Whole genome sequence analysis of serum amino acid levels
- Bing Yu†1,
- Paul S. de Vries†1,
- Ginger A. Metcalf2,
- Zhe Wang1,
- Elena V. Feofanova1,
- Xiaoming Liu1,
- Donna Marie Muzny2,
- Lynne E. Wagenknecht3,
- Richard A. Gibbs2,
- Alanna C. Morrison1 and
- Eric Boerwinkle1, 2Email author
© The Author(s). 2016
Received: 30 July 2016
Accepted: 10 November 2016
Published: 24 November 2016
Blood levels of amino acids are important biomarkers of disease and are influenced by synthesis, protein degradation, and gene–environment interactions. Whole genome sequence analysis of amino acid levels may establish a paradigm for analyzing quantitative risk factors.
In a discovery cohort of 1872 African Americans and a replication cohort of 1552 European Americans we sequenced exons and whole genomes and measured serum levels of 70 amino acids. Rare and low-frequency variants (minor allele frequency ≤5%) were analyzed by three types of aggregating motifs defined by gene exons, regulatory regions, or genome-wide sliding windows. Common variants (minor allele frequency >5%) were analyzed individually. Over all four analysis strategies, 14 gene–amino acid associations were identified and replicated. The 14 loci accounted for an average of 1.8% of the variance in amino acid levels, which ranged from 0.4 to 9.7%. Among the identified locus–amino acid pairs, four are novel and six have been reported to underlie known Mendelian conditions. These results suggest that there may be substantial genetic effects on amino acid levels in the general population that may underlie inborn errors of metabolism. We also identify a predicted promoter variant in AGA (the gene that encodes aspartylglucosaminidase) that is significantly associated with asparagine levels, with an effect that is independent of any observed coding variants.
These data provide insights into genetic influences on circulating amino acid levels by integrating -omic technologies in a multi-ethnic population. The results also help establish a paradigm for whole genome sequence analysis of quantitative traits.
Conventional wisdom holds that common complex diseases are polygenic and rare Mendelian diseases are monogenic. Indeed the biology of human health and disease is complex and there is a continuum of genetic architectures. For example, ever since the seminal work of Goldstein and Brown with familial hypercholesterolemia , it is appreciated that a subset of individuals in the far tails of the phenotype distribution (e.g., LDL-cholesterol) may have a Mendelian form of a condition while others may have a polygenic predisposition. To gain a complete understanding of the genetic architecture of health and disease will require: 1) realization of the continuum of Mendelian and polygenic conditions; 2) consideration of the whole genome; and 3) multi-omic approaches that allow measurements of intermediate phenotypes closer to gene action and that bridge genome variation with inter-individual differences in disease risk.
Circulating blood levels of amino acids and whole genome sequence data combined with state-of-the-art annotation and analysis tools can help establish a paradigm for defining the genetic architecture of quantitative phenotypes. Rare recessive mutations in genes that lead to deficiencies or excess of specific amino acids are the root cause of a number of inborn errors of metabolism . Inter-individual differences in several amino acids are risk factors for common disease (e.g., branched-chain and aromatic amino acids for diabetes) . Amino acids are important components of protein metabolism and cell signaling. They reflect a variety of cellular and physiologic processes and may, therefore, mirror gene–environment interactions. Genome-wide association studies (GWAS) have identified common variants associated with multiple amino acid levels [4–6]. Low-frequency variants that modulate amino acid levels independent of known GWAS loci have also been reported using exome arrays and a targeted analytical approach for exome sequence data [7, 8]. To date, no study has assessed the impact of rare and low-frequency variations captured by systematic and comprehensive sequencing of the protein-encoding exons and whole genomes on amino acid levels in a multi-ethnic population. We used exon and whole genome sequencing in a sample of 3424 European and African Americans to investigate the genetic determinants of 70 blood amino acid levels. Significant effects discovered in African Americans (AA) were replicated in an independent set of European Americans (EA). This study demonstrates the utility of combining multi-omic data and the importance of intermediate phenotypes close to gene action for identifying regions of the genome influencing biologically and clinically relevant traits.
We sequenced exons and whole genomes and measured serum levels of 70 amino acids in 1872 AA for the discovery stage and 1552 EA for the replication stage among participants in the Atherosclerosis Risk in Communities (ARIC) study. Baseline characteristics of both the discovery and replication samples are shown in Additional file 1: Table S1. The mean age of the AA and EA participants was 52.7 and 54.7 years, respectively, and 65.2 and 54.9% of the samples were female. Prevalent diabetes was diagnosed in 16 and 8% of the AA and EA subjects, respectively, and 52 and 31%, respectively, had prevalent hypertension. In the AA samples, a total of 330,490 single nucleotide variants (SNVs) in the exons were captured by exome sequencing and 52,094,875 in the whole genomes; 94.8% of the SNVs were rare or low-frequency (minor allele frequency (MAF) ≤5%) in the exons and this number was 82.9% in the whole genomes. The proportion of variants within frequency bins characterized as rare (0% < MAF < 1%), low-frequency (1% ≤ MAF ≤ 5%), and common (MAF > 5%) is shown in Additional file 2: Figure S1.
Gene exon approach
Gene exon-based results demonstrating a significant association among both discovery (p < 4.6 × 10−8) and replication (p < 0.003) stages for the T5 burden test
3.2 × 10−31
8.1 × 10−12
1.1 × 10−10
4.7 × 10−5
4.1 × 10−41
3.9 × 10−15
1.1 × 10−10
2.7 × 10−5
2.7 × 10−21
1.1 × 10−7
1.6 × 10−8
8.2 × 10−6
1.4 × 10−29
1.5 × 10−11
Regulatory motif approach
Defining regulatory motifs away from protein-encoding genes is a major activity of modern genome sciences. Projects such as ENCODE  and GTEx  are defining noncoding regions of the genome that have important biologic function, including regulation of gene expression. We analyzed a total of 21,040 annotated regulatory motifs with cMAC ≥7 across the genome, and statistical significance was defined as P dis < 3.4 × 10−8. Although two regulatory motifs exceeded our a priori significance threshold for discovery in the AA samples, they did not replicate in the EA samples (Additional file 1: Table S6). To help up-weight predicted functional variants, the regulatory motif analysis was repeated and weighted by the combined annotation dependent depletion (CADD) scores , but the results did not change substantially from those of the unweighted analyses (Additional file 2: Figure S2). The regulatory motif results for the meta-analysis of the discovery and replication samples with p <4.0 × 10−6 are provided in Additional file 1: Table S7.
Sliding window approach
Sliding windows demonstrating a significant association among both discovery (p < 1.1 × 10−9) and replication (p < 0.01) stages for the T5 burden test
Chr2: 73744005–73748004 (NAT8)
1.6 × 10−15
Chr2: 73744005–73748004 (NAT8)
Chr2: 73614005–73618004 (NAT8)
6.2 × 10−11
Chr2: 73614005–73618004 (NAT8)
Chr6: 132952009–132956008 (VNN1)
9.4 × 10−10
Chr6: 132952009–132956008 (VNN1)
Single variant approach
Single variant results demonstrating a significant association among both discovery (p < 7.1 × 10−10) and replication (p < 0.003) stages
4.5 × 10−19
4.9 × 10−45
2.3 × 10−14
9.5 × 10−6
2.4 × 10−10
3.9 × 10−23
3.3 × 10−75
3.4 × 10−85
9.5 × 10−14
2.5 × 10−17
4.3 × 10−10
1.2 × 10−5
2.5 × 10−17
1.4 × 10−7
5.3 × 10−10
1.6 × 10−13
7.8 × 10−12
8.1 × 10−16
4.8 × 10−34
Conditional analysis of selected regions adjusting for the lead common variant identified by previous genome-wide association studies
GWAS Lead SNV
1.3 × 10−20
1.1 × 10−20
2.1 × 10−6
4.1 × 10−6
Chr2: 73744005–73748004 (NAT8)
1.6 × 10−15
4.0 × 10−4
Chr2: 73614005–73618004 (NAT8)
6.2 × 10−11
6.8 × 10−10
9.1 × 10−10
1.5 × 10−5
6.0 × 10−8
1.1 × 10−26
4.3 × 10−27
4.4 × 10−11
4.5 × 10−10
1.5 × 10−5
3.0 × 10−5
9.3 × 10−5
2.0 × 10−4
1.4 × 10−26
1.7 × 10−26
1.3 × 10−12
1.2 × 10−13
We identified and replicated 14 associations between genetic loci and serum amino acid levels, all in or neighboring genes encoding enzymes. Four of the associated gene–amino acid pairs were novel (DDC–3-methoxytyrosine, VNN1–acisoga, ACY1–N-acetylalanine, and ACY1–N-acetylthreonine). Six of the loci–amino acid associations were identified by more than one analytical approach. In most cases, rare and low-frequency variants in the regions identified in this study were associated with amino acids independent of common variants previously identified by GWAS. Six of the gene–amino acid pairs identified here are known to underlie Mendelian disorders. Notably, among the four analytical approaches proposed in this study, analyses focusing on regulatory motifs was the only setting where there was no significant and replicated amino acid associations.
Amino acids are the building blocks of proteins. Humans can synthesize 11 of the 20 standard amino acids and the remaining nine essential amino acids must be obtained from dietary sources. The genetic loci identified in this study are all associated with non-essential amino acids or amino acid derivatives, although previous GWAS have reported multiple common variants that are associated with levels of nine essential amino acids [6, 12–14]. Given the nature of amino acid biosynthesis and the properties of the enzyme-encoding genes, it is of note that six of the identified enzymes directly catalyze reactions involving the amino acid as a substrate or end product.
Understanding the genetic bases of inherited metabolic disease has been a focus of human genetics for a long time. In this study, we identified six genes (DMGDH, AGA, ACY1, PRODH, DDC, CPS1) that have been previously implicated in recessive metabolic disorders, four of which show direct relationships to the amino acids identified here: mutations in AGA are known to cause aspartylglucosaminuria (MIM 208400); mutations in DMGDH cause dimethylglycine dehydrogenase deficiency (MIM 605850); mutations in ACY1 cause aminoacylase-1 deficiency (MIM 609924); and mutations in PRODH are known to cause hyperprolinemia type I (MIM 239500). Although the other two loci did not directly affect the identified amino acid levels, there is evidence suggesting that the two genes play a role in their regulation. DDC participates in tyrosine metabolism (DBGET: R02080) and mutations in it are known to causearomatic L-amino acid decarboxylase deficiency (AADC; MIM 608643). The identified amino acid 3-methoxytyrosine is one of the main biochemical markers of AADC . CPS1 (carbamoyl phosphate synthetase I) encodes an ammonia ligase (DBGET: R00149) and deficiency of the CPS1 protein (MIM 608307) leads to hyperammonemia. Glycine is a precursor of ammonia (DBGET: R01221) and, as such, accumulates in the liver and kidneys under the condition of excess ammonia . DMGDH–dimethylglycine, AGA–asparagine, PRODH–proline, and CPS1–glycine associations were reported by several previous studies (Additional file 1: Table S2), while the ACY1–N-acetylthreonine/N-acetylalanine and DDC–3-methoxytyrosine associations are novel. Our findings support that genetic variation impacts inter-individual differences in amino acid levels in the general population in addition to causing recessive inborn errors of metabolism.
The data reported here provide new insight into the genes influencing blood amino acid levels. For example, CCBL1, which encodes kynurenine aminotransferase 1, was associated with three lactate derivatives, including indolelactate, phenyllactate (PLA), and 3-(4-hydroxyphenyl)lactate. Kynurenine aminotransferase 1 is known to be involved in tryptophan metabolism (DBGET: T01001, hsa00380), where it converts kynurenine, an intermediate of the tryptophan degradation pathway, into kynurenic acid , a neurotoxic compound associated with schizophrenia . One of the three amino acids, indolelactate, is also part of tryptophan metabolism (DBGET: hsa00380). A common variant in CCBL1 has been reported to be related to indolelactate in populations of European ancestry , and we observed that rare and low-frequency variants in CCBL1 were associated with indolelactate in both AA and EA samples independent of the reported common variant. Because of the neurotoxic effect of kynurenic acid, inhibition of the kynurenine pathway is a therapeutic strategy for neurodegenerative disease [19, 20]. Current available drugs are indoleamine-pyrrole 2,3-dioxygenase (IDO) inhibitors, which inhibit the conversion of tryptophan to kynurenine. We identified rare and low-frequency variants in IDO1, encoding IDO, associated with low levels of kynurenine, suggesting that participants carrying functional mutations in IDO1 may show neuroprotection. Phenylalanine, tyrosine, and tryptophan have common steps in their biosynthesis pathway (DBGET:map00400). Interestingly, besides tryptophan metabolism, the other two identified lactate derivatives, PLA and 3-(4-hydroxyphenyl)lactate, are involved in phenylalanine and tyrosine metabolism. Both PLA and 3-(4-hydroxyphenyl)lactate are elevated in phenylketonuria and hyperphenylalaninemia , which if untreated may result in mental impairment and other neurologic disorders (MIM 261600 and 261640). Our results indicate that rare and low-frequency variants in CCBL1 are associated with increased levels for all three lactate derivatives. Future studies are warranted to dissect the mechanism of the observed associations and the possibility of CCBL1 as a novel drug target for neurologic disorders.
The results reported here generate new hypotheses that future studies can investigate. One example is the association between a common missense variant in VNN1 and acisoga. Acisoga is a newly described amino acid involved in polyamine metabolism. Although polyamines are ubiquitous small molecules, acisoga is the only polyamine measured in our metabolomics panel. VNN1 encodes vanin 1, which shares extensive sequence similarity with biotinidase. The function for VNN1 is not well studied; however, it possesses pantetheinase activity, which may play a role in oxidative-stress response . There is convincing evidence that altered polyamine metabolism is involved in many diseases, and drugs altering polyamine levels therefore may have a variety of important disease targets . The results presented here provide preliminary directions for further research on polyamine metabolism and the VNN1 gene.
The analysis strategy and results presented here establish a paradigm for whole genome sequence analysis of quantitative risk factor phenotypes. There is compelling evidence based on GWAS that common variants confer relatively small increments in risk and explain only a small proportion of the heritability . Assessment of rare and low-frequency variants, specifically non-coding rare and low-frequency variants, in relation to human health is largely incomplete. Whole genome sequencing data offer an opportunity to characterize rare and low-frequency variations and variations outside of the usual protein-encoding regions. The UK10K and GoT2D projects [25, 26] have demonstrated success identifying novel findings utilizing whole genome sequencing, but this success has been limited compared to GWAS, in part due to the limited statistical power. Compared to studies of complex diseases, the study of quantitative phenotypes, such as amino acid levels which are proximal to gene function, can dramatically maximize statistical power. Our study successfully identified and replicated four novel findings, demonstrating the feasibility of analyzing whole genome sequences in the context of intermediate quantitative phenotypes to promote novel biologically relevant findings.
Although the majority of the findings in our study reside in coding regions, we were able to identify non-coding loci that contribute to amino acid levels. For example, a common intronic variant, rs11131799, was shown to be associated with asparagine levels, independent of coding variants in AGA (AGA, P unadjusted = 1.1 × 10−10, P adjusted = 2.4 × 10−9). Conditioning on AGA coding variants did not markedly alter the non-coding locus association. AGA encodes the enzyme aspartylglucosaminidase, which breaks down glycoproteins by hydrolyzing N-acetylglucosamine–asparagine linkages, thereby releasing asparagine. Rs11131799, annotated as a predicted promoter variant, is highly associated with AGA expression levels (http://genenetwork.nl/biosqtlbrowser/). Some of the variants involved in the 4-kb window are annotated as predicted deleterious by CADD  and FATHMM-MKL . A previous study identified an association between asparagine and the ASPG locus, encoding asparaginase , which catalyzes the hydrolysis of asparagine to aspartic acid. Interestingly, our lead variant for the AGA–asparagine association (rs11131799) occurred in both AA and EA participants, while the previously reported lead variant (rs4690522) was only observed in EA participants. The two variants were in strong linkage disequilibrium in EA participants, but not in linkage disequilibrium in AA participants, suggesting that rs4690522 may have simply been a proxy for rs11131799 in previous studies. The data reported here suggest that blood asparagine levels may be influenced not only by the coding regions but also by some regulatory elements. Further annotation information is warranted to dissect the two non-coding regions in relation to asparagine levels.
Among the four analytical approaches proposed in this study, the analysis of regulatory motifs was the only approach that did not yield novel findings. If we consider effect sizes seen in the other analysis approaches, these results reemphasize that improvements in annotation, particularly non-coding regulatory elements, are necessary. It is likely that the high density of non-functional variants in the hypothesized regulatory motifs overwhelms the sparser functional variants included in a burden test. Alternatively, single rare and low-frequency variants with large effects may be scarce in annotated regulatory elements of the human genome.
Strengths of this study include the use of direct sequencing, as opposed to genotyping and imputation. By using sequencing data, we were able to interrogate low-frequency, rare, and private variants that are not covered by genotyping and imputation. Even for variants accessible by both approaches, sequencing avoids the measurement error generated by imputation, which can be large for rare variants. The advantages of sequencing are particularly important for fine-mapping, since differences in imputation quality among variants can obstruct the search for the most likely causal variant. An additional strength of this study is the joint calling of variants in a larger pooled sample of studies conducted in the same laboratory, including ARIC. By increasing the sample size during the calling of variants, the ability to correctly call rare variants is enhanced .
The discovery sample for this study was AA, a population with a high level of genetic diversity, to promote novel findings. Also, AA are relatively under-represented in large-scale genomics research. To our knowledge, there is no AA sample for which both whole genome sequencing and multi-amino acid measurements are available to perform replication. Therefore, EA were used as the replication sample. Our focus here is the similar associations detected in both AA and EA. For the associations that were not replicated in EA, population-specific genetic variation and effects are possible reasons in addition to the original observation being a type I error. The variants included in aggregate tests differed between our discovery (AA) and replication (EA) samples due to ancestry-specific variants as well as allele frequency differences among shared variants. The variance explained by a genetic locus provides an estimate about the proportion of phenotypic variation that is attributed to inter-individual differences in DNA sequence. In this study, the variance explaining amino acid levels ranges from 0.4 to 9.7% among AA. Our previous GWAS reported 5 to 20% variance explaining differing levels of five amino acids , and the range of variance explaining differences in amino acid levels varied among Caucasians, such as 1–10%  or 1–25% . To our knowledge, there is no trans-ethnic genetic association study of amino acid levels. Nevertheless, our exploratory trans-ethnic meta-analysis provided insights for future studies. Further investigation is warranted to evaluate these and additional findings in multiple ethnic groups.
By integrating -omic technologies into deeply phenotyped populations, we show that sequencing variants affect the levels of multiple human amino acids among two ethnicities. These data and results identify new avenues of gene function, novel molecular mechanisms, and potentially diagnostic targets for multiple diseases.
Study population and metabolome measurements
The Atherosclerosis Risk in Communities (ARIC) study is a prospective epidemiological study designed to investigate the etiology and predictors of cardiovascular disease. It enrolled 15,792 individuals aged 45–64 years from four US communities (Forsyth County, NC; Jackson, MS; suburbs of Minneapolis, MN; and Washington County, MD) in 1987–89 (baseline) and followed them for four completed visits in 1990–92, 1993–95, 1996–98, and 2011–13. A detailed description of the ARIC study design and methods is published elsewhere . Amino acid levels were measured using fasting serum samples collected at the baseline examination in 1987–1989 among ARIC selected AA and EA. A total of 89 amino acids were detected and semi-quantified by Metabolon Inc. (Durham, USA) using an untargeted, gas chromatography–mass spectrometry and liquid chromatography–mass spectrometry (GC-MS and LC-MS)-based metabolomic quantification protocol (Additional file 2: Supplemental methods) [31, 32]. Amino acids were excluded if: 1) more than 25% of the samples had values below the detection limit; or 2) the Pearson correlation coefficients between 2010 and 2014 measurements were <0.3 (Additional file 2: Supplemental methods). After this assessment, 70 metabolites were included in the present study.
Isolated DNA from AA and EA for exon sequencing were further processed using the Baylor College of Medicine Human Genome Sequencing Center (BCM-HGSC) VCRome 2.1 reagent (42 Mb, NimbleGen) , and all samples were paired-end sequenced using Illumina GAII or HiSeq instruments. Details about sequencing, variant calling, and variant quality control are provided in Additional file 2: Supplemental methods. Variants were annotated using ANNOVAR  and dbNSFP v2.0  according to the reference genome GRCh37 and National Center for Biotechnology Information RefSeq.
Whole genome sequencing
Whole genome sequencing data for AA and EA were generated at BCM-HGSC using Nano or PCR-free DNA libraries and the Hiseq 2000 instrument (Illumina, Inc., San Diego, CA, USA). Methods for the whole genome sequencing of the ARIC study samples were described elsewhere . Briefly, individuals were sequenced at sevenfold average depth on Illumina HiSeq instruments and variant calling was completed using goSNAP (https://sourceforge.net/p/gosnap/git/ci/master/tree/). Details about sequencing, variant calling, and variant quality control are provided in Additional file 2: Supplemental methods. Whole genome sequencing variants were annotated across regions and functional domains using the Whole Genome Sequencing Annotation (WGSA) pipeline . The 3′ and 5′ UTRs of a gene were determined using ANNOVAR  annotations based on the RefSeq gene model . The promoter of a gene was defined based on the overlap between the permissive set of CAGE peaks reported by the FANTOM5 project  and the 5-kb upstream region determined by the ANNOVAR annotation based on the RefSeq gene model. The enhancers and the target genes of the enhancers were defined based on the permissive set of enhancers and enhancer–promoter pairs reported by the FANTOM5 project. In the case of an undesignated enhancer–gene pair, we assigned an enhancer to the nearest gene.
Metabolomic data points lying outside the 1st–99th percentile of each amino acid level were winsorized among each measurement respectively. Levels below the detectable limit of the assay were imputed with the lowest detected value for that amino acid in all samples. Amino acid levels were then natural log-transformed prior to the analyses.
Because our primary focus was on rare and low-frequency variants, we aggregated rare and low-frequency variants (MAF ≤5%) in groups based on gene exons, regulatory motifs, or sliding windows. Gene-based aggregation tests are designed for rare and low-frequency coding variants. The analytical unit is an annotated gene. All annotated coding variants, such as splicing, stop-gain, stop-loss, nonsynonymous, and indels within the gene were aggregated for the analysis. The regulatory motifs included annotated enhancers, the 3′ and 5′ UTRs, and promoter of a gene. The sliding window approach is designed to aggregate rare and low-frequency variants according to their physical position regardless of annotated function. Based on our previous experience , sliding windows were defined as 4 kb in length and began at position 0 bp for each chromosome, with a skip length of 2 kb. Within each annotated unit, a burden test (T5)  was used, adjusting for age, sex, and the first three principal components (PCs). We further adjusted for estimated glomerular filtration rate (eGFR) , an indicator of kidney function, since multiple amino acid levels were associated with eGFR . The T5 burden test collapses variants with MAF ≤5% into a single genetic score to evaluate the joint effects of rare and low-frequency alleles. We also conducted single variant analysis for all individual variants with MAF >5% using an additive genetic model with the same adjustments. For each approach, the variance explained (VarExp) was calculated using the effect allele frequency (p) and beta (β) from the analyses and the variance of the quantitative trait (σ 2 ) using the formula VarExp = β 2 /σ 2 × 2 × p × (1 − p) . In addition, we also applied the CADD scores  as variant weights to the regulatory motifs. The weights were defined as the difference between raw CADD scores and the minimum CADD score scaled by the range of the raw CADD scores and were introduced into the T5 burden test using its quartic form. The analytical models were the same as described above. All analyses were carried out using the R seqMeta package .
The significance threshold for the gene-based analysis is defined as P dis < 4.6 × 10−8 for the discovery stage adjusting for 15,589 genes and 70 amino acids and P rep < 0.003 for the replication stage adjusting for 15 significant gene–amino acid pairs identified in the discovery stage. The significance threshold for the regulatory motifs analysis is defined as P dis < 3.4 × 10−8 for the discovery stage adjusting for 21,040 genes and 70 amino acids. The significance threshold for the sliding window approach is defined as P dis < 1.1 × 10−9 for the discovery stage adjusting for 668,748 non-overlapping windows and 70 amino acids and P rep < 0.01 for the replication stage adjusting for five significant window–amino acid pairs identified in the discovery stage. The significance threshold for the single variant analysis is defined as P dis < 7.1 × 10−10 for the discovery stage adjusting for one million independent common variants  and 70 amino acids and P rep < 0.003 for the replication stage adjusting for 16 significant single variant–amino acid pairs identified in the discovery stage. We consider an association novel if it has not been reported in previous GWAS or candidate gene study. We also performed trans-ethnic meta-analysis among the discovery and replication samples to provide additional insight into the genetic loci discovery.
Regions associated with amino acid levels using the gene-based or sliding window approaches that have already been identified by previous GWAS were selected for inclusion in the conditional analyses. We reexamined each of the selected associations, additionally adjusting the region-based association for the lead common variant identified by the GWAS, and vice versa. To adjust the GWAS variants for the identified regions, we computed the T5 burden and used it as a covariate. We also performed a conditional analysis for our single variant findings when these overlapped with regions identified by GWAS, adjusting our lead single variant for the lead variant identified by GWAS and vice versa.
We acknowledge the essential role of the ARIC study in developing and support for this article. The authors also thank the staff and participants of the ARIC study for their important contributions.
The Atherosclerosis Risk in Communities (ARIC) Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute (NHLBI) contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). Funding support for “Building on GWAS for NHLBI-diseases: the U.S. CHARGE consortium” was provided by the NIH through the American Recovery and Reinvestment Act of 2009 (ARRA) (5RC2HL102419). Metabolomics measurements were sponsored by the National Human Genome Research Institute (3U01HG004402-02S1). Sequencing was carried out at the Baylor College of Medicine Human Genome Sequencing Center (U54HG003273 and R01HL086694).
Availability of data and materials
All supporting data for the ARIC cohort for this manuscript are made available via dbGaP study accession phs000280. The summary statistics for significant and suggestive associations of this study have been deposited into the dbGaP CHARGE Summary Results site  (dbGaPstudy accession phs000930).
BY, PDV, ZW, and EVF performed statistical analyses. GAM and DMM ensured high-quality sequence variants were delivered for analyses. XL preformed variant annotation. EB and LEW were involved with study design. RAG and EB provided materials and project oversight. BY, PDV, ACM, and EB prepared the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Ethics approval and consent to participate
This study was conducted in compliance with the Helsinki Declaration and all participants have provided written informed consent. The Committee for the Protection of Human Subjects at the University of Texas Health Science Center at Houston has approved this research (IRB HSC-SPH-09-0490).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Brown MS, Goldstein JL. A receptor-mediated pathway for cholesterol homeostasis. Science. 1986;232:34–47.View ArticlePubMedGoogle Scholar
- Scriver C, Beaudet A, Sly W, Valle D, Childs B, Kinzler K, Vogelstein B. The Metabolic and Molecular Bases of Inherited Disease. 8th edn: New York City:McGraw-Hill Companies, Inc.; 2000.Google Scholar
- Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17:448–53.View ArticlePubMedPubMed CentralGoogle Scholar
- Suhre K, Shin SY, Petersen AK, Mohney RP, Meredith D, Wagele B, Altmaier E. CardioGram, Deloukas P, Erdmann J, et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature. 2011;477:54–60.View ArticlePubMedGoogle Scholar
- Rhee EP, Ho JE, Chen MH, Shen D, Cheng S, Larson MG, Ghorbani A, Shi X, Helenius IT, O’Donnell CJ, et al. A genome-wide association study of the human metabolome in a community-based cohort. Cell Metab. 2013;18:130–43.View ArticlePubMedPubMed CentralGoogle Scholar
- Yu B, Zheng Y, Alexander D, Morrison AC, Coresh J, Boerwinkle E. Genetic determinants influencing human serum metabolome among African Americans. PLoS Genet. 2014;10:e1004212.View ArticlePubMedPubMed CentralGoogle Scholar
- Yu B, Li AH, Muzny D, Veeraraghavan N, de Vries PS, Bis JC, Musani SK, Alexander D, Morrison AC, Franco OH, et al. Association of rare loss-of-function alleles in HAL, serum histidine: levels and incident coronary heart disease. Circ Cardiovasc Genet. 2015;8:351–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Rhee EP, Yang Q, Yu B, Liu X, Cheng S, Deik A, Pierce KA, Bullock K, Ho JE, Levy D, et al. An exome array study of the plasma metabolome. Nat Commun. 2016;7:12360.View ArticlePubMedPubMed CentralGoogle Scholar
- Encode Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74.View ArticleGoogle Scholar
- GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580–5.View ArticleGoogle Scholar
- Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46:310–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Williams SR, Yang Q, Chen F, Liu X, Keene KL, Jacques P, Chen WM, Weinstein G, Hsu FC, Beiser A, et al. Genome-wide meta-analysis of homocysteine and methionine metabolism identifies five one carbon metabolism loci and a novel association of ALDH1L1 with ischemic stroke. PLoS Genet. 2014;10:e1004214.View ArticlePubMedPubMed CentralGoogle Scholar
- Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, Arnold M, Erte I, Forgetta V, Yang TP, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46:543–50.View ArticlePubMedPubMed CentralGoogle Scholar
- Raffler J, Friedrich N, Arnold M, Kacprowski T, Rueedi R, Altmaier E, Bergmann S, Budde K, Gieger C, Homuth G, et al. Genome-wide association study with targeted and non-targeted NMR metabolomics identifies 15 novel loci of urinary human metabolic individuality. PLoS Genet. 2015;11:e1005487.View ArticlePubMedPubMed CentralGoogle Scholar
- Hyland K, Surtees RA, Rodeck C, Clayton PT. Aromatic L-amino acid decarboxylase deficiency: clinical features, diagnosis, and treatment of a new inborn error of neurotransmitter amine synthesis. Neurology. 1992;42:1980–8.View ArticlePubMedGoogle Scholar
- van de Poll MC, Soeters PB, Deutz NE, Fearon KC, Dejong CH. Renal metabolism of amino acids: its role in interorgan amino acid exchange. Am J Clin Nutr. 2004;79:185–97.PubMedGoogle Scholar
- Passera E, Campanini B, Rossi F, Casazza V, Rizzi M, Pellicciari R, Mozzarelli A. Human kynurenine aminotransferase II--reactivity with substrates and inhibitors. FEBS J. 2011;278:1882–900.View ArticlePubMedGoogle Scholar
- Linderholm KR, Skogh E, Olsson SK, Dahl ML, Holtze M, Engberg G, Samuelsson M, Erhardt S. Increased levels of kynurenine and kynurenic acid in the CSF of patients with schizophrenia. Schizophr Bull. 2012;38:426–32.View ArticlePubMedGoogle Scholar
- Stone TW, Forrest CM, Darlington LG. Kynurenine pathway inhibition as a therapeutic strategy for neuroprotection. FEBS J. 2012;279:1386–97.View ArticlePubMedGoogle Scholar
- Chen Y, Guillemin GJ. Kynurenine pathway metabolites in humans: disease and healthy States. Int J Tryptophan Res. 2009;2:1–19.PubMedPubMed CentralGoogle Scholar
- Spaapen LJ, Ketting D, Wadman SK, Bruinvis L, Duran M. Urinary D-4-hydroxyphenyllactate, D-phenyllactate and D-2-hydroxyisocaproate, abnormalities of bacterial origin. J Inherit Metab Dis. 1987;10:383–90.View ArticlePubMedGoogle Scholar
- Zhang B, Lo C, Shen L, Sood R, Jones C, Cusmano-Ozog K, Park-Snyder S, Wong W, Jeng M, Cowan T, et al. The role of vanin-1 and oxidative stress-related pathways in distinguishing acute and chronic pediatric ITP. Blood. 2011;117:4569–79.View ArticlePubMedGoogle Scholar
- Pegg AE. Mammalian polyamine metabolism and function. IUBMB Life. 2009;61:880–94.View ArticlePubMedPubMed CentralGoogle Scholar
- Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, et al. Finding the missing heritability of complex diseases. Nature. 2009;461:747–53.View ArticlePubMedPubMed CentralGoogle Scholar
- Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, Ma C, Fontanillas P, Moutsianas L, McCarthy DJ, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536:41–7.Google Scholar
- Consortium UK, Walter K, Min JL, Huang J, Crooks L, Memari Y, McCarthy S, Perry JR, Xu C, Futema M, et al. The UK10K project identifies rare variants in health and disease. Nature. 2015;526:82–90.View ArticleGoogle Scholar
- Shihab HA, Rogers MF, Gough J, Mort M, Cooper DN, Day IN, Gaunt TR, Campbell C. An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics. 2015;31:1536–43.View ArticlePubMedPubMed CentralGoogle Scholar
- Grove ML, Yu B, Cochran BJ, Haritunians T, Bis JC, Taylor KD, Hansen M, Borecki IB, Cupples LA, Fornage M, et al. Best practices and joint calling of the HumanExome BeadChip: the CHARGE Consortium. PLoS One. 2013;8:e68095.View ArticlePubMedPubMed CentralGoogle Scholar
- Kettunen J, Tukiainen T, Sarin AP, Ortega-Alonso A, Tikkanen E, Lyytikainen LP, Kangas AJ, Soininen P, Wurtz P, Silander K, et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet. 2012;44:269–76.View ArticlePubMedPubMed CentralGoogle Scholar
- The ARIC investigators. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. Am J Epidemiol. 1989;129:687–702.View ArticleGoogle Scholar
- Ohta T, Masutomi N, Tsutsui N, Sakairi T, Mitchell M, Milburn MV, Ryals JA, Beebe KD, Guo L. Untargeted metabolomic profiling as an evaluative tool of fenofibrate-induced toxicology in Fischer 344 male rats. Toxicol Pathol. 2009;37:521–35.View ArticlePubMedGoogle Scholar
- Evans AM, DeHaven CD, Barrett T, Mitchell M, Milgram E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal Chem. 2009;81:6656–67.View ArticlePubMedGoogle Scholar
- Bainbridge MN, Wang M, Wu Y, Newsham I, Muzny DM, Jefferies JL, Albert TJ, Burgess DL, Gibbs RA. Targeted enrichment beyond the consensus coding DNA sequence exome reveals exons with higher variant densities. Genome Biol. 2011;12:R68.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu X, Jian X, Boerwinkle E. dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum Mutat. 2013;34:E2393–402.View ArticlePubMedPubMed CentralGoogle Scholar
- Morrison AC, Voorman A, Johnson AD, Liu X, Yu J, Li A, Muzny D, Yu F, Rice K, Zhu C, et al. Whole-genome sequence-based analysis of high-density lipoprotein cholesterol. Nat Genet. 2013;45:899–901.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu X, White S, Peng B, Johnson AD, Brody JA, Li AH, Huang Z, Carroll A, Wei P, Gibbs R, et al. WGSA: an annotation pipeline for human genome sequencing studies. J Med Genet. 2016;53:111–2.View ArticlePubMedGoogle Scholar
- O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44:D733–745.View ArticlePubMedGoogle Scholar
- Consortium F, the RP, Clst, Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Haberle V, Lassmann T, et al. A promoter-level mammalian expression atlas. Nature. 2014;507:462–70.View ArticleGoogle Scholar
- Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet. 2008;83:311–21.View ArticlePubMedPubMed CentralGoogle Scholar
- Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro 3rd AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–12.View ArticlePubMedPubMed CentralGoogle Scholar
- Yu B, Zheng Y, Nettleton JA, Alexander D, Coresh J, Boerwinkle E. Serum metabolomic profiling and incident CKD among African Americans. Clin J Am Soc Nephrol. 2014;9:1410–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, Powell C, Vedantam S, Buchkovich ML, Yang J, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206.View ArticlePubMedPubMed CentralGoogle Scholar
- seqMeta R package. http://cran.r-project.org/web/packages/seqMeta/index.html.
- Pe’er I, Yelensky R, Altshuler D, Daly MJ. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol. 2008;32:381–5.View ArticlePubMedGoogle Scholar
- Rich SS, Wang ZY, Sturcke A, Ziyabari L, Feolo M, O’Donnell CJ, Rice K, Bis JC, Psaty BM. Rapid evaluation of phenotypes, SNPs and results through the dbGaP CHARGE Summary Results site. Nat Genet. 2016;48:702–3.View ArticlePubMedGoogle Scholar