Matthew P. Conomos

Detectable clonal mosaicism from birth to old age and its relationship to cancer

We detected clonal mosaicism for large chromosomal anomalies (duplications, deletions and uniparental disomy) using SNP microarray data from over 50,000 subjects recruited for genome-wide association studies. This detection method requires a relatively high frequency of cells with the same abnormal karyotype (>5–10%; presumably of clonal origin) in the presence of normal cells. The frequency of detectable clonal mosaicism in peripheral blood is low (<0.5%) from birth until 50 years of age, after which it rapidly rises to 2–3% in the elderly. Many of the mosaic anomalies are characteristic of those found in hematological cancers and identify common deleted regions with genes previously associated with these cancers. Although only 3% of subjects with detectable clonal mosaicism had any record of hematological cancer before DNA sampling, those without a previous diagnosis have an estimated tenfold higher risk of a subsequent hematological cancer (95% confidence interval = 6–18).

Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models

Linear mixed models (LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we show that LMMs are generally inappropriate for analyzing binary traits when population stratification leads to violation of the LMMs constant-residual variance assumption. To overcome this problem, we develop a computationally efficient logistic mixed model approach for genome-wide analysis of binary traits, the generalized linear mixed model association test (GMMAT). This approach fits a logistic mixed model once per GWAS and performs score tests under the null hypothesis of no association between a binary trait and individual genetic variants. We show in simulation studies and real data analysis that GMMAT effectively controls for population structure and relatedness when analyzing binary traits in a wide variety of study designs.

GWASTools: an R/Bioconductor package for quality control and analysis of Genome-Wide Association Studies

GWASTools is an R/Bioconductor package for quality control and analysis of genome-wide association studies (GWAS). GWASTools brings the interactive capability and extensive statistical libraries of R to GWAS. Data are stored in NetCDF format to accommodate extremely large datasets that cannot fit within Rs memory limits. The documentation includes instructions for converting data from multiple formats, including variants called from sequencing. GWASTools provides a convenient interface for linking genotypes and intensity data with sample and single nucleotide polymorphism annotation.

Model-free Estimation of Recent Genetic Relatedness

Genealogical inference from genetic data is essential for a variety of applications in human genetics. In genome-wide and sequencing association studies, for example, accurate inference on both recent genetic relatedness, such as family structure, and more distant genetic relatedness, such as population structure, is necessary for protection against spurious associations. Distinguishing familial relatedness from population structure with genotype data, however, is difficult because both manifest as genetic similarity through the sharing of alleles. Existing approaches for inference on recent genetic relatedness have limitations in the presence of population structure, where they either (1) make strong and simplifying assumptions about population structure, which are often untenable, or (2) require correct specification of and appropriate reference population panels for the ancestries in the sample, which might be unknown or not well defined. Here, we propose PC-Relate, a model-free approach for estimating commonly used measures of recent genetic relatedness, such as kinship coefficients and IBD sharing probabilities, in the presence of unspecified structure. PC-Relate uses principal components calculated from genome-screen data to partition genetic correlations among sampled individuals due to the sharing of recent ancestors and more distant common ancestry into two separate components, without requiring specification of the ancestral populations or reference population panels. In simulation studies with population structure, including admixture, we demonstrate that PC-Relate provides accurate estimates of genetic relatedness and improved relationship classification over widely used approaches. We further demonstrate the utility of PC-Relate in applications to three ancestrally diverse samples that vary in both size and genealogical complexity.

Genome-wide Association Study Identifies a Genetic Variant Associated with Risk for More Aggressive Prostate Cancer

Background: Of the 200,000 U.S. men annually diagnosed with prostate cancer, approximately 20% to 30% will have clinically aggressive disease. Although factors such as Gleason score and tumor stage are used to assess prognosis, there are no biomarkers to identify men at greater risk for developing aggressive prostate cancer. We therefore undertook a search for genetic variants associated with risk of more aggressive disease. Methods: A genome-wide scan was conducted in 202 prostate cancer cases with a more aggressive phenotype and 100 randomly sampled, age-matched prostate-specific antigen screened negative controls. Analysis of 387,384 autosomal single nucleotide polymorphisms (SNPs) was followed by validation testing in an independent set of 527 cases with more aggressive and 595 cases with less aggressive prostate cancer, and 1,167 age-matched controls. Results: A variant on 15q13, rs6497287, was confirmed to be most strongly associated with more aggressive (Pdiscovery = 5.20 × 10−5, Pvalidation = 0.004) than less aggressive disease (P = 0.14). Another SNP on 3q26, rs3774315, was found to be associated with prostate cancer risk; however, the association was not stronger for more aggressive disease. Conclusions: This study provides suggestive evidence for a genetic predisposition to more aggressive prostate cancer and highlights the fact that larger studies are warranted to confirm this supposition and identify further risk variants. Impact: These findings raise the possibility that assessment of genetic variation may one day be useful to discern men at higher risk for developing clinically significant prostate cancer. Cancer Epidemiol Biomarkers Prev; 20(6); 1196–203. ©2011 AACR.

Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness

Population structure inference with genetic data has been motivated by a variety of applications in population genetics and genetic association studies. Several approaches have been proposed for the identification of genetic ancestry differences in samples where study participants are assumed to be unrelated, including principal components analysis (PCA), multidimensional scaling (MDS), and model‐based methods for proportional ancestry estimation. Many genetic studies, however, include individuals with some degree of relatedness, and existing methods for inferring genetic ancestry fail in related samples. We present a method, PC‐AiR, for robust population structure inference in the presence of known or cryptic relatedness. PC‐AiR utilizes genome‐screen data and an efficient algorithm to identify a diverse subset of unrelated individuals that is representative of all ancestries in the sample. The PC‐AiR method directly performs PCA on the identified ancestry representative subset and then predicts components of variation for all remaining individuals based on genetic similarities. In simulation studies and in applications to real data from Phase III of the HapMap Project, we demonstrate that PC‐AiR provides a substantial improvement over existing approaches for population structure inference in related samples. We also demonstrate significant efficiency gains, where a single axis of variation from PC‐AiR provides better prediction of ancestry in a variety of structure settings than using 10 (or more) components of variation from widely used PCA and MDS approaches. Finally, we illustrate that PC‐AiR can provide improved population stratification correction over existing methods in genetic association studies with population structure and relatedness.

Estimating and adjusting for ancestry admixture in statistical methods for relatedness inference, heritability estimation, and association testing.

It is well known that genetic association studies are not robust to population stratification. Two widely used approaches for the detection and correction of population structure are principal component analysis and model-based estimation of ancestry. These methods have been shown to give reliable inference on population structure in unrelated samples. We evaluated these two approaches in Mexican American pedigrees provided by the Genetic Analysis Workshop 18. We also estimated identity-by-descent sharing probabilities and kinship coefficients, with adjustment for ancestry admixture, to confirm documented pedigree relationships as well as to identify cryptic relatedness in the sample. We also estimated the heritability of the first simulated replicate of diastolic blood pressure (DBP). Finally, we performed an association analysis with simulated DBP, comparing the performance of an association method that corrects for population structure but does not account for relatedness to a method that adjusts for both population and pedigree structure. Analyses with simulated DBP were performed with knowledge of the underlying trait model.

Genome-wide association study of dental caries in the Hispanic Communities Health Study/Study of Latinos (HCHS/SOL).

Dental caries is the most common chronic disease worldwide, and exhibits profound disparities in the USA with racial and ethnic minorities experiencing disproportionate disease burden. Though heritable, the specific genes influencing risk of dental caries remain largely unknown. Therefore, we performed genome-wide association scans (GWASs) for dental caries in a population-based cohort of 12 000 Hispanic/Latino participants aged 18-74 years from the HCHS/SOL. Intra-oral examinations were used to generate two common indices of dental caries experience which were tested for association with 27.7 M genotyped or imputed single-nucleotide polymorphisms separately in the six ancestry groups. A mixed-models approach was used, which adjusted for age, sex, recruitment site, five principal components of ancestry and additional features of the sampling design. Meta-analyses were used to combine GWAS results across ancestry groups. Heritability estimates ranged from 20-53% in the six ancestry groups. The most significant association observed via meta-analysis for both phenotypes was in the region of the NAMPT gene (rs190395159; P-value = 6 × 10(-10)), which is involved in many biological processes including periodontal healing. Another significant association was observed for rs72626594 (P-value = 3 × 10(-8)) downstream of BMP7, a tooth development gene. Other associations were observed in genes lacking known or plausible roles in dental caries. In conclusion, this was the largest GWAS of dental caries, to date and was the first to target Hispanic/Latino populations. Understanding the factors influencing dental caries susceptibility may lead to improvements in prediction, prevention and disease management, which may ultimately reduce the disparities in oral health across racial, ethnic and socioeconomic strata.

SeqArray—a storage-efficient high-performance data format for WGS variant calls

Motivation: Whole‐genome sequencing (WGS) data are being generated at an unprecedented rate. Analysis of WGS data requires a flexible data format to store the different types of DNA variation. Variant call format (VCF) is a general text‐based format developed to store variant genotypes and their annotations. However, VCF files are large and data retrieval is relatively slow. Here we introduce a new WGS variant data format implemented in the R/Bioconductor package ‘SeqArray’ for storing variant calls in an array‐oriented manner which provides the same capabilities as VCF, but with multiple high compression options and data access using high‐performance parallel computing. Results: Benchmarks using 1000 Genomes Phase 3 data show file sizes are 14.0 Gb (VCF), 12.3 Gb (BCF, binary VCF), 3.5 Gb (BGT) and 2.6 Gb (SeqArray) respectively. Reading genotypes in the SeqArray package are two to three times faster compared with the htslib C library using BCF files. For the allele frequency calculation, the implementation in the SeqArray package is over 5 times faster than PLINK v1.9 with VCF and BCF files, and over 16 times faster than vcftools. When used in conjunction with R/Bioconductor packages, the SeqArray package provides users a flexible, feature‐rich, high‐performance programming environment for analysis of WGS variant data. Availability and Implementation: http://www.bioconductor.org/packages/SeqArray Contact: zhengx@u.washington.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology

Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology