scholarly journals High Resolution Ancestry Deconvolution for Next Generation Genomic Data

2021 ◽  
Author(s):  
Helgi Hilmarsson ◽  
Arvind S. Kumar ◽  
Richa Rastogi ◽  
Carlos D. Bustamante ◽  
Daniel Mas Montserrat ◽  
...  
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Association Studies ◽  
Training Data ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Next Generation ◽  
Genome Data ◽  
Computational Performance ◽  
Genetic Risk Prediction

ABSTRACTAs genome-wide association studies and genetic risk prediction models are extended to globally diverse and admixed cohorts, ancestry deconvolution has become an increasingly important tool. Also known as local ancestry inference (LAI), this technique identifies the ancestry of each region of an individual’s genome, thus permitting downstream analyses to account for genetic effects that vary between ancestries. Since existing LAI methods were developed before the rise of massive, whole genome biobanks, they are computationally burdened by these large next generation datasets. Current LAI algorithms also fail to harness the potential of whole genome sequences, falling well short of the accuracy that such high variant densities can enable. Here we introduce Gnomix, a set of algorithms that address each of these points, achieving higher accuracy and swifter computational performance than any existing LAI method, while also enabling portable models that are particularly useful when training data are not shareable due to privacy or other restrictions. We demonstrate Gnomix (and its swift phase correction counterpart Gnofix) on worldwide whole-genome data from both humans and canids and utilize its high resolution accuracy to identify the location of ancient New World haplotypes in the Xoloitzcuintle, dating back over 100 generations. Code is available at https://github.com/AI-sandbox/gnomix.

scholarly journals Leveraging Functional Annotations in Genetic Risk Prediction for Human Complex Diseases

2016 ◽  
Author(s):  
Yiming Hu ◽  
Qiongshi Lu ◽  
Ryan Powles ◽  
Xinwei Yao ◽  
Fang Fang ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Genetic Risk ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Association Studies ◽  
Treatment Strategies ◽  
Complex Diseases ◽  
Genome Wide Association Studies ◽  
Genetic Risk Prediction ◽  
Human Complex

AbstractGenome wide association studies have identified numerous regions in the genome associated with hundreds of human diseases. Building accurate genetic risk prediction models from these data will have great impacts on disease prevention and treatment strategies. However, prediction accuracy remains moderate for most diseases, which is largely due to the challenges in identifying all the disease-associated variants and accurately estimating their effect sizes. We introduce AnnoPred, a principled framework that incorporates diverse functional annotation data to improve risk prediction accuracy, and demonstrate its performance on multiple human complex diseases.

Statistical Methods in Genome-Wide Association Studies

2020 ◽  
Vol 3 (1) ◽  
pp. 265-288
Author(s):  
Ning Sun ◽  
Hongyu Zhao
Keyword(s):  
Genetic Architecture ◽  
Prediction Models ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Statistical Methodology ◽  
Risk Prediction Models ◽  
Genetic Risk Prediction ◽  
Genome Wide ◽  
Genotype Information

Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges.

scholarly journals SummaryAUC: a tool for evaluating the performance of polygenic risk prediction models in validation datasets with only summary level statistics

2018 ◽  
Author(s):  
Lei Song ◽  
Aiyi Liu ◽  
Jianxin Shi ◽  
Keyword(s):  
Statistical Methods ◽  
Prediction Models ◽  
Association Studies ◽  
Complex Diseases ◽  
Training Data ◽  
Validation Dataset ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Level Data

AbstractMotivationPolygenic risk score (PRS) methods based on genome-wide association studies (GWAS) have a potential for predicting the risk of developing complex diseases and are expected to become more accurate with larger training data sets and innovative statistical methods. The area under the ROC curve (AUC) is often used to evaluate the performance of PRSs, which requires individual genotypic and phenotypic data in an independent GWAS validation dataset. We are motivated to develop methods for approximating AUC of PRSs based on the summary level data of the validation dataset, which will greatly facilitate the development of PRS models for complex diseases.ResultsWe develop statistical methods and an R package SummaryAUC for approximating the AUC and its variance of a PRS when only the summary level data of the validation dataset are available. SummaryAUC can be applied to PRSs with SNPs either genotyped or imputed in the validation dataset. We examined the performance of SummaryAUC using a large-scale GWAS of schizophrenia. SummaryAUC provides accurate approximations to AUCs and their variances. The bias of AUC is typically less than 0.5% in most analyses. SummaryAUC cannot be applied to PRSs that use all SNPs in the genome because it is computationally prohibitive.Availabilityhttps://github.com/lsncibb/[email protected]

scholarly journals Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chao-Yu Guo ◽  
Reng-Hong Wang ◽  
Hsin-Chou Yang
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Association Test ◽  
Whole Genome Sequence ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Sequence Kernel Association Test ◽  
Gene Environment ◽  
Family Based

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

scholarly journals Integration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Gabriel Costa Monteiro Moreira ◽  
Clarissa Boschiero ◽  
Aline Silva Mello Cesar ◽  
James M. Reecy ◽  
Thaís Fernanda Godoy ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Association Studies ◽  
Fat Deposition ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Genome Wide

scholarly journals 297 GWAS for complex models accounting for populations structure with GBLUP and ssGBLUP

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 32-32
Author(s):  
Juan P Steibel ◽  
Ignacio Aguilar
Keyword(s):  
Hypothesis Testing ◽  
Large Scale ◽  
Mixed Model ◽  
Prediction Models ◽  
Association Studies ◽  
Least Square ◽  
Type I ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Formal Hypothesis Testing

Abstract Genomic Best Linear Unbiased Prediction (GBLUP) is the method of choice for incorporating genomic information into the genetic evaluation of livestock species. Furthermore, single step GBLUP (ssGBLUP) is adopted by many breeders’ associations and private entities managing large scale breeding programs. While prediction of breeding values remains the primary use of genomic markers in animal breeding, a secondary interest focuses on performing genome-wide association studies (GWAS). The goal of GWAS is to uncover genomic regions that harbor variants that explain a large proportion of the phenotypic variance, and thus become candidates for discovering and studying causative variants. Several methods have been proposed and successfully applied for embedding GWAS into genomic prediction models. Most methods commonly avoid formal hypothesis testing and resort to estimation of SNP effects, relying on visual inspection of graphical outputs to determine candidate regions. However, with the advent of high throughput phenomics and transcriptomics, a more formal testing approach with automatic discovery thresholds is more appealing. In this work we present the methodological details of a method for performing formal hypothesis testing for GWAS in GBLUP models. First, we present the method and its equivalencies and differences with other GWAS methods. Moreover, we demonstrate through simulation analyses that the proposed method controls type I error rate at the nominal level. Second, we demonstrate two possible computational implementations based on mixed model equations for ssGBLUP and based on the generalized least square equations (GLS). We show that ssGBLUP can deal with datasets with extremely large number of animals and markers and with multiple traits. GLS implementations are well suited for dealing with smaller number of animals with tens of thousands of phenotypes. Third, we show several useful extensions, such as: testing multiple markers at once, testing pleiotropic effects and testing association of social genetic effects.

scholarly journals Polygenic influences associated with adolescent cognitive skills

2021 ◽  
Author(s):  
Brittany L Mitchell ◽  
Narelle K Hansell ◽  
Kerrie McAloney ◽  
Nicholas G. Martin ◽  
Margaret J. Wright ◽  
...  
Keyword(s):  
Mental Health ◽  
Genetic Predisposition ◽  
Prediction Models ◽  
Association Studies ◽  
Developmental Trajectory ◽  
Cognitive Outcomes ◽  
Cognitive Domain ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Negatively Associated

Genes play an important role in children’s cognitive ability through puberty and into adolescence. Recent advances in genomics has enabled us to test the effect of various genetic predispositions on measured cognitive outcomes. Here, we leveraged summary statistics from the most recent genome-wide association studies of eleven cognitive and mental health traits to build polygenic prediction models of measured intelligence and academic achievement in a cohort of Australian adolescent twins (N=2,335, 57% female). Additionally, we tested the association of these polygenic risk scores (PRS) with core academic skills such as the ability to comprehend, structure and sequence, evaluate and assess, communicate, and apply techniques and procedures. We show that PRSs for educational attainment, intelligence and cognitive factors explained up to 10% of the variance in educational achievement and 7% in intelligence test scores in our cohort. Additionally, we found that a genetic predisposition for ADHD was negatively associated with all cognitive outcomes and skills and a genetic predisposition for schizophrenia was negatively associated with performance IQ but no other cognitive domain. In this study, we show the potential value of genotypic data for predicting pupil achievement and cognitive developmental trajectory through puberty and into adolescence. We provide evidence that a genetic vulnerability to some mental health disorders is associated with poorer cognitive and educational outcomes, regardless of whether the individual has developed the disorder.

scholarly journals The yin–yang of kinase activation and unfolding explains the peculiarity of Val600 in the activation segment of BRAF

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Christina Kiel ◽  
Hannah Benisty ◽  
Veronica Lloréns-Rico ◽  
Luis Serrano
Keyword(s):  
Prediction Models ◽  
Association Studies ◽  
Random Forest Classifier ◽  
Driver Mutations ◽  
Genome Wide Association Studies ◽  
Folding Energy ◽  
Hydrophobic Pocket ◽  
Kinase Activation ◽  
Genome Wide ◽  
Activation Segment

Many driver mutations in cancer are specific in that they occur at significantly higher rates than – presumably – functionally alternative mutations. For example, V600E in the BRAF hydrophobic activation segment (AS) pocket accounts for >95% of all kinase mutations. While many hypotheses tried to explain such significant mutation patterns, conclusive explanations are lacking. Here, we use experimental and in silico structure-energy statistical analyses, to elucidate why the V600E mutation, but no other mutation at this, or any other positions in BRAF’s hydrophobic pocket, is predominant. We find that BRAF mutation frequencies depend on the equilibrium between the destabilization of the hydrophobic pocket, the overall folding energy, the activation of the kinase and the number of bases required to change the corresponding amino acid. Using a random forest classifier, we quantitatively dissected the parameters contributing to BRAF AS cancer frequencies. These findings can be applied to genome-wide association studies and prediction models.

scholarly journals Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services

2020 ◽  
Vol 27 (9) ◽  
pp. 1425-1430
Author(s):  
Inès Krissaane ◽  
Carlos De Niz ◽  
Alba Gutiérrez-Sacristán ◽  
Gabor Korodi ◽  
Nneka Ede ◽  
...  
Keyword(s):  
Web Services ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Cloud Platform ◽  
Human Genomics ◽  
Genome Wide ◽  
Innovative Methodology ◽  
Amazon Web Services

Abstract Objective Advancements in human genomics have generated a surge of available data, fueling the growth and accessibility of databases for more comprehensive, in-depth genetic studies. Methods We provide a straightforward and innovative methodology to optimize cloud configuration in order to conduct genome-wide association studies. We utilized Spark clusters on both Google Cloud Platform and Amazon Web Services, as well as Hail (http://doi.org/10.5281/zenodo.2646680) for analysis and exploration of genomic variants dataset. Results Comparative evaluation of numerous cloud-based cluster configurations demonstrate a successful and unprecedented compromise between speed and cost for performing genome-wide association studies on 4 distinct whole-genome sequencing datasets. Results are consistent across the 2 cloud providers and could be highly useful for accelerating research in genetics. Conclusions We present a timely piece for one of the most frequently asked questions when moving to the cloud: what is the trade-off between speed and cost?

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