DNA variants that affect alternative splicing and the relative quantities of different gene transcripts have been shown to be risk alleles for some Mendelian diseases. However, for complex traits characterized by a low odds ratio for any single contributing variant, very few studies have investigated the contribution of splicing variants. The overarching goal of this study is to discover and characterize the role that variants affecting alternative splicing may play in the genetic etiology of complex traits, which include a significant number of the common human diseases. Specifically, we hypothesize that single nucleotide polymorphisms (SNPs) in splicing regulatory elements can be characterized in silico to identify variants affecting splicing, and that these variants may contribute to the etiology of complex diseases as well as the inter-individual variability in the ratios of alternative transcripts. We leverage high-throughput expression profiling to 1) experimentally validate our in silico predictions of skipped exons and 2) characterize the molecular role of intronic genetic variations in alternative splicing events in the context of complex human traits and diseases. We propose that intronic SNPs play a role as genetic regulators within splicing regulatory elements and show that their associated exon skipping events can affect protein domains and structure. We find that SNPs we would predict to affect exon skipping are enriched among the set of SNPs reported to be associated with complex human traits.
Alternative splicing is a common eukaryotic cellular mechanism that allows for the production of multiple proteins from one gene and occurs in 40%–90% of all human genes. Alternative splicing has been shown to be important for many critical biological processes, including development, evolution, and even psychological behavior. Additionally, alternative splicing has been associated with 15%–50% of human genetic diseases, including breast cancer; however, the precise mechanism by which genetic variations regulate this process remains to be fully elucidated. In this study, we develop an integrative approach that utilizes sequence-based analysis and genome-wide expression profiling to identify genetic variations that may affect alternative splicing. We also evaluate their enrichment among established disease-associated variations. Our study provides insights into the functionality of these variations and emphasizes their importance for complex human traits and diseases.
Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.
Understanding how FI and FG levels are regulated is important because their derangement is a feature of T2D. Despite recent success from GWAS in identifying regions of the genome influencing glycemic traits, collectively these loci explain only a small proportion of trait variance. Unlocking the biological mechanisms driving these associations has been challenging because the vast majority of variants map to non-coding sequence, and the genes through which they exert their impact are largely unknown. In the current study, we sought to increase our understanding of the physiological pathways influencing both traits using exome-array genotyping in up to 33,231 non-diabetic individuals to identify coding variants and consequently genes associated with either FG or FI levels. We identified novel association signals for both traits including the receptor for GLP-1 agonists which are a widely used therapy for T2D. Furthermore, we identified coding variants at several GWAS loci which point to the genes underlying these association signals. Importantly, we found that multiple coding variants in G6PC2 result in a loss of protein function and lower fasting glucose levels.
Advances in high throughput technology have enabled the generation of unprecedented amounts of genomic data (e.g., next generation sequence data, transcriptomics, metabolomics, and proteomics), which promises to unravel the genetic architecture of complex traits. These discoveries may lead to novel therapeutic targets, guide disease prevention, and enable personalized medicine. However, the pace of data generation surpasses the ability to process and analyze the vast amounts of data. For example, in a typical study of transcription regulation, the relationship between more than 1 million genetic variants and ten thousand transcript levels are explored, requiring tens of billions of tests. In order to address this problem, we propose a fast, accurate, and robust method that can assess the significance of associations between quantitative phenotypes and genotypes. The method is an extension of the allelic test commonly used in case-control studies for the analysis of quantitative traits. We show the asymptotic equivalence of the proposed test to linear regression results. We also reduce a generalized linear regression problem to the comparison of two groups, which can handle non-normal and survival time phenotypes.
GWAS; quantitative traits; allelic methods
Black patients with neuroblastoma have a higher prevalence of high-risk disease and worse outcome than white patients. We sought to investigate the relationship between genetic variation and the disparities in survival observed in neuroblastoma.
The analytic cohort was composed of 2709 patients. Principal components were used to assign patients to genomic ethnic clusters for survival analyses. Locus-specific ancestry was calculated for use in association analysis. The shorter spans of linkage disequilibrium in African populations may facilitate the fine mapping of causal variants in regions previously implicated by genome-wide association studies conducted primarily in patients of European descent. Thus, we evaluated 13 single nucleotide polymorphisms known to be associated with susceptibility to high-risk neuroblastoma from genome-wide association studies and all variants with highly divergent allele frequencies in reference African and European populations near the known susceptibility loci. All statistical tests were two-sided.
African genomic ancestry was associated with high-risk neuroblastoma (P = .007) and lower event-free survival (P = .04, hazard ratio = 1.4, 95% confidence interval = 1.05 to 1.80). rs1033069 within SPAG16 (sperm associated antigen 16) was determined to have higher risk allele frequency in the African reference population and statistically significant association with high-risk disease in patients of European and African ancestry (P = 6.42×10−5, false discovery rate < 0.0015) in the overall cohort. Multivariable analysis using an additive model demonstrated that the SPAG16 single nucleotide polymorphism contributes to the observed ethnic disparities in high-risk disease and survival.
Our study demonstrates that common genetic variation influences neuroblastoma phenotype and contributes to the ethnic disparities in survival observed and illustrates the value of trans-population mapping.
Live attenuated influenza vaccines (LAIV) offer significant advantages over subunit or split inactivated vaccines to mitigate an eventual influenza pandemic, including simpler manufacturing processes and more cross-protective immune responses. Using an established reverse genetics (rg) system for wild-type (wt) A/Leningrad/134/1957 and cold-adapted (ca) A/Leningrad/134/17/1957 (Len17) master donor virus (MDV), we produced and characterized three rg H5N1 reassortant viruses carrying modified HA and intact NA genes from either A/Vietnam/1203/2004 (H5N1, VN1203, clade 1) or A/Egypt/321/2007 (H5N1, EG321, clade 2) virus. A mouse model of infection was used to determine the infectivity and tissue tropism of the parental wt viruses compared to the ca master donor viruses as well as the H5N1 reassortants. All ca viruses showed reduced replication in lungs and enhanced replication in nasal epithelium. In addition, the H5N1 HA and NA enhanced replication in lungs unless it was restricted by the internal genes of the ca MDV. Mice inoculated twice 4 weeks apart with the H5N1 reassortant LAIV candidate viruses developed serum hemagglutination inhibition HI and IgA antibody titers to the homologous and heterologous viruses consistent with protective immunity. These animals remained healthy after challenge inoculation with a lethal dose with homologous or heterologous wt H5N1 highly pathogenic avian influenza (HPAI) viruses. The profiles of viral replication in respiratory tissues and the immunogenicity and protective efficacy characteristics of the two ca H5N1 candidate LAIV viruses warrant further development into a vaccine for human use.
The noncovalent interactions that mediate trimerization of the influenza hemagglutinin (HA) are important determinants of its biological activities. Recent studies have demonstrated that mutations in the HA trimer interface affect the thermal and pH sensitivities of HA, suggesting a possible impact on vaccine stability (). We used size exclusion chromatography analysis of recombinant HA ectodomain to compare the differences among recombinant trimeric HA proteins from early 2009 pandemic H1N1 viruses, which dissociate to monomers, with those of more recent virus HAs that can be expressed as trimers. We analyzed differences among the HA sequences and identified intermolecular interactions mediated by the residue at position 374 (HA0 numbering) of the HA2 subdomain as critical for HA trimer stability. Crystallographic analyses of HA from the recent H1N1 virus A/Washington/5/2011 highlight the structural basis for this observed phenotype. It remains to be seen whether more recent viruses with this mutation will yield more stable vaccines in the future.
IMPORTANCE Hemagglutinins from the early 2009 H1N1 pandemic viruses are unable to maintain a trimeric complex when expressed in a recombinant system. However, HAs from 2010 and 2011 strains are more stable, and our work highlights that the improvement in stability can be attributed to an E374K substitution in the HA2 subunit of the stalk that emerged naturally in the circulating viruses.
Whereas countless highly penetrant variants have been associated with Mendelian disorders, the genetic etiologies underlying complex diseases remain largely unresolved. Here, we examine the extent to which Mendelian variation contributes to complex disease risk by mining the medical records of over 110 million patients. We detect thousands of associations between Mendelian and complex diseases, revealing a non-degenerate, phenotypic code that links each complex disorder to a unique collection of Mendelian loci. Using genome-wide association results, we demonstrate that common variants associated with complex diseases are enriched in the genes indicated by this “Mendelian code.” Finally, we detect hundreds of comorbidity associations among Mendelian disorders, and we use probabilistic genetic modeling to demonstrate that Mendelian variants likely contribute non-additively to the risk for a subset of complex diseases. Overall, this study illustrates a complementary approach for mapping complex disease loci and provides unique predictions concerning the etiologies of specific diseases.
Genome-wide association studies (GWAS) have reproducibly associated variants within introns of FTO with increased risk for obesity and type-2 diabetes (T2D) 1–3. While the molecular mechanisms linking these noncoding variants with obesity are not immediately obvious, subsequent studies in mice demonstrated that FTO expression levels influence body mass and composition phenotypes 4–6. Yet, no direct connection between the obesity-associated variants and FTO expression or function has been made 7–9. Here, we show that the obesity-associated noncoding sequences within FTO are functionally connected, at megabase distances, with the homeobox gene IRX3. The obesity-associated FTO region directly interacts with the promoters of IRX3 as well as FTO in the human, mouse, and zebrafish genomes. Furthermore, long-range enhancers within this region recapitulate aspects of IRX3 expression, suggesting that the obesity-associated interval belongs to the regulatory landscape of IRX3. Supporting this, obesity-associated SNPs are associated with expression of IRX3, but not FTO, in human brains. Directly linking IRX3 expression with regulation of body mass and composition, Irx3-deficient mice exhibit a 25–30% reduction in body weight, primarily through the loss of fat mass and increase in basal metabolic rate with browning of white adipose tissue. Furthermore, hypothalamic expression of a dominant negative form of Irx3 reproduces the metabolic phenotypes of Irx3-deficient mice. Our data posit that IRX3 is a functional long-range target of obesity-associated variants within FTO, and represents a novel determinant of body mass and composition.
Newborns characterized as large and small for gestational age are at risk for increased mortality and morbidity during the first year of life as well as for obesity and dysglycemia as children and adults. The intrauterine environment and fetal genes contribute to the fetal size at birth. To define the genetic architecture underlying the newborn size, we performed a genome-wide association study (GWAS) in 4281 newborns in four ethnic groups from the Hyperglycemia and Adverse Pregnancy Outcome Study. We tested for association with newborn anthropometric traits (birth length, head circumference, birth weight, percent fat mass and sum of skinfolds) and newborn metabolic traits (cord glucose and C-peptide) under three models. Model 1 adjusted for field center, ancestry, neonatal gender, gestational age at delivery, parity, maternal age at oral glucose tolerance test (OGTT); Model 2 adjusted for Model 1 covariates, maternal body mass index (BMI) at OGTT, maternal height at OGTT, maternal mean arterial pressure at OGTT, maternal smoking and drinking; Model 3 adjusted for Model 2 covariates, maternal glucose and C-peptide at OGTT. Strong evidence for association was observed with measures of newborn adiposity (sum of skinfolds model 3 Z-score 7.356, P = 1.90×10−13, and to a lesser degree fat mass and birth weight) and a region on Chr3q25.31 mapping between CCNL and LEKR1. These findings were replicated in an independent cohort of 2296 newborns. This region has previously been shown to be associated with birth weight in Europeans. The current study suggests that association of this locus with birth weight is secondary to an effect on fat as opposed to lean body mass.
Maternal metabolism during pregnancy impacts the developing fetus, affecting offspring birth weight and adiposity. This has important implications for metabolic health later in life (e.g., offspring of mothers with pre-existing or gestational diabetes mellitus have an increased risk of metabolic disorders in childhood). To identify genetic loci associated with measures of maternal metabolism obtained during an oral glucose tolerance test at ∼28 weeks’ gestation, we performed a genome-wide association study of 4,437 pregnant mothers of European (n = 1,367), Thai (n = 1,178), Afro-Caribbean (n = 1,075), and Hispanic (n = 817) ancestry, along with replication of top signals in three additional European ancestry cohorts. In addition to identifying associations with genes previously implicated with measures of glucose metabolism in nonpregnant populations, we identified two novel genome-wide significant associations: 2-h plasma glucose and HKDC1, and fasting C-peptide and BACE2. These results suggest that the genetic architecture underlying glucose metabolism may differ, in part, in pregnancy.
Pharmacogenomics is aimed at advancing our knowledge of the genetic basis of variable drug response. The Center for Personalized Therapeutics within the University of Chicago comprises basic, translational and clinical research as well as education including undergraduate, graduate, medical students, clinical/postdoctoral fellows and faculty. The Committee on Clinical Pharmacology and Pharmacogenomics is the educational arm of the Center aimed at training clinical and postdoctoral fellows in translational pharmacology and pharmacogenomics. Research runs the gamut from basic discovery and functional studies to pharmacogenomic implementation studies to evaluate physician adoption of genetic medicine. The mission of the Center is to facilitate research, education and implementation of pharmacogenomics to realize the true potential of personalized medicine and improve the lives of patients.
VKORC1 and CYP2C9 are important contributors to warfarin dose variability, but explain less variability for individuals of African descent than for those of European or Asian descent. We aimed to identify additional variants contributing to warfarin dose requirements in African Americans.
We did a genome-wide association study of discovery and replication cohorts. Samples from African-American adults (aged ≥18 years) who were taking a stable maintenance dose of warfarin were obtained at International Warfarin Pharmacogenetics Consortium (IWPC) sites and the University of Alabama at Birmingham (Birmingham, AL, USA). Patients enrolled at IWPC sites but who were not used for discovery made up the independent replication cohort. All participants were genotyped. We did a stepwise conditional analysis, conditioning first for VKORC1 −1639G→A, followed by the composite genotype of CYP2C9*2 and CYP2C9*3. We prespecified a genome-wide significance threshold of p<5×10−8 in the discovery cohort and p<0·0038 in the replication cohort.
The discovery cohort contained 533 participants and the replication cohort 432 participants. After the prespecified conditioning in the discovery cohort, we identified an association between a novel single nucleotide polymorphism in the CYP2C cluster on chromosome 10 (rs12777823) and warfarin dose requirement that reached genome-wide significance (p=1·51×10−8). This association was confirmed in the replication cohort (p=5·04×10−5); analysis of the two cohorts together produced a p value of 4·5×10−12. Individuals heterozygous for the rs12777823 A allele need a dose reduction of 6·92 mg/week and those homozygous 9·34 mg/week. Regression analysis showed that the inclusion of rs12777823 significantly improves warfarin dose variability explained by the IWPC dosing algorithm (21% relative improvement).
A novel CYP2C single nucleotide polymorphism exerts a clinically relevant effect on warfarin dose in African Americans, independent of CYP2C9*2 and CYP2C9*3. Incorporation of this variant into pharmacogenetic dosing algorithms could improve warfarin dose prediction in this population.
National Institutes of Health, American Heart Association, Howard Hughes Medical Institute, Wisconsin Network for Health Research, and the Wellcome Trust.
As genotyping technology has progressed, genome-wide association studies (GWAS) have matured into efficient and effective tools for mapping genes underlying human phenotypes. Recent studies have demonstrated the utility of the GWAS approach for examining pharmacogenomic traits, including drug metabolism, efficacy, and toxicity. Application of GWAS to pharmacogenomic outcomes presents unique challenges and opportunities. In the current review, we discuss the potential promises and potential caveats of this approach specifically as it relates to pharmacogenomic studies. Concerns with study design, power and sample size, and analysis are reviewed. We further examine the features of successful pharmacogenomic GWAS, and describe consortia efforts that are likely to expand the reach of pharmacogenomic GWAS in the future.
Genome-wide association; GWAS; pharmacogenetic; pharmacogenomic; drug response; drug metabolism; toxicity
Evidence implicates the serotonin transporter gene (SLC6A4) and the 15q11-q13 genes as candidates for autism as well as restricted repetitive behavior (RRB).
We conducted dense transmission disequilibrium mapping of the 15q11-q13 region with 93 single nucleotide polymorphisms (SNPs) in 86 strictly defined autism trios and tested association between SNPs and autism using the transmission disequilibrium test (TDT). As exploratory analyses, parent-of-origin effects were examined using likelihood-ratio tests (LRT) and genotype-phenotype associations for specific RRB using the Family-Based Association Test (FBAT). Additionally, gene-gene interactions between nominally associated 15q11-q13 variants and 5-HTTLPR, the common length polymorphism of SLC6A4, were examined using conditional logistic regression (CLR).
TDT revealed nominally significant transmission disequilibrium between autism and five SNPs, three of which are located within close proximity of the GABAA receptor subunit gene clusters. Three SNPs in the SNRPN/UBE3A region had marginal imprinting effects. FBAT for genotype-phenotype relations revealed nominally significant association between two SNPs and one ADI-R sub-domain item. However, both TDT and FBAT were not statistically significant after correcting for multiple comparisons. Gene-gene interaction analyses by CLR revealed additive genetic effect models, without interaction terms, fit the data best.
Lack of robust association between the 15q11-q13 SNPs and RRB phenotypes may be due to a small sample size and absence of more specific RRB measurement. Further investigation of the 15q11-q13 region with denser genotyping in a larger sample set may be necessary to determine whether this region confers risk to autism, indicated by association, or to specific autism phenotypes.
Autism; 15q11-q13; restricted repetitive behavior; 5-HTTLPR; association
Numerous single nucleotide polymorphisms (SNPs) associated with breast cancer
susceptibility have been identified by genome-wide association studies (GWAS). However,
these SNPs were primarily discovered and validated in women of European and Asian
ancestry. Because linkage disequilibrium is ancestry-dependent and heterogeneous among
racial/ethnic populations, we evaluated common genetic variants at 22 GWAS-identified
breast cancer susceptibility loci in a pooled sample of 1502 breast cancer cases and 1378
controls of African ancestry. None of the 22 GWAS index SNPs could be validated,
challenging the direct generalizability of breast cancer risk variants identified in
Caucasians or Asians to other populations. Novel breast cancer risk variants for women of
African ancestry were identified in regions including 5p12 (odds ratio [OR] = 1.40,
95% confidence interval [CI] = 1.11–1.76; P =
0.004), 5q11.2 (OR = 1.22, 95% CI = 1.09–1.36; P
= 0.00053) and 10p15.1 (OR = 1.22, 95% CI = 1.08–1.38;
P = 0.0015). We also found positive association signals in three
regions (6q25.1, 10q26.13 and 16q12.1–q12.2) previously confirmed by fine mapping
in women of African ancestry. In addition, polygenic model indicated that eight best
markers in this study, compared with 22 GWAS-identified SNPs, could better predict breast
cancer risk in women of African ancestry (per-allele OR = 1.21, 95% CI =
1.16–1.27; P = 9.7 × 10–16). Our
results demonstrate that fine mapping is a powerful approach to better characterize the
breast cancer risk alleles in diverse populations. Future studies and new GWAS in women of
African ancestry hold promise to discover additional variants for breast cancer
susceptibility with clinical implications throughout the African diaspora.
Genome-wide association (GWA) studies have identified thousands of genetic variants that contribute to disease and pharmacologic traits. More recently, high-throughput sequencing studies promise to provide a more complete catalog of genetic variants with roles in human phenotypic variation. Yet, characterizing the influence of functional variants on genes, RNAs, proteins, and ultimately disease or pharmacologic traits is a critical challenge for a vast majority of the implicated susceptibility loci. Here we describe SCAN, a bioinformatics resource we have developed to elucidate the functional consequences of genetic variants identified by genome-wide scans. In particular, this public resource implements a systems biology approach to pharmacogenomic discovery.
eQTLs; Pharmacogenomics; Expression profiling; Transcriptome; SNP function; Genetic variation
Using genome-wide genetic, gene expression, and microRNA expression (miRNA) data, we developed an integrative approach to investigate the genetic and epigenetic basis of chemotherapeutic sensitivity.
Through a sequential multi-stage framework, we identified genes and miRNAs whose expression correlated with platinum sensitivity, mapped these to genomic loci as quantitative trait loci (QTLs), and evaluated the associations between these QTLs and platinum sensitivity. A permutation analysis showed that top findings from our approach have a much lower false discovery rate compared to those from a traditional GWAS of drug sensitivity. Our approach identified five SNPs associated with 10 miRNAs and the expression level of 15 genes, all of which were associated with carboplatin sensitivity. Of particular interest was one SNP (rs11138019), which was associated with the expression of both miR-30d and the gene ABCD2, which were themselves correlated with both carboplatin and cisplatin drug-specific phenotype in the HapMap samples. Functional study found that knocking down ABCD2 in vitro led to increased apoptosis in ovarian cancer cell line SKOV3 after cisplatin treatment. Over-expression of miR-30d in vitro caused a decrease in ABCD2 expression, suggesting a functional relationship between the two.
We developed an integrative approach to the investigation of the genetic and epigenetic basis of human complex traits. Our approach outperformed standard GWAS and provided hints at potential biological function. The relationships between ABCD2 and miR-30d, and ABCD2 and platin sensitivity were experimentally validated, suggesting a functional role of ABCD2 and miR-30d in sensitivity to platinating agents.
microRNA; Gene expression; SNP; Platinum; HapMap
We demonstrate a method for the prediction of chemotherapeutic response in patients using only before-treatment baseline tumor gene expression data. First, we fitted models for whole-genome gene expression against drug sensitivity in a large panel of cell lines, using a method that allows every gene to influence the prediction. Following data homogenization and filtering, these models were applied to baseline expression levels from primary tumor biopsies, yielding an in vivo drug sensitivity prediction. We validated this approach in three independent clinical trial datasets, and obtained predictions equally good, or better than, gene signatures derived directly from clinical data.
To determine the genetic contribution to leukocyte endothelial adhesion.
Leukocyte endothelial adhesion was assessed through a novel cell-based assay using human lymphoblastoid cell lines. A high-throughput screening method was developed to evaluate the inter-individual variability in leukocyte endothelial adhesion using lymphoblastoid cell lines derived from different donors. To assess heritability, ninety-two lymphoblastoid cell lines derived from twenty-three monozygotic twin pairs and twenty-three sibling pairs were compared. These lymphoblastoid cell lines were plated with the endothelial cell line EA.hy926 and labeled with Calcein AM dye. Fluorescence was assessed to determine endothelial cell adhesion to each lymphoblastoid cell line. Intra-pair similarity was determined for monozygotic twins and siblings using Pearson pairwise correlation coefficients.
A leukocyte endothelial adhesion assay for lymphoblastoid cell lines was developed and optimized (CV = 8.68, Z′-factor = 0.67, SNR = 18.41). A higher adhesion correlation was found between the twins than that between the siblings. Intra-pair similarity for leukocyte endothelial adhesion in monozygotic twins was 0.60 compared to 0.25 in the siblings. The extent to which these differences are attributable to underlying genetic factors was quantified and the heritability of leukocyte endothelial adhesion was calculated to be 69.66% (p-value<0.0001).
There is a heritable component to leukocyte endothelial adhesion. Underlying genetic predisposition plays a significant role in inter-individual variability of leukocyte endothelial adhesion.
When planning re-sequencing studies for complex diseases, previous association and linkage studies can constrain the range of plausible genetic models for a given locus. Here, we explore the combinations of causal risk allele frequency RAFC and genotype relative risk GRRC consistent with no or limited evidence for affected sibling pair (ASP) linkage and strong evidence for case-control association. We find that significant evidence for case-control association combined with no or moderate evidence for ASP linkage can define a lower bound for the plausible RAFC. Using data from large type 2 diabetes (T2D) linkage and genome-wide association study meta-analyses, we find that under reasonable model assumptions, 23 of 36 autosomal T2D risk loci are unlikely to be due to causal variants with combined RAFC < .005, and four of the 23 are unlikely to be due to causal variants with combined RAFC < .05.
gene mapping; genetics; genetic structure; complex diseases
We sought to demonstrate the relevance of a lymphoblastoid cell line (LCL) model in the discovery of clinically relevant genetic variants affecting chemotherapeutic response by comparing LCL genome-wide association study (GWAS) results to clinical GWAS results.
A GWAS of paclitaxel-induced cytotoxicity was performed in 247 LCLs from the HapMap Project and compared to a GWAS of sensory peripheral neuropathy in breast cancer patients (n=855) treated with paclitaxel in the Cancer and Leukemia Group B (CALGB) 40101 trial. Significant enrichment was assessed by permutation resampling analysis.
We observed an enrichment of LCL cytotoxicity-associated single nucleotide polymorphisms (SNPs) in the sensory peripheral neuropathy-associated SNPs from the clinical trial with concordant allelic directions of effect (empirical P = 0.007). Of the 24 SNPs that overlap between the clinical trial (P < 0.05) and the preclinical cytotoxicity study (P < 0.001), 19 of them are expression quantitative trait loci (eQTLs), which is a significant enrichment of this functional class (empirical P = 0.0447). One of these eQTLs is located in RFX2, which encodes a member of the DNA-binding regulatory factor X family. Decreased expression of this gene by siRNA resulted in increased sensitivity of NS-1 (rat pheochromocytoma) cells to paclitaxel as measured by reduced neurite outgrowth and increased cytotoxicity, functionally validating the involvement of RFX2 in nerve cell response to paclitaxel.
The enrichment results and functional example imply that cellular models of chemotherapeutic toxicity may capture components of the underlying polygenic architecture of related traits in patients.
pharmacogenomics; clinical trial; cell lines; paclitaxel; genome-wide association
Genetic variation influences the response of an individual to drug treatments. Understanding this variation has the potential to make therapy safer and more effective by determining selection and dosing of drugs for an individual patient. In the context of cancer, tumours may have specific disease-defining mutations, but a patient’s germline genetic variation will also affect drug response (both efficacy and toxicity), and here we focus on how to study this variation. Advances in sequencing technologies, statistical genetics analysis methods and clinical trial designs have shown promise for the discovery of variants associated with drug response. We discuss the application of germline genetics analysis methods to cancer pharmacogenomics with a focus on the special considerations for study design.