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. 2018 Sep 1;50(9):758-769.
doi: 10.1152/physiolgenomics.00088.2017. Epub 2018 Jun 29.

Identification of nine novel loci related to hematological traits in a Japanese population

Yoshiki Yasukochi  1   2 , Jun Sakuma  2   3   4 , Ichiro Takeuchi  2   4   5 , Kimihiko Kato  1   6 , Mitsutoshi Oguri  1   7 , Tetsuo Fujimaki  8 , Hideki Horibe  9 , Yoshiji Yamada  1   2
Affiliations

Identification of nine novel loci related to hematological traits in a Japanese population

Yoshiki Yasukochi et al. Physiol Genomics. .

Abstract

Recent genome-wide association studies have identified various genetic variants associated with hematological traits. Although it is possible that quantitative data of hematological traits are varied among the years examined, conventional genome-wide association studies have been conducted in a cross-sectional manner that measures traits at a single point in time. To address this issue, we have traced blood profiles in 4,884 Japanese individuals who underwent annual health check-ups for several years. In the present study, longitudinal exome-wide association studies were conducted to identify genetic variants related to 13 hematological phenotypes. The generalized estimating equation model showed that a total of 67 single nucleotide polymorphisms (SNPs) were significantly [false discovery rate (FDR) of <0.01] associated with hematological phenotypes. Of the 67 SNPs, nine SNPs were identified as novel hematological markers: rs4686683 of SENP2 for red blood cell count (FDR = 0.008, P = 5.5 × 10-6); rs3917688 of SELP for mean corpuscular volume (FDR = 0.005, P = 2.4 × 10-6); rs3133745 of C8orf37-AS1 for white blood cell count (FDR = 0.003, P = 1.3 × 10-6); rs13121954 at 4q31.2 for basophil count (FDR = 0.007, P = 3.1 × 10-5); rs7584099 at 2q22.3 (FDR = 2.6 × 10-5, P = 8.8 × 10-8), rs1579219 of HCG17 (FDR = 0.003, P = 2.0 × 10-5), and rs10757049 of DENND4C (FDR = 0.008, P = 5.6 × 10-5) for eosinophil count; rs12338 of CTSB for neutrophil count (FDR = 0.007, P = 2.9 × 10-5); and rs395967 of OSMR-AS1 for monocyte count (FDR = 0.008, P = 3.2 × 10-5).

Keywords: exome-wide association study; generalized estimating equation; hematological trait; linkage disequilibrium; longitudinal data.

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Figures

Fig. 1.
Fig. 1.
Count distributions for longitudinal data of 13 hematological traits examined in the Inabe cohort (4,884 individuals). RBC, red blood cell count; Hb, hemoglobin; Ht, hematocrit; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; WBC, white blood cell count.
Fig. 2.
Fig. 2.
Quantile-quantile plots for P values in the longitudinal exome-wide association studies for 13 hematological traits in the dominant model. The observed P values (y-axis) were compared with the expected P values (x-axis) under the null hypothesis, with the values being plotted as –log10(P). RBC, red blood cell count; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; WBC, white blood cell count; λ represents the genomic inflation factor.
Fig. 3.
Fig. 3.
Quantile-quantile plots for P values in the longitudinal exome-wide association studies for 13 hematological traits in the additive model. The observed P values (y-axis) were compared with the expected P values (x-axis) under the null hypothesis, with the values being plotted as –log10(P). RBC, red blood cell count; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; WBC, white blood cell count. λ represents the genomic inflation factor.
Fig. 4.
Fig. 4.
Quantile-quantile plots for P values in the longitudinal exome-wide association studies for 13 hematological traits in the recessive model. The observed P values (y-axis) were compared with the expected P values (x-axis) under the null hypothesis, with the values being plotted as –log10(P). RBC, red blood cell count; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; WBC, white blood cell count. λ represents the genomic inflation factor.
Fig. 5.
Fig. 5.
Linkage disequilibrium of biallelic sites in a ~1.4 Mb genomic region at 6p21.3–p22.1 based on single nucleotide polymorphism (SNP) data from 4,884 Inabe residents. The SNPs with the minor allele frequency of <0.01 were removed from the analysis. The white blood cell-associated SNPs are shown in boldface.
Fig. 6.
Fig. 6.
Diagram of GeneMANIA network analysis for newly identified (pink circles) and previously reported (green circles) genes related to hematological dynamics. The hematological traits associated with newly identified SNPs are shown in parentheses.
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