scholarly journals Accurate genetic profiling of anthropometric traits using a big data approach

2015 ◽  
Author(s):  
Oriol Canela-Xandri ◽  
Konrad Rawlik ◽  
John A. Woolliams ◽  
Albert Tenesa
Keyword(s):  
Disease Management ◽  
Prediction Models ◽  
Disease Risk ◽  
Association Studies ◽  
Genomic Medicine ◽  
Full Potential ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Starting Point ◽  
The Individual

Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach that enables the prediction of multiple medically relevant phenotypes without the costs associated with developing a genetic test for each of them. As a proof of principle, we used a common panel of 319,038 SNPs to train the prediction models in 114,264 unrelated White-British for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given their explained heritable component. This represents an improvement of up to 75% over the phenotypic variance explained by the predictors developed through large collaborations, which used more than twice as many training samples. Across-population predictions in White non-British individuals were similar to those of White-British whilst those in Asian and Black individuals were informative but less accurate. The genotyping of circa 500,000 UK Biobank participants will yield predictions ranging between 66% and 83% of the maximum. We anticipate that our models and a common panel of genetic markers, which can be used across multiple traits and diseases, will be the starting point to tailor disease management to the individual. Ultimately, we will be able to capitalise on whole-genome sequence and environmental risk factors to realise the full potential of genomic medicine.

scholarly journals RECAS9: Recombining wild species introgression via mitotic gene editing in barley

2020 ◽  
Author(s):  
Shelly Lazar ◽  
Manas Ranjan Prusty ◽  
Khaled Bishara ◽  
Amir Sherman ◽  
Eyal Fridman
Keyword(s):  
Wild Species ◽  
Association Studies ◽  
Digital Pcr ◽  
Crop Wild Relatives ◽  
Linkage Drag ◽  
Genome Wide Association Studies ◽  
Starting Point ◽  
Rnp Complex ◽  
Causal Genes ◽  
The Individual

AbstractGenetic loci underlying variation in traits with agronomic importance or genetic risk factors in human diseases have been identified by linkage analysis and genome-wide association studies. However, narrowing down the mapping to the individual causal genes and variations within these is much more challenging, and so is the ability to break linkage drag between beneficial and unfavourable loci in crop breeding. We developed RECAS9 as a transgene-free approach for precisely targeting recombination events by delivering CRISPR/Cas9 ribonucleotide protein (RNP) complex into heterozygous mitotic cells for the barley (Hordeum vulgare) Heat3.1 locus. A wild species (H. spontaneum) introgression in this region carries the agronomical unfavourable tough rachis phenotype (non-brittle) allele linked with a circadian clock accelerating QTL near GIGANTEA gene. We delivered RNP, which was targeted between two single nucleotide polymorphism (SNPs), to mitotic calli cells by particle bombardment. We estimated recombination events by next generation sequencing (NGS) and droplet digital PCR (ddPCR). While NGS analysis grieved from confounding effects of PCR recombination, ddPCR analysis allowed us to associate RNP treatment on heterozygous individuals with significant increase of homologous directed repair (HDR) between cultivated and wild alleles, with recombination rate ranging between zero to 57%. These results show for the first time in plants a directed and transgene free mitotic recombination driven by Cas9 RNP, and provide a starting point for precise breeding and fine scale mapping of beneficial alleles from crop wild relatives.

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 Type 2 diabetes-related variants influence the risk of developing multiple myeloma: results from the IMMEnSE consortium

2015 ◽  
Vol 22 (4) ◽  
pp. 545-559 ◽  
Author(s):  
Rafael Ríos ◽  
Carmen Belén Lupiañez ◽  
Daniele Campa ◽  
Alessandro Martino ◽  
Joaquin Martínez-López ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Multiple Myeloma ◽  
Prediction Models ◽  
Disease Risk ◽  
Association Studies ◽  
Area Under The Curve ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Stratified Analysis

Type 2 diabetes (T2D) has been suggested to be a risk factor for multiple myeloma (MM), but the relationship between the two traits is still not well understood. The aims of this study were to evaluate whether 58 genome-wide-association-studies (GWAS)-identified common variants for T2D influence the risk of developing MM and to determine whether predictive models built with these variants might help to predict the disease risk. We conducted a case–control study including 1420 MM patients and 1858 controls ascertained through the International Multiple Myeloma (IMMEnSE) consortium. Subjects carrying the KCNQ1rs2237892T allele or the CDKN2A-2Brs2383208G/G, IGF1rs35767T/T and MADDrs7944584T/T genotypes had a significantly increased risk of MM (odds ratio (OR)=1.32–2.13) whereas those carrying the KCNJ11rs5215C, KCNJ11rs5219T and THADArs7578597C alleles or the FTOrs8050136A/A and LTArs1041981C/C genotypes showed a significantly decreased risk of developing the disease (OR=0.76–0.85). Interestingly, a prediction model including those T2D-related variants associated with the risk of MM showed a significantly improved discriminatory ability to predict the disease when compared to a model without genetic information (area under the curve (AUC)=0.645 vs AUC=0.629; P=4.05×10−06). A gender-stratified analysis also revealed a significant gender effect modification for ADAM30rs2641348 and NOTCH2rs10923931 variants (Pinteraction=0.001 and 0.0004, respectively). Men carrying the ADAM30rs2641348C and NOTCH2rs10923931T alleles had a significantly decreased risk of MM whereas an opposite but not significant effect was observed in women (ORM=0.71 and ORM=0.66 vs ORW=1.22 and ORW=1.15, respectively). These results suggest that TD2-related variants may influence the risk of developing MM and their genotyping might help to improve MM risk prediction models.

scholarly journals Efficient identification of context dependent subgroups of risk from genome-wide association studies

2014 ◽  
Vol 13 (2) ◽  
Author(s):  
Greg Dyson ◽  
Charles F. Sing
Keyword(s):  
Prediction Models ◽  
Disease Risk ◽  
Hypothesis Test ◽  
Association Studies ◽  
Genome Wide Association ◽  
Computational Time ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Genomic Variations ◽  
Genome Wide

AbstractWe have developed a modified Patient Rule-Induction Method (PRIM) as an alternative strategy for analyzing representative samples of non-experimental human data to estimate and test the role of genomic variations as predictors of disease risk in etiologically heterogeneous sub-samples. A computational limit of the proposed strategy is encountered when the number of genomic variations (predictor variables) under study is large (>500) because permutations are used to generate a null distribution to test the significance of a term (defined by values of particular variables) that characterizes a sub-sample of individuals through the peeling and pasting processes. As an alternative, in this paper we introduce a theoretical strategy that facilitates the quick calculation of Type I and Type II errors in the evaluation of terms in the peeling and pasting processes carried out in the execution of a PRIM analysis that are under-estimated and non-existent, respectively, when a permutation-based hypothesis test is employed. The resultant savings in computational time makes possible the consideration of larger numbers of genomic variations (an example genome-wide association study is given) in the selection of statistically significant terms in the formulation of PRIM prediction models.

scholarly journals Identifying Crohn’s disease signal from variome analysis

2017 ◽  
Author(s):  
Yanran Wang ◽  
Yuri Astrakhan ◽  
Britt-Sabina Petersen ◽  
Stefan Schreiber ◽  
Andre Franke ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Prediction Models ◽  
Disease Risk ◽  
Association Studies ◽  
Disease Genes ◽  
Analysis Tool ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Diagnostic Time

AbstractBackgroundAfter many years of concentrated research efforts, the exact cause of Crohn’s disease remains unknown. Its accurate diagnosis, however, helps in management and even preventing the onset of disease. Genome-wide association studies have identified 140 loci associated with CD, but these carry very small log odds ratios and are uninformative for diagnoses.ResultsHere we describe a machine learning method – AVA,Dx (Analysis of Variation for Association with Disease) – that uses whole exome sequencing data to make predictions of CD status. Using the person-specific variation in these genes from a panel of only 111 individuals, we built disease-prediction models informative of previously undiscovered disease genes. In this panel, our models differentiate CD patients from healthy controls with 71% precision and 73% recall at the default cutoff. By additionally accounting for batch effects, we are also able to predict individual CD status for previously unseen individuals from a separate CD study (84% precision, 73% recall).ConclusionsLarger training panels and additional features, including regulatory variants and environmental factors, e.g. human-associated microbiota, are expected to improve model performance. However, current results already position AVA,Dx as both an effective method for highlighting pathogenesis pathways and as a simple Crohn’s disease risk analysis tool, which can improve clinical diagnostic time and accuracy.

scholarly journals Colocalization of GWAS and eQTL Signals Detects Target Genes

2016 ◽  
Author(s):  
Farhad Hormozdiari ◽  
Martijn van de Bunt ◽  
Ayellet V. Segrè ◽  
Xiao Li ◽  
Jong Wha J Joo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Causal Variant ◽  
Genome Wide Association Studies ◽  
Specific Tissue ◽  
The Individual ◽  
Eqtl Data ◽  
Different Tissues

AbstractThe vast majority of genome-wide association studies (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual’s disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue may play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWAS and eQTL studies is challenging due to the uncertainty induced by linkage disequilibrium (LD) and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present a new method, eCAVIAR, that is capable of accounting for LD while computing the quantity we refer to as the colocalization posterior probability (CLPP). The CLPP is the probability that the same variant is responsible for both the GWAS and eQTL signal. eCAVIAR has several key advantages. First, our method can account for more than one causal variant in any loci. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Utilizing publicly available eQTL data on 45 different tissues, we demonstrate that computing CLPP can prioritize likely relevant tissues and target genes for a set of Glucose and Insulin-related traits loci. eCAVIAR is available at http://genetics.cs.ucla.edu/caviar/

Environment

2016 ◽  
Author(s):  
Christopher R. Holroyd ◽  
Nicholas C. Harvey ◽  
Mark H. Edwards ◽  
Cyrus Cooper
Keyword(s):  
Rheumatoid Arthritis ◽  
Life Course ◽  
Disease Risk ◽  
Association Studies ◽  
Population Level ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Mineral Density ◽  
The Individual

Musculoskeletal disease covers a broad spectrum of conditions whose aetiology comprises variable genetic and environmental contributions. More recently it has become clear that, particularly early in life, the interaction of gene and environment is critical to the development of later disease. Additionally, only a small proportion of the variation in adult traits such as bone mineral density has been explained by specific genes in genome-wide association studies, suggesting that gene-environment interaction may explain a much larger part of the inheritance of disease risk than previously thought. It is therefore critically important to evaluate the environmental factors which may predispose to diseases such as osteorthritis, osteoporosis, and rheumatoid arthritis both at the individual and at the population level. In this chapter we describe the environmental contributors, across the whole life course, to osteoarthritis, osteoporosis and rheumatoid arthritis, as exemplar conditions. We consider factors such as age, gender, nutrition (including the role of vitamin D), geography, occupation, and the clues that secular changes of disease pattern may yield. We describe the accumulating evidence that conditions such as osteoporosis may be partly determined by the early interplay of environment and genotype, through aetiological mechanisms such as DNA methylation and other epigenetic phenomena. Such studies, and those examining the role of environmental influences across other stages of the life course, suggest that these issues should be addressed at all ages, starting from before conception, in order to optimally reduce the burden of musculoskeletal disorders in future generations.

Environment

2013 ◽  
Author(s):  
Christopher R. Holroyd ◽  
Nicholas C. Harvey ◽  
Mark H. Edwards ◽  
Cyrus Cooper
Keyword(s):  
Rheumatoid Arthritis ◽  
Life Course ◽  
Disease Risk ◽  
Association Studies ◽  
Population Level ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Mineral Density ◽  
The Individual

Musculoskeletal disease covers a broad spectrum of conditions whose aetiology comprises variable genetic and environmental contributions. More recently it has become clear that, particularly early in life, the interaction of gene and environment is critical to the development of later disease. Additionally, only a small proportion of the variation in adult traits such as bone mineral density has been explained by specific genes in genome-wide association studies, suggesting that gene-environment interaction may explain a much larger part of the inheritance of disease risk than previously thought. It is therefore critically important to evaluate the environmental factors which may predispose to diseases such as osteorthritis, osteoporosis, and rheumatoid arthritis both at the individual and at the population level. In this chapter we describe the environmental contributors, across the whole life course, to osteoarthritis, osteoporosis and rheumatoid arthritis, as exemplar conditions. We consider factors such as age, gender, nutrition (including the role of vitamin D), geography, occupation, and the clues that secular changes of disease pattern may yield. We describe the accumulating evidence that conditions such as osteoporosis may be partly determined by the early interplay of environment and genotype, through aetiological mechanisms such as DNA methylation and other epigenetic phenomena. Such studies, and those examining the role of environmental influences across other stages of the life course, suggest that these issues should be addressed at all ages, starting from before conception, in order to optimally reduce the burden of musculoskeletal disorders in future generations.

scholarly journals Stability of Polygenic Scores Across Discovery Genome-Wide Association Studies

2021 ◽  
Author(s):  
Laura M. Schultz ◽  
Alison K. Merikangas ◽  
Kosha Ruparel ◽  
Sebastien Jacquemont ◽  
David C. Glahn ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Predictive Accuracy ◽  
Association Studies ◽  
Genetic Correlations ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Post Traumatic Stress ◽  
Individual Patient Level ◽  
Polygenic Scores ◽  
The Individual

Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual patient level when phenotypes are not necessarily already known. Hence, we investigated the stability of PGS in European-American (EUR)- and African-American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort (PNC) and the Adolescent Brain Cognitive Development Study (ABCD) using different discovery GWAS for post-traumatic stress disorder (PTSD), type-2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations > 0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was higher for GWAS that explained more of the trait variance, with height PGS being more stable than PTSD or T2D PGS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the level of the individual patient, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.

scholarly journals AB0730 ASSOCIATION OF SPONDYLOARTHRITIS AND FAMILIAL MEDITERRANEAN FEVER AND IMPACT ON DISEASE PHENOTYPE: A SYSTEMATIC REVIEW OF THE LITERATURE

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1659.3-1659
Author(s):  
N. Ziade ◽  
A. Nassar
Keyword(s):  
Disease Management ◽  
Familial Mediterranean Fever ◽  
Literature Search ◽  
Association Studies ◽  
Case Reports ◽  
Genome Wide Association Studies ◽  
Systematic Literature Search ◽  
Disease Phenotype ◽  
Mefv Mutation ◽  
Mediterranean Fever

Background:Spondyloarthritis (SpA) and Familial Meditaerranean fever (FMF) may co-exist in certain populations, and have some overlapping manifestations (oligo-arthritis, hip involvement). Their association may impact disease phenotype and may affect disease management.Objectives:To evaluate the association of SpA and FMF and its impact on disease phenotype and management.Methods:A systematic literature search was conducted with the keywords spondyloarthritis and familial mediterranean fever from Janurary 1990 to January 2020 in PubMed and using manual cross-reference methods.Results:The search retrieved 74 articles, out of which 37 articles were relevant to the study question; most of the articles were case reports, with some large cohort studies of FMF and SpA (Flowchart in Figure 1).In large FMF cohorts, the prevalence of SpA was higher compared to the general population (7.5-13%, OR around 10). M694V was a risk factor for SpA. These FMF-SpA patients were older at diagnosis, had lower fever attacks, and higher disease duration, inflammatory back pain, chronic arthritis, enthesopathy, persistent inflammation and higher resistance to Colchicine. In case series, they were responsive to anti-TNF therapy.In large SpA cohorts, MEFV mutation, particularly M694V, was found in 15-35% (even without associated FMF). In most cohorts, MEFV mutation carriers didn’t have any distinct disease phenotype, except for some reports of higher ESR, more hip involvement, higher BASFI and higher BASDAI. Genome-wide association studies and case reports suggest an implication for IL-1 and thus a role for Anakinra therapy in these patients.Conclusion:In FMF or SpA patients with resistance to conventional therapy, the evaluation of disease association should be performed as it may have significant impact on disease management.References:[1]Li et al, Plos Genetics 2019. Watad et al, Frontiers Immunol 2019. Atas et al, Rheumatol Int 2019. Cherqaoui et al, JBS 2017. Zhong et al. Plos One 2017. Ornek et al, Arch Rheumatol 2016. Cinar et al, Rheumatol Int 2008. Durmur et al, JBS 2007.Figure 1.Flowchart of the systematic literature search (Spondyloarthritis, Familial Mediterranean Fever; January 1990-2020).Disclosure of Interests:Nelly Ziade Speakers bureau: Abbvie, Janssen, Lilly, Novartis, Pfizer, Roche, Sanofi, Aref Nassar: None declared

Sign in / Sign up

Export Citation Format

Share Document