An exome-wide sequencing study of lipid response to high-fat meal and fenofibrate in Caucasians from the GOLDN cohort

Our understanding of genetic influences on the response of lipids to specific interventions is limited. In this study, we sought to elucidate effects of rare genetic variants on lipid response to a high-fat meal challenge and fenofibrate (FFB) therapy in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) cohort using an exome-wide sequencing-based association study. Our results showed that the rare coding variants in ITGA7 , SIPA1L2 , and CEP72 are significantly associated with fasting low-density lipoprotein cholesterol (LDL-C) response to FFB (P=1.24E-07), triglyceride postprandial area under the increase (AUI) (P=2.31E-06), and triglyceride postprandial AUI response to FFB (P=1.88E-06) respectively. We sought to replicate the association for SIPA1L2 in the Heredity and Phenotype Intervention (HAPI) Heart Study, which included a high-fat meal challenge but not FFB treatment. The associated rare variants in GOLDN were not observed in the HAPI Heart study and thus the gene-based result was not replicated. For functional validation, we found gene transcript level of SIPA1L2 is associated with triglyceride postprandial AUI (P≤0.05) in GOLDN. Our study suggests unique genetic mechanisms contributing to the lipid response to the high-fat meal challenge and FFB therapy. 6 conducted prior to the availability of high-resolution genomic data. To augment the discoveries from Genome Wide Association Study (GWAS), account for further missing heritability, and identify additional functional loci contributing to variation in lipid response to a high-fat meal and treatment with FFB, we performed an exome-wide sequencing study in 894 European Americans from the GOLDN study. We sought to replicate the PPL result using the Heredity and Phenotype Intervention (HAPI) Heart Study, which included a high-fat meal challenge but not FFB treatment. In addition, we sought to validate the associations for our findings using DNA methylation and RNA-Seq data previously collected in GOLDN.


Introduction
Dyslipidemia, defined as abnormal levels of lipids and/or lipoproteins in the blood (1), is a critical modifiable risk factor for chronic diseases, accounting for almost half of the population attributable risk for adverse cardiovascular events (2). Circulating lipid levels are influenced by both environment (e.g. diet, smoking, and prescription drugs) and genetic variation; twin studies estimate the genetic contribution to explain ~60% of phenotypic variation (3). Until recently (4), findings from genome-wide association studies have accounted for about 12% of blood lipid variance. The proportion of unexplained variance (~48%) could be lessened with the inclusion of rare variant analyses, illustrating the important role of the low-frequency polymorphisms in the genetic architecture of lipid traits (5). These rare variants, which were not covered by previous genome-wide association studies but contributing to lipid traits, could be located in coding regions, as well as introns, untranslated regions and inter-gene regions.
To date, such high-resolution genetic investigations of lipids have focused only on fasting phenotypes, while knowledge of genetic determinants of lipid response to interventions remains limited.
The Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study cohort provides a unique opportunity to study the effects of rare genetic variants on response to two interventions-a high-fat meal (PPL) and fenofibrate treatment (FFB) due to its carefully controlled intervention by standardizing the environmental perturbation. The high-fat meal intervention provides a unique and impactful way to study dyslipidemia in a dynamic test, not only because humans spend most of their waking hours in the postprandial state, but also because of the high fat content of western diets. Furthermore, an elevated postprandial lipid response, followed by delayed clearance, has been shown to predict future risk of cardiovascular disease (6). Additionally, the second GOLDN intervention-the three-week treatment with micronized FFB-creates an opportunity for pharmacogenomic discovery and a deeper understanding of individual variation to lipid-lowering drugs (7). Prior studies in GOLDN have scanned common variants to identify multiple promising determinants of response to both interventions (8)(9)(10); however, they were by guest, on  www.jlr.org Downloaded from 6 conducted prior to the availability of high-resolution genomic data. To augment the discoveries from Genome Wide Association Study (GWAS), account for further missing heritability, and identify additional functional loci contributing to variation in lipid response to a high-fat meal and treatment with FFB, we performed an exome-wide sequencing study in 894 European Americans from the GOLDN study. We sought to replicate the PPL result using the Heredity and Phenotype Intervention (HAPI) Heart Study, which included a high-fat meal challenge but not FFB treatment. In addition, we sought to validate the associations for our findings using DNA methylation and RNA-Seq data previously collected in GOLDN.

Study Population
GOLDN (clinicaltrials.gov-NCT00083369) was designed to characterize genetic factors that determine response of lipids to two environmental interventions: a 3-week FFB treatment and a high fat meal challenge (8,9,11). Only Caucasian families with at least two siblings were included. Participants were asked to discontinue any lipid-lowering agents (pharmaceuticals or nutraceuticals) for at least 4 weeks prior to the initial visit. GOLDN recruited and sequenced 894 subjects from 186 families recruited at two centers (Minneapolis, MN and Salt Lake City, UT). Of the 894 subjects, 810 participants participated in the high fat meal intervention. After that, 797 GOLDN participants received daily treatment with 160 mg micronized FFB for three weeks and were followed for treatment response. In the minimal model, all the associations were adjusted for sex, age, age 2 , age 3 , and recruiting center as fixed effects (24,25). For the FFB analyses, an additional variable measuring the number of pills taken per day (to adjust for compliance) was included as a covariate. In addition, a kinship coefficient considered as a random effect was used to adjust for family relatedness. In the full model, apart from those covariates included in minimal models, additional related lipid levels were included as covariates.
For the fasting level response to FFB and pre-FFB postprandial AUI and uptake, respective baseline levels were included as covariates. For the pre-FFB postprandial clearance phenotype, draw 2 lipid level was used as a covariate. For the three postprandial lipid level response to FFB, their corresponding pre-FFB treatment level and fasting level response to FFB were included as covariates (Supplemental Table   S1). We sought to replicate our associations for TG postprandial phenotypes using the Heredity and Phenotype Intervention Heart Study (29), in which postprandial TG levels were measured at 0, 1, 2, 3, 4 and 6 hours after 770 Old Order Amish participants underwent a high fat feeding intervention identical to the one used in GOLDN. More study procedure details can be found in previous reports (10, 29). HAPI Heart Study participants were genotyped as part of the Trans-Omics for Precision Medicine (TOPMed) effort using whole genome sequencing methods. TG postprandial phenotypes were defined in the HAPI Heart Study similarly as described above in GOLDN but calculated with measured values at 0, 3, and 6 hours. Demographic and clinical characteristics of HAPI Heart Study were listed in Supplemental Table   S2.
The association of GOLDN top hits in the replication cohort was tested using RAREMETALWORKER with an identical model to GOLDN. Next, we also performed a joint metaanalysis of GOLDN top hits across all participating cohorts using RAREMETAL.

Functional Validation
We sought to validate the associations for our findings using DNA methylation and RNA-Seq data previously collected in GOLDN. CpG site methylation was quantified using the Illumina (San Diego, CA) Infinium Human Methylation450 Beadchip with 991 participants as described previously (24). The CpG sites within the genes containing significantly associated variants and the intergenic CpG sites near these genes were examined to test whether their methylation levels were associated with lipid levels or not using linear mixed models.
For transcriptional profiling, 100 unrelated GOLDN participants were selected from the extremes of the BMI distribution. RNA was extracted from buffy coats using the TRIzol method (ThermoFisher Scientific, Waltham, MA, USA) and the quality was evaluated using Bioanalyzer (Agilent Technologies,  12 Santa Clara, CA, USA). After sequencing and alignment, we fitted linear mixed models to test for associations between gene transcript level and lipid phenotypes.

Demographic and Clinical Characteristics
Demographic and clinical characteristics of the study subjects were listed in Supplemental Table   S2 and Table 1. From 186 families, we included 435 males and 459 females from the GOLDN study.
Levels of TG, LDL-C, HDL-C after FFB treatment were significantly different from pre-treatment levels (P≤0.05) (  Table 2.

Associations with Rare Variants from Known Lipid Genes from Previous GOLDN Studies
We also sought to replicate known candidate genes including APOA5 (34), APOE (11), and APOC3 (35), which were reported to carry variants significantly associated with one or more lipid class in GOLDN (for response phenotypes) as well as fasting lipids in other studies (21). The variants identified in those genes and their P values are listed in Supplemental Table S3 and Supplemental Table S4.
Overall, we report APOA5 and APOE but not APOC3 show associations with one or more GOLDN lipid trait in single variant or gene-based tests. We note some known mutations in these three genes were not identified in our study, or were filtered out during QC. For example, a known missense SNP (rs7412) in APOE was filtered out because of low reading depth.

Replication Results
The significant gene SIPA1L2 for TG postprandial AUI was tested for association in the HAPI Heart Study. In total, seven exonic functional SNPs were identified in this gene within HAPI Heart Study, one of which was shared with GOLDN (Supplemental Table S5 and Supplemental Table S6). None of the seven SNPs in HAPI Heart Study was significant (Supplemental Table S5). The association of SIPA1L2 with TG postprandial AUI in HAPI Heart Study is not significant (P=0.97) according to gene-based test, and the effect direction is different from that in GOLDN. The FFB treatment protocol was not a part of the HAPI Heart study and thus we could not attempt replication for ITGA7 or CEP72.

Functional Validation Results
We examined whether differential methylation at cytosine-guanine dinucleotides (CpGs) within and neighboring our top gene findings were associated with our lipid traits. After Bonferroni correction for multiple testing, no methylation of CpG site within or neighboring those three genes was associated with lipid intervention traits.
We also examined the gene transcript level of candidate genes using RNA-Seq data of GOLDN.
The gene transcript level of SIPA1L2 was significantly associated with TG postprandial AUI (P≤0.05), and the gene transcript level of ITGA7 was borderline associated with LDL-C fasting level response to FFB (P=0.07).

Discussion
High-resolution genetic investigations of lipids have focused only on fasting phenotypes. In this study, we sought to elucidate effects of rare genetic variants on lipid response to a high-fat meal and FFB by guest, on July 21, 2018 www.jlr.org Downloaded from treatment using an exome-wide sequencing-based association study for the first time. Here, we presented preliminary evidence of genetic determinants of lipid response to two interventions. Those genes include ITGA7, SIPA1L2, and CEP72. The SIPA1L2 gene transcript level was associated with the TG postprandial response. In addition, we identified associations related with lipid response to two interventions in candidate genes whose common variants were found to be associated with baseline lipid phenotypes in previous studies.
We identified three genes with rare coding-sequence mutations whose carriers exhibited clinically relevant phenotypic differences compared with non-carriers. Previous studies offer insights into the observed associations. For example, common polymorphisms in ITGA2 (an ITGA7 homolog) were associated with coronary atherosclerosis in a candidate gene association study of the Chinese Han population (36). Variants in CEP164 and CEP68, which are homologs of CEP72, were reported to be associated with TG and LDL-C fasting levels (5,21,37). Although no study indicated that SIPA1L2 is  Table S3) where APOA5 rare variant carriers exhibited significantly higher baseline TG and lower baseline HDL-C levels (P≤0.05) compared with non-carriers.
After 3 weeks of FFB treatment, reduction of TG fasting level in APOA5 rare variant carriers was significantly larger than that in non-carriers (P≤0.05), and the HDL-C response was also higher (34). Our by guest, on July 21, 2018 www.jlr.org Downloaded from study adds to that study by identifying rs3135506 in APOA5 was also associated with TG postprandial clearance slope, a trait not previously analyzed. The effects of two APOE variants (rs7412 and rs429358) on TG levels has also been examined in GOLDN (11). Our study further showed rs429358 in APOE to be associated with baseline LDL-C and LDL-C response to FFB. Liu, et al. (35) reported two intronic variants in APOC3 were associated with enhanced TG response to FFB treatment. Our study did not cover those variants and we did not uncover any exonic APOC3 variations with allele count greater than 2 (Supplemental Table S3 and Supplemental Table S4). rs76353203, a stop-gain variant in APOC3, was reported to be associated with decreased baseline TG, increased baseline HDL-C and decreased baseline LDL-C in the Amish population (40). In our study, we observed consistent directions of this association, but the variant was carried by only one sample and the association was not significant. Overall these results continue to add to the body of literature on these important lipid candidate genes.
This study has several limitations. The efficiency of capture probes varies considerably for exome sequencing (Supplemental Figure S1). Thus, the read depths for partial SNPs were low so that they were excluded by the quality control procedure. For example, rs7412, which was known to be associated with lipid levels, was excluded due to low read depth. In our study, novel rare variants in three genes, ITGA7, SIPA1L2, and CEP72, associated with lipid response to a high-fat meal and/or a 3-week FFB treatment were found. Moreover, the gene transcript level of SIPA1L2 was associated with the postprandial TG AUI. We identified new intervention trait associations within two known candidate genes (APOA5 and APOE). In conclusion, we found novel genetic variants that contribute to lipid intervention traits and revealed potential underlying molecular mechanisms, which may inform biomarkers of disease risk and treatment targets.