Sequential sentinel SNP Regional Association Plots (SSS-RAP): an approach for testing independence of SNP association signals using meta-analysis data

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis

MOTIVATION

LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.

RESULTS

In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies.

AVAILABILITY AND IMPLEMENTATION

The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/ CONTACT: jie.zheng@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics

MOTIVATION

Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients ([Formula: see text]) of the variants. However, haplotypes rather than pairwise [Formula: see text], are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel.

RESULTS

Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well with a small training sample size (N < 2000) while other methods become suboptimal. Moreover, HAPRAP's performance is not affected substantially by single nucleotide polymorphisms (SNPs) with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization).

AVAILABILITY AND IMPLEMENTATION

The HAPRAP package and documentation are available at http://apps.biocompute.org.uk/haprap/ CONTACT: : jie.zheng@bristol.ac.uk or tom.gaunt@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Lipids, obesity and gallbladder disease in women: insights from genetic studies using the cardiovascular gene-centric 50K SNP array

Gallbladder disease (GBD) has an overall prevalence of 10–40% depending on factors such as age, gender, population, obesity and diabetes, and represents a major economic burden. Although gallstones are composed of cholesterol by-products and are associated with obesity, presumed causal pathways remain unproven, although BMI reduction is typically recommended. We performed genetic studies to discover candidate genes and define pathways involved in GBD. We genotyped 15 241 women of European ancestry from three cohorts, including 3216 with GBD, using the Human cardiovascular disease (HumanCVD) BeadChip containing up to ~53 000 single-nucleotide polymorphisms (SNPs). Effect sizes with P-values for development of GBD were generated. We identify two new loci associated with GBD, GCKR rs1260326:T>C (P=5.88 × 10⁻⁷, ß=−0.146) and TTC39B rs686030:C>A (P=6.95x10⁻⁷, ß=0.271) and detect four independent SNP effects in ABCG8 rs4953023:G>A (P=7.41 × 10⁻⁴⁷, ß=0.734), ABCG8 rs4299376:G^>T (P=2.40 × 10⁻¹⁸, ß=0.278), ABCG5 rs6544718:T>C (P=2.08 × 10⁻¹⁴, ß=0.044) and ABCG5 rs6720173:G>C (P=3.81 × 10⁻¹², ß⁼0.262) in conditional analyses taking genotypes of rs4953023:G>A as a covariate. We also delineate the risk effects among many genotypes known to influence lipids. These data, from the largest GBD genetic study to date, show that specific, mainly hepatocyte-centred, components of lipid metabolism are important to GBD risk in women. We discuss the potential pharmaceutical implications of our findings.