gwas methods review

| 0000025412 00000 n 0 The Human Genome Epidemiology Network (HuGE Net) (http://www.hugenet.ca) also has a GWAS integrator webpage and contains a list of publications, hits, variants, disease and trait information etc. 0000008029 00000 n 0000029224 00000 n KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters. 0000038981 00000 n Summary of GWAS meta-analysis review: (A) type of meta-analysis; (B) type of paper; (C) type of meta-analysis method; (D) software used.

0000006689 00000 n Copyright © 2020 Elsevier B.V. or its licensors or contributors. Tel: 412-624-5318; Fax: The simplest GWAS meta-analysis approach is to combine, A major improvement over Fisher's method is a weighted, The random effects model assumes that the mean effect (of each SNP) in each study is different, with those means usually assumed to be chosen from a Gaussian distribution. 0000036305 00000 n

Review Manager (RevMan) (http://ims.cochrane.org/revman/about-revman-5) (24) is another package that does meta-analysis and provides results in tabular format and graphically.

This review introduces the pipeline of statistical methods used in GWAS analysis, from data quality control, association tests, population structure control, interaction effects and results visualization, through to post-GWAS validation methods and related issues.

0000007873 00000 n <<38640D7DABF9E5469CFAD9E1ADC0A6D6>]>> Your comment will be reviewed and published at the journal's discretion. Stable gene expression for normalisation and single-sample scoring, ZFAT binds to centromeres to control noncoding RNA transcription through the KAT2B–H4K8ac–BRD4 axis, Rewiring of growth-dependent transcription regulation by a point mutation in region 1.1 of the housekeeping σ factor, The HMGB chromatin protein Nhp6A can bypass obstacles when traveling on DNA, A hand-off of DNA between archaeal polymerases allows high-fidelity replication to resume at a discrete intermediate three bases past 8-oxoguanine, Chemical Biology and Nucleic Acid Chemistry, Gene Regulation, Chromatin and Epigenetics, http://genome.sph.umich.edu/wiki/METAL_Program, http://www.stats.ox.ac.uk/~jsliu/meta.html, http://pngu.mgh.harvard.edu/~purcell/plink/metaanal.shtml, http://www.broadinstitute.org/mpg/magenta/, http://ims.cochrane.org/revman/about-revman-5, http://bioinformatics.biol.uoa.gr/~pbagos/metagen, https://chgr.mc.vanderbilt.edu/synthesisview, Receive exclusive offers and updates from Oxford Academic, PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations, The NHGRI GWAS Catalog, a curated resource of SNP-trait associations, WormBase 2014: new views of curated biology, GWAS3D: detecting human regulatory variants by integrative analysis of genome-wide associations, chromosome interactions and histone modifications. | xref Opposite coding of SNPs in different studies can cause what should be similar effects to look precisely opposite.

To overcome this problem, there are some other statistics available, such as H, R and I2, defined as ⁠, and ⁠, where is the genetic effect under the random effects model.

In genetic association studies for targeted SNPs, there have been three ways to deal with HWE: including all studies irrespective of the HWE tests (34), doing sensitivity analysis to verify differential genetic effects in subgroups (15,35–37), and excluding studies with statistically significant deviation from HWE(15,38).

Most GWAS meta-analysis uses relatively straightforward methods. Any of those methods can be applied either across all studies at once, or cumulatively as each study is added. There are, however, some remaining barriers and open methodological issues. 0000003578 00000 n By continuing you agree to the use of cookies. All the methods of meta-analysis for our case study were implemented by us in R. Table 2 shows the results of our case study. While such analyses are becoming more and more common, statistical methods have lagged somewhat behind. Population structure causes false positive associations in GWAS if not accounted for, and methods to deal with this are presented.

One hundred and sixty-four papers (80%) use fixed or random effects models, 28 (14%) combine weighted Z- scores from P- values, 6 (3%) use Fisher's method, and 7 (3%) use direct data merging. 0000018977 00000 n (2009) (15). In R, a few other available packages for meta-analysis are Metafor (http://www.metafor-project.org/) (27), rmeta, and CATMAP. For software packages, METAL (41 papers; 45%) and R packages (23 papers; 25%) are the most popular.

Figure 2B shows that the majority of reports are biological applications (226 papers; 91%) while 10 papers (4%) are for novel methodology, 4 papers (1%) are databases and software, and 9 papers (4%) are review papers. 0000035660 00000 n 2020 Jan;21(1):3-16. doi: 10.1111/mpp.12874. View Article Google Scholar 2.

0000018590 00000 n Funding for open access charge: University of Pittsburgh. For example, in our recombination example we know that males and females are likely to be different, so we could fit a model that explicitly has different fixed male and female effects.

Search for other works by this author on: *To whom correspondence should be addressed. %PDF-1.4 %�� There are also a number of databases that contain selected results from GWAS studies, some of which are suitable for inclusion in meta-analyses of targeted regions. In addition to these basic methods, almost any meta-analysis method in the statistical literature can be applied to GWAS, and some of the software packages discussed below do so.

0000039697 00000 n 2010; 86: 6–22. We suggest that this might not be the right approach for GWAS, because (i) the number of studies being combined is often not very large (leading to an imprecise heterogeneity estimate) and (ii) the form of the heterogeneity typically does not fit a Gaussian random effects model. Prior to meta-analysis, it is clearly important that all data sets undergo thorough standard GWAS data cleaning, such as filtering out ‘bad’ SNPs and samples using genotype call rates, tests of Hardy–Weinberg equilibrium (HWE), etc (33). This is probably an artifact caused by fitting a random effects model to just two data sets. 0000026648 00000 n The standard solution to this problem is to impute the genotypes of all SNPs in all samples, and a variety of good methods is available for doing so (39). In combining the female data sets, all four meta-analysis methods also behave similarly, reflecting the lack of significant association. Over the last 10 years, high-density SNP arrays and DNA re-sequencing have illuminated the majority of the genotypic space for a number of organisms, including humans, maize, rice and Arabidopsis. https://doi.org/10.1016/j.ajhg.2017.06.005.

A substantial number of recent GWAS indicate that for most disorders, only a few common variants are implicated … For the fixed effects model, inverse-variance weighting is widely used, although other methods are also available. 0000023482 00000 n

The difference between the fixed effects and random effects models is that fixed effects meta-analysis assumes that the genetic effects are the same across the different studies. 0000036595 00000 n Clipboard, Search History, and several other advanced features are temporarily unavailable. 0000008106 00000 n 0000025769 00000 n The most important GWAS database is the NIH Database of Genotype and Phenotype (dbGaP), which is the repository for both raw data and results from most NIH-funded GWAS. Even with those large sample sizes, discoveries have been modest for many or most phenotypes studied because typical effect sizes are quite small, and many results do not appear to replicate in subsequent studies. Methods. 0000037169 00000 n

The same SNP assay can behave differently on different chips, or even on the same chip in different batches, and thus it is common to scan data sets for SNPs with widely differing allele frequencies and eliminate them before combining.

PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/metaanal.shtml) (21) is a free, open-source software for GWAS analysis, which also has some meta-analysis tools to do fixed effects and random effects meta-analysis. 0000004724 00000 n