Participants were pooled from 3 large consortia that provided clinical and genotyping data: the Colon Cancer Family Registry, the Colorectal Transdisciplinary Study, and the Genetics and Epidemiology of Colorectal Cancer Consortium and were all of genetically defined European descent. Findings were replicated in an independent cohort of 72,573 participants.

Results: Overall associations with CRC per standard deviation of PRS were significant for early-onset cancer, and were stronger compared with late-onset cancer (P for interaction = .01); when we compared the highest PRS quartile with the lowest, risk increased 3.7-fold for early-onset CRC (95% CI 3.28-4.24) vs 2.9-fold for late-onset CRC (95% CI 2.80-3.04). This association was strongest for participants without a first-degree family history of CRC (P for interaction = 5.61 × 10). When we compared the highest with the lowest quartiles in this group, risk increased 4.3-fold for early-onset CRC (95% CI 3.61-5.01) vs 2.9-fold for late-onset CRC (95% CI 2.70-3.00). Sensitivity analyses were consistent with these findings.

Conclusions: In an analysis of associations with CRC per standard deviation of PRS, we found the cumulative burden of CRC-associated common genetic variants to associate with early-onset cancer, and to be more strongly associated with early-onset than late-onset cancer, particularly in the absence of CRC family history. Analyses of PRS, along with environmental and lifestyle risk factors, might identify younger individuals who would benefit from preventive measures.
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http://dx.doi.org/10.1053/j.gastro.2019.12.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7103489PMC
April 2020

Genetic associations of breast and prostate cancer are enriched for regulatory elements identified in disease-related tissues.

Hum Genet 2019 Oct 22;138(10):1091-1104. Epub 2019 Jun 22.

Department of Epidemiology, University of Washington, Seattle, WA, USA.

Although genome-wide association studies (GWAS) have identified hundreds of risk loci for breast and prostate cancer, only a few studies have characterized the GWAS association signals across functional genomic annotations with a particular focus on single nucleotide polymorphisms (SNPs) located in DNA regulatory elements. In this study, we investigated the enrichment pattern of GWAS signals for breast and prostate cancer in genomic functional regions located in normal tissue and cancer cell lines. We quantified the overall enrichment of SNPs with breast and prostate cancer association p values < 1 × 10 across regulatory categories. We then obtained annotations for DNaseI hypersensitive sites (DHS), typical enhancers, and super enhancers across multiple tissue types, to assess if significant GWAS signals were selectively enriched in annotations found in disease-related tissue. Finally, we quantified the enrichment of breast and prostate cancer SNP heritability in regulatory regions, and compared the enrichment pattern of SNP heritability with GWAS signals. DHS, typical enhancers, and super enhancers identified in the breast cancer cell line MCF-7 were observed with the highest enrichment of genome-wide significant variants for breast cancer. For prostate cancer, GWAS signals were mostly enriched in DHS and typical enhancers identified in the prostate cancer cell line LNCaP. With progressively stringent GWAS p value thresholds, an increasing trend of enrichment was observed for both diseases in DHS, typical enhancers, and super enhancers located in disease-related tissue. Results from heritability enrichment analysis supported the selective enrichment pattern of functional genomic regions in disease-related cell lines for both breast and prostate cancer. Our results suggest the importance of studying functional annotations identified in disease-related tissues when characterizing GWAS results, and further demonstrate the role of germline DNA regulatory elements from disease-related tissue in breast and prostate carcinogenesis.
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http://dx.doi.org/10.1007/s00439-019-02041-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6745259PMC
October 2019

Genetic analyses of diverse populations improves discovery for complex traits.

Nature 2019 06 19;570(7762):514-518. Epub 2019 Jun 19.

Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.
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http://dx.doi.org/10.1038/s41586-019-1310-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6785182PMC
June 2019

DNA repair and cancer in colon and rectum: Novel players in genetic susceptibility.

Int J Cancer 2020 01 4;146(2):363-372. Epub 2019 Jul 4.

Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA.

Interindividual differences in DNA repair systems may play a role in modulating the individual risk of developing colorectal cancer. To better ascertain the role of DNA repair gene polymorphisms on colon and rectal cancer risk individually, we evaluated 15,419 single nucleotide polymorphisms (SNPs) within 185 DNA repair genes using GWAS data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), which included 8,178 colon cancer, 2,936 rectum cancer cases and 14,659 controls. Rs1800734 (in MLH1 gene) was associated with colon cancer risk (p-value = 3.5 × 10 ) and rs2189517 (in RAD51B) with rectal cancer risk (p-value = 5.7 × 10 ). The results had statistical significance close to the Bonferroni corrected p-value of 5.8 × 10 . Ninety-four SNPs were significantly associated with colorectal cancer risk after Binomial Sequential Goodness of Fit (BSGoF) procedure and confirmed the relevance of DNA mismatch repair (MMR) and homologous recombination pathways for colon and rectum cancer, respectively. Defects in MMR genes are known to be crucial for familial form of colorectal cancer but our findings suggest that specific genetic variations in MLH1 are important also in the individual predisposition to sporadic colon cancer. Other SNPs associated with the risk of colon cancer (e.g., rs16906252 in MGMT) were found to affect mRNA expression levels in colon transverse and therefore working as possible cis-eQTL suggesting possible mechanisms of carcinogenesis.
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http://dx.doi.org/10.1002/ijc.32516DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7301215PMC
January 2020

Moving from one to many: insights from the growing list of pleiotropic cancer risk genes.

Br J Cancer 2019 06 21;120(12):1087-1089. Epub 2019 May 21.

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.

Pleiotropy, a phenomenon in which a single gene affects multiple phenotypes, is becoming very common among different cancer types and cancer-related phenotypes, such as those in hormonal, cardiometabolic and inflammatory/immune conditions. The discovery of pleiotropic associations can improve our understanding of cancer and help to target investigation of genes with greater clinical relevance.
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http://dx.doi.org/10.1038/s41416-019-0475-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6738109PMC
June 2019

Colorectal cancer susceptibility variants and risk of conventional adenomas and serrated polyps: results from three cohort studies.

Int J Epidemiol 2020 02;49(1):259-269

Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Background: Increasing evidence suggests that conventional adenomas (CAs) and serrated polyps (SPs) represent two distinct groups of precursor lesions for colorectal cancer (CRC). The influence of common genetic variants on risk of CAs and SPs remain largely unknown.

Methods: Among 27 426 participants within three prospective cohort studies, we created a weighted genetic risk score (GRS) based on 40 CRC-related single nucleotide polymorphisms (SNPs) identified in previous genome-wide association studies; and we examined the association of GRS (per one standard deviation increment) with risk of CAs, SPs and synchronous CAs and SPs, by multivariable logistic regression. We also analysed individual variants in the secondary analysis.

Results: During 18-20 years of follow-up, we documented 2952 CAs, 1585 SPs and 794 synchronous CAs and SPs. Higher GRS was associated with increased risk of CAs [odds ratio (OR) = 1.17, 95% confidence interval (CI): 1.12-1.21] and SPs (OR = 1.09, 95% CI: 1.03-1.14), with a stronger association for CAs than SPs (Pheterogeneity=0.01). An even stronger association was found for patients with synchronous CAs and SPs (OR = 1.32), advanced CAs (OR = 1.22) and multiple CAs (OR = 1.25). Different sets of variants were associated with CAs and SPs, with a Spearman correlation coefficient of 0.02 between the ORs associating the 40 SNPs with the two lesions. After correcting for multiple testing, three variants were associated with CAs (rs3802842, rs6983267 and rs7136702) and two with SPs (rs16892766 and rs4779584).

Conclusions: Common genetic variants play a potential role in the conventional and serrated pathways of CRC. Different sets of variants are identified for the two pathways, further supporting the aetiological heterogeneity of CRC.
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http://dx.doi.org/10.1093/ije/dyz096DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7426026PMC
February 2020

The Future of Genomic Studies Must Be Globally Representative: Perspectives from PAGE.

Annu Rev Genomics Hum Genet 2019 08 12;20:181-200. Epub 2019 Apr 12.

Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA; email:

The past decade has seen a technological revolution in human genetics that has empowered population-level investigations into genetic associations with phenotypes. Although these discoveries rely on genetic variation across individuals, association studies have overwhelmingly been performed in populations of European descent. In this review, we describe limitations faced by single-population studies and provide an overview of strategies to improve global representation in existing data sets and future human genomics research via diversity-focused, multiethnic studies. We highlight the successes of individual studies and meta-analysis consortia that have provided unique knowledge. Additionally, we outline the approach taken by the Population Architecture Using Genomics and Epidemiology (PAGE) study to develop best practices for performing genetic epidemiology in multiethnic contexts. Finally, we discuss how limiting investigations to single populations impairs findings in the clinical domain for both rare-variant identification and genetic risk prediction.
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http://dx.doi.org/10.1146/annurev-genom-091416-035517DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7012212PMC
August 2019

Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer.

Hum Genet 2019 Apr 28;138(4):307-326. Epub 2019 Feb 28.

Ontario Institute for Cancer Research, Toronto, ON, Canada.

Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10, replication P = 0.01), and PYGL (discovery P = 2.3 × 10, replication P = 6.7 × 10). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci.
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http://dx.doi.org/10.1007/s00439-019-01989-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6483948PMC
April 2019

Shared heritability and functional enrichment across six solid cancers.

Nat Commun 2019 01 25;10(1):431. Epub 2019 Jan 25.

Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Calle de Melchor Fernández Almagro, 3, 28029, Madrid, Spain.

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r = 0.57, p = 4.6 × 10), breast and ovarian cancer (r = 0.24, p = 7 × 10), breast and lung cancer (r = 0.18, p =1.5 × 10) and breast and colorectal cancer (r = 0.15, p = 1.1 × 10). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.
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http://dx.doi.org/10.1038/s41467-018-08054-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347624PMC
January 2019

Discovery of common and rare genetic risk variants for colorectal cancer.

Nat Genet 2019 01 3;51(1):76-87. Epub 2018 Dec 3.

Department of Epidemiology, German Institute of Human Nutrition (DIfE), Potsdam-Rehbrücke, Germany.

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P < 5 × 10, bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development.
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http://dx.doi.org/10.1038/s41588-018-0286-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358437PMC
January 2019

Mendelian randomisation study of age at menarche and age at menopause and the risk of colorectal cancer.

Br J Cancer 2018 06 24;118(12):1639-1647. Epub 2018 May 24.

Department of Family Medicine and Community Health, Case Western Reserve University, Cleveland, OH, 44106, USA.

Background: Substantial evidence supports an association between use of menopausal hormone therapy and decreased colorectal cancer (CRC) risk, indicating a role of exogenous sex hormones in CRC development. However, findings on endogenous oestrogen exposure and CRC are inconsistent.

Methods: We used a Mendelian randomisation approach to test for a causal effect of age at menarche and age at menopause as surrogates for endogenous oestrogen exposure on CRC risk. Weighted genetic risk scores based on 358 single-nucleotide polymorphisms associated with age at menarche and 51 single-nucleotide polymorphisms associated with age at menopause were used to estimate the association with CRC risk using logistic regression in 12,944 women diagnosed with CRC and 10,741 women without CRC from three consortia. Sensitivity analyses were conducted to address pleiotropy and possible confounding by body mass index.

Results: Genetic risk scores for age at menarche (odds ratio per year 0.98, 95% confidence interval: 0.95-1.02) and age at menopause (odds ratio 0.98, 95% confidence interval: 0.94-1.01) were not significantly associated with CRC risk. The sensitivity analyses yielded similar results.

Conclusions: Our study does not support a causal relationship between genetic risk scores for age at menarche and age at menopause and CRC risk.
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http://dx.doi.org/10.1038/s41416-018-0108-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6008474PMC
June 2018

Determining Risk of Colorectal Cancer and Starting Age of Screening Based on Lifestyle, Environmental, and Genetic Factors.

Gastroenterology 2018 06 17;154(8):2152-2164.e19. Epub 2018 Feb 17.

Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.

Background & Aims: Guidelines for initiating colorectal cancer (CRC) screening are based on family history but do not consider lifestyle, environmental, or genetic risk factors. We developed models to determine risk of CRC, based on lifestyle and environmental factors and genetic variants, and to identify an optimal age to begin screening.

Methods: We collected data from 9748 CRC cases and 10,590 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colorectal Transdisciplinary study, from 1992 through 2005. Half of the participants were used to develop the risk determination model and the other half were used to evaluate the discriminatory accuracy (validation set). Models of CRC risk were created based on family history, 19 lifestyle and environmental factors (E-score), and 63 CRC-associated single-nucleotide polymorphisms identified in genome-wide association studies (G-score). We evaluated the discriminatory accuracy of the models by calculating area under the receiver operating characteristic curve values, adjusting for study, age, and endoscopy history for the validation set. We used the models to project the 10-year absolute risk of CRC for a given risk profile and recommend ages to begin screening in comparison to CRC risk for an average individual at 50 years of age, using external population incidence rates for non-Hispanic whites from the Surveillance, Epidemiology, and End Results program registry.

Results: In our models, E-score and G-score each determined risk of CRC with greater accuracy than family history. A model that combined both scores and family history estimated CRC risk with an area under the receiver operating characteristic curve value of 0.63 (95% confidence interval, 0.62-0.64) for men and 0.62 (95% confidence interval, 0.61-0.63) for women; area under the receiver operating characteristic curve values based on only family history ranged from 0.53 to 0.54 and those based only E-score or G-score ranged from 0.59 to 0.60. Although screening is recommended to begin at age 50 years for individuals with no family history of CRC, starting ages calculated based on combined E-score and G-score differed by 12 years for men and 14 for women, for individuals with the highest vs the lowest 10% of risk.

Conclusions: We used data from 2 large international consortia to develop CRC risk calculation models that included genetic and environmental factors along with family history. These determine risk of CRC and starting ages for screening with greater accuracy than the family history only model, which is based on the current screening guideline. These scoring systems might serve as a first step toward developing individualized CRC prevention strategies.
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http://dx.doi.org/10.1053/j.gastro.2018.02.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5985207PMC
June 2018

Enrichment of colorectal cancer associations in functional regions: Insight for using epigenomics data in the analysis of whole genome sequence-imputed GWAS data.

PLoS One 2017 21;12(11):e0186518. Epub 2017 Nov 21.

Department Genome Sciences, University of Washington, Seattle, Washington, United States of America.

Background: The evaluation of less frequent genetic variants and their effect on complex disease pose new challenges for genomic research. To investigate whether epigenetic data can be used to inform aggregate rare-variant association methods (RVAM), we assessed whether variants more significantly associated with colorectal cancer (CRC) were preferentially located in non-coding regulatory regions, and whether enrichment was specific to colorectal tissues.

Methods: Active regulatory elements (ARE) were mapped using data from 127 tissues and cell-types from NIH Roadmap Epigenomics and Encyclopedia of DNA Elements (ENCODE) projects. We investigated whether CRC association p-values were more significant for common variants inside versus outside AREs, or 2) inside colorectal (CR) AREs versus AREs of other tissues and cell-types. We employed an integrative epigenomic RVAM for variants with allele frequency <1%. Gene sets were defined as ARE variants within 200 kilobases of a transcription start site (TSS) using either CR ARE or ARE from non-digestive tissues. CRC-set association p-values were used to evaluate enrichment of less frequent variant associations in CR ARE versus non-digestive ARE.

Results: ARE from 126/127 tissues and cell-types were significantly enriched for stronger CRC-variant associations. Strongest enrichment was observed for digestive tissues and immune cell types. CR-specific ARE were also enriched for stronger CRC-variant associations compared to ARE combined across non-digestive tissues (p-value = 9.6 × 10-4). Additionally, we found enrichment of stronger CRC association p-values for rare variant sets of CR ARE compared to non-digestive ARE (p-value = 0.029).

Conclusions: Integrative epigenomic RVAM may enable discovery of less frequent variants associated with CRC, and ARE of digestive and immune tissues are most informative. Although distance-based aggregation of less frequent variants in CR ARE surrounding TSS showed modest enrichment, future association studies would likely benefit from joint analysis of transcriptomes and epigenomes to better link regulatory variation with target genes.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0186518PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5697874PMC
December 2017

Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions.

Am J Epidemiol 2017 Oct;186(7):771-777

A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.
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http://dx.doi.org/10.1093/aje/kwx229DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860556PMC
October 2017

Transethnic insight into the genetics of glycaemic traits: fine-mapping results from the Population Architecture using Genomics and Epidemiology (PAGE) consortium.

Diabetologia 2017 12 13;60(12):2384-2398. Epub 2017 Sep 13.

Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA.

Aims/hypothesis: Elevated levels of fasting glucose and fasting insulin in non-diabetic individuals are markers of dysregulation of glucose metabolism and are strong risk factors for type 2 diabetes. Genome-wide association studies have discovered over 50 SNPs associated with these traits. Most of these loci were discovered in European populations and have not been tested in a well-powered multi-ethnic study. We hypothesised that a large, ancestrally diverse, fine-mapping genetic study of glycaemic traits would identify novel and population-specific associations that were previously undetectable by European-centric studies.

Methods: A multiethnic study of up to 26,760 unrelated individuals without diabetes, of predominantly Hispanic/Latino and African ancestries, were genotyped using the Metabochip. Transethnic meta-analysis of racial/ethnic-specific linear regression analyses were performed for fasting glucose and fasting insulin. We attempted to replicate 39 fasting glucose and 17 fasting insulin loci. Genetic fine-mapping was performed through sequential conditional analyses in 15 regions that included both the initially reported SNP association(s) and denser coverage of SNP markers. In addition, Metabochip-wide analyses were performed to discover novel fasting glucose and fasting insulin loci. The most significant SNP associations were further examined using bioinformatic functional annotation.

Results: Previously reported SNP associations were significantly replicated (p ≤ 0.05) in 31/39 fasting glucose loci and 14/17 fasting insulin loci. Eleven glycaemic trait loci were refined to a smaller list of potentially causal variants through transethnic meta-analysis. Stepwise conditional analysis identified two loci with independent secondary signals (G6PC2-rs477224 and GCK-rs2908290), which had not previously been reported. Population-specific conditional analyses identified an independent signal in G6PC2 tagged by the rare variant rs77719485 in African ancestry. Further Metabochip-wide analysis uncovered one novel fasting insulin locus at SLC17A2-rs75862513.

Conclusions/interpretation: These findings suggest that while glycaemic trait loci often have generalisable effects across the studied populations, transethnic genetic studies help to prioritise likely functional SNPs, identify novel associations that may be population-specific and in turn have the potential to influence screening efforts or therapeutic discoveries.

Data Availability: The summary statistics from each of the ancestry-specific and transethnic (combined ancestry) results can be found under the PAGE study on dbGaP here: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000356.v1.p1.
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http://dx.doi.org/10.1007/s00125-017-4405-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5918310PMC
December 2017

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits.

Nat Commun 2017 04 26;8:14977. Epub 2017 Apr 26.

Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Australia.

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.
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http://dx.doi.org/10.1038/ncomms14977DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5414044PMC
April 2017

Strategies for Enriching Variant Coverage in Candidate Disease Loci on a Multiethnic Genotyping Array.

PLoS One 2016 14;11(12):e0167758. Epub 2016 Dec 14.

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.

Investigating genetic architecture of complex traits in ancestrally diverse populations is imperative to understand the etiology of disease. However, the current paucity of genetic research in people of African and Latin American ancestry, Hispanic and indigenous peoples in the United States is likely to exacerbate existing health disparities for many common diseases. The Population Architecture using Genomics and Epidemiology, Phase II (PAGE II), Study was initiated in 2013 by the National Human Genome Research Institute to expand our understanding of complex trait loci in ethnically diverse and well characterized study populations. To meet this goal, the Multi-Ethnic Genotyping Array (MEGA) was designed to substantially improve fine-mapping and functional discovery by increasing variant coverage across multiple ethnicities at known loci for metabolic, cardiovascular, renal, inflammatory, anthropometric, and a variety of lifestyle traits. Studying the frequency distribution of clinically relevant mutations, putative risk alleles, and known functional variants across multiple populations will provide important insight into the genetic architecture of complex diseases and facilitate the discovery of novel, sometimes population-specific, disease associations. DNA samples from 51,650 self-identified African ancestry (17,328), Hispanic/Latino (22,379), Asian/Pacific Islander (8,640), and American Indian (653) and an additional 2,650 participants of either South Asian or European ancestry, and other reference panels have been genotyped on MEGA by PAGE II. MEGA was designed as a new resource for studying ancestrally diverse populations. Here, we describe the methodology for selecting trait-specific content for use in multi-ethnic populations and how enriching MEGA for this content may contribute to deeper biological understanding of the genetic etiology of complex disease.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0167758PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5156387PMC
July 2017
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