Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits

Nat Genet. 2012 Mar 18;44(4):369-75, S1-3. doi: 10.1038/ng.2213.


We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Body Height
  • Body Mass Index
  • Case-Control Studies
  • Cyclin-Dependent Kinase Inhibitor p15 / genetics
  • Data Interpretation, Statistical*
  • Diabetes Mellitus, Type 2 / genetics*
  • Genetic Loci / genetics
  • Genetic Variation
  • Genome, Human
  • Genome-Wide Association Study*
  • Genotype
  • Humans
  • Linkage Disequilibrium
  • Meta-Analysis as Topic
  • Phenotype
  • Polymorphism, Single Nucleotide*


  • CDKN2B protein, human
  • Cyclin-Dependent Kinase Inhibitor p15

Grants and funding