Dr. Gaffney performed one of the first genome-wide linkage scans in human SLE as a Molecular Genetics Fellow at the University of Minnesota. This study demonstrated significant linkage signals in the HLA region and on chromosomes 16q13, 14q21-23, and 20p12 (Gaffney, et al. Proc Natl Acad Sci USA 1998). Dr. Gaffney’s subsequent scans in independent sets of families replicated the linkage effects in the HLA and 16q13, and provided suggestive support for several additional regions (Gaffney, et al. Am J Hum Genet 2000; Gray-McGuire, et al. Am J Hum Genet 2000).
Dr. Gaffney has also made significant contributions to several of the large-scale genome-wide association studies (GWAS) on SLE patients of European American, Hispanic, Asian and Amerindian ancestries (SLEGEN, et al. Nat Genet 2008; Alarcón-Riquelme, et al. Arthritis Rheumatol 2016; Lessard, et al., Arthritis Rheumatol 2016). Collectively, these studies identified >20 novel genetic associations with SLE, and shed light on how genetic ancestry influences SLE susceptibility. Further, these studies played an integral role in defining the genetic landscape of SLE and prioritizing specific genomic intervals for gene discovery efforts and genetic fine-mapping of the risk loci.
Towards this end, Dr. Gaffney led the Large Lupus Association Study 2 (LLAS2) aimed at fine-mapping and characterizing the genomic architecture of SLE risk loci. LLAS2 genotyped 32,216 SNPs in 16,712 subjects and leveraged differences in linkage disequilibrium (LD) across European American and non-European American cohorts to fine-map and replicate GWAS-identified genetic loci in independent SLE cases for causal variant discovery. Then, instead of performing a centralized analysis for a single publication, the LLAS2 data was distributed to collaborators with interests on specific loci. This approach successfully produced >40 publications, including several from Dr. Gaffney’s research team focused on TNFAIP3 (Graham, et al. Nat Genet 2008; Adrianto, et al. Nat Genet 2011), UBE2L3 (Wang, et al. Genes Immun 2012), and TNIP1 (Adrianto, et al. Arthritis Rheumatol 2012).
More recently, Dr. Gaffney and his collaborators used Immunochip genotyping of SLE patients from multiple different ancestries to refine the association signals of several established regions. Using this data, they were also able to predict accelerated patterns of SLE risk by calculating the genetic load (risk allele count) of individuals. These findings support a “cumulative hit hypothesis” whereby individuals with accumulating genetic risk alleles are likely to exhibit earlier onset and more severe clinical manifestations of autoimmune disease (Langefeld, et al. Nat Commun 2017).