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.

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 The Genetics of Type 2 Diabetes: A Realistic Appraisal in 2008

2008 ◽  
Vol 93 (12) ◽  
pp. 4633-4642 ◽  
Author(s):  
Jose C. Florez
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Models ◽  
Association Studies ◽  
Evidence Synthesis ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Major Work ◽  
Therapeutic Implications ◽  
Genome Wide

Context: Over the last few months, genome-wide association studies have contributed significantly to our understanding of the genetic architecture of type 2 diabetes. If and how this information will impact clinical practice is not yet clear. Evidence Acquisition: Primary papers reporting genome-wide association studies in type 2 diabetes or establishing a reproducible association for specific candidate genes were compiled. Further information was obtained from background articles, authoritative reviews, and relevant meeting conferences and abstracts. Evidence Synthesis: As many as 17 genetic loci have been convincingly associated with type 2 diabetes; 14 of these were not previously known, and most of them were unsuspected. The associated polymorphisms are common in populations of European descent but have modest effects on risk. These loci highlight new areas for biological exploration and allow the initiation of experiments designed to develop prediction models and test possible pharmacogenetic and other applications. Conclusions: Although substantial progress in our knowledge of the genetic basis of type 2 diabetes is taking place, these new discoveries represent but a small proportion of the genetic variation underlying the susceptibility to this disorder. Major work is still required to identify the causal variants, test their role in disease prediction and ascertain their therapeutic implications.

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 Using DNA to predict behaviour problems from preschool to adulthood

2021 ◽  
Author(s):  
Agnieszka Gidziela ◽  
Kaili Rimfeld ◽  
Margherita Malanchini ◽  
Andrea G. Allegrini ◽  
Andrew McMillan ◽  
...  
Keyword(s):  
Prediction Models ◽  
Association Studies ◽  
Penalized Regression ◽  
Effect Sizes ◽  
Behaviour Problems ◽  
List Type ◽  
Genome Wide Association Studies ◽  
General Behaviour ◽  
Genome Wide ◽  
Polygenic Scores

AbstractBackgroundOne goal of the DNA revolution is to predict problems in order to prevent them. We tested here if the prediction of behaviour problems from genome-wide polygenic scores (GPS) can be improved by creating composites across ages and across raters and by using a multi-GPS approach that includes GPS for adult psychiatric disorders as well as for childhood behaviour problems.MethodOur sample included 3,065 genotyped unrelated individuals from the Twins Early Development Study who were assessed longitudinally for hyperactivity, conduct, emotional problems and peer problems as rated by parents, teachers and children themselves. GPS created from 15 genome-wide association studies were used separately and jointly to test the prediction of behaviour problems composites (general behaviour problems, externalizing and internalizing) across ages (from age 2 to age 21) and across raters in penalized regression models. Based on the regression weights, we created multi-trait GPS reflecting the best prediction of behaviour problems. We compared GPS prediction to twin heritability using the same sample and measures.ResultsMulti-GPS prediction of behaviour problems increased from less than 2% of the variance for observed traits to up to 6% for cross-age and cross-rater composites. Twin study estimates of heritability mirrored patterns of multi-GPS prediction as they increased from less than 40% to up to 83%.ConclusionsThe ability of GPS to predict behaviour problems can be improved by using multiple GPS, cross-age composites and cross-rater composites, although the effect sizes remain modest, up to 6%. Our results can be used in any genotyped sample to create multi-trait GPS predictors of behaviour problems that will be more predictive than polygenic scores based on a single age, rater or GPS.Key pointsGenome-wide polygenic scores (GPS) can be used to predict behaviour problems in childhood, but the effect sizes are generally less than 3.5%.DNA-based prediction models of achieve greater accuracy if holistic approaches are employed, that is cross-trait, longitudinal and trans-situational approaches.The prediction of childhood behaviour problems can be improved by using multiple GPS to predict composites that aggregate behaviour problems across ages and across raters.Our results yield weights that can be applied to GPS in any study to create multi-trait GPS predictors of behaviour problems based on cross-age and cross-rater composites.As compared to individuals in the lowest multi-trait GPS decile, nearly three times as many individuals in the highest internalizing multi-trait GPS decile were diagnosed with anxiety disorder and 25% more individuals in the highest general behaviour problems and externalizing multi-trait GPS deciles have taken medication for mental health.

scholarly journals A New Strategy for Using Historical Imbalanced Yield Data to Conduct Genome-Wide Association Studies and Develop Genomic Prediction Models for Wheat Breeding

2021 ◽  
Author(s):  
Chenggen Chu ◽  
Shichen Wang ◽  
Jackie C. Rudd ◽  
Amir M.H. Ibrahim ◽  
Qingwu Xue ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Models ◽  
Association Studies ◽  
Wheat Breeding ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Yield Data ◽  
Breeding Lines ◽  
Genome Wide ◽  
Genomic Regions

Abstract Using imbalanced historical yield data to predict performance and select new lines is an arduous breeding task. Genome-wide association studies (GWAS) and high throughput genotyping based on sequencing techniques can increase prediction accuracy. An association mapping panel of 227 Texas elite (TXE) wheat breeding lines was used for GWAS and a training population to develop prediction models for grain yield selection. An imbalanced set of yield data collected from 102 environments (year-by-location) over ten years, through testing yield in 40–66 lines each year at 6–14 locations with 38–41 lines repeated in the test in any two consecutive years, was used. Based on correlations among data from different environments within two adjacent years and heritability estimated in each environment, yield data from 87 environments were selected and assigned to two correlation-based groups. The yield best linear unbiased estimation (BLUE) from each group, along with reaction to greenbug and Hessian fly in each line, were used for GWAS to reveal genomic regions associated with yield and insect resistance. A total of 74 genomic regions were associated with grain yield and two of them were commonly detected in both correlation-based groups. Greenbug resistance in TXE lines was mainly controlled by Gb3 on chromosome 7DL in addition to two novel regions on 3DL and 6DS, and Hessian fly resistance was conferred by the region on 1AS. Genomic prediction models developed in two correlation-based groups were validated using a set of 105 new advanced breeding lines and the model from correlation-based group G2 was more reliable for prediction. This research not only identified genomic regions associated with yield and insect resistance but also established the method of using historical imbalanced breeding data to develop a genomic prediction model for crop improvement.

scholarly journals Transcriptome-wide association and prediction for carotenoids and tocochromanols in fresh sweet corn kernels

2021 ◽  
Author(s):  
Jenna Hershberger ◽  
Ryokei Tanaka ◽  
Joshua C. Wood ◽  
Nicholas Kaczmar ◽  
Di Wu ◽  
...  
Keyword(s):  
Statistical Power ◽  
Prediction Models ◽  
Sweet Corn ◽  
Association Studies ◽  
A Priori ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Causal Genes ◽  
Corn Kernels

Sweet corn is consistently one of the most highly consumed vegetables in the U.S., providing a valuable opportunity to increase nutrient intake through biofortification. Significant variation for carotenoid (provitamin A, lutein, zeaxanthin) and tocochromanol (vitamin E, antioxidants) levels is present in temperate sweet corn germplasm, yet previous genome-wide association studies (GWAS) of these traits have been limited by low statistical power and mapping resolution. Here, we employed a high-quality transcriptomic dataset collected from fresh sweet corn kernels to conduct transcriptome-wide association studies (TWAS) and transcriptome prediction studies for 39 carotenoid and tocochromanol traits. In agreement with previous GWAS findings, TWAS detected significant associations for four causal genes, β-carotene hydroxylase (crtRB1), lycopene epsilon cyclase (lcyE), γ-tocopherol methyltransferase (vte4), and homogentisate geranylgeranyltransferase (hggt1) on a transcriptome-wide level. Pathway-level analysis revealed additional associations for deoxy-xylulose synthase2 (dxs2), diphosphocytidyl methyl erythritol synthase2 (dmes2), cytidine methyl kinase1 (cmk1), and geranylgeranyl hydrogenase1 (ggh1), of which, dmes2, cmk1, and ggh1 have not previously been identified through maize association studies. Evaluation of prediction models incorporating genome-wide markers and transcriptome-wide abundances revealed a trait-dependent benefit to the inclusion of both genomic and transcriptomic data over solely genomic data, but both transcriptome- and genome-wide datasets outperformed a priori candidate gene-targeted prediction models for most traits. Altogether, this study represents an important step towards understanding the role of regulatory variation in the accumulation of vitamins in fresh sweet corn kernels.

scholarly journals Genetic predictors of gene expression associated with risk of bipolar disorder

2016 ◽  
Author(s):  
Kaanan Shah ◽  
Heather E Wheeler ◽  
Eric R Gamazon ◽  
Dan L Nicolae ◽  
Nancy J Cox ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Prediction Models ◽  
Association Studies ◽  
Anterior Cingulate ◽  
Health Concern ◽  
Ras Signaling ◽  
Genome Wide Association Studies ◽  
Mrna Levels ◽  
Genome Wide ◽  
Increased Risk

Bipolar disorder (BD) affects the quality of life of approximately 1% of the population and represents a major public health concern. It is known to be highly heritable but large-scale genome-wide association studies (GWAS) have discovered only a handful of markers associated with the disease. Furthermore, the biological mechanisms underlying these markers need to be elucidated. We recently published a gene-level association test, PrediXcan that integrates transcriptome regulation data to characterize the function of these markers in a tissue specific manner. In this study, we developed prediction models for mRNA levels in 10 brain regions using data from the GTEx project and performed PrediXcan analysis in WTCCC as well as in an independent cohort, GAIN. We replicate the association between predicted expression of PTPRE and BD risk in whole blood and recapitulate the association in brain tissues. PTPRE encodes the protein tyrosine phosphatase, receptor type E, that is known to be involved in RAS signaling and activation of voltage-gated K+ channels. We also found a new genome-wide significant association between lower predicted expression of BBX (bobby sox homolog) in the anterior cingulate cortex region of the brain and increased risk of BD (pWTCCC = 7.02 x 10e-6, pGAIN = 4.68 x 10e-3, pmeta = 1.11 x 10e-7). In sum, we used our mechanistically informed approach, PrediXcan, to identify and replicate two novel genome-wide significant genes using existing GWAS studies.

scholarly journals Colorectal Cancer Risk by Genetic Variants in Populations With and Without Colonoscopy History

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Feng Guo ◽  
Xuechen Chen ◽  
Jenny Chang-Claude ◽  
Michael Hoffmeister ◽  
Hermann Brenner
Keyword(s):  
Colorectal Cancer ◽  
Prediction Models ◽  
Association Studies ◽  
Statistical Tests ◽  
The United States ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Large Genome ◽  
Genome Wide ◽  
History Of

Abstract Background Polygenic risk scores (PRS), which are derived from results of large genome-wide association studies, are increasingly propagated for colorectal cancer (CRC) risk stratification. The majority of studies included in the large genome-wide association studies consortia were conducted in the United States and Germany, where colonoscopy with detection and removal of polyps has been widely practiced over the last decades. We aimed to assess if and to what extent the history of colonoscopy with polypectomy may alter metrics of the predictive ability of PRS for CRC risk. Methods A PRS based on 140 single nucleotide polymorphisms was compared between 4939 CRC patients and 3797 control persons of the Darmkrebs: Chancen der Verhütung durch Screening (DACHS) study, a population-based case-control study conducted in Germany. Risk discrimination was quantified according to the history of colonoscopy and polypectomy by areas under the curves (AUCs) and their 95% confidence intervals (CIs). All statistical tests were 2-sided. Results AUCs and 95% CIs were higher among subjects without previous colonoscopy (AUC = 0.622, 95% CI = 0.606 to 0.639) than among those with previous colonoscopy and polypectomy (AUC = 0.568, 95% CI = 0.536 to 0.601; difference [Δ AUC] = 0.054, P = .004). Such differences were consistently seen in sex-specific groups (women: Δ AUC = 0.073, P = .02; men: Δ AUC = 0.046, P = .048) and age-specific groups (younger than 70 years: Δ AUC = 0.052, P = .07; 70 years or older: Δ AUC = 0.049, P = .045). Conclusions Predictive performance of PRS may be underestimated in populations with widespread use of colonoscopy. Future studies using PRS to develop CRC prediction models should carefully consider colonoscopy history to provide more accurate estimates.

scholarly journals Identifying novel genetic variants for brain amyloid deposition: a genome-wide association study in the Korean population

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Hang-Rai Kim ◽  
Sang-Hyuk Jung ◽  
Jaeho Kim ◽  
Hyemin Jang ◽  
Sung Hoon Kang ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Prediction Models ◽  
Association Studies ◽  
Genome Wide Association ◽  
Korean Population ◽  
European Ancestry ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
A Genome

Abstract Background Genome-wide association studies (GWAS) have identified a number of genetic variants for Alzheimer’s disease (AD). However, most GWAS were conducted in individuals of European ancestry, and non-European populations are still underrepresented in genetic discovery efforts. Here, we performed GWAS to identify single nucleotide polymorphisms (SNPs) associated with amyloid β (Aβ) positivity using a large sample of Korean population. Methods One thousand four hundred seventy-four participants of Korean ancestry were recruited from multicenters in South Korea. Discovery dataset consisted of 1190 participants (383 with cognitively unimpaired [CU], 330 with amnestic mild cognitive impairment [aMCI], and 477 with AD dementia [ADD]) and replication dataset consisted of 284 participants (46 with CU, 167 with aMCI, and 71 with ADD). GWAS was conducted to identify SNPs associated with Aβ positivity (measured by amyloid positron emission tomography). Aβ prediction models were developed using the identified SNPs. Furthermore, bioinformatics analysis was conducted for the identified SNPs. Results In addition to APOE, we identified nine SNPs on chromosome 7, which were associated with a decreased risk of Aβ positivity at a genome-wide suggestive level. Of these nine SNPs, four novel SNPs (rs73375428, rs2903923, rs3828947, and rs11983537) were associated with a decreased risk of Aβ positivity (p < 0.05) in the replication dataset. In a meta-analysis, two SNPs (rs7337542 and rs2903923) reached a genome-wide significant level (p < 5.0 × 10−8). Prediction performance for Aβ positivity increased when rs73375428 were incorporated (area under curve = 0.75; 95% CI = 0.74–0.76) in addition to clinical factors and APOE genotype. Cis-eQTL analysis demonstrated that the rs73375428 was associated with decreased expression levels of FGL2 in the brain. Conclusion The novel genetic variants associated with FGL2 decreased risk of Aβ positivity in the Korean population. This finding may provide a candidate therapeutic target for AD, highlighting the importance of genetic studies in diverse populations.

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