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Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.
BACKGROUND - Observational studies examining associations between adult height and risk of colorectal, prostate, and lung cancers have generated mixed results. We conducted meta-analyses using data from prospective cohort studies and further carried out Mendelian randomization analyses, using height-associated genetic variants identified in a genome-wide association study (GWAS), to evaluate the association of adult height with these cancers.
METHODS AND FINDINGS - A systematic review of prospective studies was conducted using the PubMed, Embase, and Web of Science databases. Using meta-analyses, results obtained from 62 studies were summarized for the association of a 10-cm increase in height with cancer risk. Mendelian randomization analyses were conducted using summary statistics obtained for 423 genetic variants identified from a recent GWAS of adult height and from a cancer genetics consortium study of multiple cancers that included 47,800 cases and 81,353 controls. For a 10-cm increase in height, the summary relative risks derived from the meta-analyses of prospective studies were 1.12 (95% CI 1.10, 1.15), 1.07 (95% CI 1.05, 1.10), and 1.06 (95% CI 1.02, 1.11) for colorectal, prostate, and lung cancers, respectively. Mendelian randomization analyses showed increased risks of colorectal (odds ratio [OR] = 1.58, 95% CI 1.14, 2.18) and lung cancer (OR = 1.10, 95% CI 1.00, 1.22) associated with each 10-cm increase in genetically predicted height. No association was observed for prostate cancer (OR = 1.03, 95% CI 0.92, 1.15). Our meta-analysis was limited to published studies. The sample size for the Mendelian randomization analysis of colorectal cancer was relatively small, thus affecting the precision of the point estimate.
CONCLUSIONS - Our study provides evidence for a potential causal association of adult height with the risk of colorectal and lung cancers and suggests that certain genetic factors and biological pathways affecting adult height may also affect the risk of these cancers.
Greater genetic variability in an individual is protective against recessive disease. However, existing quantifications of autozygosity, such as runs of homozygosity (ROH), have proved highly sensitive to genotyping density and have yielded inconclusive results about the relationship of diversity and disease risk. Using genotyping data from three data sets with >43,000 subjects, we demonstrated that an alternative approach to quantifying genetic variability, the heterozygosity ratio, is a robust measure of diversity and is positively associated with the nondisease trait height and several disease phenotypes in subjects of European ancestry. The heterozygosity ratio is the number of heterozygous sites in an individual divided by the number of nonreference homozygous sites and is strongly affected by the degree of genetic admixture of the population and varies across human populations. Unlike quantifications of ROH, the heterozygosity ratio is not sensitive to the density of genotyping performed. Our results establish the heterozygosity ratio as a powerful new statistic for exploring the patterns and phenotypic effects of different levels of genetic variation in populations.
Copyright © 2016 by the Genetics Society of America.
Although the role of complete gene inactivation by two loss-of-function mutations inherited in trans is well-established in recessive Mendelian diseases, we have not yet explored how such gene knockouts (KOs) could influence complex human phenotypes. Here, we developed a statistical framework to test the association between gene KOs and quantitative human traits. Our method is flexible, publicly available, and compatible with common genotype format files (e.g. PLINK and vcf). We characterized gene KOs in 4498 participants from the NHLBI Exome Sequence Project (ESP) sequenced at high coverage (>100×), 1976 French Canadians from the Montreal Heart Institute Biobank sequenced at low coverage (5.7×), and >100 000 participants from the Genetic Investigation of ANthropometric Traits (GIANT) Consortium genotyped on an exome array. We tested associations between gene KOs and three anthropometric traits: body mass index (BMI), height and BMI-adjusted waist-to-hip ratio (WHR). Despite our large sample size and multiple datasets available, we could not detect robust associations between specific gene KOs and quantitative anthropometric traits. Our results highlight several limitations and challenges for future gene KO studies in humans, in particular when there is no prior knowledge on the phenotypes that might be affected by the tested gene KOs. They also suggest that gene KOs identified with current DNA sequencing methodologies probably do not strongly influence normal variation in BMI, height, and WHR in the general human population.
© The Author 2016. Published by Oxford University Press.
BACKGROUND - Epidemiological studies have linked adult height with breast cancer risk in women. However, the magnitude of the association, particularly by subtypes of breast cancer, has not been established. Furthermore, the mechanisms of the association remain unclear.
METHODS - We performed a meta-analysis to investigate associations between height and breast cancer risk using data from 159 prospective cohorts totaling 5216302 women, including 113178 events. In a consortium with individual-level data from 46325 case patients and 42482 control patients, we conducted a Mendelian randomization analysis using a genetic score that comprised 168 height-associated variants as an instrument. This association was further evaluated in a second consortium using summary statistics data from 16003 case patients and 41335 control patients.
RESULTS - The pooled relative risk of breast cancer was 1.17 (95% confidence interval [CI] = 1.15 to 1.19) per 10cm increase in height in the meta-analysis of prospective studies. In Mendelian randomization analysis, the odds ratio of breast cancer per 10cm increase in genetically predicted height was 1.22 (95% CI = 1.13 to 1.32) in the first consortium and 1.21 (95% CI = 1.05 to 1.39) in the second consortium. The association was found in both premenopausal and postmenopausal women but restricted to hormone receptor-positive breast cancer. Analyses of height-associated variants identified eight new loci associated with breast cancer risk after adjusting for multiple comparisons, including three loci at 1q21.2, DNAJC27, and CCDC91 at genome-wide significance level P < 5×10(-8).
CONCLUSIONS - Our study provides strong evidence that adult height is a risk factor for breast cancer in women and certain genetic factors and biological pathways affecting adult height have an important role in the etiology of breast cancer.
© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: email@example.com.
Height has an extremely polygenic pattern of inheritance. Genome-wide association studies (GWAS) have revealed hundreds of common variants that are associated with human height at genome-wide levels of significance. However, only a small fraction of phenotypic variation can be explained by the aggregate of these common variants. In a large study of African-American men and women (n = 14,419), we genotyped and analyzed 966,578 autosomal SNPs across the entire genome using a linear mixed model variance components approach implemented in the program GCTA (Yang et al Nat Genet 2010), and estimated an additive heritability of 44.7% (se: 3.7%) for this phenotype in a sample of evidently unrelated individuals. While this estimated value is similar to that given by Yang et al in their analyses, we remain concerned about two related issues: (1) whether in the complete absence of hidden relatedness, variance components methods have adequate power to estimate heritability when a very large number of SNPs are used in the analysis; and (2) whether estimation of heritability may be biased, in real studies, by low levels of residual hidden relatedness. We addressed the first question in a semi-analytic fashion by directly simulating the distribution of the score statistic for a test of zero heritability with and without low levels of relatedness. The second question was addressed by a very careful comparison of the behavior of estimated heritability for both observed (self-reported) height and simulated phenotypes compared to imputation R2 as a function of the number of SNPs used in the analysis. These simulations help to address the important question about whether today's GWAS SNPs will remain useful for imputing causal variants that are discovered using very large sample sizes in future studies of height, or whether the causal variants themselves will need to be genotyped de novo in order to build a prediction model that ultimately captures a large fraction of the variability of height, and by implication other complex phenotypes. Our overall conclusions are that when study sizes are quite large (5,000 or so) the additive heritability estimate for height is not apparently biased upwards using the linear mixed model; however there is evidence in our simulation that a very large number of causal variants (many thousands) each with very small effect on phenotypic variance will need to be discovered to fill the gap between the heritability explained by known versus unknown causal variants. We conclude that today's GWAS data will remain useful in the future for causal variant prediction, but that finding the causal variants that need to be predicted may be extremely laborious.
BACKGROUND - Associations between anthropometry and head and neck cancer (HNC) risk are inconsistent. We aimed to evaluate these associations while minimizing biases found in previous studies.
METHODS - We pooled data from 1,941,300 participants, including 3760 cases, in 20 cohort studies and used multivariable-adjusted Cox proportional hazard regression models to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association of anthropometric measures with HNC risk overall and stratified by smoking status.
RESULTS - Greater waist circumference (per 5 cm: HR = 1.04, 95% CI 1.03-1.05, P-value for trend = <0.0001) and waist-to-hip ratio (per 0.1 unit: HR = 1.07, 95% CI 1.05-1.09, P-value for trend = <0.0001), adjusted for body mass index (BMI), were associated with higher risk and did not vary by smoking status (P-value for heterogeneity = 0.85 and 0.44, respectively). Associations with BMI (P-value for interaction = <0.0001) varied by smoking status. Larger BMI was associated with higher HNC risk in never smokers (per 5 kg/m(2): HR = 1.15, 95% CI 1.06-1.24, P-value for trend = 0.0006), but not in former smokers (per 5 kg/m(2): HR = 0.99, 95% CI 0.93-1.06, P-value for trend = 0.79) or current smokers (per 5 kg/m(2): HR = 0.76, 95% CI 0.71-0.82, P-value for trend = <0.0001). Larger hip circumference was not associated with a higher HNC risk. Greater height (per 5 cm) was associated with higher risk of HNC in never and former smokers, but not in current smokers.
CONCLUSIONS - Waist circumference and waist-to-hip ratio were associated positively with HNC risk regardless of smoking status, whereas a positive association with BMI was only found in never smokers.
© The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.
Human height is associated with risk of multiple diseases and is profoundly determined by an individual's genetic makeup and shows a high degree of ethnic heterogeneity. Large-scale genome-wide association (GWA) analyses of adult height in Europeans have identified nearly 180 genetic loci. A recent study showed high replicability of results from Europeans-based GWA studies in Asians; however, population-specific loci may exist due to distinct linkage disequilibrium patterns. We carried out a GWA meta-analysis in 93 926 individuals from East Asia. We identified 98 loci, including 17 novel and 81 previously reported loci, associated with height at P < 5 × 10(-8), together explaining 8.89% of phenotypic variance. Among the newly identified variants, 10 are commonly distributed (minor allele frequency, MAF > 5%) in Europeans, with comparable frequencies with in Asians, and 7 single-nucleotide polymorphisms are with low frequency (MAF < 5%) in Europeans. In addition, our data suggest that novel biological pathway such as the protein tyrosine phosphatase family is involved in regulation of height. The findings from this study considerably expand our knowledge of the genetic architecture of human height in Asians.
© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: firstname.lastname@example.org.
Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.
Individuals with sickle cell anemia (SCA) exhibit delayed growth trajectories and lower blood pressure (BP) measurements than individuals without SCA. We evaluated factors associated with height, weight, and BP and established reference growth curves and BP tables using data from the Silent Cerebral Infarct Multi-Center Clinical (SIT) Trial (NCT00072761). Quantile regression models were used to determine the percentiles of growth and BP measurements. Multivariable quantile regression was used to test associations of baseline variables with height, weight, and BP measurements. Height and weight measurements were collected from a total of 949 participants with median age of 10.5 years [Interquartile range (IQR) 8.2-12.9] and median follow-up time of 3.2 years (IQR 1.8-4.7, range 0-12.9). Serial BP measurements were collected from a total of 944 and 943 participants, respectively, with median age of 10.6 years (IQR = 8.3-12.9 years), and median follow-up time of 3.3 years (IQR = 1.7-4.8). Multivariable quantile regression analysis revealed that higher hemoglobin measurements at baseline were associated with greater height (P < 0.001), weight (P = 0.000), systolic BP (P < 0.001), and diastolic BP (P = 0.003) measurements. We now provide new reference values for height, weight, and BP measurements that are now readily available for medical management.
© 2014 Wiley Periodicals, Inc.