Common variants identified in genome-wide association studies of testicular germ cell tumour: an update, biological insights and clinical application

Andrology ◽  
2015 ◽  
Vol 3 (1) ◽  
pp. 34-46 ◽  
Author(s):  
K. Litchfield ◽  
J. Shipley ◽  
C. Turnbull
Keyword(s):  
Germ Cell ◽  
Clinical Application ◽  
Association Studies ◽  
Germ Cell Tumour ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Common Variants ◽  
Testicular Germ Cell Tumour ◽  
Testicular Germ Cell ◽  
Genome Wide

scholarly journals Meta-analysis of five genome-wide association studies identifies multiple new loci associated with testicular germ cell tumor

2017 ◽  
Vol 49 (7) ◽  
pp. 1141-1147 ◽  
Author(s):  
Zhaoming Wang ◽  
◽  
Katherine A McGlynn ◽  
Ewa Rajpert-De Meyts ◽  
D Timothy Bishop ◽  
...  
Keyword(s):  
Germ Cell ◽  
Germ Cell Tumor ◽  
Association Studies ◽  
Meta Analysis ◽  
Testicular Germ Cell Tumor ◽  
Cell Tumor ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Testicular Germ Cell ◽  
Genome Wide

scholarly journals A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS)

2018 ◽  
Vol 17 (6) ◽  
Author(s):  
Huaqing Zhao ◽  
Nandita Mitra ◽  
Peter A. Kanetsky ◽  
Katherine L. Nathanson ◽  
Timothy R. Rebbeck
Keyword(s):  
Principal Components ◽  
Population Stratification ◽  
Propensity Scores ◽  
Association Studies ◽  
Germ Cell Tumors ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Testicular Germ Cell ◽  
Genome Wide

Abstract Genome-wide association studies (GWAS) are susceptible to bias due to population stratification (PS). The most widely used method to correct bias due to PS is principal components (PCs) analysis (PCA), but there is no objective method to guide which PCs to include as covariates. Often, the ten PCs with the highest eigenvalues are included to adjust for PS. This selection is arbitrary, and patterns of local linkage disequilibrium may affect PCA corrections. To address these limitations, we estimate genomic propensity scores based on all statistically significant PCs selected by the Tracy-Widom (TW) statistic. We compare a principal components and propensity scores (PCAPS) approach to PCA and EMMAX using simulated GWAS data under no, moderate, and severe PS. PCAPS reduced spurious genetic associations regardless of the degree of PS, resulting in odds ratio (OR) estimates closer to the true OR. We illustrate our PCAPS method using GWAS data from a study of testicular germ cell tumors. PCAPS provided a more conservative adjustment than PCA. Advantages of the PCAPS approach include reduction of bias compared to PCA, consistent selection of propensity scores to adjust for PS, the potential ability to handle outliers, and ease of implementation using existing software packages.

scholarly journals Genome-wide linkage screen for testicular germ cell tumour susceptibility loci

2006 ◽  
Vol 15 (3) ◽  
pp. 443-451 ◽  
Author(s):  
Gillian P. Crockford ◽  
Rachel Linger ◽  
Sarah Hockley ◽  
Darshna Dudakia ◽  
Lola Johnson ◽  
...  
Keyword(s):  
Germ Cell ◽  
Germ Cell Tumour ◽  
Cell Tumour ◽  
Susceptibility Loci ◽  
Testicular Germ Cell Tumour ◽  
Testicular Germ Cell ◽  
Genome Wide

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

2017 ◽  
Author(s):  
Dominic Holland ◽  
Oleksandr Frei ◽  
Rahul Desikan ◽  
Chun-Chieh Fan ◽  
Alexey A. Shadrin ◽  
...  
Keyword(s):  
Association Studies ◽  
Causal Snps ◽  
Reference Panel ◽  
Causal Effects ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Common Variants ◽  
Genome Wide ◽  
Causal Variants

AbstractEstimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-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. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities ranging from ≃ 2 × 10−5to ≃ 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 reaching genome-wide significance 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 (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.Author SummaryThere are ~10 million common variants in the genome of humans with European ancestry. For any particular phenotype a number of these variants will have some causal effect. It is of great interest to be able to quantify the number of these causal variants and the strength of their effect on the phenotype.Genome wide association studies (GWAS) produce very noisy summary statistics for the association between subsets of common variants and phenotypes. For any phenotype, these statistics collectively are difficult to interpret, but buried within them is the true landscape of causal effects. In this work, we posit a probability distribution for the causal effects, and assess its validity using simulations. Using a detailed reference panel of ~11 million common variants – among which only a small fraction are likely to be causal, but allowing for non-causal variants to show an association with the phenotype due to correlation with causal variants – we implement an exact procedure for estimating the number of causal variants and their mean strength of association with the phenotype. We find that, across different phenotypes, both these quantities – whose product allows for lower bound estimates of heritability – vary by orders of magnitude.

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