Characterizing rare and low-frequency height-associated variants in the Japanese population

Akiyama, Masato; Ishigaki, Kazuyoshi; Sakaue, Saori; Momozawa, Yukihide; Horikoshi, Momoko; Hirata, Makoto; Matsuda, Koichi; Ikegawa, Shiro; Takahashi, Atsushi; Kanai, Masahiro; Suzuki, Sadao; Matsui, Daisuke; Naito, Mariko; Yamaji, Taiki; Iwasaki, Motoki; Sawada, Norie; Tanno, Kozo; Sasaki, Makoto; Hozawa, Atsushi; Minegishi, Naoko; Wakai, Kenji; Tsugane, Shoichiro; Shimizu, Atsushi; Yamamoto, Masayuki; Okada, Yukinori; Murakami, Yoshinori; Kubo, Michiaki; Kamatani, Yoichiro

doi:10.1038/s41467-019-12276-5

Cited by 162 publications

(167 citation statements)

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“…We analyzed GWAS summary statistics of 30 diseases and complex traits, primarily from UK Biobank, 69 Biobank Japan, 19 and CONVERGE. 17 These include: atrial fibrillation (AF), 70,71 age at menarche(AMN), 72,73 age at menopause (AMP), 72,73 basophil count(BASO), 19,74 body mass index (BMI), 19,75 blood sugar(BS), 19,75 diastolic blood pressure (DBP), 19,75 eosinophil count(EO), 19,75 estimated glomerular filtration rate (EGFR), 19,76 hemoglobin A1c(HBA1C), 19,75 height (HEIGHT), 75,77 high density lipoprotein (HDL), 19,75 hemoglobin (HGB), 19,74 hematocrit (HTC), 19,74 low density lipoprotein (LDL), 19,75 lymphocyte count(LYMPH), 19,75 mean corpuscular hemoglobin (MCH), 19,75 mean corpuscular hemoglobin concentration (MCHC), 19,74 mean corpuscular volume (MCV), 19,74 major depressive disorder (MDD), 17,78 monocyte count (MONO), 19,75 neutrophil count(NEUT), 19,74 platelet count (PLT), 19,75 rheumatoid arthritis(RA), 79 red blood cell count (RBC), 19,75 systolic blood pressure (SBP), 19,75 type 2 diabetes (T2D), 80,81 total cholesterol (TC), 19,75 triglyceride (TG), 19,75 and white blood cell count (WBC). 19,75 Further information for the GWAS summary statistics analyzed is provided in Table S10.…”

Section: Methodsmentioning

confidence: 99%

Population-specific causal disease effect sizes in functionally important regions impacted by selection

Shi

Gazal

Kanai

et al. 2019

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30Many diseases and complex traits exhibit population-specific causal effect sizes 31 with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic 32 polygenic risk prediction. We developed a new method, S-LDXR, for stratifying 33 squared trans-ethnic genetic correlation across genomic annotations, and applied S-34 LDXR to genome-wide association summary statistics for 30 diseases and complex 35 traits in East Asians (EAS) and Europeans (EUR) (average N EAS =93K, N EUR =274K) 36 with an average trans-ethnic genetic correlation of 0.83 (s.e. 0.01). We determined 37 that squared trans-ethnic genetic correlation was 0.81¢ (s.e. 0.01) smaller than the 38 genome-wide average at SNPs in the top quintile of background selection statistic, 39 implying more population-specific causal effect sizes. Accordingly, causal effect sizes 40 were more population-specific in functionally important regions, including coding, con-41 served, and regulatory regions. In analyses of regions surrounding specifically expressed 42 genes, causal effect sizes were most population-specific for skin and immune genes and 43 least population-specific for brain genes. Our results could potentially be explained 44 by stronger gene-environment interaction at loci impacted by selection, particularly 45 positive selection. 46 2 Trans-ethnic genetic correlations are significantly less than 1 for many diseases and 48 complex traits, 1-6 implying that population-specific causal disease effect sizes contribute to 49 the incomplete portability of genome-wide association study (GWAS) findings and poly-50 genic risk scores to non-European populations. 6-12 However, current methods for estimating 51 genome-wide trans-ethnic genetic correlations assume the same trans-ethnic genetic correla-52 tion for all categories of SNPs, 2,5,13 providing little insight into why causal disease effect sizes 53 are population-specific. Understanding the biological processes contributing to population-54 specific causal disease effect sizes can help inform polygenic risk prediction in non-European 55 populations and alleviate health disparities. 6,14,15 56 Here, we introduce a new method, S-LDXR, for stratifying squared trans-ethnic ge-57 netic correlation across functional categories of SNPs using GWAS summary statistics and 58 population-matched linkage disequilibrium (LD) reference panels (e.g. the 1000 Genomes 59Project 16 ); we stratify the squared trans-ethnic genetic correlation across functional cate-60 gories to robustly handle noisy heritability estimates. We confirm that S-LDXR yields ro-61 bust estimates in extensive simulations. We apply S-LDXR to 30 diseases and complex traits 62 with GWAS summary statistics available in both East Asian (EAS) and European (EUR) 63 populations, leveraging recent large studies in East Asian populations from the CONVERGE 64 consortium and Biobank Japan; 17-19 we analyze a broad set of genomic annotations from the 65 baseline-LD model, 20-22 as well as tissue-specific annotations based o...

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Section: Methodsmentioning

confidence: 99%

Population-specific causal disease effect sizes in functionally important regions impacted by selection

Shi

Gazal

Kanai

et al. 2019

Preprint

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“…In this project, we analyzed 179,066 participants of Japanese ancestry as determined by the principal component analysis (PCA)-based sample selection criteria. The genotype data was further imputed with 1000 Genomes Project Phase 3 version 5 genotype ( n = 2,504) and Japanese whole-genome sequencing data ( n = 1,037) 36 using Minimac3 software. After the imputation, we excluded variants with an imputation quality of Rsq < 0.7 or those with a minor allele frequency (MAF) < 1%.…”

Section: Methodsmentioning

confidence: 99%

Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan

Sakaue

Kanai

Karjalainen

et al. 2019

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50Human genetics seeks a way to improve human health on a global scale. Expectations are 51 running high for polygenic risk scores (PRSs) to be translated into clinical practice to predict 52 an inborn susceptibility to health risks. While risk stratification based on PRS is one way to 53 promote population health, a strategy to utilize genetics to prioritize modifiable risk factors 54 and biomarkers driving heath outcome is also warranted. To this end, here we utilized PRSs 55 to comprehensively investigate the association of the genetic susceptibility to complex traits 56 with human lifespan in collaboration with three worldwide biobanks (ntotal = 675,898). First, 57we conducted genome-wide association studies for 45 quantitative clinical phenotypes, 58 constructed the individual PRSs, and associated them with the age at death of 179,066 59 participants in BioBank Japan. The PRSs revealed that the genetic susceptibility of high 60 systolic blood pressure (sBP) was strongly associated with a shorter lifespan (hazard ratio 61[HR] = 1.03, P = 1.4×10 -7 ). Next, we sought to replicate these associations in individuals of 62European ancestry in UK Biobank (n = 361,194) and FinnGen (n = 135,638). Among the 63 investigated traits, the individuals with higher blood pressure-related PRSs were trans-64 ethnically associated with a shorter lifespan (HR = 1.03, Pmeta = 3.9×10 -13 for sBP) and 65 parental lifespan (HR = 1.06, PUKBB = 2.0×10 -86 for sBP). Further, our trans-biobank study 66 identified additional complex traits associated with lifespan (e.g., obesity, height, serum lipids, 67 and platelet counts). Of them, obesity-related traits showed strikingly heterogeneous effects 68 on lifespan between Japanese and European populations (Pheterogeneity = 9.5×10 -8 for body 69 mass index). Through trans-ethnic biobank collaboration, we elucidated the novel value of 70 the PRS study in genetics-driven prioritization of risk factors and biomarkers which can be 71 medically intervened to improve population health. 72 73 5 Main 74 differences among populations, trans-ethnic comparison is also warranted. A collaboration 108 with three trans-ethnic nation-wide biobanks collecting genotype, phenotype and survival 109 data (ntotal = 675,898) has enabled us to uncover the modifiable risk factors and monitorable 110 biomarkers affecting human lifespan across the populations, on an unprecedented scale and 111 without any clinical intervention. 112 113 7 Results 114 Study overview 115

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“…We genotyped samples using i) the Illumina HumanOmniExpressExome BeadChip or ii) a combination of the Illumina HumanOmniExpress and the HumanExome BeadChip. We applied standard quality-control criteria for samples and variants as described elsewhere 15 . The genotypes were prephased using Eagle and imputed using Minimac3 with a reference panel using a combination of the 1000 Genomes Project Phase 3 (version 5) samples (n = 2,504) and whole-genome sequencing data of Japanese individuals (n = 1,037) 15 .…”

Section: Genotyping and Imputationmentioning

confidence: 99%

“…77Purposely non-representative study designs can also be valuable, for example case-control 78 studies seeking to enrich cases with non-genetic risk factors can maximize power to detect 79 genetic effects 12 . 80 81Recent studies have highlighted that genetic factors are associated with aspects of study 82 engagement 13,14,15 . For example, individuals with high genetic risk for schizophrenia enrolled in 83 a study are less likely to complete health questionnaires, attend clinical assessments and 84 continue participation in longitudinal studies than those with lower genetic risk 13,16 .…”

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confidence: 99%

Genetic analyses identify widespread sex-differential participation bias

Pirastu

Cordioli

Nandakumar

et al. 2020

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48Genetic association results are often interpreted with the assumption that study participation 49 does not affect downstream analyses. Understanding the genetic basis of this participation bias 50 is challenging as it requires the genotypes of unseen individuals. However, we demonstrate that 51 it is possible to estimate comparative biases by performing GWAS contrasting one subgroup 52 versus another. For example, we show that sex exhibits autosomal heritability in the presence of 53 sex-differential participation bias. By performing a GWAS of sex in ~3.3 million males and 54 females, we identify over 150 autosomal loci significantly associated with sex and highlight 55 complex traits underpinning differences in study participation between sexes. For example, the 56 body mass index (BMI) increasing allele at the FTO locus was observed at higher frequency in 57 males compared to females (OR 1.02 [1.02-1.03], P=4.4x10 -36 ). Finally, we demonstrate how 58 these biases can potentially lead to incorrect inferences in downstream analyses and propose a 59 conceptual framework for addressing such biases. Our findings highlight a new challenge that 60 genetic studies may face as sample sizes continue to grow. 61 62 63 64 65 66Individuals who enroll in research studies or purchase direct-to-consumer genetic tests are often 67 not representative of the general population 1,2,3 . 68For example, the UK Biobank study invited ~9 million individuals and achieved an overall 69 participation rate of 5.45% 4 . These enrolled individuals clearly demonstrated a "healthy 70 volunteer bias", with lower rates of obesity, smoking and fewer self-reported health conditions 71 than the sampling frame 4 . Achieving accurate representation of the sampling population in any 72 study is challenging. Examples do exist, however, such as the iPSYCH study which enrolled a 73 random sample of the population, based on DNA extracted from a nationwide collection of 74 neonatal dried blood spots 5 . The benefits of achieving such representativeness have long been 75 discussed 6,7,8,9 , with many arguing that unrepresentative samples can bias prevalence estimates 76 but do not necessarily create substantial biases on exposure-disease associations 10,11 . 77Purposely non-representative study designs can also be valuable, for example case-control 78 studies seeking to enrich cases with non-genetic risk factors can maximize power to detect 79 genetic effects 12 . 80 81Recent studies have highlighted that genetic factors are associated with aspects of study 82 engagement 13,14,15 . For example, individuals with high genetic risk for schizophrenia enrolled in 83 a study are less likely to complete health questionnaires, attend clinical assessments and 84 continue participation in longitudinal studies than those with lower genetic risk 13,16 . It remains 85 unclear to what extent genetic factors influence initial study participation, or what the 86 downstream consequences of such bias are, though there are prior attempts to quantify the bias 87 with simul...

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Characterizing rare and low-frequency height-associated variants in the Japanese population

Cited by 162 publications

References 35 publications

Population-specific causal disease effect sizes in functionally important regions impacted by selection

Population-specific causal disease effect sizes in functionally important regions impacted by selection

Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan

Genetic analyses identify widespread sex-differential participation bias

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