Combined linkage and association analysis identifies rare and low frequency variants for blood pressure at 1q31

Abstract

High blood pressure (BP) is a major risk factor for cardiovascular disease (CVD) and is more prevalent in African Americans as compared to other US groups. Although large, population-based genome-wide association studies (GWAS) have identified over 300 common polymorphisms modulating inter-individual BP variation, largely in European ancestry subjects, most of them do not localize to regions previously identified through family-based linkage studies. This discrepancy has remained unexplained despite the statistical power differences between current GWAS and prior linkage studies. To address this issue, we performed genome-wide linkage analysis of BP traits in African-American families from the Family Blood Pressure Program (FBPP) and genotyped on the Illumina Human Exome BeadChip v1.1. We identified a genomic region on chromosome 1q31 with LOD score 3.8 for pulse pressure (PP), a region we previously implicated in DBP studies of European ancestry families. Although no reported GWAS variants map to this region, combined linkage and association analysis of PP identified 81 rare and low frequency exonic variants accounting for the linkage evidence. Replication analysis in eight independent African ancestry cohorts (N = 16,968) supports this specific association with PP (P = 0.0509). Additional association and network analyses identified multiple potential candidate genes in this region expressed in multiple tissues and with a strong biological support for a role in BP. In conclusion, multiple genes and rare variants on 1q31 contribute to PP variation. Beyond producing new insights into PP, we demonstrate how family-based linkage and association studies can implicate specific rare and low frequency variants for complex traits.

Rare coding variants in RCN3 are associated with blood pressure

“…1. After conducting linkage analysis on chromosomes 1, 17, and 19 using TOPMed Freeze 6a WGS data, the linkage peaks on chromosomes 1 and 19 from Wang et al [11] remained but the peak on chromosome 17 was no longer significant (Fig. 2).…”

“…edu/ beagle/ genet ic_ maps/). The set of linkage disequilibrium pruned SNPs that was used in the exome array linkage analysis by Wang et al [11] (MAF > 0.2 and linkage disequilibrium r 2 < 0.1), which consists of 813 markers for chr1, 347 markers for chr17, and 384 markers for chr19, was used again in the linkage analysis with TOPMed WGS data. Linkage region was defined as a two-LOD score drop from the linkage peak SNP, which has the highest LOD score.…”

“…The same approach could be applied to family-based studies with AA. A linkage analysis using 4394 AA in 1802 families from the Family Blood Pressure Program (FBPP) identified several linkage peaks on chromosomes 1, 17, and 19 (maximum logarithm of the odds [LOD] > 3) for BP traits [11]. Wang et al have examined the 1q31 region using exome array data and have detected multiple genes and rare variants contributing to pulse pressure (PP) variation [11].…”

“…A linkage analysis using 4394 AA in 1802 families from the Family Blood Pressure Program (FBPP) identified several linkage peaks on chromosomes 1, 17, and 19 (maximum logarithm of the odds [LOD] > 3) for BP traits [11]. Wang et al have examined the 1q31 region using exome array data and have detected multiple genes and rare variants contributing to pulse pressure (PP) variation [11]. Because exome array data is limited to exonic regions with mostly coding variants, regulatory non-coding variants as well as very rare variants (minor allele frequency [MAF] < 0.001) cannot be studied with high confidence.…”

Sequencing Analysis at 8p23 Identifies Multiple Rare Variants in DLC1 Associated with Sleep-Related Oxyhemoglobin Saturation Level

“…[6][7][8][9] Here we present an analytical approach based on a strategy that integrates linkage and whole-genome sequencing (WGS) analysis, complemented with gene expression and methylation data (Figure 1), and aims to increase statistical power to identify rare variants. Many disease variants have been identified through linkage analysis; [10][11][12] however, the utility of linkage information in combination with WGS data for complex traits has not been evaluated.…”