Common, low-frequency, and rare genetic variants associated with lipoprotein subclasses and triglyceride measures in Finnish men from the METSIM study

<div><p>Lipid and lipoprotein subclasses are associated with metabolic and cardiovascular diseases, yet the genetic contributions to variability in subclass traits are not fully understood. We conducted single-variant and gene-based association tests between 15.1M variants from genome-wide and exome array and imputed genotypes and 72 lipid and lipoprotein traits in 8,372 Finns. After accounting for 885 variants at 157 previously identified lipid loci, we identified five novel signals near established loci at <i>HIF3A</i>, <i>ADAMTS3</i>, <i>PLTP</i>, <i>LCAT</i>, and <i>LIPG</i>. Four of the signals were identified with a low-frequency (0.005LCAT. Gene-based associations (<i>P</i><10⁻¹⁰) support a role for coding variants in <i>LIPC</i> and <i>LIPG</i> with lipoprotein subclass traits. 30 established lipid-associated loci had a stronger association for a subclass trait than any conventional trait. These novel association signals provide further insight into the molecular basis of dyslipidemia and the etiology of metabolic disorders.</p></div

Comparison of METSIM association data between conventional lipid traits and subclass traits at established loci.

<p>Comparison of METSIM association data between conventional lipid traits and subclass traits at established loci.</p

Gene-based tests of association with HDL subclass traits for <i>LIPC</i> and <i>LIPG</i>.

<p>The distribution of the inverse normalized residuals of the trait values for all individuals (histogram) compared to individuals carrying variants included in the gene-based tests of association (triangles) (A) at <i>LIPC</i> with triglycerides in very large HDL and (B) at <i>LIPG</i> with phospholipids in medium HDL. The histograms indicate counts of individuals per trait bin in the METSIM study, and the dashed gray line below the histograms indicates the mean trait level. The rows of black and red triangles represent individuals that are heterozygous and homozygous, respectively for each variant indicated, and the solid black lines indicate the mean trait level for variant carriers. <i>P</i>_{<i>discovery</i>}, p-value for the individual variant-trait association; <i>P</i>_<i>gene</i>, p-value for the gene-based test of association; Annotation, functional annotation of the variants; Splice accept., splice acceptor variant. Figure created with VARV (<a href="https://github.com/shramdas/varv" target="_blank">https://github.com/shramdas/varv</a>).</p

Novel independent signal at <i>LIPG</i>.

<p>Association with phospholipids in medium HDL at the <i>LIPG</i> locus. The colors and shapes distinguish the association signals and are based on the LD (r²) in METSIM samples between each variant and a reference variant, rs538509310 or rs1943973, represented in red and blue, respectively. X-axis, genomic (GRCh37/hg19) position in Mb. Left y-axis, p- value of variant-trait association in–log₁₀. Right y-axis, local estimates of genomic recombination rate in cM/Mb, represented by blue lines. (A) Unconditional association with phospholipids in medium HDL. Black squares indicate the five coding variants (rs200435657, rs201922257, rs142545730, rs138438163, and rs77960347) used in the <i>LIPG</i> gene-based association tests. (B) Association with phospholipids in medium HDL after genome-wide conditional analysis of known lipid-associated variants (n = 885). (C) Association with phospholipids in medium HDL after conditioning on rs538509310. The association plots for four additional signals at <i>HIF3A</i>, <i>ALB</i>, <i>SYS1</i>, and <i>LCAT</i> are provided in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007079#pgen.1007079.s004" target="_blank">S4 Fig</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007079#pgen.1007079.s005" target="_blank">S5 Fig</a>.</p

Newly identified signals associated with lipoprotein subclasses and triglyceride measures.

<p>Newly identified signals associated with lipoprotein subclasses and triglyceride measures.</p

reQTL results FDR 10%.

<p>reQTL results FDR 10%.</p

GTEx replication.

<p>Replication rate (y axis) as a function of FUSION reQTL p-value cutoff (x axis). Dashed line represents two standard deviations from the null distribution, calculated using the hypergeometric distribution.</p

<i>FHOD3</i> locus.

<p>(A) Top wiggle tracks show ATAC-seq signal in multiple cell types, followed by ChromHMM chromatin state tracks. Beneath are <i>FHOD3</i> GWAS loci and the SNPs from this study (reQTL and tSNP). The bottom track shows the FUSION <i>FHOD3</i> RNA-seq signal. (B) ATAC-seq signal highlights potential regulatory regions with the skeletal muscle stretch enhancer. (C) Effects of SNPs overlapping ATAC-seq peaks in the reQTL haplotype on in silico predicted TF binding.</p

<i>FHOD3</i> reQTL, rs17746240 (18:33970347).

<p>The data for each of the three possible reQTL genotypes are presented in separate plots (columns). The top row plots show the relationship between gene expression (y axis) and the clinical variable (x axis). The bottom row plots show the relationship between the allelic imbalance of the tSNP and the clinical variable (x axis). Note the bottom row has fewer samples because it is limited to samples heterozygous for the tSNP. (A) LDLc GxE effect with rs72895597 (18:34232657) as the tSNP. (B) SBP GxE effect with rs2303510 (18:34324091) as the tSNP.</p

Genetic and environmental effects on gene expression.

<p>Blood insulin levels represent a cellular environment for tissues such as skeletal muscle. The left panel depicts a single genome with color-coded genomic elements and various heterozygous sites. The right panel shows the relative transcript abundance for the corresponding locus on the left panel. Some genomic elements contain genetic variants. When the variant is the same color as the element, the element is active. In some cases the variant is black, indicating that the variant renders the regulatory element nonfunctional and only basal transcription occurs. The purple element represents a gene with a transcribed SNP (tSNP), shown in the transcripts. Allele specific expression is calculated across both chromosomes and compared to the high and low environment. (A) When regulated by an insulin-responsive element (green), gene expression changes according to insulin concentrations in the extracellular environment. (B) When regulated by an insulin-independent element (orange) containing genetic variation, gene expression changes according to the presence of a genetic variant (eQTL), but not to insulin levels. The tSNP shows allelic bias due to the eQTL effect, but is not associated with the insulin environment. (C) When regulated by both an insulin-responsive element and an insulin-independent element containing genetic variation, the effects of the insulin environment and the genetic variation on gene expression may be additive, although more complex relationships are possible. The tSNP shows some imbalance due to the eQTL effect and is associated to insulin levels. Such cases may be identified as weak reQTLs. (D) When regulated by an insulin-responsive element containing genetic variation, there may exist an interaction effect between the genetic variant and insulin levels such that changes in gene expression across insulin environments depend on the genetic variant. The tSNP shows allelic imbalance associated with insulin levels due to the reQTL effect. One of several possible interaction effects depicted.</p