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Genome Biology

, 17:237 | Cite as

Whole genome sequence analysis of serum amino acid levels

  • Bing Yu
  • Paul S. de Vries
  • Ginger A. Metcalf
  • Zhe Wang
  • Elena V. Feofanova
  • Xiaoming Liu
  • Donna Marie Muzny
  • Lynne E. Wagenknecht
  • Richard A. Gibbs
  • Alanna C. Morrison
  • Eric Boerwinkle
Open Access
Research
Part of the following topical collections:
  1. The Biology of Human Diseases, as Revealed Through Genomics

Abstract

Background

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.

Results

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.

Conclusions

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.

Keywords

Amino acids Whole genome sequence Metabolomics Rare variants Multi-ethnic 

Background

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 [1], 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 [2]. Inter-individual differences in several amino acids are risk factors for common disease (e.g., branched-chain and aromatic amino acids for diabetes) [3]. 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, 5, 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.

Results

Baseline characteristics

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.

We used four approaches to examine the association of amino acid levels with genetic variants across the genome: 1) a gene exon approach; 2) an annotated regulatory motif approach; 3) a genome-wide sliding window approach; and 4) a single variant approach. The single variant approach analyzes the variants individually and the other three approaches collapse rare and low-frequency variants into a burden test because insufficient information is available for any one rare variant within a fixed sample size. The gene exon approach leverages the strength of the exome sequence data and the regulatory motif and sliding window approaches highlight the utility of whole genome sequence data. Each of these approaches is separately addressed in the following paragraphs. Overall, a total of 14 genetic loci–amino acid paired associations exceeded our a priori defined threshold for statistical significance in the discovery analysis in AA samples and were replicated in the EA samples. Within the 14 pairs, six loci–amino acid relationships were detected by more than one analytical approach (Fig. 1). Ten out of 14 pairs have been reported by previous GWAS, and the other four pairs are novel. A comparison between the 14 pairs and previous GWAS findings is provided in Additional file 1: Table S2.
Fig. 1

Identified significant genetic associations with serum amino acid levels. Gene names with a single line underneath indicate the association was reported in previous studies for European ancestry; gene names with double lines underneath indicate the association was reported in previous studies for both African and European ancestry. Gene names shown in the single variant test were assigned according to the leading common variant annotations

Gene exon approach

For the gene exon approach, we restricted our analysis to predicted functional variants with MAF ≤5%. A total of 15,589 genes with cumulative minor allele counts (cMAC) ≥7 were analyzed. We identified and replicated seven gene–amino acid pairs (HAO2–alpha-hydroxyisovalerate, AGA–asparagine, DMGDH–dimethylglycine, CCBL1–indolelactate, ACY1–N-acetylalanine and ACY1–N-acetylthreonine, PRODH–proline) with significant discovery p values (P dis ) < 4.6 × 10−8 and a replication p value (P rep ) <0.003 (Table 1). There were 12 to 30 rare and low-frequency variants involved within each of the identified genes. Detailed results for each rare and low-frequency variant involved in these genes are provided in Additional file 1: Table S3. A full list of identified gene–amino acid pairs regardless of successful replication is provided in Additional file 1: Table S4. Annotated functional variants in the six genes of the seven gene–amino acid pairs accounted for 0.6–3.6% of the variance in the amino acid levels, with the average being 1.8%. The six genes all encode enzymes, four of which directly catalyze reactions involving the identified amino acids as substrates or end products. The relationships between AGA and asparagine (P dis  = 1.3 × 10−10, P rep  = 2.7 × 10−5), dimethylglycine and DMGDH (P dis  = 3.2 × 10−31, P rep  = 8.1 × 10−12), N-acetylalanine, N-acetylthreonine and ACY1 (P dis  = 4.1 × 10−41 and 1.1 × 10−10, P rep  = 3.9 × 10−15 and 4.7 × 10−5), proline and PRODH (P dis  = 1.4 × 10−29, P rep  = 1.5 × 10−11) are consistent with known autosomal recessive metabolic disorders. The gene exon results for the meta-analysis of the discovery and replication samples with p < 4.0 × 10−6 are provided in Additional file 1: Table S5.
Table 1

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

Metabolite

Gene

Discovery (AA)

Replication (EA)

P

Beta

cMAC

VarExp

P

Beta

cMAC

VarExp

Dimethylglycine

DMGDH

3.2 × 10−31

0.64

96

3.6%

8.1 × 10−12

0.39

73

1.7%

N-acetylthreonine

ACY1

1.1 × 10−10

0.12

239

0.6%

4.7 × 10−5

0.26

24

0.4%

N-acetylalanine

ACY1

4.1 × 10−41

0.16

239

1.5%

3.9 × 10−15

0.25

24

0.6%

Asparagine

AGA

1.1 × 10−10

0.34

157

1.4%

2.7 × 10−5

0.38

58

0.9%

Indolelactate

CCBL1

2.7 × 10−21

0.39

87

1.6%

1.1 × 10−7

0.26

33

0.5%

Alpha-hydroxyisovalerate

HAO2

1.6 × 10−8

0.64

21

0.8%

8.2 × 10−6

0.41

18

0.5%

Proline

PRODH

1.4 × 10−29

0.14

324

1.4%

1.5 × 10−11

0.09

295

0.7%

cMAC cumulative minor allele count, VarExp variance explained by the loci

Regulatory motif approach

Defining regulatory motifs away from protein-encoding genes is a major activity of modern genome sciences. Projects such as ENCODE [9] and GTEx [10] 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 [11], 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

We next applied a sliding window approach to analyze rare and low-frequency variation (MAF ≤5%) aggregated by 4-kb windows with a 2-kb skip length using burden tests to scan the entire genome. A total of 1,337,499 windows (668,748 non-overlapping windows) with cMAC ≥7 were analyzed. We identified and replicated two genomic regions influencing two amino acid levels (P dis  < 1.1 × 10−9 and P rep  < 0.01; Table 2). One is a 130-kb region at 2p13.2, where two windows in the region were associated with N-acetyl-1-methylhistidine levels (lowest window P dis  = 1.6 × 10−15, P rep  = 3.9 × 10−4). ALMS1 and NAT8, two neighboring genes residing in this 130-kb region, have been previously reported to be related to N-acetyl amino acids levels [4, 6]. The other region is located at 6q23.2 where a single window 46 kb downstream of VNN1 was associated with acisoga. Detailed results for each rare and low-frequency variant involved in the identified windows are provided in Additional file 1: Table S3. A full list of identified significant sliding window–amino acid pairs regardless of successful replication is provided in Additional file 1: Table S8. The sliding window results for the meta-analysis of the discovery and replication samples with p < 4.0 × 10−6 are provided in Additional file 1: Table S9.
Table 2

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

Metabolite

Discovery (AA)

Replication (EA)

Window (gene)

P

Beta

cMAC

VarExp

Window (gene)

P

Beta

cMAC

VarExp

N-acetyl-1-methylhistidine

Chr2: 73744005–73748004 (NAT8)

1.6 × 10−15

0.12

933

1.0%

Chr2: 73744005–73748004 (NAT8)

0.0004

0.17

156

0.5%

N-acetyl-1-methylhistidine

Chr2: 73614005–73618004 (NAT8)

6.2 × 10−11

−0.11

728

0.7%

Chr2: 73614005–73618004 (NAT8)

0.005

−0.07

336

0.2%

Acisoga

Chr6: 132952009–132956008 (VNN1)

9.4 × 10−10

0.06

1504

0.4%

Chr6: 132952009–132956008 (VNN1)

0.009

−0.04

764

0.1%

cMAC cumulative minor allele count, VarExp variance explained by the loci

Single variant approach

In addition to rare and low-frequency variants, we conducted a survey of the genome investigating common SNVs with MAF >5%. Eleven single variant–amino acid associations reached the significance threshold at both the discovery and replication stages (P dis  < 7.1 × 10−10 and P rep  < 0.003; Table 3). These 11 common variants accounted for 0.7–9.7% of the variance of amino acids levels, with an average of 2.3%. The 11 SNVs all resided in protein-encoding gene regions, six of which encode enzymes that catalyze the reaction of the corresponding metabolite as a substrate or product. Among the significant findings, two gene–amino acid associations are novel (3-methoxytyrosine and DDC, and acisoga and VNN1) and there are two loci, DDC and CPS1, in which mutations are known to cause autosomal recessive metabolic disorders. A full list of identified significant single variant–amino acid pairs regardless of successful replication is provided in Additional file 1: Table S10. The single variant results for the meta-analysis of the discovery and replication samples with p < 5.0 × 10−8 are provided in Additional file 1: Table S11.
Table 3

Single variant results demonstrating a significant association among both discovery (p < 7.1 × 10−10) and replication (p < 0.003) stages

Metabolite

Variant information

Discovery (AA)

Replication (EA)

Gene

SNP

Function

Chr:position

REF/ALT

MAF

Beta

P

Var Exp

MAF

Beta

P

Var Exp

Glycine

CPS1

rs1047891

Missense

2:211540507

C/A

0.37

0.09

4.5 × 10−19

1.3%

0.31

0.16

4.9 × 10−45

3.6%

Dimethylglycine

DMGDH

rs933683

Intronic

5:78324003

G/T

0.44

−0.15

2.3 × 10−14

1.9%

0.29

−0.09

9.5 × 10−6

0.7%

Asparagine

AGA

rs11131799

Intronic

4:178363378

G/A

0.49

−0.14

2.4 × 10−10

2.5%

0.36

−0.26

3.9 × 10−23

4.5%

N-acetyl-1-methylhistidine

NAT8

rs13538

Missense

2:73868328

A/G

0.48

0.34

3.3 × 10−75

9.7%

0.23

0.51

3.4 × 10−85

14.2%

Glutarylcarnitine

SYCE2

rs8012

Missense

19:13010520

A/G

0.19

−0.12

9.5 × 10−14

1.2%

0.46

−0.11

2.5 × 10−17

1.5%

N-acetyl phenylalanine

ALMS1P

rs13431529

Intronic

2:73876041

G/C

0.49

0.09

4.3 × 10−10

1.0%

0.23

0.06

1.2 × 10−5

0.4%

3-Methoxytyrosine

DDC

rs11575302

Silent

7:50607694

G/A

0.15

0.15

2.5 × 10−17

1.5%

0.02

0.19

1.4 × 10−7

0.5%

Indolepropionate

ACSM5

rs8044331

Intronic

16:20450302

T/C

0.42

−0.17

5.3 × 10−10

1.8%

0.22

−0.11

0.001

0.5%

Alpha-hydroxyisovalerate

HAO2

rs17023507

UTR5

1:119923247

C/T

0.10

−0.25

1.6 × 10−13

1.9%

0.002

−0.64

0.001

0.4%

Proline

PRODH

rs1814288

Intronic

22:18923383

C/T

0.30

−0.06

7.8 × 10−12

0.7%

0.21

−0.03

0.003

0.1%

Acisoga

VNN1

rs2272996

Missense

6: 133015271

T/C

0.19

0.18

8.1 × 10−16

0.2%

0.27

0.26

4.8 × 10−34

5.1%

REF/ALT reference allele and alternative allele, MAF minor allele frequency, VarExp variance explained by the loci

Conditional analyses

Across all analytic approaches, six of the region–amino acid associations have been reported in previous GWAS: AGA–asparagine, DMGDH–dimethylglycine, HAO2–alpha-hydroxyisovalerate, PRODH–proline, CCBL1–idnolelactate, and two sliding windows close to NAT8 with N-acetyl-1-methylhistidine. We performed conditional analyses in order to examine whether sequencing data were able to identify independent region-based effects at loci highlighted by previous GWAS. Results of the region-based conditional analyses are shown in Table 4. Low-frequency variants in AGA, DMGDH, HAO2, PRODH, and CCBL1 were associated with amino acid levels independent of the known GWAS lead variants. The association of low-frequency variants in the two sliding windows near NAT8, however, was strongly attenuated after adjusting for rs13538, the lead variant identified by previous GWAS. Among these six associations, we examined whether any GWAS findings can be explained by rare and low-frequency variants. In one case, rs248386, the significance of the lead variant identified by previous GWAS of dimethylglycine levels was largely diminished after conditioning on the burden of rare and low-frequency variants in DMGDH (Additional file 1: Table S12). We next performed conditional analyses to determine whether the lead single common variants for nine locus–amino acid associations were independent from the lead variants identified by GWAS. In three of these cases (rs13538–NAT8, rs1047891–CPS1, and rs8012–SYCE2), we identified the same lead variant as previous GWAS. The remaining lead variants we discovered in AA samples (rs11131799–AGA, rs933683–DMGDH, rs1814288–PRODH, rs13431529–ALMS1P, rs8044331–ACSM5, and rs17023507–HAO2) were generally independent of those identified by previous GWAS (Additional file 1: Table S13).
Table 4

Conditional analysis of selected regions adjusting for the lead common variant identified by previous genome-wide association studies

Metabolite

Region

Type

GWAS Lead SNV

Discovery (AA)

Replication (EA)

P unadjusted

P adjusted

P unadjusted

P adjusted

Indolelactate*

CCBL1

Gene

rs15676

1.3 × 10−20

1.1 × 10−20

2.1 × 10−6

4.1 × 10−6

N-acetyl-1-methylhistidine

Chr2: 73744005–73748004 (NAT8)

Window

rs13538

1.6 × 10−15

0.005

4.0 × 10−4

0.2

N-acetyl-1-methylhistidine

Chr2: 73614005–73618004 (NAT8)

Window

rs13538

6.2 × 10−11

0.9

0.005

0.8

Asparagine*

AGA

Gene

rs4690522

6.8 × 10−10

9.1 × 10−10

1.5 × 10−5

6.0 × 10−8

Dimethlyglycine*

DMGDH

Gene

rs248386

1.1 × 10−26

4.3 × 10−27

4.4 × 10−11

4.5 × 10−10

Alpha-hydroxyisovalerate*

HAO2

Gene

rs12141041

1.5 × 10−5

3.0 × 10−5

9.3 × 10−5

2.0 × 10−4

Proline*

PRODH

Gene

rs2540641

1.4 × 10−26

1.7 × 10−26

1.3 × 10−12

1.2 × 10−13

*Unadjusted results may differ from main analysis because only individuals with both exome sequencing and whole genome sequencing were included in the conditional analysis. SNV single nucleotide variant

Discussion

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, 13, 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 [15]. 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 [16]. 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 [17], a neurotoxic compound associated with schizophrenia [18]. 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 [13], 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 [21], 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 [22]. 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 [23]. 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 [24]. 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 [11] and FATHMM-MKL [27]. A previous study identified an association between asparagine and the ASPG locus, encoding asparaginase [13], 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 [28].

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 [6], and the range of variance explaining differences in amino acid levels varied among Caucasians, such as 1–10% [29] or 1–25% [13]. 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.

Conclusions

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.

Methods

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 [30]. 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.

Exome sequencing

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) [33], 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 [34] and dbNSFP v2.0 [35] 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 [36]. 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 [37]. The 3′ and 5′ UTRs of a gene were determined using ANNOVAR [34] annotations based on the RefSeq gene model [38]. The promoter of a gene was defined based on the overlap between the permissive set of CAGE peaks reported by the FANTOM5 project [39] 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.

Statistical analyses

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 [36], 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) [40] was used, adjusting for age, sex, and the first three principal components (PCs). We further adjusted for estimated glomerular filtration rate (eGFR) [41], an indicator of kidney function, since multiple amino acid levels were associated with eGFR [42]. 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) [43]. In addition, we also applied the CADD scores [11] 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 [44].

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 [45] 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.

Notes

Acknowledgements

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.

Funding

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 [46] (dbGaPstudy accession phs000930).

Authors’ contributions

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.

Competing interests

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).

Supplementary material

13059_2016_1106_MOESM1_ESM.xlsx (196 kb)
Additional file 1: Tables S1–S13. (XLSX 195 kb)
13059_2016_1106_MOESM2_ESM.pdf (215 kb)
Additional file 2: Supplemental methods and Figures S1–S2. (PDF 215 kb)

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Copyright information

© The Author(s). 2016

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.

Authors and Affiliations

  • Bing Yu
    • 1
  • Paul S. de Vries
    • 1
  • Ginger A. Metcalf
    • 2
  • Zhe Wang
    • 1
  • Elena V. Feofanova
    • 1
  • Xiaoming Liu
    • 1
  • Donna Marie Muzny
    • 2
  • Lynne E. Wagenknecht
    • 3
  • Richard A. Gibbs
    • 2
  • Alanna C. Morrison
    • 1
  • Eric Boerwinkle
    • 1
    • 2
  1. 1.Human Genetics Center, University of Texas Health Science Center at HoustonHoustonUSA
  2. 2.Human Genome Sequencing Center, Baylor College of MedicineHoustonUSA
  3. 3.Public Health Sciences, Wake Forest School of MedicineWinston-SalemUSA

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