Association Studies Of Complex Diseases Research Articles

Abstract P1116: Genetic Mapping Of Cardiomyocyte Ploidy Phenotypes To Identify Genetic Determinants Of Outcomes After Infarction

Outcomes following cardiac injury are influenced by lifestyle and environment, as well as genetic background. Our interest lies in the latter, where very few genes have successfully been linked to heart failure. Contributors to this low success rate include high genetic diversity of patient populations, inability to control for non-genetic factors, and cellular complexities that contribute to outcomes. We propose utilizing the Hybrid Rat Diversity Panel (HRDP) to assess high throughput surrogate phenotypes, which can be predictive of outcomes after injury when measured in the basal state. This approach can offer a solution to the limitations of association studies for complex diseases like heart failure. Our surrogate phenotype of interest is cardiomyocyte ploidy, where high frequency of diploid cardiomyocytes is predictive of ability to regenerate and functional recovery. Conversely, an increased proportion of polyploid (≥8N) cardiomyocytes is associated with adverse ventricular remodeling and dilated cardiomyopathy. Having analyzed 50 of the 96 strains, we see that frequency of the MNDCMs varies from 1.3-20.3% across strains, while frequency of the highly polyploid CMs (≥8N) varies from 0.8-20.9%. Four strains have been selected for myocardial infarction (MI) studies, including M520 (17.4%) and F344 (20.3%) with high MNDCM content and GK (2.8%) and HXB23 (1.8%) with low MNDCM content. Following MI, both M520 and F344 rats exhibit a higher percentage of BrdU+ cardiomyocytes in the border zone region compared to HXB23 rats, supporting the hypothesis that strains with high MNDCM frequency would be better at mounting a proliferative response. This also translated to a greater functional improvement and smaller scars in M520 rats, but not in F344. Additionally, we looked at the size of cardiomyocytes following MI and found that HXB23 rats (20.9% ≥8N CMs) have an increased cross-sectional area in comparison to the other strains, which may be indicative of concentric hypertrophy in response to injury. The HRDP could serve as a powerful tool for mapping traits related to outcomes following MI, allowing us to identify potential gene candidates that may be interrogated further for future use in precision medicine-based therapies. Mapping studies are ongoing.

Complex Feline Disease Mapping Using a Dense Genotyping Array.

The current feline genotyping array of 63 k single nucleotide polymorphisms has proven its utility for mapping within breeds, and its use has led to the identification of variants associated with Mendelian traits in purebred cats. However, compared to single gene disorders, association studies of complex diseases, especially with the inclusion of random bred cats with relatively low linkage disequilibrium, require a denser genotyping array and an increased sample size to provide statistically significant associations. Here, we undertook a multi-breed study of 1,122 cats, most of which were admitted and phenotyped for nine common complex feline diseases at the Cornell University Hospital for Animals. Using a proprietary 340 k single nucleotide polymorphism mapping array, we identified significant genome-wide associations with hyperthyroidism, diabetes mellitus, and eosinophilic keratoconjunctivitis. These results provide genomic locations for variant discovery and candidate gene screening for these important complex feline diseases, which are relevant not only to feline health, but also to the development of disease models for comparative studies.

Robust estimates of heritable coronary disease risk in individuals with type 2 diabetes.

Type 2 diabetes (T2D) is an important heritable risk factor for coronary artery disease (CAD), the risk of both diseases being increased by metabolic syndrome (MS). With the availability of large‐scale genome‐wide association data, we aimed to elucidate the genetic burden of CAD risk in T2D predisposed individuals within the context of MS and their shared genetic architecture. Mendelian randomization (MR) analyses supported a causal relationship between T2D and CAD [odds ratio (OR) = 1.13 per log‐odds unit 95% confidence interval (CI): 1.10–1.16; p = 1.59 × 10−17]. Simultaneously adjusting MR analyses for the effects of the T2D instrument including blood pressure, dyslipidaemia, and obesity attenuated the association between T2D and CAD (OR = 1.07, 95% CI: 1.04–1.11). Bayesian locus‐overlap analysis identified 44 regions with the same causal variant underlying T2D and CAD genetic signals (FDR < 1%) at a posterior probability >0.7; five (MHC, LPL, ABO, RAI1 and MC4R) of these regions contain genome‐wide significant (p < 5 × 10−8) associations for both traits. Given the small effect sizes observed in genome‐wide association studies for complex diseases, even with 44 potential target regions, this has implications for the likely magnitude of CAD risk reduction that might be achievable by pure T2D therapies.

Interpreting type 1 diabetes risk with genetics and single-cell epigenomics

Genetic risk variants that have been identified in genome-wide association studies of complex diseases are primarily non-coding1. Translating these risk variants into mechanistic insights requires detailed maps of gene regulation in disease-relevant cell types2. Here we combined two approaches: a genome-wide association study of type 1 diabetes (T1D) using 520,580 samples, and the identification of candidate cis-regulatory elements (cCREs) in pancreas and peripheral blood mononuclear cells using single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC–seq) of 131,554 nuclei. Risk variants for T1D were enriched in cCREs that were active in T cells and other cell types, including acinar and ductal cells of the exocrine pancreas. Risk variants at multiple T1D signals overlapped with exocrine-specific cCREs that were linked to genes with exocrine-specific expression. At the CFTR locus, the T1D risk variant rs7795896 mapped to a ductal-specific cCRE that regulated CFTR; the risk allele reduced transcription factor binding, enhancer activity and CFTR expression in ductal cells. These findings support a role for the exocrine pancreas in the pathogenesis of T1D and highlight the power of large-scale genome-wide association studies and single-cell epigenomics for understanding the cellular origins of complex disease. A genome-wide association study combined with single-cell epigenomics identifies risk loci for type 1 diabetes.

Interpreting type 1 diabetes risk with genetics and single-cell epigenomics.

Genetic risk variants that have been identified in genome-wide association studies of complex diseases are primarily non-coding1. Translating these risk variants into mechanistic insights requires detailed maps of gene regulation in disease-relevant cell types2. Here we combined two approaches: a genome-wide association study of type 1 diabetes (T1D) using 520,580 samples, and the identification of candidate cis-regulatory elements (cCREs) in pancreas and peripheral blood mononuclear cells using single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) of 131,554 nuclei. Risk variants for T1D were enriched in cCREs that were active in T cells and other cell types, including acinar and ductal cells of the exocrine pancreas. Risk variants at multiple T1D signals overlapped with exocrine-specific cCREs that were linked to genes with exocrine-specific expression. At the CFTR locus, theT1D risk variant rs7795896 mapped to a ductal-specific cCRE that regulated CFTR; the risk allele reduced transcription factor binding, enhancer activity and CFTR expression in ductal cells. These findings support a role for the exocrine pancreas in the pathogenesis of T1D and highlight the power of large-scale genome-wide association studies and single-cell epigenomics for understanding the cellular origins of complex disease.

Pleiotropy within gene variants associated with nonalcoholic fatty liver disease and traits of the hematopoietic system.

Genome-wide association studies of complex diseases, including nonalcoholic fatty liver disease (NAFLD), have demonstrated that a large number of variants are implicated in the susceptibility of multiple traits — a phenomenon known as pleiotropy that is increasingly being explored through phenome-wide association studies. We focused on the analysis of pleiotropy within variants associated with hematologic traits and NAFLD. We used information retrieved from large public National Health and Nutrition Examination Surveys, Genome-wide association studies, and phenome-wide association studies based on the general population and explored whether variants associated with NAFLD also present associations with blood cell-related traits. Next, we applied systems biology approaches to assess the potential biological connection/s between genes that predispose affected individuals to NAFLD and nonalcoholic steatohepatitis, and genes that modulate hematological-related traits—specifically platelet count. We reasoned that this analysis would allow the identification of potential molecular mediators that link NAFLD with platelets. Genes associated with platelet count are most highly expressed in the liver, followed by the pancreas, heart, and muscle. Conversely, genes associated with NAFLD presented high expression levels in the brain, lung, spleen, and colon. Functional mapping, gene prioritization, and functional analysis of the most significant loci (P < 1 × 10-8) revealed that loci involved in the genetic modulation of platelet count presented significant enrichment in metabolic and energy balance pathways. In conclusion, variants in genes influencing NAFLD exhibit pleiotropic associations with hematologic traits, particularly platelet count. Likewise, significant enrichment of related genes with variants influencing platelet traits was noted in metabolic-related pathways. Hence, this approach yields novel mechanistic insights into NAFLD pathogenesis.

Searching Far and Genome-Wide: The Relevance of Association Studies in Amyotrophic Lateral Sclerosis.

Genome-wide association studies (GWAS) and rare variant association studies (RVAS) are applied across many areas of complex disease to analyze variation in whole genomes of thousands of unrelated patients. These approaches are able to identify variants and/or biological pathways which are associated with disease status and, in contrast to traditional linkage studies or candidate gene approaches, do so without requiring multigenerational affected families, prior hypotheses, or known genes of interest. However, the novel associations identified by these methods typically have lower effect sizes than those found in classical family studies. In the motor neuron disease amyotrophic lateral sclerosis (ALS), GWAS, and RVAS have been used to identify multiple disease-associated genes but have not yet resulted in novel therapeutic interventions. There is significant urgency within the ALS community to identify additional genetic markers of disease to uncover novel biological mechanisms, stratify genetic subgroups of disease, and drive drug development. Given the widespread and increasing application of genetic association studies of complex disease, it is important to recognize the strengths and limitations of these approaches. Here, we review ALS gene discovery via GWAS and RVAS.

Parkinson's disease genetic risk in a midbrain neuronal cell line

In genome-wide association studies of complex diseases, many risk polymorphisms are found to lie in non-coding DNA and likely confer risk through allele-dependent differences in gene regulatory elements. However, because distal regulatory elements can alter gene expression at various distances on linear DNA, the identity of relevant genes is unknown for most risk loci. In Parkinson's disease, at least some genetic risk is likely intrinsic to a neuronal subpopulation of cells in the brain regions affected. In order to compare neuron-relevant methods of pairing risk polymorphisms to target genes as well as to further characterize a single-cell model of a neurodegenerative disease, we used the portionally-dopaminergic, neuronal, mesencephalic-derived cell line LUHMES to dissect differentiation-specific mechanisms of gene expression. We compared genome-wide gene expression in undifferentiated and differentiated cells with genome-wide histone H3K27ac and CTCF-bound regions. Whereas promoters and CTCF binding were largely consistent between differentiated and undifferentiated cells, enhancers were mostly unique. We matched the differentiation-specific appearance or disappearance of enhancers with changes in gene expression and identified 22,057 enhancers paired with 6388 differentially expressed genes by proximity. These enhancers are enriched with at least 13 transcription factor response elements, driving a cluster of genes involved in neurogenesis. We show that differentiated LUHMES cells, but not undifferentiated cells, show enrichment for PD-risk SNPs. Candidate genes for these loci are largely unrelated, though a subset is linked to synaptic vesicle cycling and transport, implying that PD-related disruption of these pathways is intrinsic to dopaminergic neurons.

A fast and exhaustive method for heterogeneity and epistasis analysis based on multi-objective optimization.

The existing epistasis analysis approaches have been criticized mainly for their: (i) ignoring heterogeneity during epistasis analysis; (ii) high computational costs; and (iii) volatility of performances and results. Therefore, they will not perform well in general, leading to lack of reproducibility and low power in complex disease association studies. In this work, a fast scheme is proposed to accelerate exhaustive searching based on multi-objective optimization named ESMO for concurrently analyzing heterogeneity and epistasis phenomena. In ESMO, mutual entropy and Bayesian network approaches are combined for evaluating epistatic SNP combinations. In order to be compatible with heterogeneity of complex diseases, we designed an adaptive framework based on non-dominant sort and top k selection algorithm with improved time complexity O(k*M*N) . Moreover, ESMO is accelerated by strategies such as trading space for time, calculation sharing and parallel computing. Finally, ESMO is nonparametric and model-free. We compared ESMO with other recent or classic methods using different evaluating measures. The experimental results show that our method not only can quickly handle epistasis, but also can effectively detect heterogeneity of complex population structures. https://github.com/XiongLi2016/ESMO/tree/master/ESMO-common-master . lx_hncs@163.com.

Ancestry-based stratified analysis of Immunochip data identifies novel associations with celiac disease.

To identify candidate genes in celiac disease (CD), we reanalyzed the whole Immunochip CD cohort using a different approach that clusters individuals based on immunoancestry prior to disease association analysis, rather than by geographical origin. We detected 636 new associated SNPs (P<7.02 × 10-07) and identified 5 novel genomic regions, extended 8 others previously identified and also detected 18 isolated signals defined by one or very few significant SNPs. To test whether we could identify putative candidate genes, we performed expression analyses of several genes from the top novel region (chr2:134533564-136169524), from a previously identified locus that is now extended, and a gene marked by an isolated SNP, in duodenum biopsies of active and treated CD patients, and non-celiac controls. In the largest novel region, CCNT2 and R3HDM1 were constitutively underexpressed in disease, even after gluten removal. Moreover, several genes within this region were coexpressed in patients, but not in controls. Other novel genes like KIF21B, REL and SORD also showed altered expression in active disease. Apart from the identification of novel CD loci, these results suggest that ancestry-based stratified analysis is an efficient strategy for association studies in complex diseases.

Genetic simulation tools for post-genome wide association studies of complex diseases.

Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.

JOINT ANALYSIS OF SNP AND GENE EXPRESSION DATA IN GENETIC ASSOCIATION STUDIES OF COMPLEX DISEASES.

Genetic association studies have been a popular approach for assessing the association between common Single Nucleotide Polymorphisms (SNPs) and complex diseases. However, other genomic data involved in the mechanism from SNPs to disease, e.g., gene expressions, are usually neglected in these association studies. In this paper, we propose to exploit gene expression information to more powerfully test the association between SNPs and diseases by jointly modeling the relations among SNPs, gene expressions and diseases. We propose a variance component test for the total effect of SNPs and a gene expression on disease risk. We cast the test within the causal mediation analysis framework with the gene expression as a potential mediator. For eQTL SNPs, the use of gene expression information can enhance power to test for the total effect of a SNP-set, which are the combined direct and indirect effects of the SNPs mediated through the gene expression, on disease risk. We show that the test statistic under the null hypothesis follows a mixture of χ2 distributions, which can be evaluated analytically or empirically using the resampling-based perturbation method. We construct tests for each of three disease models that is determined by SNPs only, SNPs and gene expression, or includes also their interactions. As the true disease model is unknown in practice, we further propose an omnibus test to accommodate different underlying disease models. We evaluate the finite sample performance of the proposed methods using simulation studies, and show that our proposed test performs well and the omnibus test can almost reach the optimal power where the disease model is known and correctly specified. We apply our method to re-analyze the overall effect of the SNP-set and expression of the ORMDL3 gene on the risk of asthma.

The Impact of Phenotypic and Genetic Heterogeneity on Results of Genome Wide Association Studies of Complex Diseases

Phenotypic misclassification (between cases) has been shown to reduce the power to detect association in genetic studies. However, it is conceivable that complex traits are heterogeneous with respect to individual genetic susceptibility and disease pathophysiology, and that the effect of heterogeneity has a larger magnitude than the effect of phenotyping errors. Although an intuitively clear concept, the effect of heterogeneity on genetic studies of common diseases has received little attention. Here we investigate the impact of phenotypic and genetic heterogeneity on the statistical power of genome wide association studies (GWAS). We first performed a study of simulated genotypic and phenotypic data. Next, we analyzed the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes mellitus (DM) type 1 (T1D) and type 2 (T2D), using varying proportions of each type of diabetes in order to examine the impact of heterogeneity on the strength and statistical significance of association previously found in the WTCCC data. In both simulated and real data, heterogeneity (presence of “non-cases”) reduced the statistical power to detect genetic association and greatly decreased the estimates of risk attributed to genetic variation. This finding was also supported by the analysis of loci validated in subsequent large-scale meta-analyses. For example, heterogeneity of 50% increases the required sample size by approximately three times. These results suggest that accurate phenotype delineation may be more important for detecting true genetic associations than increase in sample size.

Integration of expression profiles and endo-phenotypes in genetic association studies: A Bayesian approach to determine the path from gene to disease

In genetic association studies of complex diseases, endo-phenotypes such as expression profiles, epigenetic data, or clinical intermediate-phenotypes provide insight to understand the underlying biological path of the disease. In such situations, in order to establish the path from the gene to the disease, we have to decide whether the gene acts on the disease phenotype primarily through a specific endo-phenotype or whether the gene influences the disease through an unidentified path which is characterized by different intermediate phenotypes. Here, we address the question that a genetic locus, given its effect on an endo-phenotype, influences the trait of interest primarily through the path of the endo-phenotype. We propose a Bayesian approach that can evaluate the genetic association between the genetic locus and the phenotype of interest in the presence of the genetic effect on the endo-phenotype. Using simulation studies, we verify that our approach has the desired properties and compare this approach with a mediation approach. The proposed Bayesian approach is illustrated by an application to genome-wide association study for childhood asthma (CAMP) that contains expression profiles.

A fast and accurate algorithm for single individual haplotyping

BackgroundDue to the difficulty in separating two (paternal and maternal) copies of a chromosome, most published human genome sequences only provide genotype information, i.e., the mixed information of the underlying two haplotypes. However, phased haplotype information is needed to completely understand complex genetic polymorphisms and to increase the power of genome-wide association studies for complex diseases. With the rapid development of DNA sequencing technologies, reconstructing a pair of haplotypes from an individual's aligned DNA fragments by computer algorithms (i.e., Single Individual Haplotyping) has become a practical haplotyping approach.ResultsIn the paper, we combine two measures "errors corrected" and "fragments cut" and propose a new optimization model, called Balanced Optimal Partition (BOP), for single individual haplotyping. The model generalizes two existing models, Minimum Error Correction (MEC) and Maximum Fragments Cut (MFC), and could be made either model by using some extreme parameter values. To solve the model, we design a heuristic dynamic programming algorithm H-BOP. By limiting the number of intermediate solutions at each iteration to an appropriately chosen small integer k, H-BOP is able to solve the model efficiently.ConclusionsExtensive experimental results on simulated and real data show that when k = 8, H-BOP is generally faster and more accurate than a recent state-of-art algorithm ReFHap in haplotype reconstruction. The running time of H-BOP is linearly dependent on some of the key parameters controlling the input size and H-BOP scales well to large input data. The code of H-BOP is available to the public for free upon request to the corresponding author.

Next generation analytic tools for large scale genetic epidemiology studies of complex diseases

Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled "Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases" on September 15-16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.

Genetic copy number variants in myocardial infarction patients with hyperlipidemia.

BackgroundCardiovascular disease is the chief cause of death in Taiwan and many countries, of which myocardial infarction (MI) is the most serious condition. Hyperlipidemia appears to be a significant cause of myocardial infarction, because it causes atherosclerosis directly. In recent years, copy number variation (CNV) has been analyzed in genomewide association studies of complex diseases. In this study, CNV was analyzed in blood samples and SNP arrays from 31 myocardial infarction patients with hyperlipidemia.ResultsWe identified seven CNV regions that were associated significantly with hyperlipidemia and myocardial infarction in our patients through multistage analysis (P<0.001), at 1p21.3, 1q31.2 (CDC73), 1q42.2 (DISC1), 3p21.31 (CDCP1), 10q11.21 (RET) 12p12.3 (PIK3C2G) and 16q23.3 (CDH13), respectively. In particular, the CNV region at 10q11.21 was examined by quantitative real-time PCR, the results of which were consistent with microarray findings.ConclusionsOur preliminary results constitute an alternative method of evaluating the relationship between CNV regions and cardiovascular disease. These susceptibility CNV regions may be used as biomarkers for early-stage diagnosis of hyperlipidemia and myocardial infarction, rendering them valuable for further research and discussion.

Enrichment analysis of genetic association in genes and pathways by aggregating signals from both rare and common variants

New high-throughput sequencing technologies have brought forth opportunities for unbiased analysis of thousands of rare genomic variants in genome-wide association studies of complex diseases. Because it is hard to detect single rare variants with appreciable effect sizes at the population level, existing methods mostly aggregate effects of multiple markers by collapsing the rare variants in genes (or genomic regions). We hypothesize that a higher level of aggregation can further improve association signal strength. Using the Genetic Analysis Workshop 17 simulated data, we test a two-step strategy that first applies a collapsing method in a gene-level analysis and then aggregates the gene-level test results by performing an enrichment analysis in gene sets. We find that the gene set approach which combines signals across multiple genes outperforms testing individual genes separately and that the power of the gene set enrichment test is further improved by proper adjustment of statistics to account for gene-wise differences.

Identification of DEEP Vein Thrombosis GENETIC RISK Variants by NEXT GENERATION Sequencing of Hemostatic Genes

Abstract 710Next-generation DNA sequencing is opening new avenues for genetic association studies in complex diseases that, like deep vein thrombosis (DVT), have a strong genetic basis only partially accounted for by currently identified risk variants. Using next-generation DNA sequencing, we resequenced the entire protein-coding area and intron-exon boundaries of ≈200 hemostatic/pro-inflammatory genes (including all coagulation factor and anticoagulant protein genes) in 26 Italian individuals with idiopathic DVT of the lower limbs and 28 age-, gender- and ethnicity-matched healthy controls.In order to enrich for genetic component, DVT patients were selected from a cohort of 730 cases of idiopathic DVT referred to the Angelo Bianchi Bonomi Hemophilia and Thrombosis Center (Milan, Italy) on the basis of the following criteria: (a) negative for anti-phospholipid autoantibodies, anticoagulant protein deficiencies, factor V Leiden and prothrombin G20210A, (b) age of onset <55 years. Genomic libraries with barcode sequence-tags were pooled (in pools of 8 or 16 samples) and enriched for target DNA sequences by capture on Roche NimbleGen HD2 2.1M-probe custom chips. Sequencing was performed on ABI SOLiD 4 platforms. After sequencing, reads with the barcodes were assigned to the corresponding sample and mapped to reference human genome, NCBI36/hg18, using BFAST software.We produced more than 18 gigabases of raw sequence data to sequence at high depth of coverage (>40X after duplicate read removal) the 700-kilobase target region. A total of 2351 single nucleotide variants (SNVs) and 121 short indels were identified. We developed a dedicated software in order to be able to run association analyses in our dataset using PLINK. A plain association analysis was used to compare the frequencies of common SNVs, whereas a gene-based analysis of restricted non-synonymous mutations was used to reveal potential associations of rare variants.Of 626 common SNVs tested for association, 32 SNVs from 13 genes were associated with p<0.01. These include 8 missense variants at 6 genes, 2 in genes already reported in association with DVT (FGA, ABO) and 6 in genes not previously reported (LPA, COL4A6, PLCG2 and KNG1). As a positive control we confirmed the association of rs6050 at FGA in ∼1400 Italian individuals (p=1.9 × 10−5, OR 1.45; 95% CI, 1.22–1.72). Replication of the other variants is ongoing. Gene-based analyses revealed an excess of private missense variants at anticoagulant protein genes (13 vs 3 variants; binomial probability, p=0.005) and at collagen 4 genes (14 vs 6 variants; binomial probability, p=0.02) in DVT cases compared to controls. We are currently expanding our investigations to 100 DVT cases and 100 controls.Although obtained in a relatively small number of patients and controls, our results illustrate the potential of next-generation sequencing for the discovery of genetic variation predisposing to complex diseases like DVT. Disclosures:No relevant conflicts of interest to declare.

Necessary and Sufficient Conditions for Equality of Sibling and Parent-Offspring Risk of a Disease Endophenotype in Case Families

A large number of linkage and association studies of complex diseases focus on analysis of a more common or more easily measured disease endophenotype. The motivation for this approach is that there is a pleiotropic locus common to both the disease and the endophenotype and that this locus is a major genetic determinant of the endophenotype. In this paper, we determine the conditions under which the risk of the endophenotype in siblings of affected probands with disease equals the risk of the endophenotype in the offspring (parents) of affected parents (offspring) with disease. In doing so we prove that this equality holds if and only if the penetrance of either the endophenotype or the disease (but not necessarily both) is additive.