pinecone/core-2020-05-10-deduplication huggingface dataset:
Brief review of regression‐based and machine learning methods in genetic epidemiology: the Genetic Analysis Workshop 17 experience

Genetics Analysis Workshop 17 provided common and rare genetic variants from exome sequencing data and simulated binary and quantitative traits in 200 replicates. We provide a brief review of the machine learning and regression‐based methods used in the analyses of these data. Several regression and machine learning methods were used to address different problems inherent in the analyses of these data, which are high‐dimension, low‐sample‐size data typical of many genetic association studies. Unsupervised methods, such as cluster analysis, were used for data segmentation and, subset selection. Supervised learning methods, which include regression‐based methods (e.g., generalized linear models, logic regression, and regularized regression) and tree‐based methods (e.g., decision trees and random forests), were used for variable selection (selecting genetic and clinical features most associated or predictive of outcome) and prediction (developing models using common and rare genetic variants to accurately predict outcome), with the outcome being case‐control status or quantitative trait value. We include a discussion of cross‐validation for model selection and assessment, and a description of available software resources for these methods.  Genet. Epidemiol . 35:S5–S11, 2011. © 2011 Wiley Periodicals, Inc
Next generation analytic tools for large scale genetic epidemiology studies of complex diseases

Over the past several years, genome‐wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled “Next Generation Analytic Tools for Large‐Scale Genetic Epidemiology Studies of Complex Diseases” on September 15–16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large‐scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene‐gene and gene‐environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.  Genet. Epidemiol . 36 : 22–35, 2012. © 2011 Wiley Periodicals, Inc
A better coefficient of determination for genetic profile analysis

Genome-wide association studies have facilitated the construction of risk predictors for disease from multiple Single Nucleotide Polymorphism markers. The ability of such "genetic profiles" to predict outcome is usually quantified in an independent data set. Coefficients of determination (R-2) have been a useful measure to quantify the goodness-of-fit of the genetic profile. Various pseudo-R-2 measures for binary responses have been proposed. However, there is no standard or consensus measure because the concept of residual variance is not easily defined on the observed probability scale. Unlike other nongenetic predictors such as environmental exposure, there is prior information on genetic predictors because for most traits there are estimates of the proportion of variation in risk in the population due to all genetic factors, the heritability. It is this useful ability to benchmark that makes the choice of a measure of goodness-of-fit in genetic profiling different from that of nongenetic predictors. In this study, we use a liability threshold model to establish the relationship between the observed probability scale and underlying liability scale in measuring R-2 for binary responses. We show that currently used R-2 measures are difficult to interpret, biased by ascertainment, and not comparable to heritability. We suggest a novel and globally standard measure of R-2 that is interpretable on the liability scale. Furthermore, even when using ascertained case-control studies that are typical in human disease studies, we can obtain an R-2 measure on the liability scale that can be compared directly to heritability. Genet. Epidemiol. 36:214-224, 2012. (C) 2012 Wiley Periodicals, Inc
Robust and Powerful Tests for Rare Variants Using Fisher’s Method ...

This is the peer reviewed version of the following article: ``Derkach, A., Lawless, J.F. and Sun, L. (2013). Robust and powerful tests for rare variants using Fisher's method to combine evidence of association from two or more complementary tests. Genetic Epidemiology, 37 (1), 110--121", which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/gepi.21689/full DOI: 10.1002/gepi.21689.  This article may be used for non-commercial purposes in accordance with
http://olabout.wiley.com/WileyCDA/Section/id-828039.html. Wiley Terms and Conditions for Self-Archiving.Many association tests have been proposed for rare variants, but the choice of a powerful test is uncertain
when there is limited information on the underlying genetic model. Proposed methods use
either linear statistics, which are powerful when most variants are causal and have the same direction
of effect, or quadratic statistics, which are more powerful in other scenarios. To achieve robustness,
it is natural to combine the evidence of association from two or more complementary tests. To
this end, we consider the minimum-p and Fisher’s methods of combining P-values from linear and
quadratic statistics. Extensive simulation studies show that both methods are robust across models
with varying proportions of causal, deleterious, and protective rare variants, allele frequencies, and
effect sizes. When the majority (>75%) of the causal effects are in the same direction (deleterious
or protective), Fisher’s method consistently outperforms the minimum-p and the individual linear and
quadratic tests, as well as the optimal sequence kernel association test, SKAT-O. When the individual
test has moderate power, Fisher’s test has improved power for 90% of the  5000 models considered,
with >20% relative efficiency gain for 40% of the models. The maximum absolute power loss is 8%
for the remaining 10% of the models. An application to the GAW17 quantitative trait Q2 data based
on sequence data of the 1000 Genomes Project shows that, compared with linear and quadratic tests,
Fisher’s test has comparable power for all 13 functional genes and provides the best power for more
than half of them.Natural Sciences and Engineering Research Council of Canada || (JFL RGPIN 8597
Importance of different types of prior knowledge in selecting genome-wide findings for follow-up.

Biological plausibility and other prior information could help select genome-wide association (GWA) findings for further follow-up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts opinions and empirical evidence to estimate the relative importance of 15 types of information at the single-nucleotide polymorphism (SNP) and gene levels. Opinions were elicited from 10 experts using a two-round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNPs established as being associated with seven disease traits through GWA meta-analysis and independent replication, with the corresponding frequency in a randomly selected set of SNPs. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta-analysis or more than one study as conferring the highest relative probability of true association, whereas previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, although location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research
A Geometric Framework for Evaluating Rare Variant Tests of Association

The wave of next‐generation sequencing data has arrived. However, many questions still remain about how to best analyze sequence data, particularly the contribution of rare genetic variants to human disease. Numerous statistical methods have been proposed to aggregate association signals across multiple rare variant sites in an effort to increase statistical power; however, the precise relation between the tests is often not well understood. We present a geometric representation for rare variant data in which rare allele counts in case and control samples are treated as vectors in Euclidean space. The geometric framework facilitates a rigorous classification of existing rare variant tests into two broad categories: tests for a difference in the lengths of the case and control vectors, and joint tests for a difference in either the lengths or angles of the two vectors. We demonstrate that genetic architecture of a trait, including the number and frequency of risk alleles, directly relates to the behavior of the length and joint tests. Hence, the geometric framework allows prediction of which tests will perform best under different disease models. Furthermore, the structure of the geometric framework immediately suggests additional classes and types of rare variant tests. We consider two general classes of tests which show robustness to noncausal and protective variants. The geometric framework introduces a novel and unique method to assess current rare variant methodology and provides guidelines for both applied and theoretical researchers
Novel Likelihood Ratio Tests for Screening Gene‐Gene and Gene‐Environment Interactions With Unbalanced Repeated‐Measures Data

There has been extensive literature on modeling gene‐gene interaction (GGI) and gene‐environment interaction (GEI) in case‐control studies with limited literature on statistical methods for GGI and GEI in longitudinal cohort studies. We borrow ideas from the classical two‐way analysis of variance literature to address the issue of robust modeling of interactions in repeated‐measures studies. While classical interaction models proposed by Tukey and Mandel have interaction structures as a function of main effects, a newer class of models, additive main effects and multiplicative interaction (AMMI) models, do not have similar restrictive assumptions on the interaction structure. AMMI entails a singular value decomposition of the cell residual matrix after fitting the additive main effects and has been shown to perform well across various interaction structures. We consider these models for testing GGI and GEI from two perspectives: likelihood ratio test based on cell means and a regression‐based approach using individual observations. Simulation results indicate that both approaches for AMMI models lead to valid tests in terms of maintaining the type I error rate, with the regression approach having better power properties. The performance of these models was evaluated across different interaction structures and 12 common epistasis patterns. In summary, AMMI model is robust with respect to misspecified interaction structure and is a useful screening tool for interaction even in the absence of main effects. We use the proposed methods to examine the interplay between the hemochromatosis gene and cumulative lead exposure on pulse pressure in the Normative Aging Study
Statistical testing of shared genetic control for potentially related
  traits

Integration of data from genome-wide single nucleotide polymorphism (SNP)
association studies of different traits should allow researchers to disentangle
the genetics of potentially related traits within individually associated
regions. Formal statistical colocalisation testing of individual regions, which
requires selection of a set of SNPs summarizing the association in a region. We
show that the SNP selection method greatly affects type 1 error rates, with
published studies having used methods expected to result in substantially
inflated type 1 error rates. We show that either avoiding variable selection
and instead testing the most informative principal components or integrating
over variable selection using Bayesian model averaging can lead to correct
control of type 1 error rates. Application to data from Graves' disease and
Hashimoto's thyroiditis reveals a common genetic signature across seven regions
shared between the diseases, and indicates that in five of six regions
associated with Graves' disease and not Hashimoto's thyroiditis, this more
likely reflects genuine absence of association with the latter rather than lack
of power. Our examination, by simulation, of the performance of colocalisation
tests and associated software will foster more widespread adoption of formal
colocalisation testing. Given the increasing availability of large expression
and genetic association data sets from disease-relevant tissue and purified
cell populations, coupled with identification of regulatory sequences by
projects such as ENCODE, colocalisation analysis has the potential to reveal
both shared genetic signatures of related traits and causal disease genes and
tissues.Comment: Supplementary information attache
Accounting for population stratification in DNA methylation studies

DNA methylation is an important epigenetic mechanism that has been linked to complex diseases and is of great interest to researchers as a potential link between genome, environment, and disease. As the scale of DNA methylation association studies approaches that of genome-wide association studies, issues such as population stratification will need to be addressed. It is well-documented that failure to adjust for population stratification can lead to false positives in genetic association studies, but population stratification is often unaccounted for in DNA methylation studies. Here, we propose several approaches to correct for population stratification using principal components (PCs) from different subsets of genome-wide methylation data. We first illustrate the potential for confounding due to population stratification by demonstrating widespread associations between DNA methylation and race in 388 individuals (365 African American and 23 Caucasian). We subsequently evaluate the performance of our PC-based approaches and other methods in adjusting for confounding due to population stratification. Our simulations show that (1) all of the methods considered are effective at removing inflation due to population stratification, and (2) maximum power can be obtained with single-nucleotide polymorphism (SNP)-based PCs, followed by methylation-based PCs, which outperform both surrogate variable analysis and genomic control. Among our different approaches to computing methylation-based PCs, we find that PCs based on CpG sites chosen for their potential to proxy nearby SNPs can provide a powerful and computationally efficient approach to adjust for population stratification in DNA methylation studies when genome-wide SNP data are unavailable
A Generalized Genetic Random Field Method for the Genetic Association Analysis of Sequencing Data

With the advance of high‐throughput sequencing technologies, it has become feasible to investigate the influence of the entire spectrum of sequencing variations on complex human diseases. Although association studies utilizing the new sequencing technologies hold great promise to unravel novel genetic variants, especially rare genetic variants that contribute to human diseases, the statistical analysis of high‐dimensional sequencing data remains a challenge. Advanced analytical methods are in great need to facilitate high‐dimensional sequencing data analyses. In this article, we propose a generalized genetic random field (GGRF) method for association analyses of sequencing data. Like other similarity‐based methods (e.g., SIMreg and SKAT), the new method has the advantages of avoiding the need to specify thresholds for rare variants and allowing for testing multiple variants acting in different directions and magnitude of effects. The method is built on the generalized estimating equation framework and thus accommodates a variety of disease phenotypes (e.g., quantitative and binary phenotypes). Moreover, it has a nice asymptotic property, and can be applied to small‐scale sequencing data without need for small‐sample adjustment. Through simulations, we demonstrate that the proposed GGRF attains an improved or comparable power over a commonly used method, SKAT, under various disease scenarios, especially when rare variants play a significant role in disease etiology. We further illustrate GGRF with an application to a real dataset from the Dallas Heart Study. By using GGRF, we were able to detect the association of two candidate genes,  ANGPTL 3 and  ANGPTL 4, with serum triglyceride
Genome‐Wide Family‐Based Linkage Analysis of Exome Chip Variants and Cardiometabolic Risk

Linkage analysis of complex traits has had limited success in identifying trait‐influencing loci. Recently, coding variants have been implicated as the basis for some biomedical associations. We tested whether coding variants are the basis for linkage peaks of complex traits in 42 African‐American ( n  = 596) and 90 Hispanic ( n  = 1,414) families in the Insulin Resistance Atherosclerosis Family Study (IRASFS) using Illumina HumanExome Beadchips. A total of 92,157 variants in African Americans (34%) and 81,559 (31%) in Hispanics were polymorphic and tested using two‐point linkage and association analyses with 37 cardiometabolic phenotypes. In African Americans 77 LOD scores greater than 3 were observed. The highest LOD score was 4.91 with the  APOE  SNP rs7412 (MAF = 0.13) with plasma apolipoprotein B (ApoB). This SNP was associated with ApoB ( P ‐value = 4 × 10 −19 ) and accounted for 16.2% of the variance in African Americans. In Hispanic families, 104 LOD scores were greater than 3. The strongest evidence of linkage (LOD = 4.29) was with rs5882 (MAF = 0.46) in  CETP  with HDL.  CETP  variants were strongly associated with HDL (0.00049 <  P ‐value <4.6 × 10 −12 ), accounting for up to 4.5% of the variance. These loci have previously been shown to have effects on the biomedical traits evaluated here. Thus, evidence of strong linkage in this genome wide survey of primarily coding variants was uncommon. Loci with strong evidence of linkage was characterized by large contributions to the variance, and, in these cases, are common variants. Less compelling evidence of linkage and association was observed with additional loci that may require larger family sets to confirm
Efficient Generalized Least Squares Method for Mixed Population and Family‐based Samples in Genome‐wide Association Studies

Genome‐wide association studies (GWAS) that draw samples from multiple studies with a mixture of relationship structures are becoming more common. Analytical methods exist for using mixed‐sample data, but few methods have been proposed for the analysis of genotype‐by‐environment (G×E) interactions. Using GWAS data from a study of sarcoidosis susceptibility genes in related and unrelated African Americans, we explored the current analytic options for genotype association testing in studies using both unrelated and family‐based designs. We propose a novel method—generalized least squares (GLX)—to estimate both SNP and G×E interaction effects for categorical environmental covariates and compared this method to generalized estimating equations (GEE), logistic regression, the Cochran–Armitage trend test, and the W QLS  and M QLS  methods. We used simulation to demonstrate that the GLX method reduces type I error under a variety of pedigree structures. We also demonstrate its superior power to detect SNP effects while offering computational advantages and comparable power to detect G×E interactions versus GEE. Using this method, we found two novel SNPs that demonstrate a significant genome‐wide interaction with insecticide exposure—rs10499003 and rs7745248, located in the intronic and 3' UTR regions of the  FUT9  gene on chromosome 6q16.1
A method for gene-based pathway analysis using genomewide association study summary statistics reveals nine new type 1 diabetes associations

Pathway analysis can complement point-wise single nucleotide polymorphism (SNP) analysis in exploring genomewide association study (GWAS) data to identify specific disease-associated genes that can be candidate causal genes. We propose a straightforward methodology that can be used for conducting a gene-based pathway analysis using summary GWAS statistics in combination with widely available reference genotype data. We used this method to perform a gene-based pathway analysis of a type 1 diabetes (T1D) meta-analysis GWAS (of 7,514 cases and 9,045 controls). An important feature of the conducted analysis is the removal of the major histocompatibility complex gene region, the major genetic risk factor for T1D. Thirty-one of the 1,583 (2%) tested pathways were identified to be enriched for association with T1D at a 5% false discovery rate. We analysed these 31 pathways and their genes to identify SNPs in or near these pathway genes that showed potentially novel association with T1D and attempted to replicate the association of 22 SNPs in additional samples. Replication p-values were skewed (p = 9.85 x 10^-11) with 12 of the 22 SNPs showing p < 0:05. Support, including replication evidence, was obtained for nine T1D associated variants in genes ITGB7 (rs11170466, p = 7.86 x 10^-9), NRP1 (rs722988, 4.88 x 10^-8), BAD (rs694739, 2.37 x 10^-7), CTSB (rs1296023, 2.79 x 10^-7), FYN (rs11964650, p = 5.60 x 10^-7), UBE2G1 (rs9906760, 5.08 x 10^-7), MAP3K14 (rs17759555, 9.67 x 10^-7), ITGB1 (rs1557150, 1.93 x 10^-6) and IL7R (rs1445898, 2.76 x 10^-6). The proposed methodology can be applied to other GWAS datasets for which only summary level data are available.This is the final version. It was first published by Wiley at http://onlinelibrary.wiley.com/doi/10.1002/gepi.21853/abstract