Miriam S. Udler

FGFR2 variants and breast cancer risk: fine-scale mapping using African American studies and analysis of chromatin conformation

Genome-wide association studies have identified FGFR2 as a breast cancer (BC) susceptibility gene in populations of European and Asian descent, but a causative variant has not yet been conclusively identified. We hypothesized that the weaker linkage disequilibrium across this associated region in populations of African ancestry might help refine the set of candidate-causal single nucleotide polymorphisms (SNPs) previously identified by our group. Eight candidate-causal SNPs were evaluated in 1253 African American invasive BC cases and 1245 controls. A significant association with BC risk was found with SNP rs2981578 (unadjusted per-allele odds ratio = 1.20, 95% confidence interval 1.03-1.41, P(trend) = 0.02), with the odds ratio estimate similar to that reported in European and Asian subjects. To extend the fine-mapping, genotype data from the African American studies were analyzed jointly with data from European (n = 7196 cases, 7275 controls) and Asian (n = 3901 cases, 3205 controls) studies. In the combined analysis, SNP rs2981578 was the most strongly associated. Five other SNPs were too strongly correlated to be excluded at a likelihood ratio of < 1/100 relative to rs2981578. Analysis of DNase I hypersensitive sites indicated that only two of these map to highly accessible chromatin, one of which, SNP rs2981578, has previously been implicated in up-regulating FGFR2 expression. Our results demonstrate that the association of SNPs in FGFR2 with BC risk extends to women of African American ethnicity, and illustrate the utility of combining association analysis in datasets of diverse ethnic groups with functional experiments to identify disease susceptibility variants.

Common germline polymorphisms in COMT, CYP19A1, ESR1, PGR, SULT1E1 and STS and survival after a diagnosis of breast cancer

Although preliminary evidence suggests that germline variation in genes involved in steroid hormone metabolism may alter breast cancer prognosis, this has not been systematically evaluated. We examined associations between germline polymorphisms in 6 genes involved in the steroid hormone metabolism and signaling pathway (COMT, CYP19A1, ESR1, PGR, SULT1E1, STS) and survival among women with breast cancer participating in SEARCH, a population‐based case–control study. Blood samples from up to 4,470 women were genotyped for 4 possible functional SNPs in CYP19A1 and 106 SNPs tagging the common variation in the remainder of the genes. The genotypes of each polymorphism were tested for association with survival after breast cancer diagnosis using Cox regression analysis. Significant evidence of an association was observed for a COMT polymorphism (rs4818 p = 0.016) under the codominant model. This SNP appeared to fit a dominant model better (HR = 0.80 95% CI: 0.69–0.95, p = 0.009); however, the result was only marginally significant after permutation analysis adjustment for multiple hypothesis tests (p = 0.047). To further evaluate this finding, somatic expression microarray data from 8 publicly available datasets were used to test the association between survival and tumor COMT gene expression; no statistically significant associations were observed. A correlated SNP in COMT, rs4860, has recently been associated with breast cancer prognosis in Chinese women in a dominant model. These results suggest that COMT rs4818, or a variant it tags, is associated with breast cancer prognosis. Further study of COMT and its putative association with breast cancer prognosis is warranted.

Evaluating the power to discriminate between highly correlated SNPs in genetic association studies.

Neighboring common polymorphisms are often correlated (in linkage disequilibrium (LD)) as a result of shared ancestry. An association between a polymorphism and a disease trait may therefore be the indirect result of a correlated functional variant, and identifying the true causal variant(s) from an initial disease association is a major challenge in genetic association studies. Here, we present a method to estimate the sample size needed to discriminate between a functional variant of a given allele frequency and effect size, and other correlated variants. The sample size required to conduct such fine‐scale mapping is typically 1–4 times larger than required to detect the initial association. Association studies in populations with different LD patterns can substantially improve the power to isolate the causal variant. An online tool to perform these calculations is available at http://moya.srl.cam.ac.uk/ocac/FineMappingPowerCalculator.html. Genet. Epidemiol. 34:463–468, 2010.

The Role of the BRCA2 Gene in Susceptibility to Prostate Cancer Revisited

Prostate cancer is a genetically complex disease with multiple predisposing factors affecting presentation, progression, and outcome. Epidemiologic studies have long shown an aggregation of breast and prostate cancer in some families. More recently, studies have reported an apparent excess of prostate cancer cases among BRCA2 mutation–carrying families. Additionally, population-based screens of early-onset prostate cancer patients have suggested that the prevalence of deleterious BRCA2 mutations in this group is 1% to 2%, imparting a significantly increased risk of the disease compared with noncarrier cases. However, studies of high-risk prostate cancer families suggest that BRCA2 plays at most a minimal role in these individuals, highlighting the potential genetic heterogeneity of the disease. In this commentary, we review the current literature and hypotheses surrounding the relationship between BRCA2 mutations and susceptibility to prostate cancer and speculate on the potential for involvement of additional genes. (Cancer Epidemiol Biomarkers Prev 2008;17(8):1843–8)

Effect of Genetic African Ancestry on eGFR and Kidney Disease

Self-reported ancestry, genetically determined ancestry, and APOL1 polymorphisms are associated with variation in kidney function and related disease risk, but the relative importance of these factors remains unclear. We estimated the global proportion of African ancestry for 9048 individuals at Mount Sinai Medical Center in Manhattan (3189 African Americans, 1721 European Americans, and 4138 Hispanic/Latino Americans by self-report) using genome-wide genotype data. CKD-EPI eGFR and genotypes of three APOL1 coding variants were available. In admixed African Americans and Hispanic/Latino Americans, serum creatinine values increased as African ancestry increased (per 10% increase in African ancestry, creatinine values increased 1% in African Americans and 0.9% in Hispanic/Latino Americans; P≤1x10(-7)). eGFR was likewise significantly associated with African genetic ancestry in both populations. In contrast, APOL1 risk haplotypes were significantly associated with CKD, eGFR<45 ml/min per 1.73 m(2), and ESRD, with effects increasing with worsening disease states and the contribution of genetic African ancestry decreasing in parallel. Using genetic ancestry in the eGFR equation to reclassify patients as black on the basis of ≥50% African ancestry resulted in higher eGFR for 14.7% of Hispanic/Latino Americans and lower eGFR for 4.1% of African Americans, affecting CKD staging in 4.3% and 1% of participants, respectively. Reclassified individuals had electrolyte values consistent with their newly assigned CKD stage. In summary, proportion of African ancestry was significantly associated with normal-range creatinine and eGFR, whereas APOL1 risk haplotypes drove the associations with CKD. Recalculation of eGFR on the basis of genetic ancestry affected CKD staging and warrants additional investigation.

Clustering of Type 2 Diabetes Genetic Loci by Multi-Trait Associations Identifies Disease Mechanisms and Subtypes

Background Type 2 diabetes (T2D) is a heterogeneous disease for which 1) disease-causing pathways are incompletely understood and 2) sub-classification may improve patient management. Unlike other biomarkers, germline genetic markers do not change with disease progression or treatment. In this paper we test whether a germline genetic approach informed by physiology can be used to deconstruct T2D heterogeneity. First, we aimed to categorize genetic loci into groups representing likely disease mechanistic pathways. Second, we asked whether the novel clusters of genetic loci we identified have any broad clinical consequence, as assessed in four independent cohorts of individuals with T2D. Methods and Findings In an effort to identify mechanistic pathways driven by established T2D genetic loci, we applied Bayesian nonnegative matrix factorization clustering to genome-wide association results for 94 independent T2D genetic loci and 47 diabetes-related traits. We identified five robust clusters of T2D loci and traits, each with distinct tissue-specific enhancer enrichment based on analysis of epigenomic data from 28 cell types. Two clusters contained variant-trait associations indicative of reduced beta-cell function, differing from each other by high vs. low proinsulin levels. The three other clusters displayed features of insulin resistance: obesity-mediated (high BMI, waist circumference), “lipodystrophy-like” fat distribution (low BMI, adiponectin, HDL-cholesterol, and high triglycerides), and disrupted liver lipid metabolism (low triglycerides). Increased cluster GRS’s were associated with distinct clinical outcomes, including increased blood pressure, coronary artery disease, and stroke risk. We evaluated the potential for clinical impact of these clusters in four studies containing participants with T2D (METSIM, N=487; Ashkenazi, N=509; Partners Biobank, N=2,065; UK Biobank N=14,813). Individuals with T2D in the top genetic risk score decile for each cluster reproducibly exhibited the predicted cluster-associated phenotypes, with ~30% of all participants assigned to just one cluster top decile. Conclusion Our approach identifies salient T2D genetically anchored and physiologically informed pathways, and supports use of genetics to deconstruct T2D heterogeneity. Classification of patients by these genetic pathways may offer a step toward genetically informed T2D patient management.

Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum

There is a limited understanding about the impact of rare protein-truncating variants across multiple phenotypes. We explore the impact of this class of variants on 13 quantitative traits and 10 diseases using whole-exome sequencing data from 100,296 individuals. Protein-truncating variants in genes intolerant to this class of mutations increased risk of autism, schizophrenia, bipolar disorder, intellectual disability, and ADHD. In individuals without these disorders, there was an association with shorter height, lower education, increased hospitalization, and reduced age at enrollment. Gene sets implicated from GWASs did not show a significant protein-truncating variants burden beyond what was captured by established Mendelian genes. In conclusion, we provide a thorough investigation of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.

Germline genetic variation and breast cancer survival: prognostic and therapeutic implications

‘Not all tumors go through all stages of progression, and the rate of progression varies from person to person. The determinants of this process are multifaceted and incompletely understood.’ Breast cancer development and progression are complex multistep processes. Following malignant transformation, a mammary tumor progresses from in situ disease to invasive disease. There may be local spread to neighboring tissues or local or regional lymph nodes, as well as metastasis to distal organs including the brain, bone, lungs or liver. Not all tumors go through all stages of progression, and the rate of progression varies from person to person. The determinants of this process are multifaceted and incompletely understood. They include factors related to the individual, such as BMI, tumorspecific factors, such as histopathological grade and hormone receptor status, and exogenous factors, such as treatment and environmental exposures. Inherited genetic variation is likely to either directly modulate these factors or interact with them to influence the course of disease. For the purpose of this editorial we will use the term outcome as a general proxy for a range of concepts related to disease progression.

Genetic discovery and translational decision support from exome sequencing of 20,791 type 2 diabetes cases and 24,440 controls from five ancestries

Protein-coding genetic variants that strongly affect disease risk can provide important clues into disease pathogenesis. Here we report an exome sequence analysis of 20,791 type 2 diabetes (T2D) cases and 24,440 controls from five ancestries. We identify rare (minor allele frequency<0.5%) variant gene-level associations in (a) three genes at exome-wide significance, including a T2D-protective series of >30 SLC30A8 alleles, and (b) within 12 gene sets, including those corresponding to T2D drug targets (p=6.1×10−3) and candidate genes from knockout mice (p=5.2×10−3). Within our study, the strongest T2D rare variant gene-level signals explain at most 25% of the heritability of the strongest common single-variant signals, and the rare variant gene-level effect sizes we observe in established T2D drug targets will require 110K-180K sequenced cases to exceed exome-wide significance. To help prioritize genes using associations from current smaller sample sizes, we present a Bayesian framework to recalibrate association p-values as posterior probabilities of association, estimating that reaching p<0.05 (p<0.005) in our study increases the odds of causal T2D association for a nonsynonymous variant by a factor of 1.8 (5.3). To help guide target or gene prioritization efforts, our data are freely available for analysis at www.type2diabetesgenetics.org.

Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis

Background Type 2 diabetes (T2D) is a heterogeneous disease for which (1) disease-causing pathways are incompletely understood and (2) subclassification may improve patient management. Unlike other biomarkers, germline genetic markers do not change with disease progression or treatment. In this paper, we test whether a germline genetic approach informed by physiology can be used to deconstruct T2D heterogeneity. First, we aimed to categorize genetic loci into groups representing likely disease mechanistic pathways. Second, we asked whether the novel clusters of genetic loci we identified have any broad clinical consequence, as assessed in four separate subsets of individuals with T2D. Methods and findings In an effort to identify mechanistic pathways driven by established T2D genetic loci, we applied Bayesian nonnegative matrix factorization (bNMF) clustering to genome-wide association study (GWAS) results for 94 independent T2D genetic variants and 47 diabetes-related traits. We identified five robust clusters of T2D loci and traits, each with distinct tissue-specific enhancer enrichment based on analysis of epigenomic data from 28 cell types. Two clusters contained variant-trait associations indicative of reduced beta cell function, differing from each other by high versus low proinsulin levels. The three other clusters displayed features of insulin resistance: obesity mediated (high body mass index [BMI] and waist circumference [WC]), “lipodystrophy-like” fat distribution (low BMI, adiponectin, and high-density lipoprotein [HDL] cholesterol, and high triglycerides), and disrupted liver lipid metabolism (low triglycerides). Increased cluster genetic risk scores were associated with distinct clinical outcomes, including increased blood pressure, coronary artery disease (CAD), and stroke. We evaluated the potential for clinical impact of these clusters in four studies containing individuals with T2D (Metabolic Syndrome in Men Study [METSIM], N = 487; Ashkenazi, N = 509; Partners Biobank, N = 2,065; UK Biobank [UKBB], N = 14,813). Individuals with T2D in the top genetic risk score decile for each cluster reproducibly exhibited the predicted cluster-associated phenotypes, with approximately 30% of all individuals assigned to just one cluster top decile. Limitations of this study include that the genetic variants used in the cluster analysis were restricted to those associated with T2D in populations of European ancestry. Conclusion Our approach identifies salient T2D genetically anchored and physiologically informed pathways, and supports the use of genetics to deconstruct T2D heterogeneity. Classification of patients by these genetic pathways may offer a step toward genetically informed T2D patient management.