scholarly journals Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

2018 ◽  
Author(s):  
Dominic Holland ◽  
Oleksandr Frei ◽  
Rahul Desikan ◽  
Chun-Chieh Fan ◽  
Alexey A. Shadrin ◽  
...  
Keyword(s):  
Association Studies ◽  
Gaussian Mixture ◽  
Causal Snps ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Sample Sizes ◽  
Narrow Sense Heritability ◽  
Basic Model ◽  
Current Sample

AbstractOf signal interest in the genetics of human traits is estimating their polygenicity (the proportion of causally associated single nucleotide polymorphisms (SNPs)) and the discoverability (or effect size variance) of the causal SNPs. Narrow-sense heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from an extensive reference panel, to estimate these quantities from genome-wide association studies (GWAS) summary statistics for SNPs with minor allele frequency >1%. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities ranging from ≃ 2 × 10−5 to ≃ 4 × 10−3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation.

Admixture mapping of growth related traits in F2 mice dataset using ancestry informative markers

2014 ◽  
Vol 12 (02) ◽  
pp. 1441010
Author(s):  
Burak Karacaören
Keyword(s):  
Association Studies ◽  
Statistical Tests ◽  
Multiple Hypothesis Testing ◽  
Admixture Mapping ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Ancestry Informative Markers ◽  
Single Marker ◽  
Genomic Signals

Most of the associated single nucleotide polymorphisms (SNPs) for genome wide association studies (GWAS) explain very little proportion of phenotypic variance in outbred populations. One reason is; large number of markers raises the problem of multiple hypothesis testing correction using conservative statistical tests in single marker models. Admixture mapping could be used as alternative model to detect the genes associated with quantitative traits by less number of ancestry informative markers. Ancestral genotypes of founder populations were available for the F 2 mice dataset for growth related traits. The objectives of this study were (1) to detect genomic signals by admixture mapping for growth related traits by ancestry informative markers and ancestral genotypes (2) to detect genomic signals for growth related traits by Bayes C(π) model and compare results with those obtained by use of admixture mapping. Bayes C(π) model detected more SNPs that has high ancestry informative markers. But due to stringent significance tests and small SNPs effects admixture model did not detect the same SNPs in Bayes C(π). As was expected higher ancestral informative markers lead to higher Z values in admixture model with a little variation. Admixture model could incorporate and use ancestral genomic information.

Genetic Burden Analyses of Phenotypes Relevant to Aging in the Berlin Aging Study II (BASE-II)

Gerontology ◽  
2016 ◽  
Vol 62 (3) ◽  
pp. 316-322 ◽  
Author(s):  
Christina M. Lill ◽  
Tian Liu ◽  
Kristina Norman ◽  
Antje Meyer ◽  
Elisabeth Steinhagen-Thiessen ◽  
...  
Keyword(s):  
Telomere Length ◽  
Association Studies ◽  
Receiver Operating Curve ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Mineral Density ◽  
Association Analyses ◽  
Genome Wide ◽  
Aging Study

Background: Body mass index (BMI), bone mineral density (BMD), and telomere length are phenotypes that modulate the course of aging. Over 40% of their phenotypic variance is determined by genetics. Genome-wide association studies (GWAS) have recently uncovered >100 independent single-nucleotide polymorphisms (SNPs) showing genome-wide significant (p < 5 × 10-8) association with these traits. Objective: To test the individual and combined impact of previously reported GWAS SNPs on BMI, BMD, and relative leukocyte telomere length (rLTL) in ∼1,750 participants of the Berlin Aging Study II (BASE-II), a cohort consisting predominantly of individuals >60 years of age. Methods: Linear regression analyses were performed on a total of 101 SNPs and BMI, BMD measurements of the femoral neck (FN) and lumbar spine (LS), and rLTL. The combined effect of all trait-specific SNPs was evaluated by generating a weighted genomic profile score (wGPS) used in the association analyses. The predictive capability of the wGPS was estimated by determining the area under the receiver operating curve (AUC) for osteoporosis status (determined by BMD) with and without the wGPS. Results: Five loci showed experiment-wide significant association with BMI (FTO rs1558902, p = 1.80 × 10-5) or BMD (MEPE rs6532023, pFN = 5.40 × 10-4, pLS = 1.09 × 10-4; TNFRSF11B rs2062377, pLS = 8.70 × 10-4; AKAP11 rs9533090, pLS = 1.05 × 10-3; SMG6 rs4790881, pFN = 3.41 × 10-4) after correction for multiple testing. Several additional loci showed nominally significant (p < 0.05) association with BMI and BMD. The trait-specific wGPS was highly significantly associated with BMD (p < 2 × 10-16) and BMI (p = 1.10 × 10-6). No significant association was detected for rLTL in either single-SNP or wGPS-based analyses. The AUC for osteoporosis improved modestly from 0.762 (95% CI 0.733-0.800) to 0.786 (95% CI 0.756-0.823) and 0.785 (95% CI 0.757-0.824) upon inclusion of the FN- and LS-BMD wGPS, respectively. Conclusion: Our study provides an independent validation of previously reported genetic association signals for BMI and BMD in the BASE-II cohort. Additional studies are needed to pinpoint the factors underlying the proportion of phenotypic variance that remains unexplained by the current models.

scholarly journals A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

2021 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Real Data ◽  
Causal Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

scholarly journals Causal Association between Periodontitis and Parkinson’s Disease: A Bidirectional Mendelian Randomization Study

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 772
Author(s):  
João Botelho ◽  
Vanessa Machado ◽  
José João Mendes ◽  
Paulo Mascarenhas
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Instrumental Variables ◽  
Mendelian Randomization ◽  
Association Studies ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genetic Liability ◽  
Bidirectional Association

The latest evidence revealed a possible association between periodontitis and Parkinson’s disease (PD). We explored the causal relationship of this bidirectional association through two-sample Mendelian randomization (MR) in European ancestry populations. To this end, we used openly accessible data of genome-wide association studies (GWAS) on periodontitis and PD. As instrumental variables for periodontitis, seventeen single-nucleotide polymorphisms (SNPs) from a GWAS of periodontitis (1817 periodontitis cases vs. 2215 controls) and eight non-overlapping SNPs of periodontitis from an additional GWAS for validation purposes. Instrumental variables to explore for the reverse causation included forty-five SNPs from a GWAS of PD (20,184 cases and 397,324 controls). Multiple approaches of MR were carried-out. There was no evidence of genetic liability of periodontitis being associated with a higher risk of PD (B = −0.0003, Standard Error [SE] 0.0003, p = 0.26). The eight independent SNPs (B = −0.0000, SE 0.0001, p = 0.99) validated this outcome. We also found no association of genetically primed PD towards periodontitis (B = −0.0001, SE 0.0001, p = 0.19). These MR study findings do not support a bidirectional causal genetic liability between periodontitis and PD. Further GWAS studies are needed to confirm the consistency of these results.

scholarly journals Pharmacogenomics of Lithium Response in Bipolar Disorder

2021 ◽  
Vol 14 (4) ◽  
pp. 287
Author(s):  
Courtney M. Vecera ◽  
Gabriel R. Fries ◽  
Lokesh R. Shahani ◽  
Jair C. Soares ◽  
Rodrigo Machado-Vieira
Keyword(s):  
Bipolar Disorder ◽  
Association Studies ◽  
Mood Stabilizer ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Non Coding Rna ◽  
Genome Wide ◽  
Lithium Response ◽  
Genetic Loading ◽  
Long Non Coding Rna

Despite being the most widely studied mood stabilizer, researchers have not confirmed a mechanism for lithium’s therapeutic efficacy in Bipolar Disorder (BD). Pharmacogenomic applications may be clinically useful in the future for identifying lithium-responsive patients and facilitating personalized treatment. Six genome-wide association studies (GWAS) reviewed here present evidence of genetic variations related to lithium responsivity and side effect expression. Variants were found on genes regulating the glutamate system, including GAD-like gene 1 (GADL1) and GRIA2 gene, a mutually-regulated target of lithium. In addition, single nucleotide polymorphisms (SNPs) discovered on SESTD1 may account for lithium’s exceptional ability to permeate cell membranes and mediate autoimmune and renal effects. Studies also corroborated the importance of epigenetics and stress regulation on lithium response, finding variants on long, non-coding RNA genes and associations between response and genetic loading for psychiatric comorbidities. Overall, the precision medicine model of stratifying patients based on phenotype seems to derive genotypic support of a separate clinical subtype of lithium-responsive BD. Results have yet to be expounded upon and should therefore be interpreted with caution.

scholarly journals Investigation of gene–environment interactions in relation to tic severity

2021 ◽  
Author(s):  
Mohamed Abdulkadir ◽  
Dongmei Yu ◽  
Lisa Osiecki ◽  
Robert A. King ◽  
Thomas V. Fernandez ◽  
...  
Keyword(s):  
Tourette Syndrome ◽  
Association Studies ◽  
Autism Spectrum ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Linear Regression Models ◽  
Compulsive Disorder ◽  
Gene Environment ◽  
Tic Severity

AbstractTourette syndrome (TS) is a neuropsychiatric disorder with involvement of genetic and environmental factors. We investigated genetic loci previously implicated in Tourette syndrome and associated disorders in interaction with pre- and perinatal adversity in relation to tic severity using a case-only (N = 518) design. We assessed 98 single-nucleotide polymorphisms (SNPs) selected from (I) top SNPs from genome-wide association studies (GWASs) of TS; (II) top SNPs from GWASs of obsessive–compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD); (III) SNPs previously implicated in candidate-gene studies of TS; (IV) SNPs previously implicated in OCD or ASD; and (V) tagging SNPs in neurotransmitter-related candidate genes. Linear regression models were used to examine the main effects of the SNPs on tic severity, and the interaction effect of these SNPs with a cumulative pre- and perinatal adversity score. Replication was sought for SNPs that met the threshold of significance (after correcting for multiple testing) in a replication sample (N = 678). One SNP (rs7123010), previously implicated in a TS meta-analysis, was significantly related to higher tic severity. We found a gene–environment interaction for rs6539267, another top TS GWAS SNP. These findings were not independently replicated. Our study highlights the future potential of TS GWAS top hits in gene–environment studies.

scholarly journals Transcriptional Regulation of RUNX1: An Informatics Analysis

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1175
Author(s):  
Amarni L. Thomas ◽  
Judith Marsman ◽  
Jisha Antony ◽  
William Schierding ◽  
Justin M. O’Sullivan ◽  
...  
Keyword(s):  
Association Studies ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Runx1 Gene ◽  
Gene Regulatory Elements ◽  
Important Regulator ◽  
Aml1 Gene ◽  
Regulatory Landscape

The RUNX1/AML1 gene encodes a developmental transcription factor that is an important regulator of haematopoiesis in vertebrates. Genetic disruptions to the RUNX1 gene are frequently associated with acute myeloid leukaemia. Gene regulatory elements (REs), such as enhancers located in non-coding DNA, are likely to be important for Runx1 transcription. Non-coding elements that modulate Runx1 expression have been investigated over several decades, but how and when these REs function remains poorly understood. Here we used bioinformatic methods and functional data to characterise the regulatory landscape of vertebrate Runx1. We identified REs that are conserved between human and mouse, many of which produce enhancer RNAs in diverse tissues. Genome-wide association studies detected single nucleotide polymorphisms in REs, some of which correlate with gene expression quantitative trait loci in tissues in which the RE is active. Our analyses also suggest that REs can be variant in haematological malignancies. In summary, our analysis identifies features of the RUNX1 regulatory landscape that are likely to be important for the regulation of this gene in normal and malignant haematopoiesis.

scholarly journals Common genetic variants with fetal effects on birth weight are enriched for proximity to genes implicated in rare developmental disorders

2021 ◽  
Author(s):  
Robin N Beaumont ◽  
Isabelle K Mayne ◽  
Rachel M Freathy ◽  
Caroline F Wright
Keyword(s):  
Birth Weight ◽  
Statistical Power ◽  
Developmental Disorders ◽  
Association Studies ◽  
Later Life ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
Causal Genes

Abstract Birth weight is an important factor in newborn survival; both low and high birth weights are associated with adverse later-life health outcomes. Genome-wide association studies (GWAS) have identified 190 loci associated with maternal or fetal effects on birth weight. Knowledge of the underlying causal genes is crucial to understand how these loci influence birth weight and the links between infant and adult morbidity. Numerous monogenic developmental syndromes are associated with birth weights at the extreme ends of the distribution. Genes implicated in those syndromes may provide valuable information to prioritize candidate genes at the GWAS loci. We examined the proximity of genes implicated in developmental disorders (DDs) to birth weight GWAS loci using simulations to test whether they fall disproportionately close to the GWAS loci. We found birth weight GWAS single nucleotide polymorphisms (SNPs) fall closer to such genes than expected both when the DD gene is the nearest gene to the birth weight SNP and also when examining all genes within 258 kb of the SNP. This enrichment was driven by genes causing monogenic DDs with dominant modes of inheritance. We found examples of SNPs in the intron of one gene marking plausible effects via different nearby genes, highlighting the closest gene to the SNP not necessarily being the functionally relevant gene. This is the first application of this approach to birth weight, which has helped identify GWAS loci likely to have direct fetal effects on birth weight, which could not previously be classified as fetal or maternal owing to insufficient statistical power.

A nonparametric test for association with multiple loci in the retrospective case-control study

2019 ◽  
Vol 29 (2) ◽  
pp. 589-602
Author(s):  
Chan Wang ◽  
Shufang Deng ◽  
Leiming Sun ◽  
Liming Li ◽  
Yue-Qing Hu
Keyword(s):  
Rare Variants ◽  
Association Studies ◽  
Nonparametric Test ◽  
Case Control ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Retrospective Case ◽  
Multiple Loci ◽  
Common Diseases ◽  
The Difference

The genome-wide association studies aim at identifying common or rare variants associated with common diseases and explaining more heritability. It is well known that common diseases are influenced by multiple single nucleotide polymorphisms (SNPs) that are usually correlated in location or function. In order to powerfully detect association signals, it is highly desirable to take account of correlations or linkage disequilibrium (LD) information among multiple SNPs in testing for association. In this article, we propose a test SLIDE that depicts the difference of the average multi-locus genotypes between cases and controls and derive its variance–covariance matrix in the retrospective design. This matrix is composed of the pairwise LD between SNPs. Thus SLIDE can borrow the strength from an external database in the population of interest with a few thousands to hundreds of thousands individuals to improve the power for detecting association. Extensive simulations show that SLIDE has apparent superiority over the existing methods, especially in the situation involving both common and rare variants, both protective and deleterious variants. Furthermore, the efficiency of the proposed method is demonstrated in the application to the data from the Wellcome Trust Case Control Consortium.

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

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