Correction For Population Stratification Research Articles

Bayesian LASSO for population stratification correction in rare haplotype association studies.

Population stratification (PS) is one major source of confounding in both single nucleotide polymorphism (SNP) and haplotype association studies. To address PS, principal component regression (PCR) and linear mixed model (LMM) are the current standards for SNP associations, which are also commonly borrowed for haplotype studies. However, the underfitting and overfitting problems introduced by PCR and LMM, respectively, have yet to be addressed. Furthermore, there have been only a few theoretical approaches proposed to address PS specifically for haplotypes. In this paper, we propose a new method under the Bayesian LASSO framework, QBLstrat, to account for PS in identifying rare and common haplotypes associated with a continuous trait of interest. QBLstrat utilizes a large number of principal components (PCs) with appropriate priors to sufficiently correct for PS, while shrinking the estimates of unassociated haplotypes and PCs. We compare the performance of QBLstrat with the Bayesian counterparts of PCR and LMM and a current method, haplo.stats. Extensive simulation studies and real data analyses show that QBLstrat is superior in controlling false positives while maintaining competitive power for identifying true positives under PS.

Population stratification correction using Bayesian shrinkage priors for genetic association studies.

Population stratification (PS) is a major source of confounding in population-based genetic association studies of quantitative traits. Principal component regression (PCR) and linear mixed model (LMM) are two commonly used approaches to account for PS in association studies. Previous studies have shown that LMM can be interpreted as including all principal components (PCs) as random-effect covariates. However, including all PCs in LMM may dilute the influence of relevant PCs in some scenarios, while including only a few preselected PCs in PCR may fail to fully capture the genetic diversity. To address these shortcomings, we introduce Bayestrat-a method to detect associated variants with PS correction under the Bayesian LASSO framework. To adjust for PS, Bayestrat accommodates a large number of PCs and utilizes appropriate shrinkage priors to shrink the effects of nonassociated PCs. Simulation results show that Bayestrat consistently controls type I error rates and achieves higher power compared to its non-shrinkage counterparts, especially when the number of PCs included in the model is large. As a demonstration of the utility of Bayestrat, we apply it to the Multi-Ethnic Study of Atherosclerosis (MESA). Variants and genes associated with serum triglyceride or HDL cholesterol are identified in our analyses. The automatic and self-selection features of Bayestrat make it particularly suited in situations with complex underlying PS scenarios, where it is unknown a priori which PCs are potential confounders, yet the number that needs to be considered could be large in order to fully account for PS.

Fast and efficient correction for population stratification in multi-locus genome-wide association studies

Reducing false discoveries caused by population stratification (PS) has always been a challenge in genome-wide association studies (GWAS). The current literature established several single marker approaches including genomic control (GC), EIGENSTRAT and generalized linear mixed model association test (GMMAT) and multi-marker methods such as LASSO mixed model (LASSOMM). However, the single-marker methods require prespecifying an arbitrary p value threshold in the selection process, likely resulting in suboptimal precision or recall. On the other hand, it appears that LASSOMM is extremely computationally intensive and may not suitable for large-scale GWAS. In this paper, we proposed a simple multi-marker approach (PCA-LASSO) combining principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). We utilize PCA to correct for the confounding effects of PS and LASSO with built-in cross-validation for a data-driven selection. Compared to the current single-marker approaches, the proposed PCA-LASSO provides optimal balance between precision and recall, and consequently superior F1 scores. Similarly, compared to LASSOMM, PCA-LASSO markedly increases the precision while minimizing the loss of recall, and therefore improves the overall F1 score in presence of PS. More importantly, PCA-LASSO drastically reduces the computational time by > 1000 times when compared to LASSOMM. We applied PCA-LASSO to a real dataset of Alzheimer's disease and successfully identified SNP rs429358 (Gene APOE4) which has been widely reported to be associated with the onset and elevated risk of Alzheimer's disease. In conclusion, PCA-LASSO is a simple, fast, but accurate approach for GWAS in presence of latent PS.

AssocTests: An R Package for Genetic Association Studies

The R package AssocTests provides some procedures which are commonly used in genetic association studies. These procedures are population stratification correction through eigenvectors, principal coordinates of clusterings, Tracy-Widom test, distance regression, single-marker test, maximum test based on three Cochran-Armitage trend tests, non-parametric trend test, and non-parametric maximum test. The trait values for these methods should be discrete or continuous. The discrete traits can be coded by 1/0 for cases/controls. The genotype values can be 0, 1, or 2 indicating the number of risk alleles for a biallelic single-nucleotide polymorphism. This article introduces the methods and algorithms implemented in the package. Some examples are provided to illustrate the package's capability.

Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies

BackgroundGenome-wide Association Studies (GWAS) have contributed to unraveling associations between genetic variants in the human genome and complex traits for more than a decade. While many works have been invented as follow-ups to detect interactions between SNPs, epistasis are still yet to be modeled and discovered more thoroughly.ResultsIn this paper, following the previous study of detecting marginal epistasis signals, and motivated by the universal approximation power of deep learning, we propose a neural network method that can potentially model arbitrary interactions between SNPs in genetic association studies as an extension to the mixed models in correcting confounding factors. Our method, namely Deep Mixed Model, consists of two components: 1) a confounding factor correction component, which is a large-kernel convolution neural network that focuses on calibrating the residual phenotypes by removing factors such as population stratification, and 2) a fixed-effect estimation component, which mainly consists of an Long-short Term Memory (LSTM) model that estimates the association effect size of SNPs with the residual phenotype.ConclusionsAfter validating the performance of our method using simulation experiments, we further apply it to Alzheimer’s disease data sets. Our results help gain some explorative understandings of the genetic architecture of Alzheimer’s disease.

Assessment of Genetic Differentiation and Linkage Disequilibrium in Solanum pimpinellifolium Using Genome-Wide High-Density SNP Markers.

To mine new favorable alleles for tomato breeding, we investigated the feasibility of utilizing Solanum pimpinellifolium as a diverse panel of genome-wide association study through the restriction site-associated DNA sequencing technique. Previous attempts to conduct genome-wide association studies using S. pimpinellifolium were impeded by an inability to correct for population stratification and by lack of high-density markers to address the issue of rapid linkage disequilibrium decay. In the current study, a set of 24,330 SNPs was identified using 99 S. pimpinellifolium accessions from the Tomato Genetic Resource Center. Approximately 84% of PstI site-associated DNA sequencing regions were located in the euchromatic regions, resulting in the tagging of most SNPs on or near genes. Our genotypic data suggested that S. pimpinellifolium were divided into three single-ancestry subpopulations and four mixed-ancestry subpopulations. Additionally, our SNP genotypic data consistently confirmed the genetic differentiation, achieving a relatively reliable correction of population stratification. Previous studies utilized the 8K tomato SNP array, SolCAP, to investigate the genetic variation of S. pimpinellifolium and we performed a meta-analysis of these genotypes. The result suggested SolCAP array was less appropriate to profile the genetic differentiation of S. pimpinellifolium when more accessions were involved because the samples belonging to the same accession demonstrated different genome patterns. Moreover, as expected, rapid linkage disequilibrium decay was observed in S. pimpinellifolium, especially in euchromatic regions. Approximately two-thirds of the flanking SNP markers did not display linkage disequilibrium based on r2 = 0.1. However, the 18-Kb linkage disequilibrium decay indeed reveals the potential of single-gene resolution in GWAS when markers are saturated.

A panel of 32 AIMs suitable for population stratification correction and global ancestry estimation in Mexican mestizos

BackgroundAssociation studies are useful to unravel the genetic basis of common human diseases. However, the presence of undetected population structure can lead to both false positive results and failures to detect genuine associations. Even when most of the approaches to deal with population stratification require genome-wide data, the use of a well-selected panel of ancestry informative markers (AIMs) may appropriately correct for population stratification. Few panels of AIMs have been developed for Latino populations and most contain a high number of markers (> 100 AIMs). For some association studies such as candidate gene approaches, it may be unfeasible to genotype a numerous set of markers to avoid false positive results. In such cases, methods that use fewer AIMs may be appropriate.ResultsWe validated an accurate and cost-effective panel of AIMs, for use in population stratification correction of association studies and global ancestry estimation in Mexicans, as well as in populations having large proportions of both European and Native American ancestries.Based on genome-wide data from 1953 Mexican individuals, we performed a PCA and SNP weights were calculated to select subsets of unlinked AIMs within percentiles 0.10 and 0.90, ensuring that all chromosomes were represented. Correlations between PC1 calculated using genome-wide data versus each subset of AIMs (16, 32, 48 and 64) were r2 = 0.923, 0.959, 0.972 and 0.978, respectively. When evaluating PCs performance as population stratification adjustment covariates, no correlation was found between P values obtained from uncorrected and genome-wide corrected association analyses (r2 = 0.141), highlighting that population stratification correction is compulsory for association analyses in admixed populations. In contrast, high correlations were found when adjusting for both PC1 and PC2 for either subset of AIMs (r2 > 0.900). After multiple validations, including an independent sample, we selected a minimal panel of 32 AIMs, which are highly informative of the major ancestral components of Mexican mestizos, namely European and Native American ancestries. Finally, the correlation between the global ancestry proportions calculated using genome-wide data and our panel of 32 AIMs was r2 = 0.972.ConclusionsOur panel of 32 AIMs accurately estimated global ancestry and corrected for population stratification in association studies in Mexican individuals.

Secure genome-wide association analysis using multiparty computation.

Most sequenced genomes are currently stored in strict access-controlled repositories1–3. Free access to these data could improve the power of genome-wide association studies (GWAS) to identify disease-causing genetic variants and may aid in the discovery of new drug targets4,5. However, concerns over genetic data privacy6–9 may deter individuals from contributing their genomes to scientific studies10 and in many cases, prevent researchers from sharing data with the scientific community11. Although several cryptographic techniques for secure data analysis exist12–14, none scales to computationally intensive analyses, such as GWAS. Here we describe an end-to-end protocol for large-scale genome-wide analysis that facilitates quality control and population stratification correction in 9K, 13K, and 23K individuals while maintaining the confidentiality of underlying genotypes and phenotypes. We show the protocol could feasibly scale to a million individuals. This approach may help to make currently restricted data available to the scientific community and could potentially enable ‘secure genome crowdsourcing,’ allowing individuals to contribute their genomes to a study without compromising their privacy.

Genetic parameters for periodontal disease: an analysis of electronic dental treatment records linked to pedigree, genomic, sociodemographic and clinical data

ABSTRACT
 BackgroundElectronic health records provides unprecedented opportunity for their re-use in genetic epidemiological research. However, electronic health records data from clinical settings, such as dental practices may be inaccurate or of insufficient granularity to be of use in research. In this study, we wish to determine the utility of National Health Service (NHS) electronic dental treatment records in genetic epidemiological research.
 ObjectiveTo estimate the heritability of periodontal disease using NHS electronic dental treatment records linked to health and non-health data within the Generation Scotland: Scottish Family Health Study (GS:SFHS).
 ApproachWe linked 852,355 NHS Scotland electronic dental treatment records from April 2000 to July 2015 to 20,626 participants within the GS:SFHS with pedigree, genomic, sociodemographic and clinical data. We then conducted a proof-of-principle genetic epidemiological analysis using periodontal (gum) disease treatment records. The data set analysed, consisted of 160,508 dental treatment records from 13,717 study participants; 3,387 of which were periodontal treatment records (from 2,192 study participants). We adjusted for the effects of previous treatment record, interval since last treatment, age, sex, treatment year, and treatment month, Scottish index of multiple deprivation, alcohol consumption, diabetes diagnosis, and smoking status in a linear model in the statistical software ASReml. We then calculated the mean risk of periodontal disease for each study participant based on residuals extracted from the aforementioned model. Genome-complex trait analysis (GCTA; with correction for population stratification) was used to estimate the pedigree and genomic based heritability of periodontal disease.
 ResultsWe estimate the familial heritability of periodontal (gum) disease at 10.42% (95% confidence interval 5.97-14.88%). The genomic component did not contribute significantly to the heritability estimate.
 Conclusionwe have demonstrated the usefulness of electronic dental treatment records in population based genetic epidemiological research .This study has also, to the best of our knowledge provided the first population based estimates of the genetic parameters for periodontal disease; confirming its familial nature. This invaluable and unique data resource will allow the acceleration of oral health research in Scotland and the exploration of research questions that could not be considered previously.

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data.

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper, we reexamine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (six CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1,388 individuals in 124 pedigrees) takes less than 2 min and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 min and 1.5 GB of memory. The algorithm is implemented as the Ped-GWAS Analysis (Option 29) in the Mendel statistical genetics package, which is freely available for Macintosh, Linux, and Windows platforms from http://genetics.ucla.edu/software/mendel.

Single Nucleotide Polymorphism Clustering in Systemic Autoimmune Diseases.

Systemic Autoimmune Diseases, a group of chronic inflammatory conditions, have variable symptoms and difficult diagnosis. In order to reclassify them based on genetic markers rather than clinical criteria, we performed clustering of Single Nucleotide Polymorphisms. However naive approaches tend to group patients primarily by their geographic origin. To reduce this “ancestry signal”, we developed SNPClust, a method to select large sources of ancestry-independent genetic variations from all variations detected by Principal Component Analysis. Applied to a Systemic Lupus Erythematosus case control dataset, SNPClust successfully reduced the ancestry signal. Results were compared with association studies between the cases and controls without or with reference population stratification correction methods. SNPClust amplified the disease discriminating signal and the ratio of significant associations outside the HLA locus was greater compared to population stratification correction methods. SNPClust will enable the use of ancestry-independent genetic information in the reclassification of Systemic Autoimmune Diseases. SNPClust is available as an R package and demonstrated on the public Human Genome Diversity Project dataset at https://github.com/ThomasChln/snpclust.

Association Between Three SNPs and Thromboangiitis Obliterans in Xinjiang Uyghur Population

Thromboangiitis obliterans (TAO), also called Buerger's disease, is a chronic peripheral vascular occlusive disease. It is an obliterative vasculitis characterized by arterial thrombosis and strongly associated with tobacco exposure. The pathogenesis and etiology of TAO are not well understood, but genetic factors may be important in its development. A case-control study was undertaken to identify genetic factors potentially involved in the pathogenesis of TAO in a Xinjiang Uyghur population of China, where TAO is common. We ascertained 177 TAO patients by clinical screening and 86 healthy individuals from the HAPMAP database. The genotypes of single-nucleotide polymorphisms (SNPs) of the participants were identified using the Affymetrix Genome-Wide Human SNP Array 6.0 to perform a genome wide association study (GWAS). The association between the SNPs and incidence of TAO was quantified using race stratification exposure. Through a case-control GWAS study 26 SNPs were significantly associated with incidence of TAO following a Bonferroni correction. However, after genomic control correction for population stratification only three of these SNPS were highly significantly associated with TAO: rs376511 in IL17RC (OR = 24.4, 95% CI:8.68 - 68.62, p < 0.0001), rs7632505 in SEMA5B (OR = 29.47, 95% CI:7.16 - 121.3, p < 0.0001), and rs10178082 (OR = 18.09, 95% CI: 6.56 - 49.92, p < 0.0001) showed a significant risk of TAO in the Uyghur population. This study shows an association between these 3 SNPs and susceptibility to TAO in the Uyghur population, suggesting that polymorphisms in the IL-17RC and Sema 5B genes may pre-dispose individuals in this population to development of TAO. These findings require replication.

Severity of Mitral Valve Degeneration Is Associated with Chromosome 15 Loci in Whippet Dogs.

Mitral valve degeneration (MVD) is the most common form of heart disease in dogs, frequently leading to left-sided congestive heart failure and cardiac mortality. Although breed-specific disease characteristics and overrepresentation point towards a genetic origin for MVD, a causative mutation and complete molecular pathogenesis are unknown. Whippet dogs are overrepresented in incidence of MVD, suggesting an inherited component in this breed. Expressivity of this condition is variable with some dogs showing evidence of more severe disease at earlier ages than other dogs. This phenomenon makes a traditional case versus control genetic study prone to phenotyping error. This study sought to avoid these common pitfalls by identifying genetic loci associated with severity of MVD in Whippets through a genome-wide association study (GWAS). 138 Whippet dogs were characterized for MVD by echocardiographic examination and a novel disease severity score was developed and adjusted for age in each subject. Single nucleotide polymorphism (SNP) genotype data (170k Illumina CanineHD SnpChip) was obtained for DNA isolated from blood of each study subject. Continuous variable genome wide association was performed after correction for population stratification by efficient mixed model association expedited (EMMAX) in 130 dogs. A genome wide significant association was identified on chromosome 15 (peak locus 57,770,326; Padj = 0.049) and secondary loci of suggestive association were identified on chromosome 2 (peak locus 37,628,875; Padj = 0.079). Positional candidate genes were identified within the primary and secondary loci including follistatin-related protein 5 precursor (FSTL5) and Rho GTPase-activating protein 26 (ARHGAP26). These results support the hypothesis that severity of MVD in whippets has a genetic basis and warrants further study by either candidate gene sequencing or next-generation techniques.

Genetic approaches to identify genomic regions associated with decreased susceptibility to bovine respiratory disease complex

One approach to reducing losses from BRDC is the implementation of selection of cattle that are less likely to be affected by disease by the use of dairy predicted transmitting ability (PTA) or beef expected progeny differences (EPD) for susceptibility to BRDC. The PTAs or EPDs would be based on genotypes associated with animals less susceptible to disease and incorporated into commercial genotyping platforms for industry use. The discovery of quantitative trait loci (QTL) associated with BRDC susceptibility is the first step in implementing the selection of cattle for enhanced resistance to BRDC. Feedlot crossbred Bos taurus (n=408) heifers from Washington were used to illustrate how genetic approaches identify BRDC susceptibility QTL that will form the basis for selection of cattle with increased resistance to the disease. The crossbred cattle consisted of 234 cases and 174 controls as defined by the McGuirk Health scoring system. Cases and controls remained in the same pens together throughout the study. The incidence of BRDC in the feedlot was 2.5% over a 15-month period. The distinction between cases and controls was based on the McGuirk health scoring system with possible integer scores ranging between 0 and 12; calves with scores &lt;4 were identified as controls and those with a score &gt;5 were identified as a case. Of the 174 controls, 4 (1.7%) subsequently became ill with BRDC and were reclassified as a case. The mean health score was 9.44 ± 1.19 for cases and 2.36 ± 0.61 for controls. Deeppharyngeal and mid-nasal swabs were taken for bacteriology and virology diagnostics. Histophilus somni had the greatest difference in prevalence between cases and controls (odds ratio = 1.65, 95% confidence interval 1.05 - 2.58, p &lt; 0.05) among the detected pathogens. A genome-wide association analysis (GWAA) and genomic heritability estimate was computed using Illumina BovineHD BeadChip genotypes. Heritability of susceptibility to BRDC was estimated at 37%. After correction for population stratification using 30 principal components, the genomic inflation factor was 1.02. Single nucleotide polymorphisms (SNPs) were filtered for low call rate (&lt;90%), Hardy-Weinberg disequilibrium (p &lt; 1x10-50) and low minor allele frequency (&lt;1%) which left 669,933 SNPs for the GWAA. Animals were also filtered for low genotyping call rate (&lt;90%) leaving 220 cases and 164 controls. The GWAA identified 43 QTL (p &lt; 5x10-5) on 15 chromosomes. The identification of QTL associated with BRDC susceptibility is the first step towards implementing selection of cattle that are less likely to be affected by BRDC through the use of genomic PTAs or EPDs.

Genetics of aging, health, and survival: dynamic regulation of human longevity related traits.

Background: The roles of genetic factors in human longevity would be better understood if one can use more efficient methods in genetic analyses and investigate pleiotropic effects of genetic variants on aging and health related traits.Data and methods: We used EMMAX software with modified correction for population stratification to perform genome wide association studies (GWAS) of female lifespan from the original FHS cohort. The male data from the original FHS cohort and male and female data combined from the offspring FHS cohort were used to confirm findings. We evaluated pleiotropic effects of selected genetic variants as well as gene-smoking interactions on health and aging related traits. Then we reviewed current knowledge on functional properties of genes related to detected variants.Results: The eight SNPs with genome-wide significant variants were negatively associated with lifespan in both males and females. After additional QC, two of these variants were selected for further analyses of their associations with major diseases (cancer and CHD) and physiological aging changes. Gene-smoking interactions contributed to these effects. Genes closest to detected variants appear to be involved in similar biological processes and health disorders, as those found in other studies of aging and longevity e.g., in cancer and neurodegeneration.Conclusions: The impact of genes on longevity may involve trade-off-like effects on different health traits. Genes that influence lifespan represent various molecular functions but may be involved in similar biological processes and health disorders, which could contribute to genetic heterogeneity of longevity and the lack of replication in genetic association studies.

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.

Improving the power of GWAS and avoiding confounding from population stratification with PC-Select.

Using a reduced subset of SNPs in a linear mixed model can improve power for genome-wide association studies, yet this can result in insufficient correction for population stratification. We propose a hybrid approach using principal components that does not inflate statistics in the presence of population stratification and improves power over standard linear mixed models.

Fast genome-wide pedigree quantitative trait loci analysis using MENDEL.

The linkage era left a rich legacy of pedigree samples that can be used for modern genome-wide association sequencing (GWAS) or next-generation sequencing (NGS) studies. Family designs are naturally equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Unfortunately, pedigree likelihoods are notoriously hard to compute, and current software for association mapping in pedigrees is prohibitively slow in processing dense marker maps. In a recent release of the comprehensive genetic analysis software MENDEL, we implemented an ultra-fast score test for association mapping with pedigree-based GWAS or NGS study data. Our implementation (a) works for random sample data, pedigree data, or a mix of both;(b) allows for covariate adjustment, including correction for population stratification;(c) accommodates both univariate and multivariate quantitative traits; and (d) allows missing values in multivariate traits. In this paper, we assess the capabilities of MENDEL on the Genetic Analysis Workshop 18 sequencing data. For instance, when jointly testing the 4 longitudinally measured diastolic blood pressure traits, it takes MENDEL less than 51 minutes on a standard laptop computer to read, quality check, and analyze a data set with 959 individuals and 8.3 million single-nucleotide polymorphisms (SNPs). Our analysis reveals association of one SNP in the q32.2 region of chromosome 1. MENDEL is freely available on http://www.genetics.ucla.edu/software.

E-19 Free Communication/Slide - Genetics

PURPOSE: Three genome-wide association studies (GWAS) of elite Jamaican, African-American and Japanese sprint athletes and their matched controls were performed to identify common genetic variants associated with elite athlete status. METHODS: 95 Jamaican sprint athletes and 102 Jamaican controls, 108 African-American sprint athletes and 397 African-American controls, and 54 Japanese sprint athletes and 118 Japanese controls were genotyped using the Illumina®Human OmniExpress/Omni1-Quad BeadChips. Standard GWAS quality control (QC) and population stratification correction were applied to the genotype data. Genetic associations were evaluated by logistic regression/standard allelic association analysis. Meta-analyses were subsequently performed for single nucleotide polymorphisms (SNPs) with a predetermined threshold of 5 × 10-5for the three sprint GWAS sample sets using a fixed-effects model. RESULTS: After QC, 609,801 autosomal SNPs in 88 Jamaican sprint athletes and 87 Jamaican controls, 637,991 autosomal SNPs in 79 African-American sprint athletes and 391 African-American controls, and 541,179 autosomal SNPs in 54 Japanese sprint athletes and 116 Japanese controls were available for analysis. The genomic inflation factor values were 1.075, 1.070 and 1.031 for Jamaican, African-American and Japanese GWAS sample sets, respectively. No SNPs exceeded the conventional GWAS significance threshold of 5x10-8 in respective cohorts. Two SNPs within the GALNT13(Pcombined= 4.66 × 10-7 for rs10196189) and theCREMgenes (Pcombined= 1.88 × 10-6 for rs1531550) attained P< 2 × 10-6 after meta-analysis. CONCLUSIONS: Two putative loci for elite sprint performance across Jamaicans, African-Americans and Japanese sprinters have been discovered using a GWAS approach followed by meta-analysis. These signals require further replication before functional dissection can be carried out.

Breast cancer prediction using genome wide single nucleotide polymorphism data.

BackgroundThis paper introduces and applies a genome wide predictive study to learn a model that predicts whether a new subject will develop breast cancer or not, based on her SNP profile.ResultsWe first genotyped 696 female subjects (348 breast cancer cases and 348 apparently healthy controls), predominantly of Caucasian origin from Alberta, Canada using Affymetrix Human SNP 6.0 arrays. Then, we applied EIGENSTRAT population stratification correction method to remove 73 subjects not belonging to the Caucasian population. Then, we filtered any SNP that had any missing calls, whose genotype frequency was deviated from Hardy-Weinberg equilibrium, or whose minor allele frequency was less than 5%. Finally, we applied a combination of MeanDiff feature selection method and KNN learning method to this filtered dataset to produce a breast cancer prediction model. LOOCV accuracy of this classifier is 59.55%. Random permutation tests show that this result is significantly better than the baseline accuracy of 51.52%. Sensitivity analysis shows that the classifier is fairly robust to the number of MeanDiff-selected SNPs. External validation on the CGEMS breast cancer dataset, the only other publicly available breast cancer dataset, shows that this combination of MeanDiff and KNN leads to a LOOCV accuracy of 60.25%, which is significantly better than its baseline of 50.06%. We then considered a dozen different combinations of feature selection and learning method, but found that none of these combinations produces a better predictive model than our model. We also considered various biological feature selection methods like selecting SNPs reported in recent genome wide association studies to be associated with breast cancer, selecting SNPs in genes associated with KEGG cancer pathways, or selecting SNPs associated with breast cancer in the F-SNP database to produce predictive models, but again found that none of these models achieved accuracy better than baseline.ConclusionsWe anticipate producing more accurate breast cancer prediction models by recruiting more study subjects, providing more accurate labelling of phenotypes (to accommodate the heterogeneity of breast cancer), measuring other genomic alterations such as point mutations and copy number variations, and incorporating non-genetic information about subjects such as environmental and lifestyle factors.