Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model

Autoři: Dominic Holland aff001;  Oleksandr Frei aff003;  Rahul Desikan aff004;  Chun-Chieh Fan aff001;  Alexey A. Shadrin aff003;  Olav B. Smeland aff003;  V. S. Sundar aff001;  Paul Thompson aff008;  Ole A. Andreassen aff003;  Anders M. Dale aff001
Působiště autorů: Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, California, United States of America aff001;  Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America aff002;  NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway aff003;  Department of Radiology, University of California, San Francisco, San Francisco, California, United States of America aff004;  Department of Radiology, University of California, San Diego, La Jolla, California, United States of America aff005;  Department of Cognitive Sciences, University of California at San Diego, La Jolla, California, United States of America aff006;  Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway aff007;  Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America aff008;  Department of Psychiatry, University of California, San Diego, La Jolla, California, United States of America aff009
Vyšlo v časopise: Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model. PLoS Genet 16(5): e1008612. doi:10.1371/journal.pgen.1008612
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008612


Estimating 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 a reference panel of 11 million SNPs, 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 (as a fraction of the reference panel) 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 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.

Klíčová slova:

Alzheimer's disease – Amyotrophic lateral sclerosis – Fourier analysis – Genome-wide association studies – Heredity – Heterozygosity – Molecular genetics – Schizophrenia


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