Evaluation of IFITM3 rs12252 Association With Severe Pediatric Influenza Infection.

Background: Interferon-induced transmembrane protein 3 (IFITM3) restricts endocytic fusion of influenza virus. IFITM3 rs12252_C, a putative alternate splice site, has been associated with influenza severity in adults. IFITM3 has not been evaluated in pediatric influenza. Methods: The Pediatric Influenza (PICFLU) study enrolled children with suspected influenza infection across 38 pediatric intensive care units during November 2008 to April 2016. IFITM3 was sequenced in patients and parents were genotyped for specific variants for family-based association testing. rs12252 was genotyped in 54 African-American pediatric outpatients with influenza (FLU09), included in the population-based comparisons with 1000 genomes. Splice site analysis of rs12252_C was performed using PICFLU and FLU09 patient RNA. Results: In PICFLU, 358 children had influenza infection. We identified 22 rs12252_C homozygotes in 185 white non-Hispanic children. rs12252_C was not associated with influenza infection in population or family-based analyses. We did not identify the Δ21 IFITM3 isoform in RNAseq data. The rs12252 genotype was not associated with IFITM3 expression levels, nor with critical illness severity. No novel rare IFITM3 functional variants were identified. Conclusions: rs12252 was not associated with susceptibility to influenza-related critical illness in children or with critical illness severity. Our data also do not support it being a splice site.

Staphylococcus aureus α-Toxin Response Distinguishes Respiratory Virus-Methicillin-Resistant S. aureus Coinfection in Children.

BACKGROUND: Development of methicillin-resistant Staphylococcus aureus (MRSA) pneumonia after a respiratory viral infection is frequently fatal in children. In mice, S. aureus α-toxin directly injures pneumocytes and increases mortality, whereas α-toxin blockade mitigates disease. The role of α-toxin in pediatric staphylococcal-viral coinfection is unclear. METHODS: We enrolled children across 34 North American pediatric intensive care units with acute respiratory failure and suspected influenza virus infection. Serial serum anti-α-toxin antibody titers and functional α-toxin neutralization capacity were compared across children coinfected with MRSA or methicillin-susceptible S. aureus (MSSA) and control children infected with influenza virus only. MRSA isolates were tested for α-toxin production and lethality in a murine pneumonia model. RESULTS: Influenza virus was identified in 22 of 25 children with MRSA coinfection (9 died) and 22 patients with MSSA coinfection (all survived). Initial α-toxin-specific antibody titers were similar, compared with those in the 13 controls. In patients with serial samples, only MRSA-coinfected patients showed time-dependent increases in anti-α-toxin titer and functional neutralization capacity. MRSA α-toxin production from patient isolates correlated with initial serologic titers and with mortality in murine pneumonia. CONCLUSIONS: These data implicate α-toxin as a relevant antigen in severe pediatric MRSA pneumonia associated with respiratory viral infection, supporting a potential role for toxin-neutralizing therapy.

Identifying treatment effect heterogeneity in clinical trials using subpopulations of events: STEPP.

BACKGROUND: Investigators conducting randomized clinical trials often explore treatment effect heterogeneity to assess whether treatment efficacy varies according to patient characteristics. Identifying heterogeneity is central to making informed personalized healthcare decisions. Treatment effect heterogeneity can be investigated using subpopulation treatment effect pattern plot (STEPP), a non-parametric graphical approach that constructs overlapping patient subpopulations with varying values of a characteristic. Procedures for statistical testing using subpopulation treatment effect pattern plot when the endpoint of interest is survival remain an area of active investigation. METHODS: A STEPP analysis was used to explore patterns of absolute and relative treatment effects for varying levels of a breast cancer biomarker, Ki-67, in the phase III Breast International Group 1-98 randomized clinical trial, comparing letrozole to tamoxifen as adjuvant therapy for postmenopausal women with hormone receptor-positive breast cancer. Absolute treatment effects were measured by differences in 4-year cumulative incidence of breast cancer recurrence, while relative effects were measured by the subdistribution hazard ratio in the presence of competing risks using O-E (observed-minus-expected) methodology, an intuitive non-parametric method. While estimation of hazard ratio values based on O-E methodology has been shown, a similar development for the subdistribution hazard ratio has not. Furthermore, we observed that the subpopulation treatment effect pattern plot analysis may not produce results, even with 100 patients within each subpopulation. After further investigation through simulation studies, we observed inflation of the type I error rate of the traditional test statistic and sometimes singular variance-covariance matrix estimates that may lead to results not being produced. This is due to the lack of sufficient number of events within the subpopulations, which we refer to as instability of the subpopulation treatment effect pattern plot analysis. We introduce methodology designed to improve stability of the subpopulation treatment effect pattern plot analysis and generalize O-E methodology to the competing risks setting. Simulation studies were designed to assess the type I error rate of the tests for a variety of treatment effect measures, including subdistribution hazard ratio based on O-E estimation. This subpopulation treatment effect pattern plot methodology and standard regression modeling were used to evaluate heterogeneity of Ki-67 in the Breast International Group 1-98 randomized clinical trial. RESULTS: We introduce methodology that generalizes O-E methodology to the competing risks setting and that improves stability of the STEPP analysis by pre-specifying the number of events across subpopulations while controlling the type I error rate. The subpopulation treatment effect pattern plot analysis of the Breast International Group 1-98 randomized clinical trial showed that patients with high Ki-67 percentages may benefit most from letrozole, while heterogeneity was not detected using standard regression modeling. CONCLUSION: The STEPP methodology can be used to study complex patterns of treatment effect heterogeneity, as illustrated in the Breast International Group 1-98 randomized clinical trial. For the subpopulation treatment effect pattern plot analysis, we recommend a minimum of 20 events within each subpopulation.

Subpopulation Treatment Effect Pattern Plot (STEPP) analysis for continuous, binary, and count outcomes.

BACKGROUND: For the past few decades, randomized clinical trials have provided evidence for effective treatments by comparing several competing therapies. Their successes have led to numerous new therapies to combat many diseases. However, since their conclusions are based on the entire cohort in the trial, the treatment recommendation is for everyone, and may not be the best option for an individual. Medical research is now focusing more on providing personalized care for patients, which requires investigating how patient characteristics, including novel biomarkers, modify the effect of current treatment modalities. This is known as heterogeneity of treatment effects. A better understanding of the interaction between treatment and patient-specific prognostic factors will enable practitioners to expand the availability of tailored therapies, with the ultimate goal of improving patient outcomes. The Subpopulation Treatment Effect Pattern Plot (STEPP) approach was developed to allow researchers to investigate the heterogeneity of treatment effects on survival outcomes across values of a (continuously measured) covariate, such as a biomarker measurement. METHODS: Here, we extend the Subpopulation Treatment Effect Pattern Plot approach to continuous, binary, and count outcomes, which can be easily modeled using generalized linear models. With this extension of Subpopulation Treatment Effect Pattern Plot, these additional types of treatment effects within subpopulations defined with respect to a covariate of interest can be estimated, and the statistical significance of any observed heterogeneity of treatment effect can be assessed using permutation tests. The desirable feature that commonly used models are applied to well-defined patient subgroups to estimate treatment effects is retained in this extension. RESULTS: We describe a simulation study to confirm that the proper Type I error rate is maintained when there is no treatment heterogeneity, and a power study to show that the statistics have power to detect treatment heterogeneity under alternative scenarios. As an illustration, we apply the methods to data from the Aspirin/Folate Polyp Prevention Study, a clinical trial evaluating the effect of oral aspirin, folic acid, or both as a chemoprevention agent against colorectal adenomas. The pre-existing R software package stepp has been extended to handle continuous, binary, and count data using Gaussian, Bernoulli, and Poisson models, and it is available on the Comprehensive R Archive Network. CONCLUSION: The extension of the method and the availability of new software now permit STEPP to be applied to the full range of clinical trial end points.

A novel method for detecting association between DNA methylation and diseases using spatial information.

DNA methylation may represent an important contributor to the missing heritability described in complex trait genetics. However, technology to measure DNA methylation has outpaced statistical methods for analysis. Taking advantage of the recent finding that methylated sites cluster together, we propose a Spatial Clustering Method (SCM) to detect differentially methylated regions (DMRs) in the genome in case and control studies using spatial location information. This new method compares the distribution of distances in cases and controls between DNA methylation marks in the genomic region of interest. A statistic is computed based on these distances. Proper type I error rate is maintained and statistical significance is evaluated using permutation test. The effectiveness of the SCM we propose is evaluated by a simulation study. By simulating a simple disease model, we demonstrate that SCM has good power to detect DMRs associated with the disease. Finally, we applied the SCM to an exploratory analysis of chromosome 14 from a colorectal cancer data set and identified statistically significant genomic regions. Identification of these regions should lead to a better understanding of methylated sites and their contribution to disease. The SCM can be used as a reliable statistical method for the identification of DMRs associated with disease states in exploratory epigenetic analyses.

A general semi-parametric approach to the analysis of genetic association studies in population-based designs.

BACKGROUND: For genetic association studies in designs of unrelated individuals, current statistical methodology typically models the phenotype of interest as a function of the genotype and assumes a known statistical model for the phenotype. In the analysis of complex phenotypes, especially in the presence of ascertainment conditions, the specification of such model assumptions is not straight-forward and is error-prone, potentially causing misleading results. RESULTS: In this paper, we propose an alternative approach that treats the genotype as the random variable and conditions upon the phenotype. Thereby, the validity of the approach does not depend on the correctness of assumptions about the phenotypic model. Misspecification of the phenotypic model may lead to reduced statistical power. Theoretical derivations and simulation studies demonstrate both the validity and the advantages of the approach over existing methodology. In the COPDGene study (a GWAS for Chronic Obstructive Pulmonary Disease (COPD)), we apply the approach to a secondary, quantitative phenotype, the Fagerstrom nicotine dependence score, that is correlated with COPD affection status. The software package that implements this method is available. CONCLUSIONS: The flexibility of this approach enables the straight-forward application to quantitative phenotypes and binary traits in ascertained and unascertained samples. In addition to its robustness features, our method provides the platform for the construction of complex statistical models for longitudinal data, multivariate data, multi-marker tests, rare-variant analysis, and others.

Handling the data management needs of high-throughput sequencing data: SpeedGene, a compression algorithm for the efficient storage of genetic data.

BACKGROUND: As Next-Generation Sequencing data becomes available, existing hardware environments do not provide sufficient storage space and computational power to store and process the data due to their enormous size. This is and will be a frequent problem that is encountered everyday by researchers who are working on genetic data. There are some options available for compressing and storing such data, such as general-purpose compression software, PBAT/PLINK binary format, etc. However, these currently available methods either do not offer sufficient compression rates, or require a great amount of CPU time for decompression and loading every time the data is accessed. RESULTS: Here, we propose a novel and simple algorithm for storing such sequencing data. We show that, the compression factor of the algorithm ranges from 16 to several hundreds, which potentially allows SNP data of hundreds of Gigabytes to be stored in hundreds of Megabytes. We provide a C++ implementation of the algorithm, which supports direct loading and parallel loading of the compressed format without requiring extra time for decompression. By applying the algorithm to simulated and real datasets, we show that the algorithm gives greater compression rate than the commonly used compression methods, and the data-loading process takes less time. Also, The C++ library provides direct-data-retrieving functions, which allows the compressed information to be easily accessed by other C++ programs. CONCLUSIONS: The SpeedGene algorithm enables the storage and the analysis of next generation sequencing data in current hardware environment, making system upgrades unnecessary.

On the analysis of sequence data: testing for disease susceptibility loci using patterns of linkage disequilibrium.

Despite the numerous and successful applications of genome-wide association studies (GWASs), there has been a lot of difficulty in discovering disease susceptibility loci (DSLs). This is due to the fact that the GWAS approach is an indirect mapping technique, often identifying markers. For the identification of DSLs, which is required for the understanding of the genetic pathways for complex diseases, sequencing data that examines every genetic locus directly is necessary. Yet, there is currently a lack of methodology targeted at the identification of the DSLs in sequencing data: existing methods localize the causal variant to a region but not to a single variant, and therefore do not allow one to identify unique loci that cause the phenotype association. Here, we have developed such a method to determine if there is evidence that an individual loci affects case/control status with sequencing data. This methodology differs from other rare variant approaches: rather than testing an entire region comprised of many loci for association with the phenotype, we can identify the individual genetic locus that causes the association between the phenotype and the genetic region. For each variant, the test determines if the pattern of linkage disequilibrium (LD) across the other variants coincides with the pattern expected if that variant were a DSL. Power simulations show that the method successfully detects the causal variant, distinguishing it from other nearby variants (in high LD with the causal variant), and outperforms the standard tests. The efficiency of the method is especially apparent with small samples, which are currently realistic for studies due to sequencing data costs. The practical relevance of the approach is illustrated by an application to a sequencing dataset for nonsyndromic cleft lip with or without cleft palate. The proposed method implicated one variant (P = 0.002, 0.062 after Bonferroni correction), which was not found by standard analyses. Code for implementation is available.

Quantitative trait prediction based on genetic marker-array data, a simulation study.

MOTIVATION: Using simulation studies for quantitative trait loci (QTL), we evaluate the prediction quality of regression models that include as covariates single-nucleotide polymorphism (SNP) genetic markers which did not achieve genome-wide significance in the original genome-wide association study, but were among the SNPs with the smallest P-value for the selected association test. We compare the results of such regression models to the standard approach which is to include only SNPs that achieve genome-wide significance. Using mean square prediction error as the model metric, our simulation results suggest that by using the coefficient of determination (R(2)) value as a guideline to increase or reduce the number of SNPs included in the regression model, we can achieve better prediction quality than the standard approach. However, important parameters such as trait heritability, the approximate number of QTLs, etc. have to be determined from previous studies or have to be estimated accurately.

Clinical and analytical validation of FoundationOne®CDx, a comprehensive genomic profiling assay for solid tumors.

FoundationOne®CDx (F1CDx) is a United States (US) Food and Drug Administration (FDA)-approved companion diagnostic test to identify patients who may benefit from treatment in accordance with the approved therapeutic product labeling for 28 drug therapies. F1CDx utilizes next-generation sequencing (NGS)-based comprehensive genomic profiling (CGP) technology to examine 324 cancer genes in solid tumors. F1CDx reports known and likely pathogenic short variants (SVs), copy number alterations (CNAs), and select rearrangements, as well as complex biomarkers including tumor mutational burden (TMB) and microsatellite instability (MSI), in addition to genomic loss of heterozygosity (gLOH) in ovarian cancer. CGP services can reduce the complexity of biomarker testing, enabling precision medicine to improve treatment decision-making and outcomes for cancer patients, but only if test results are reliable, accurate, and validated clinically and analytically to the highest standard available. The analyses presented herein demonstrate the extensive analytical and clinical validation supporting the F1CDx initial and subsequent FDA approvals to ensure high sensitivity, specificity, and reliability of the data reported. The analytical validation included several in-depth evaluations of F1CDx assay performance including limit of detection (LoD), limit of blank (LoB), precision, and orthogonal concordance for SVs (including base substitutions [SUBs] and insertions/deletions [INDELs]), CNAs (including amplifications and homozygous deletions), genomic rearrangements, and select complex biomarkers. The assay validation of >30,000 test results comprises a considerable and increasing body of evidence that supports the clinical utility of F1CDx to match patients with solid tumors to targeted therapies or immunotherapies based on their tumor's genomic alterations and biomarkers. F1CDx meets the clinical needs of providers and patients to receive guideline-based biomarker testing, helping them keep pace with a rapidly evolving field of medicine.

Evaluation of Mannose Binding Lectin Gene Variants in Pediatric Influenza Virus-Related Critical Illness.

Background: Mannose-binding lectin (MBL) is an innate immune protein with strong biologic plausibility for protecting against influenza virus-related sepsis and bacterial co-infection. In an autopsy cohort of 105 influenza-infected young people, carriage of the deleterious MBL gene MBL2_Gly54Asp("B") mutation was identified in 5 of 8 individuals that died from influenza-methicillin-resistant Staphylococcus aureus (MRSA) co-infection. We evaluated MBL2 variants known to influence MBL levels with pediatric influenza-related critical illness susceptibility and/or severity including with bacterial co-infections. Methods: We enrolled children and adolescents with laboratory-confirmed influenza infection across 38 pediatric intensive care units from November 2008 to June 2016. We sequenced MBL2 "low-producer" variants rs11003125("H/L"), rs7096206("Y/X"), rs1800450Gly54Asp("B"), rs1800451Gly57Glu("C"), rs5030737Arg52Cys("D") in patients and biologic parents. We measured serum levels and compared complement activity in low-producing homozygotes ("B/B," "C/C") to HYA/HYA controls. We used a population control of 1,142 healthy children and also analyzed family trios (PBAT/HBAT) to evaluate disease susceptibility, and nested case-control analyses to evaluate severity. Results: We genotyped 420 patients with confirmed influenza-related sepsis: 159 (38%) had acute lung injury (ALI), 165 (39%) septic shock, and 30 (7%) died. Although bacterial co-infection was diagnosed in 133 patients (32%), only MRSA co-infection (n = 33, 8% overall) was associated with death (p < 0.0001), present in 11 of 30 children that died (37%). MBL2 variants predicted serum levels and complement activation as expected. We found no association between influenza-related critical illness susceptibility and MBL2 variants using family trios (633 biologic parents) or compared to population controls. MBL2 variants were not associated with admission illness severity, septic shock, ALI, or bacterial co-infection diagnosis. Carriage of low-MBL producing MBL2 variants was not a risk factor for mortality, but children that died did have higher carriage of one or more B alleles (OR 2.3; p = 0.007), including 7 of 11 with influenza MRSA-related death (vs. 2 of 22 survivors: OR 14.5, p = 0.0002). Conclusions:MBL2 variants that decrease MBL levels were not associated with susceptibility to pediatric influenza-related critical illness or with multiple measures of critical illness severity. We confirmed a prior report of higher B allele carriage in a relatively small number of young individuals with influenza-MRSA associated death.

Body mass index change in gastrointestinal cancer and chronic obstructive pulmonary disease is associated with Dedicator of Cytokinesis 1.

BACKGROUND: There have been a number of candidate gene association studies of cancer cachexia-related traits, but no genome-wide association study (GWAS) has been published to date. Cachexia presents in patients with a number of complex traits, including both cancer and COPD. The objective of the current investigation was to search for a shared genetic aetiology for change in body mass index (ΔBMI) among cancer and COPD by using GWAS data in the Framingham Heart Study. METHODS: A linear mixed effects model accounting for age, sex, and change in smoking status was used to calculate ΔBMI in participants over 40 years of age with three consecutive BMI time points (n = 4162). Four GWAS of ΔBMI using generalized estimating equations were performed among 1085 participants with a cancer diagnosis, 204 with gastrointestinal (GI) cancer, 112 with lung cancer, and 237 with COPD to test for association with 418 365 single-nucleotide polymorphisms (SNPs). RESULTS: Two SNPs reached a level of genome-wide significance (P < 5 × 10-8 ) with ΔBMI: (i) rs41526344 within the CNTN4 gene, among COPD cases (ß = 0.13, P = 4.3 × 10-8 ); and (ii) rs4751240 in the gene Dedicator of Cytokinesis 1 (DOCK1) among GI cancer cases (ß = 0.10, P = 1.9 × 10-8 ). The DOCK1 SNP association replicated in the ΔBMI GWAS among COPD cases (ßmeta-analyis = 0.10, Pmeta-analyis = 9.3 × 10-10 ). The DOCK1 gene codes for the dedicator of cytokinesis 1 protein, which has a role in myoblast fusion. CONCLUSIONS: In sum, one statistically significant common variant in the DOCK1 gene was associated with ΔBMI in GI cancer and COPD cases providing support for at least partially shared aetiology of ΔBMI in complex diseases.

Phase III Randomized Study of 4 Weeks of High-Dose Interferon-α-2b in Stage T2bNO, T3a-bNO, T4a-bNO, and T1-4N1a-2a (microscopic) Melanoma: A Trial of the Eastern Cooperative Oncology Group-American College of Radiology Imaging Network Cancer Research Group (E1697).

Purpose To test the efficacy of 4 weeks of intravenous (IV) induction with high-dose interferon (IFN) as part of the Eastern Cooperative Oncology Group regimen compared with observation (OBS) in patients with surgically resected intermediate-risk melanoma. Patients and Methods In this intergroup international trial, eligible patients had surgically resected cutaneous melanoma in the following categories: (1) T2bN0, (2) T3a-bN0, (3) T4a-bN0, and (4) T1-4N1a-2a (microscopic). Patients were randomly assigned to receive IFN α-2b at 20 MU/m2/d IV for 5 days (Monday to Friday) every week for 4 weeks (IFN) or OBS. Stratification factors were pathologic lymph node status, lymph node staging procedure, Breslow depth, ulceration of the primary lesion, and disease stage. The primary end point was relapse-free survival. Secondary end points included overall survival, toxicity, and quality of life. Results A total of 1,150 patients were randomly assigned. At a median follow-up of 7 years, the 5-year relapse-free survival rate was 0.70 (95% CI, 0.66 to 0.74) for OBS and 0.70, (95% CI, 0.66 to 0.74) for IFN ( P = .964). The 5-year overall survival rate was 0.83 (95% CI, 0.79 to 0.86) for OBS and 0.83 (95% CI, 0.80 to 0.86) for IFN ( P = .558). Treatment-related grade 3 and higher toxicity was 4.6% versus 57.9% for OBS and IFN, respectively ( P < .001). Quality of life was worse for the treated group. Conclusion Four weeks of IV induction as part of the Eastern Cooperative Oncology Group high-dose IFN regimen is not better than OBS alone for patients with intermediate-risk melanoma as defined in this trial.

A comparative analysis of family-based and population-based association tests using whole genome sequence data.

The revolution in next-generation sequencing has made obtaining both common and rare high-quality sequence variants across the entire genome feasible. Because researchers are now faced with the analytical challenges of handling a massive amount of genetic variant information from sequencing studies, numerous methods have been developed to assess the impact of both common and rare variants on disease traits. In this report, whole genome sequencing data from Genetic Analysis Workshop 18 was used to compare the power of several methods, considering both family-based and population-based designs, to detect association with variants in the MAP4 gene region and on chromosome 3 with blood pressure. To prioritize variants across the genome for testing, variants were first functionally assessed using prediction algorithms and expression quantitative trait loci (eQTLs) data. Four set-based tests in the family-based association tests (FBAT) framework--FBAT-v, FBAT-lmm, FBAT-m, and FBAT-l--were used to analyze 20 pedigrees, and 2 variance component tests, sequence kernel association test (SKAT) and genome-wide complex trait analysis (GCTA), were used with 142 unrelated individuals in the sample. Both set-based and variance-component-based tests had high power and an adequate type I error rate. Of the various FBATs, FBAT-l demonstrated superior performance, indicating the potential for it to be used in rare-variant analysis. The updated FBAT package is available at: http://www.hsph.harvard.edu/fbat/.

The rare TREM2 R47H variant exerts only a modest effect on Alzheimer disease risk.

OBJECTIVES: Recently, 2 independent studies reported that a rare missense variant, rs75932628 (R47H), in exon 2 of the gene encoding the "triggering receptor expressed on myeloid cells 2" (TREM2) significantly increases the risk of Alzheimer disease (AD) with an effect size comparable to that of the APOE ε4 allele. METHODS: In this study, we attempted to replicate the association between rs75932628 and AD risk by directly genotyping rs75932628 in 2 independent Caucasian family cohorts consisting of 927 families (with 1,777 affected and 1,235 unaffected) and in 2 Caucasian case-control cohorts composed of 1,314 cases and 1,609 controls. In addition, we imputed genotypes in 3 independent Caucasian case-control cohorts containing 1,906 cases and 1,503 controls. RESULTS: Meta-analysis of the 2 family-based and the 5 case-control cohorts yielded a p value of 0.0029, while the overall summary estimate (using case-control data only) resulted in an odds ratio of 1.67 (95% confidence interval 0.95-2.92) for the association between the TREM2 R47H and increased AD risk. CONCLUSIONS: While our results serve to confirm the association between R47H and risk of AD, the observed effect on risk was substantially smaller than that previously reported.

Rare variant analysis for family-based design.

Genome-wide association studies have been able to identify disease associations with many common variants; however most of the estimated genetic contribution explained by these variants appears to be very modest. Rare variants are thought to have larger effect sizes compared to common SNPs but effects of rare variants cannot be tested in the GWAS setting. Here we propose a novel method to test for association of rare variants obtained by sequencing in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency SNPs that may be enriched in functional variants can be upweighted compared to common variants. Using simulations we show that the family-based methods perform at par with the population-based methods under no population stratification. By construction, family-based tests are completely robust to population stratification; we show that our proposed methods remain valid even when population stratification is present.

Identifying causal rare variants of disease through family-based analysis of Genetics Analysis Workshop 17 data set.

Linkage- and association-based methods have been proposed for mapping disease-causing rare variants. Based on the family information provided in the Genetic Analysis Workshop 17 data set, we formulate a two-pronged approach that combines both methods. Using the identity-by-descent information provided for eight extended pedigrees (n = 697) and the simulated quantitative trait Q1, we explore various traditional nonparametric linkage analysis methods; the best result is obtained by assuming between-family heterogeneity and applying the Haseman-Elston regression to each pedigree separately. We discover strong signals from two genes in two different families and weaker signals for a third gene from two other families. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency < 1% or < 3%) designated as causal for Q1. Family-based association tests correctly identified causal single-nucleotide polymorphisms for four genes (KDR, VEGFA, VEGFC, and FLT1). Our results suggest that both linkage and association tests with families show promise for identifying rare variants.