Leveraging global multi-ancestry meta-analysis in the study of idiopathic pulmonary fibrosis genetics

Identification of a Genetic Susceptibility Locus for Idiopathic Pulmonary Fibrosis in the 16p Subtelomere Using Whole-Genome Sequencing

“…We were initially surprised that this large-effect signal had not been reported by larger IPF GWASs, such as that of Allen and colleagues ( 2 ) or Zhou and colleagues ( 3 ). However, we noted that previous association testing of rs367849850, rs3930589, and other variants with which they are in strong linkage disequilibrium was inconsistent because of poor regional coverage after array genotyping and imputation ( Figure 1D ); additionally, using biobank-derived IPF cases may attenuate association signals ( 1 , 8 ). Nevertheless, we observed replication in data from two independent studies: the Global Biobank Meta-analysis Initiative (rs367849850: OR, 1.3; P = 6.5 × 10 −4 ; rs3930589: OR, 1.2; P = 7.0 × 10 −4 ) ( 5 ) and the discovery phase of a published IPF GWAS with clinically derived cases (rs367849850: OR, 2.4; P = 1.8 × 10 −6 ) ( 9 ).…”

“…The etiology of IPF is complex and includes a substantial genetic component. Genome-wide association studies (GWASs) from increasingly large, ancestrally diverse cohorts have identified more than 20 common variant signals for IPF ( 1 – 3 ), and rare variant analyses have implicated telomere-related genes ( 4 , 5 ). Here, we performed the first genome-wide search for common and rare IPF risk variants fully based on whole-genome sequencing (WGS), enabling a more comprehensive evaluation of genetic variation than that available from array-based genotyping.…”

Using population biobanks to understand complex traits, rare diseases, and their shared genetic architecture

“…For some phenotypes such as IPF(Partanen et al 2021) and asthma(Tsuo et al 2021), we observed attenuation of effect size estimates in GBMI compared to disease-specific cohorts which generally study highly ascertained patients. For IPF, the attenuation was seen compared to the IPF-specific cohort: the Allenet al study(Allen et al 2020).…”

“…To boost statistical power, we can meta-analyze GBMI GWAS with other non-overlapping cohorts as shown in other GBMI working groups Partanen et al 2022). However, we should note that more heterogeneity might be introduced from different resources such as population structure and phenotype definitions, which we cannot control with summary statistics data and that could exacerbate the heterogeneous performance of PRS across target populations.…”

“…This would help researchers quantify both baseline exposure and the development of the disease, as well as the response to treatment, while also accounting for recruitment differences. A successful example of this is FinnGen, which has fully integrated the biobank idea into their health policy so that virtually all of the population can be included (Kurki et al, 2022), and the effect heterogeneity between recruitment strategies can be measured (Partanen et al, 2022). However, this kind of solution only works in a fully developed country with a health system that is equally accessible regardless of socio-economic conditions, which is not always the case.…”