Summarizing polygenic risks for complex diseases in a clinical whole-genome report

Abstract

Purpose: Disease-causing mutations and pharmacogenomic variants are of primary interest for clinical whole-genome sequencing. However, estimating genetic liability for common complex diseases using established risk alleles might one day prove clinically useful.

Methods: We compared polygenic scoring methods using a case-control data set with independently discovered risk alleles in the MedSeq Project. For eight traits of clinical relevance in both the primary-care and cardiomyopathy study cohorts, we estimated multiplicative polygenic risk scores using 161 published risk alleles and then normalized them using the population median estimated from the 1000 Genomes Project.

Results: Our polygenic score approach identified the overrepresentation of independently discovered risk alleles in cases as compared with controls using a large-scale genome-wide association study data set. In addition to normalized multiplicative polygenic risk scores and rank in a population, the disease prevalence and proportion of heritability explained by known common risk variants provide important context in the interpretation of modern multilocus disease risk models.

Conclusion: Our approach in the MedSeq Project demonstrates how complex trait risk variants from an individual genome can be summarized and reported for the general clinician and also highlights the need for definitive clinical studies to obtain reference data for such estimates and to establish clinical utility.

Conflict of interest statement

DISCLOSURE

The authors declare no conflict of interest.

Figures

Figure 1. Comparison of polygenic score calculation methods

Using the risk alleles and allele frequencies reported in the GWAS catalog, we calculated polygenic scores for 379 individuals of the 1000 Genomes Project European cohort. We counted the number of risk alleles in an individual—counting method—and compared with the multiplicative polygenic risk score (MPRS) and multiple single-nucleotide polymorphism (SNP) population attribution risk (PAR) using odd ratios (ORs) and ORs with risk allele frequency, respectively. Red circles represent the individuals in the same decile according to MPRS and PAR. The resulting decile of the counting method was different from those from MPRS and PAR, although they were significantly correlated (c and g). The results for coronary heart disease (60 risk alleles, a–c) and type 2 diabetes (70 risk alleles, e–g) showed the same trend. Venn diagrams show the agreement between polygenic scoring methods for the individuals in the lOth deciles by three methods (d and h). GWAS, genome-wide association study.

Figure 2. Distribution of polygenic scores in a case-control data set

The Wellcome Trust Case Control Consortium (WTCCC) phase I data set (N = 16,179 individuals) consisted of two control groups—the 1958 British Birth Cohort (58BC) and common controls recruited from the UK Blood Services (NBS)—and six disease groups; Crohn disease (CD), bipolar disorder (BD), coronary heart disease (CHD), type 1 diabetes (T1D), type 2 diabetes (T2D), and rheumatoid arthritis (RA). We compared the multiplicative polygenic risk score (MPRS) distributions between cases and controls, except for the hypertension group because of the small number of risk alleles (see Table 1). For all phenotypes, no significant difference was found between 58BC and NBS, and the mean MPRS of case groups was significantly higher as compared with the two control groups (Tukey's honestly significant difference P values < 0.001 for all case versus control groups).