European Journal of Human Genetics https://doi.org/10.1038/s41431-018-0296-y ARTICLE Genetic basis for plasma amino acid concentrations based on absolute quantification: a genome-wide association study in the Japanese population 1 1 2 2,3 2 Akira Imaizumi ● Yusuke Adachi ● Takahisa Kawaguchi ● Koichiro Higasa ● Yasuharu Tabara ● 2,4 4 2 1 1 Kazuhiro Sonomura ● Taka-aki Sato ● Meiko Takahashi ● Toshimi Mizukoshi ● Hiro-o Yoshida ● 1 1 1 1 1 5 Naoko Kageyama ● Chisato Okamoto ● Mariko Takasu ● Maiko Mori ● Yasushi Noguchi ● Nobuhisa Shimba ● 1 2 2 Hiroshi Miyano ● Ryo Yamada ● Fumihiko Matsuda Received: 5 March 2018 / Revised: 15 October 2018 / Accepted: 25 October 2018 © The Author(s) 2019. This article is published with open access Abstract To assess the use of plasma free amino acids (PFAAs) as biomarkers for metabolic disorders, it is essential to identify genetic factors that influence PFAA concentrations. PFAA concentrations were absolutely quantified by liquid chromatography–mass spectrometry using plasma samples from 1338 Japanese individuals, and genome-wide quantitative 1234567890();,: 1234567890();,: trait locus (QTL) analysis was performed for the concentrations of 21 PFAAs. We next conducted a conditional QTL analysis using the concentration of each PFAA adjusted by the other 20 PFAAs as covariates to elucidate genetic determinants that influence PFAA concentrations. We identified eight genes that showed a significant association with PFAA concentrations, of which two, SLC7A2 and PKD1L2, were identified. SLC7A2 was associated with the plasma levels of arginine and ornithine, and PKD1L2 with the level of glycine. The significant associations of these two genes were revealed in the conditional QTL analysis, but a significant association between serine and the CPS1 gene disappeared when glycine was used as a covariate. We demonstrated that conditional QTL analysis is useful for determining the metabolic pathways predominantly used for PFAA metabolism. Our findings will help elucidate the physiological roles of genetic components that control the metabolism of amino acids. Introduction Circulating metabolite concentrations in the body can serve Supplementary information The online version of this article (https:// as useful biomarkers for the diagnosis, prognosis, and risk doi.org/10.1038/s41431-018-0296-y) contains supplementary assessment of diseases, particularly for metabolic disorders material, which is available to authorized users. such as diabetes, dyslipidemia, and hypertension [1–8]. * Akira Imaizumi Among these metabolites, the free amino acids in plasma [email protected] (PFAAs) are key regulators of metabolic pathways, and * Fumihiko Matsuda their concentrations are influenced by both genetic and [email protected] environmental factors, such as the diet [9–14]. Recently, several genome-wide association studies 1 Institute for Innovation, Ajinomoto Co., Inc., (GWAS) also identified genetic variations associated with Kawasaki, Kanagawa 210-8681, Japan PFAAs in European populations [9–11, 13, 15]. However, 2 Center for Genomic Medicine, Kyoto University Graduate School the influence of heritability and whether these loci are of Medicine, Kyoto 606-8507, Japan shared among other human populations are still unknown. 3 Department of Genome Analysis, Institute of Biomedical Science, In addition, metabolite concentrations are influenced by Kansai Medical University, Hirakata, Osaka 573-1010, Japan other metabolites within the same metabolic pathway. 4 Life Science Laboratories, Shimadzu Corporation, Seika, Kyoto Therefore, genome-wide quantitative trait locus (QTL) 619-0237, Japan analyses conditioned on the other amino acids sharing the 5 R&D Planning Dept., Ajinomoto Co., Inc, Tokyo 104-8315, Japan same pathway are necessary. A. Imaizumi et al. Fig. 1 Flow diagram of the QC processes and QTL analyses using the PFAA concentrations of Japanese subjects from the Nagahama Study In this study, we sought to elucidate genetic determinants (HPLC–ESI–MS). The methods were previously developed that influence PFAA concentrations. We conducted a QTL and verified by the authors [16–19]. analysis of PFAAs measured by an absolute quantification Quantification was considered successful when the method using plasma samples from 1338 Japanese obtained value was within the determination range of the individuals. calibration curve. PFAA concentrations for QTL analysis Materials and methods For the QTL analysis, we prepared three adjusted PFAA Subjects and ethics concentrations from the measured absolute concentrations of PFAAs. The first adjusted concentration was adjusted for Participants were recruited from the Nagahama sex and age by linear regression after Box-Cox transfor- Prospective Genome Cohort for Comprehensive Human mation. The second was adjusted by the other 20 PFAAs Bioscience (the Nagahama Study). All of the subjects by multiple linear regression after the first adjustment. were approved by the Institutional Review Board and the For this regression analysis, explanatory variables were ethics committees of each institute, to which donors selected by the step-wise function (stepwiseglm) in gave written informed consent, in accordance with the MATLAB, with P = 0.001 and P = 0.01 as inclusion and national guidelines. exclusion criteria, respectively. The third was adjusted by one of the other PFAAs by linear regression after the Absolute quantification of PFAA concentrations first adjustment. The concentrations of 21 PFAAs from 2,084 individuals SNP genotyping and quality control (QC) process who participated in the Nagahama study in 2008 (n = 1124) and 2009 (n = 960) were quantified. Blood samples (5 ml) A total of 1594 samples were genotyped using three com- were collected from forearm veins after overnight mercially available Illumina genotyping arrays (Illumina, fasting into tubes containing ethylenediaminetetraacetic Inc., San Diego, CA): Human610-Quad BeadChip (610 K), acid (EDTA; Termo, Tokyo, Japan). The plasma was HumanOmni-2.5-Quad BeadChip (2.5M-4), and extracted by centrifugation at 2010×g at 4 °C for 15 min and HumanOmni-2.5-8 BeadChip (2.5M-8). The 1,124 subjects then stored at –80 °C. After deproteinizing the thawed recruited in 2008 were genotyped using 610 K (n = 1,113) plasma samples using 80% acetonitrile, the samples were or both 610 K and 2.5M-4 (n = 11). The 470 subjects subjected to pre-column derivatization, then the absolute recruited in 2009 were genotyped using 610 K (n = 101), concentrations, the absolute concentrations of the 2.5M-4 (n = 293), 2.5M-8 (n = 62), or both 610 K and PFAAs were measured by high performance liquid chro- 2.5M-4 (n = 14). In total, 2,638,338 SNPs were genotyped matography - electrospray ionization mass spectrometry in the arrays. As shown in Fig. 1, through a sample QC Genetic basis for plasma amino acid concentrations based on absolute quantification: a genome-wide. process, 256 samples were excluded from the analysis: In silico analysis of genetic variants 3 genetic outliers identified by principal component analysis (PCA), 138 relatives, and 115 samples with a The exome sequencing data of 300 Japanese individuals call rate of SNPs < 0.95. Through a marker QC process, from the Human Genetic Variation Database (HGVD) were 1,200,574 SNPs were excluded: 138,990 SNPs with a used to identify candidates for genetic variants with a call rate < 0.99, 1,187 SNPs with deviation from Hardy– functional impact on PFAA concentrations [22, 23]. Pair- Weinberg equilibrium (P < 1.0 × 10–6), and 1,060,397 wise linkage disequilibrium (LD) coefficients (r2) were SNPs with a variant allele frequency < 1%. After the calculated using PLINK [20]. The impacts of the non- QC processes, 1,437,764 SNPs in 1338 samples remained synonymous variants were predicted using the Ensembl for the GWA studies. Of these, 266,274 SNPs shared Variant Effect Predictor [24], which is based on the SIFT among all of the arrays were defined as intersectional [25] and PolyPhen [26] algorithms. Expression QTL SNPs. All of the QC procedures were processed using (eQTL) analysis data (release version 8.0) were also PLINK ver. 1.07 [20]. Both genotype and PFAA con- downloaded from the HGVD [22]. centrations data of Nagahama study is deposited on the Japanese Genotype-phenotype Archive affiliated to the DDBJ (DNA Data Bank of Japan), via National Bioscience Results DataBase (NBDC), Japan. The data is accessible on hum0012 at https://ddbj.nig.ac.jp/jga/viewer/permit/dataset/ PFAA profiling JGAD00000000012. We measured the PFAA concentrations by an absolute Imputation quantification method using HPLC–ESI–MS [16–19]. The concentrations of 21 PFAAs were quantified successively in The 1,437,764 SNPs from 1,338 samples used for the GWA all of the samples (N = 2,094). The PFAA concentrations in studies were imputed using MACH ver. 1.0 [21]. An the 1,338 samples used for GWAS are summarized with imputation panel was generated using the genotyping data biochemical parameters in Table 1. The means, standard of 665 Nagahama Study samples that were not used for the deviations, and ranges of the absolute concentrations were present GWA analyses and contained the results of comparable to those obtained in an independent study in a 1,560,699 SNPs with Illumina HumanCoreExome Bead- Japanese population, except for arginine, glutamate, and Chip (Exome), HumanOmni2.5 S BeadChip (2.5 S), 2.5M- ornithine [27]. The averaged levels of glutamate and orni- 4, and 2.5M-8 arrays. Of these 665 samples, 478 were thine higher, and that of arginine was lower, than in the genotyped using all of the arrays, and 187 were genotyped previous study. using the Exome, 2.5 S, and 2.5M-4. Imputed SNPs with a variant allele frequency > 1% or an r2 < 0.5 were excluded QTL analysis of PFAAs (GWAS-1) from the subsequent association analysis. Finally, 1,288,202 SNPs from the 1338 samples were fixed with 1,021,918 A flow diagram of the QC processes and QTL analyses is additional SNPs. shown in Fig. 1. The first QTL analysis was conducted for each PFAA concentration, which was adjusted for sex and QTL analysis age after Box-Cox transformation, with 266,274 intersec- tional SNPs of 1338 samples (GWAS-1).