Analysis Of Genetic Data Research Articles

GenPop-An Online Tool to Analyze Human Population Genetic Data.

GenPop is a web based online cross platform tool developed to help Geneticist and Epidemiologist to deal with association studies in analyzing human population genetic data. The tool features include descriptive analysis such as Hardy-Weinberg equilibrium test, chi-square p-value and analysis of single nucleotide polymorphisms (SNPs) with multiple inheritance models such as dominant, recessive, allelic, genotype, odd's ratio and relative risk at 95% confidence interval and analysis of multiple SNPs including haplotype frequencies and linkage disequilibrium for a pair of biallelic markers. This is a user-driven human population genetic data analysis tool that is easily scalable and acceptable with multiple implementations of different algorithms. GenPop has been developed using PHP, JavaScript and with PHPExcel library to analyse the genetic data for case control studies.

Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity.

Current developments of neuroimaging and genetics promote an integrative and compressive study of schizophrenia. However, it is still difficult to explore how gene mutations are related to brain abnormalities due to the high dimension but low sample size of these data. Conventional approaches reduce the dimension of dataset separately and then calculate the correlation, but ignore the effects of the response variables and the structure of data. To improve the identification of risk genes and abnormal brain regions on schizophrenia, in this paper, we propose a novel method called Independence and Structural sparsity Canonical Correlation Analysis (ISCCA). ISCCA combines independent component analysis (ICA) and Canonical Correlation Analysis (CCA) to reduce the collinear effects, which also incorporate graph structure of the data into the model to improve the accuracy of feature selection. The results from simulation studies demonstrate its higher accuracy in discovering correlations compared with other competing methods. Moreover, applying ISCCA to a real imaging genetics dataset collected by Mind Clinical Imaging Consortium (MCIC), a set of distinct gene-ROI interactions are identified, which are verified to be both statistically and biologically significant.

Identification of 34 genes conferring genetic and pharmacological risk for the comorbidity of schizophrenia and smoking behaviors.

The prevalence of smoking is significantly higher in persons with schizophrenia (SCZ) than in the general population. However, the biological mechanisms of the comorbidity of smoking and SCZ are largely unknown. This study aimed to reveal shared biological pathways for the two diseases by analyzing data from two genome-wide association studies with a total sample size of 153,898. With pathway-based analysis, we first discovered 18 significantly enriched pathways shared by SCZ and smoking, which were classified into five groups: postsynaptic density, cadherin binding, dendritic spine, long-term depression, and axon guidance. Then, by using an integrative analysis of genetic, epigenetic, and expression data, we found not only 34 critical genes (e.g., PRKCZ, ARHGEF3, and CDKN1A) but also various risk-associated SNPs in these genes, which convey susceptibility to the comorbidity of the two disorders. Finally, using both in vivo and in vitro data, we demonstrated that the expression profiles of the 34 genes were significantly altered by multiple psychotropic drugs. Together, this multi-omics study not only reveals target genes for new drugs to treat SCZ but also reveals new insights into the shared genetic vulnerabilities of SCZ and smoking behaviors.

Machine Learning Algorithm for Predicting Warfarin Dose in Caribbean Hispanics Using Pharmacogenetic Data.

Despite some previous examples of successful application to the field of pharmacogenomics, the utility of machine learning (ML) techniques for warfarin dose predictions in Caribbean Hispanic patients has yet to be fully evaluated. This study compares seven ML methods to predict warfarin dosing in Caribbean Hispanics. This is a secondary analysis of genetic and non-genetic clinical data from 190 cardiovascular Hispanic patients. Seven ML algorithms were applied to the data. Data was divided into 80 and 20% to be used as training and test sets. ML algorithms were trained with the training set to obtain the models. Model performance was determined by computing the corresponding mean absolute error (MAE) and % patients whose predicted optimal dose were within ±20% of the actual stabilization dose, and then compared between groups of patients with “normal” (i.e., > 21 but <49 mg/week), low (i.e., ≤21 mg/week, “sensitive”), and high (i.e., ≥49 mg/week, “resistant”) dose requirements. Random forest regression (RFR) significantly outperform all other methods, with a MAE of 4.73 mg/week and 80.56% of cases within ±20% of the actual stabilization dose. Among those with “normal” dose requirements, RFR performance is also better than the rest of models (MAE = 2.91 mg/week). In the “sensitive” group, support vector regression (SVR) shows superiority over the others with lower MAE of 4.79 mg/week. Finally, multivariate adaptive splines (MARS) shows the best performance in the resistant group (MAE = 7.22 mg/week) and 66.7% of predictions within ±20%. Models generated by using RFR, MARS, and SVR algorithms showed significantly better predictions of weekly warfarin dosing in the studied cohorts than other algorithms. Better performance of the ML models for patients with “normal,” “sensitive,” and “resistant” to warfarin were obtained when compared to other populations and previous statistical models.

SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity

SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity

Tools for Evolutionary and Genetic Analysis (TEGA): A new platform for the management of molecular and environmental data.

Population genetics studies the distributions and changes in population allele frequencies in response to processes, such as mutation, natural selection, gene flow, and genetic drift. Researchers daily manage genetic, biological, and environmental data of the samples, storing them in text files or spreadsheets, which makes it difficult to maintain consistency and traceability. Here we present TEGA, a WEB-based stand-alone software developed for the easy analysis and management of population genetics data. It was designed to: 1) facilitate data management, 2) provide a way to execute the analysis procedures, and 3) supply a means to publish data, procedures, and results. TEGA is distributed under the GNU AGPL v3 license. The documentation, source code, and screenshots are available at https://github.com/darioelias/TEGA. In addition, we present Rabid Fish, the first implementation of TEGA in the Genetics Labortory of the Faculty of Humanities and Sciences at the National University of the Litoral, where research focuses on population genetics studies applied to non-model organisms.

Design protein-protein interaction network and protein-drug interaction network for common cancer diseases: A bioinformatics approach

In terms of genomics, bioinformatics conceptualizes biological processes and utilizes computer science to generate knowledge from biological data. Bioinformatics is multidisciplinary in that it merges the available methods in multiple fields, including computation, inventory, statistical analysis, collection, and processing of genetic data, to provide approaches in the fields of genetics, education, and healthcare. Our study, described herein, has attempted to analyze the relationships between genetic variants in breast, colorectal, lung, and ovarian cancer, to establish a common pathway, or PPI network and PDI network. ology: A computational investigation was implemented to understand the PPI network and PDI network for breast, colorectal, lung, and ovarian cancer. Related genes were collected from the NCBI gene database for each disease. A data mining technique was utilized to discover the common genes between the four diseases. Based upon our computational model, a total of 10 common genes: TP53, EGFR, TNF, APOE, VEGFA, IL6, TGFB1, MTHFR, ERBB2, and ESR1 were observed. A novel pathway was found in the investigation. This research was therefore assistive in the identification and study of the PPI network and PDI network for common cancer diseases. Numerous prior studies have also demonstrated a strong association between breast, colorectal, lung, and ovarian cancer. The PPI network and PDI network were developed based on common genes between four studied diseases. This information can be helpful to understand the ligand binding of the PPI network. In this area of drug design, our study therefore provides additional insight.

Use of Museum Specimens to Refine Historical Pronghorn Subspecies Boundaries

ABSTRACTEndangered Sonoran (Antilocapra americana sonoriensis) and Peninsular (A. a. peninsularis) pronghorn persist largely because of captive breeding and reintroduction efforts. Recovery team managers want to re‐establish pronghorn in their native range, but there is currently uncertainty regarding the subspecies status of extinct pronghorn populations that historically inhabited southern California, USA, and northern Baja California, Mexico. To address this uncertainty, we genotyped museum specimens and conducted phylogenetic and population genetic analyses of historical data in the context of 3 contemporary pronghorn populations. The historical northern Baja California pronghorn share the most ancestry with contemporary Peninsular pronghorn, whereas pronghorn in southern California share more ancestry with contemporary American (A. a. americana) pronghorn. For reintroductions into northern Baja California, the Peninsular subspecies is more appropriate based on museum genetic data. For reintroductions into Southern California, ecological and genetic factors are both important, as the subspecies most genetically related to historical populations (American) may not be well‐adapted to the hot, low‐elevation deserts of the reintroduction area. © 2019 The Wildlife Society.

Polygene: Population genetics analyses for autopolyploids based on allelic phenotypes

Abstract Polyploidy has appeared in almost every ancestral plant lineage, and in extant species, occurs frequently. When present, polyploidy presents problems for genetic data analysis, which are caused by both genotypic ambiguities and double‐reduction. To address these problems, we developed a new software package, polygene, which enables the estimation of genotypic frequencies for a number of polysomic inheritance models. Specifically, polygene obtains posterior probabilities for genotypes hidden within allelic phenotypes. Comprehensive modes of genetic analyses are provided by polygene, which include genetic diversity analysis, tests for allelic phenotypic or genotypic distributions, linkage disequilibrium and genetic differentiation, genetic distance analysis, principal coordinates analysis, hierarchical clustering analysis, individual inbreeding coefficient estimation, individual heterozygosity index estimation, population assignment, pairwise relatedness estimation, parentage analysis, analysis of molecular variance and Bayesian clustering. polygene enables easy and convenient allelic phenotype‐ or genotype‐based analysis for both autopolyploids and diploids. polygene will thus facilitate molecular ecology research involving autopolyploids.

Physicochemical characterization and trait stability in a genetically diverse ex situ collection of pomegranate (Punica granatum L.) germplasm from Cyprus

Proximity to the center of origin and geographical isolation shaped a unique genetic diversity of pomegranate in Cyprus that constitutes a valuable resource for the crop. Physicochemical characters and trait stability were studied for three years in an ex situ collection of 29 pomegranate accessions from Cyprus. Accession signature traits with exceptional yearly stability were titratable acidity and the citrate/malate ratio. Overall, the Cypriot germplasm was characterized by juice of low anthocyanin content (x¯ = 20.6 mg/L) and moderate phenolic content (x¯ = 613.0 mg/L) with glucose (x¯ = 69.1 g/L) and fructose (x¯ = 74.2 g/L) as predominant sugars. Juice antioxidant capacity was associated primarily with total phenolics and less so with juice color and anthocyanin content. Total sugar content was higher in large-fruited accessions with darker juice. In most accessions the titratable acidity was low (<0.50% w/v), with citric (x¯ = 44.7%), malic (x¯ = 39.1%) and succinic (x¯ = 15.8%) being the main organic acid fractions detected. Based on the maturity index (SSC/TA), six accessions were sweet-sour or borderline sweet-sour and 23 accessions were sweet. Cluster analysis of phenotypic characteristics and genetic data revealed a core group of thirteen genetically and phenotypically close accessions constituting the Cypriot pomegranate landrace, characterized by moderate fruit weight, high juiciness, thin rind, moderately hard seeds and light-colored juice low in acidity, anthocyanins, phenolics and antioxidant capacity. The present work advances the understanding of genetic and environmental contribution to the configuration of pomegranate physicochemical fruit composition.

Genetic patterns and changes in availability of suitable habitat support a colonisation history of a North American perennial plant.

Climatic fluctuations during the Pleistocene influenced the geographical distribution of plant species across the southern region of California. Following an integrative approach, we combined genetic data analysis with Environmental Niche Models (ENMs) to assess the historical range expansion of Yucca schidigera, a long-lived desert perennial native of the Baja California Peninsula. We genotyped 240 individuals with seven nuclear microsatellite to investigate genetic diversity distribution across 13 populations. Indeed, we used Environmental Niche Models to examine the changes on the distribution of suitable climatic conditions for this species during the LIG (~120 ka), LGM (~22 ka) and Mid Holocene (~6 ka). We detected high genetic diversity across Y. schidigera populations (AR = 9.94 ± 0.38 SE; Hexp = 0.791 ± 0.011 SE) with genetic variation decreasing significantly with latitude (allelic richness: R 2 = 0.38, P = 0.023; expected heterocigosity: R2 = 0.32, P = 0.042). We observed low, but significant genetic differentiation (FST = 0.0678; P < 0.001) which was consistent with the parapatric distribution of the three genetic groupings detected by the Bayesian clustering algorithm. The ENMs suggest that suitable habitat for this species increased since the LGM. Our results support a range expansion of Y. schidigera across northwestern Baja California during the late Quaternary. Genetic data suggest that colonization of the current distribution followed a southward directionality as suitable climatic conditions became widely available in this region. High genetic variation across our sample suggests large historic effective population sizes for this section of the geographical range.

Germline mutations in the transcription factor IKZF5 cause thrombocytopenia

AbstractTo identify novel causes of hereditary thrombocytopenia, we performed a genetic association analysis of whole-genome sequencing data from 13 037 individuals enrolled in the National Institute for Health Research (NIHR) BioResource, including 233 cases with isolated thrombocytopenia. We found an association between rare variants in the transcription factor-encoding gene IKZF5 and thrombocytopenia. We report 5 causal missense variants in or near IKZF5 zinc fingers, of which 2 occurred de novo and 3 co-segregated in 3 pedigrees. A canonical DNA-zinc finger binding model predicts that 3 of the variants alter DNA recognition. Expression studies showed that chromatin binding was disrupted in mutant compared with wild-type IKZF5, and electron microscopy revealed a reduced quantity of α granules in normally sized platelets. Proplatelet formation was reduced in megakaryocytes from 7 cases relative to 6 controls. Comparison of RNA-sequencing data from platelets, monocytes, neutrophils, and CD4+ T cells from 3 cases and 14 healthy controls showed 1194 differentially expressed genes in platelets but only 4 differentially expressed genes in each of the other blood cell types. In conclusion, IKZF5 is a novel transcriptional regulator of megakaryopoiesis and the eighth transcription factor associated with dominant thrombocytopenia in humans.

The Radiogenomics of Late-onset Alzheimer Disease.

Radiogenomics, defined as the integrated analysis of radiologic imaging and genetic data, is a well-established tool shown to augment neuroimaging in the clinical diagnosis, prognostication, and scientific study of late-onset Alzheimer disease (LOAD). Early work using candidate single nucleotide polymorphisms (SNPs) identified genetic variation in APOE, BIN1, CLU, and CR1 as key modifiers of brain structure and function using magnetic resonance imaging (MRI). More recently, polygenic risk scores used in conjunction with MRI and positron emission tomography have shown great promise as a risk-stratification tool for clinical trials and care-management decisions. In addition, recent work using multimodal MRI and positron emission tomography as proxies of LOAD progression has identified novel risk variants that are enhancing our understanding of LOAD pathophysiology and progression. Herein, we highlight key studies and trends in the radiogenomics of LOAD over the past two decades and their implications for clinical practice and scientific research.

Spatiotemporal reconstruction and transmission dynamics during the 2016-17 H5N8 highly pathogenic avian influenza epidemic in Italy.

SummaryEffective control of avian diseases in domestic populations requires understanding of the transmission dynamics facilitating viral emergence and spread. In 2016–17, Italy experienced a significant avian influenza epidemic caused by a highly pathogenic A(H5N8) virus, which affected domestic premises housing around 2.7 million birds, primarily in the north‐eastern regions with the highest density of poultry farms (Lombardy, Emilia‐Romagna and Veneto). We perform integrated analyses of genetic, spatiotemporal and host data within a Bayesian phylogenetic framework. Using continuous and discrete phylogeography, we estimate the locations of movements responsible for the spread and persistence of the epidemic. The information derived from these analyses on rates of transmission between regions through time can be used to assess the success of control measures. Using an approach based on phylogenetic–temporal distances between domestic cases, we infer the presence of cryptic wild bird‐mediated transmission, information that can be used to complement existing epidemiological methods for distinguishing transmission within the domestic population from incursions across the wildlife–domestic interface, a common challenge in veterinary epidemiology. Spatiotemporal reconstruction of the epidemic reveals a highly skewed distribution of virus movements with a high proportion of shorter distance local movements interspersed with occasional long‐distance dispersal events associated with wild birds. We also show how such inference be used to identify possible instances of human‐mediated movements where distances between phylogenetically linked domestic cases are unusually high.

TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies

One of the most important tasks in genome-wide association analysis (GWAS) is the detection of single-nucleotide polymorphisms (SNPs) which are related to target traits. With the development of sequencing technology, traditional statistical methods are difficult to analyze the corresponding high-dimensional massive data or SNPs. Recently, machine learning methods have become more popular in high-dimensional genetic data analysis for their fast computation speed. However, most of machine learning methods have several drawbacks, such as poor generalization ability, over-fitting, unsatisfactory classification and low detection accuracy. This study proposed a two-stage algorithm based on least angle regression and random forest (TSLRF), which firstly considered the control of population structure and polygenic effects, then selected the SNPs that were potentially related to target traits by using least angle regression (LARS), furtherly analyzed this variable subset using random forest (RF) to detect quantitative trait nucleotides (QTNs) associated with target traits. The new method has more powerful detection in simulation experiments and real data analyses. The results of simulation experiments showed that, compared with the existing approaches, the new method effectively improved the detection ability of QTNs and model fitting degree, and required less calculation time. In addition, the new method significantly distinguished QTNs and other SNPs. Subsequently, the new method was applied to analyze five flowering-related traits in Arabidopsis. The results showed that, the distinction between QTNs and unrelated SNPs was more significant than the other methods. The new method detected 60 genes confirmed to be related to the target trait, which was significantly higher than the other methods, and simultaneously detected multiple gene clusters associated with the target trait.

The Dundee Resource for Sequence Analysis and Structure Prediction.

The Dundee Resource for Sequence Analysis and Structure Prediction (DRSASP; http://www.compbio.dundee.ac.uk/drsasp.html) is a collection of web services provided by the Barton Group at the University of Dundee. DRSASP's flagship services are the JPred4 webserver for secondary structure and solvent accessibility prediction and the JABAWS 2.2 webserver for multiple sequence alignment, disorder prediction, amino acid conservation calculations, and specificity‐determining site prediction. DRSASP resources are available through conventional web interfaces and APIs but are also integrated into the Jalview sequence analysis workbench, which enables the composition of multitool interactive workflows. Other existing Barton Group tools are being brought under the banner of DRSASP, including NoD (Nucleolar localization sequence detector) and 14‐3‐3‐Pred. New resources are being developed that enable the analysis of population genetic data in evolutionary and 3D structural contexts. Existing resources are actively developed to exploit new technologies and maintain parity with evolving web standards. DRSASP provides substantial computational resources for public use, and since 2016 DRSASP services have completed over 1.5 million jobs.

Novel HLA Typing Method Identifies HLA Alleles Associated with Pediatric ITP

Background: Human leukocyte antigen (HLA) genes on chromosome 6p21 encode the major histocompatibility complex (MHC). MHC molecules are expressed on all nucleated cells and are essential to the immune system's ability to distinguish healthy native cells from foreign or abnormal ones. Specialized antigen presenting cells display MHC molecules loaded with foreign peptides to initiate T cell responses. HLA sequencing variability is critical to function; studies have shown even single allelic changes can alter the ability of MHC molecules to recognize cognate T cell receptors. Given the role of MHC molecules in identifying cells as "self", associations between HLA sequence variation and autoimmunity is a research focus. Like other autoimmune disorders, association between ITP and HLA genes has been investigated. Prior studies are limited by outdated HLA typing methods, genetically homogenous study populations, and lack of validation studies. Preexisting exome data has historically not been used to provide accurate HLA sequencing information given the polymorphic, highly repetitive sequence in this region. This study applied a novel bioinformatics pipeline, xHLA, to an existing set of whole exome sequencing in a cohort of chronic pediatric ITP patients. xHLA, reported by Xie et al. in 2017, yields high-resolution, two field HLA typing for HLA-A, -B, -C, -DQB1, -DPB1, and -DRB1. A prior genetic analysis of chronic ITP patient whole exome data identified a missense variant in BTNL2, a gene which plays a role in peripheral regulatory T cell induction. This variant (rs143211074) is upregulated in chronic ITP versus healthy controls (minor allele frequency= 4.17% in ITP v. 0.54% in controls, p=1.43x10-4). Linkage of rs143211074 and HLA alleles was examined in this study. Methods: Whole exome sequencing data for a cohort of 272 chronic ITP patients was processed using the xHLA method. In order to eliminate sequencing variability related to ethnicity alone, initial analyses were focused on Caucasians only (n=170), using Eigenvectors for principle components analysis. The frequency of individual HLA alleles and HLA haplotypes for Caucasians from the National Marrow Donor Program (NMDP) Be The Match Registry® were compared to the ITP cohort. HLA haplotypes were inferred using the NMDP HaploStats program. A chi-squared test was utilized to compare nonparametric categorical data using GraphPad Prism version 8.0.1 for Windows, GraphPad Software, San Diego, California, USA, www.graphpad.com. A Bonferroni correction was applied and a p-value of &lt;0.05 was statistically significant. Results: 12 HLA alleles had increased frequency in the chronic ITP cohort versus controls. (Table 1) The BTNL2 variant, rs143211074, was found in linkage disequilibrium with the HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype. Of the 10 patients heterozygous for the variant allele, 80% had the HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype. Both patients homozygous for rs143211074 were also homozygous for the HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype. In the entire cohort, this haplotype was identified in 3.63% of ITP patients, and only in 1.27% of controls. Our group recently published that a positive direct antiglobulin test is associated with chronic ITP and need for second line treatments. Of the patients with the HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype with DAT testing performed (n=9), 66% had a positive DAT test and 33% had a negative DAT result. Of those without the HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype who had DAT testing, 17.2% had a positive DAT and 82.8% had a negative test (p=0.0026). Conclusions: HLA alleles are seen at increased frequency in a cohort of Caucasian chronic ITP patients. The HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype is linked to a variant previously identified in BTNL2. Coupled with prior data suggesting that a positive DAT result is associated with chronic disease and use of second line agents, this presence of the HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype may also be linked to refractory disease. Confirmation of the accuracy of the xHLA method in a cohort of patients with both exome data and traditional HLA typing is underway. In the future, a separate cohort of chronic and acute ITP patients will undergo next generation sequencing and have similar HLA typing performed as an independent validation set. Table 1 Disclosures Despotovic: Novartis: Research Funding; Amgen: Research Funding; Dova: Honoraria.

Analysis of secondary phenotypes in multigroup association studies.

Although case-control association studies have been widely used, they are insufficient for many complex diseases, such as Alzheimer's disease and breast cancer, since these diseases may have multiple subtypes with distinct morphologies and clinical implications. Many multigroup studies, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), have been undertaken by recruiting subjects based on their multiclass primary disease status, while extensive secondary outcomes have been collected. The aim of this paper is to develop a general regression framework for the analysis of secondary phenotypes collected in multigroup association studies. Our regression framework is built on a conditional model for the secondary outcome given the multigroup status and covariates and its relationship with the population regression of interest of the secondary outcome given the covariates. Then, we develop generalized estimation equations to estimate the parameters of interest. We use both simulations and a large-scale imaging genetic data analysis from the ADNI to evaluate the effect of the multigroup sampling scheme on standard genome-wide association analyses based on linear regression methods, while comparing it with our statistical methods that appropriately adjust for the multigroup sampling scheme. Data used in preparation of this article were obtained from the ADNI database.

Integrative Deep Learning for Identifying Differentially Expressed (DE) Biomarkers.

As a large amount of genetic data are accumulated, an effective analytical method and a significant interpretation are required. Recently, various methods of machine learning have emerged to process genetic data. In addition, machine learning analysis tools using statistical models have been proposed. In this study, we propose adding an integrated layer to the deep learning structure, which would enable the effective analysis of genetic data and the discovery of significant biomarkers of diseases. We conducted a simulation study in order to compare the proposed method with metalogistic regression and meta-SVM methods. The objective function with lasso penalty is used for parameter estimation, and the Youden J index is used for model comparison. The simulation results indicate that the proposed method is more robust for the variance of the data than metalogistic regression and meta-SVM methods. We also conducted real data (breast cancer data (TCGA)) analysis. Based on the results of gene set enrichment analysis, we obtained that TCGA multiple omics data involve significantly enriched pathways which contain information related to breast cancer. Therefore, it is expected that the proposed method will be helpful to discover biomarkers.

Extending Tests of Hardy-Weinberg Equilibrium to Structured Populations.

Testing for Hardy-Weinberg equilibrium (HWE) is an important component in almost all analyses of population genetic data. Genetic markers that violate HWE are often treated as special cases; for example, they may be flagged as possible genotyping errors, or they may be investigated more closely for evolutionary signatures of interest. The presence of population structure is one reason why genetic markers may fail a test of HWE. This is problematic because almost all natural populations studied in the modern setting show some degree of structure. Therefore, it is important to be able to detect deviations from HWE for reasons other than structure. To this end, we extend statistical tests of HWE to allow for population structure, which we call a test of "structural HWE." Additionally, our new test allows one to automatically choose tuning parameters and identify accurate models of structure. We demonstrate our approach on several important studies, provide theoretical justification for the test, and present empirical evidence for its utility. We anticipate the proposed test will be useful in a broad range of analyses of genome-wide population genetic data.