Reproduction and In-Depth Evaluation of Genome-Wide Association Studies and Genome-Wide Meta-analyses Using Summary Statistics

In line with open-source genetics, we report a novel linear regression technique for genome-wide association studies (GWAS), called Open GWAS algoriTHm (OATH). When individual-level data are not available, OATH can not only completely reproduce reported results from an experimental model, but also recover underreported results from other alternative models with a different combination of nuisance parameters using naïve summary statistics (NSS). OATH can also reliably evaluate all reported results in-depth (e.g., p-value variance analysis), as demonstrated for 42 Arabidopsis phenotypes under three magnesium (Mg) conditions. In addition, OATH can be used for consortium-driven genome-wide association meta-analyses (GWAMA), and can greatly improve the flexibility of GWAMA. A prototype of OATH is available in the Genetic Analysis Repository (https://github.com/gc5k/GEAR).

[1]  Zoltán Kutalik,et al.  Across-cohort QC analyses of GWAS summary statistics from complex traits , 2016, European Journal of Human Genetics.

[2]  R. Fisher,et al.  On the Mathematical Foundations of Theoretical Statistics , 1922 .

[3]  M. Cotton,et al.  Sequence and analysis of chromosome 4 of the plant Arabidopsis thaliana , 1999, Nature.

[4]  S. Shabala,et al.  Screening broad beans (Vicia faba) for magnesium deficiency. II. Photosynthetic performance and leaf bioelectrical responses. , 2004, Functional plant biology : FPB.

[5]  On the reconciliation of missing heritability for genome-wide association studies , 2016, European Journal of Human Genetics.

[6]  A. Auton,et al.  Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel , 2011, Nature Genetics.

[7]  Guo-Bo Chen,et al.  Estimating heritability of complex traits from genome-wide association studies using IBS-based Haseman–Elston regression , 2014, Front. Genet..

[8]  Rory A. Fisher,et al.  The Arrangement of Field Experiments , 1992 .

[9]  Peter Kraft,et al.  Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. , 2015, American journal of human genetics.

[10]  Po-Ru Loh,et al.  A Robust Example of Collider Bias in a Genetic Association Study. , 2016, American journal of human genetics.

[11]  K. Roeder,et al.  Genomic Control for Association Studies , 1999, Biometrics.

[12]  Bjarni J. Vilhjálmsson,et al.  A mixed-model approach for genome-wide association studies of correlated traits in structured populations , 2012, Nature Genetics.

[13]  R. Yadegari,et al.  RASPBERRY3 Gene Encodes a Novel Protein Important for Embryo Development , 2002, Plant Physiology.

[14]  E. Mignot,et al.  Genome Wide Analysis of Narcolepsy in China Implicates Novel Immune Loci and Reveals Changes in Association Prior to Versus After the 2009 H1N1 Influenza Pandemic , 2013, PLoS genetics.

[15]  Ying Wang,et al.  Genome-wide association study in a Chinese Han population identifies nine new susceptibility loci for systemic lupus erythematosus , 2009, Nature Genetics.

[16]  The Chinese Human Genome Sequencing Consortium,et al.  Sequence and analysis of chromosome 5 of the plant Arabidopsis thaliana , 2000, Nature.

[17]  D. Gianola,et al.  Genomic Heritability: What Is It? , 2014, PLoS genetics.