Hundreds of variants clustered in genomic loci and biological pathways affect human height

Functional Validation of Candidate Genes Detected by Genomic Feature Models

Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) categories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach.

New Genetic Diagnoses of Short Stature Provide Insights into Local Regulation of Childhood Growth

Idiopathic short stature is a common condition with a heterogeneous etiology. Advances in genetic methods, including genome sequencing techniques and bioinformatics approaches, have emerged as important tools to identify the genetic defects in families with monogenic short stature. These findings have contributed to the understanding of growth regulation and indicate that growth plate chondrogenesis, and therefore linear growth, is governed by a large number of genes important for different signaling pathways and cellular functions, including genetic defects in hormonal regulation, paracrine signaling, cartilage matrix, and fundamental cellular processes. In addition, mutations in the same gene can cause a wide phenotypic spectrum depending on the severity and mode of inheritance of the mutation.
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Relationships of bone characteristics in MYO9B deficient femurs

The objective of this study was to examine relationships among a variety of bone characteristics, including volumetric, mineral density, geometric, dynamic mechanical analysis, and static fracture mechanical properties. As MYO9B is an unconventional myosin in bone cells responsible for normal skeletal growth, bone characteristics of wild-type (WT), heterozygous (HET), and MYO9B knockout (KO) mice groups were compared as an animal model to express different bone quantity and quality. Forty-five sex-matched 12-week-old mice were used in this study. After euthanization, femurs were isolated and scanned using microcomputed tomography (micro-CT) to assess bone volumetric, tissue mineral density (TMD), and geometric parameters. Then, a non-destructive dynamic mechanical analysis (DMA) was performed by applying oscillatory bending displacement on the femur. Finally, the same femur was subject to static fracture testing. KO group had significantly lower length, bone mineral density (BMD), bone mass and volume, dynamic and static stiffness, and strength than WT and HET groups (p < 0.019). On the other hand, TMD parameters of KO group were comparable with those of WT group. HET group showed volumetric, geometric, and mechanical properties similar to WT group, but had lower TMD (p < 0.014). Non-destructive micro-CT and DMA parameters had significant positive correlations with strength (p < 0.015) without combined effect of groups and sex on the correlations (p > 0.077). This comprehensive characterization provides a better understanding of interactive behavior between the tissue- and organ-level of the same femur. The current findings elucidate that MYO9B is responsible for controlling bone volume to determine the growth rate and fracture risk of bone.

FUN-LDA: A Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: Methods and Applications

We describe a method based on a latent Dirichlet allocation model for predicting functional effects of noncoding genetic variants in a cell-type- and/or tissue-specific way (FUN-LDA). Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome in 127 different tissues and cell types. We demonstrate the usefulness of our predictions by using several validation experiments. Using eQTL data from several sources, including the GTEx project, Geuvadis project, and TwinsUK cohort, we show that eQTLs in specific tissues tend to be most enriched among the predicted functional variants in relevant tissues in Roadmap. We further show how these integrated functional scores can be used for (1) deriving the most likely cell or tissue type causally implicated for a complex trait by using summary statistics from genome-wide association studies and (2) estimating a tissue-based correlation matrix of various complex traits. We found large enrichment of heritability in functional components of relevant tissues for various complex traits, and FUN-LDA yielded higher enrichment estimates than existing methods. Finally, using experimentally validated functional variants from the literature and variants possibly implicated in disease by previous studies, we rigorously compare FUN-LDA with state-of-the-art functional annotation methods and show that FUN-LDA has better prediction accuracy and higher resolution than these methods. In particular, our results suggest that tissue- and cell-type-specific functional prediction methods tend to have substantially better prediction accuracy than organism-level prediction methods. Scores for each position in the human genome and for each ENCODE and Roadmap tissue are available online (see Web Resources).

Genetic Variants Associated With Obesity and Insulin Resistance in Hispanic Boys With Nonalcoholic Fatty Liver Disease

BACKGROUND AND OBJECTIVES

Nonalcoholic fatty liver disease (NAFLD) disproportionately affects Hispanic boys. Further, obesity and insulin resistance are major risk factors for NAFLD. No gene localization studies had been performed on children with biopsy-proven NAFLD. This study aims to identify genomic variants associated with increased adiposity and insulin resistance in a population of children with varying histologic severity of NAFLD.

METHODS

We conducted a genome-wide association scan (GWAS) including 624,297 single-nucleotide polymorphisms (SNPs) distributed among all 22 autosomal chromosomes in 234 Hispanic boys (up to 18 years of age) who were consecutively recruited in a prospective cohort study in the Nonalcoholic Steatohepatitis Clinical Research Network Studies. Traits were examined quantitatively using linear regression. SNPs with P value <10 and a minor allele frequency >5% were considered potentially significant.

RESULTS

Evaluated subjects had a median age of 12.0 years, body mass index (BMI) of 31.4, and hemoglobin A1C (Hgb A1C) of 5.3. The prevalence of NAFL, borderline NASH, and definite NASH were 23%, 53%, and 22%, respectively. The GWAS identified 10 SNPs that were associated with BMI z score, 6 within chromosome 2, and 1 within CAMK1D, which has a potential role in liver gluconeogenesis. In addition, the GWAS identified 9 novel variants associated with insulin resistance: HOMA-IR (6) and HbA1c (3).

CONCLUSIONS

This study of Hispanic boys with biopsy-proven NAFLD with increased risk for the metabolic syndrome revealed novel genetic variants that are associated with obesity and insulin resistance.

Evaluation and application of summary statistic imputation to discover new height-associated loci

As most of the heritability of complex traits is attributed to common and low frequency genetic variants, imputing them by combining genotyping chips and large sequenced reference panels is the most cost-effective approach to discover the genetic basis of these traits. Association summary statistics from genome-wide meta-analyses are available for hundreds of traits. Updating these to ever-increasing reference panels is very cumbersome as it requires reimputation of the genetic data, rerunning the association scan, and meta-analysing the results. A much more efficient method is to directly impute the summary statistics, termed as summary statistics imputation, which we improved to accommodate variable sample size across SNVs. Its performance relative to genotype imputation and practical utility has not yet been fully investigated. To this end, we compared the two approaches on real (genotyped and imputed) data from 120K samples from the UK Biobank and show that, genotype imputation boasts a 3- to 5-fold lower root-mean-square error, and better distinguishes true associations from null ones: We observed the largest differences in power for variants with low minor allele frequency and low imputation quality. For fixed false positive rates of 0.001, 0.01, 0.05, using summary statistics imputation yielded a decrease in statistical power by 9, 43 and 35%, respectively. To test its capacity to discover novel associations, we applied summary statistics imputation to the GIANT height meta-analysis summary statistics covering HapMap variants, and identified 34 novel loci, 19 of which replicated using data in the UK Biobank. Additionally, we successfully replicated 55 out of the 111 variants published in an exome chip study. Our study demonstrates that summary statistics imputation is a very efficient and cost-effective way to identify and fine-map trait-associated loci. Moreover, the ability to impute summary statistics is important for follow-up analyses, such as Mendelian randomisation or LD-score regression.

Association between Growth Hormone-Insulin-Like Growth Factor-1 Axis Gene Polymorphisms and Short Stature in Chinese Children

Objective

This study was designed to analyze the association between the growth hormone-insulin-like growth factor-1 (GH-IGF-1) axis gene polymorphisms and short stature in Chinese children.

Methods

181 growth hormone deficiency (GHD) patients and 206 normal stature controls were enrolled to attend this study. Five single-nucleotide polymorphisms in the GH receptor (GHR) and 5 SNPs within the GH-signaling pathway were genotyped by matrix-assisted laser desorption/ionization time of flight mass spectrometry. We conducted an association study between these SNPs and the risk of developing short stature. Linkage disequilibrium analysis was performed using Haploview software and the associations of the SNPs frequencies with short stature were analyzed using X² tests.

Results

No significant difference was found in gender, weight, height, and BMI between the GHD and control groups, except that the age of GHD group was older than the control one. Allele and genotype frequencies were consistent with those expected from Hardy-Weinberg equilibrium. Compared with the controls, heterozygous genotype frequencies (CT) of rs12515480 and rs6873545 of GHR gene were significantly lower. Genotype frequencies of the other 8 SNPs did not show significant difference between these two groups. Considering a dominant model, an OR < 1 was observed for genotypes rs12515480 (OR = 0.532, P = 0.019) and rs6873545 (OR = 0.587, P = 0.017).

Conclusions

The heterozygous genotypes of rs12515480 and rs6873545 of GHR gene were associated with decreased risk of GHD in Chinese children.

Genome-wide meta-analysis identifies novel loci associated with parathyroid hormone level

Background

Parathyroid hormone (PTH) is one of the principal regulators of calcium homeostasis. Although serum PTH level is mostly accounted by genetic factors, genetic background underlying PTH level is insufficiently known. Therefore, the aim of this study was to identify novel genetic variants associated with PTH levels.

Methods

We performed GWAS meta-analysis within two genetically isolated Croatian populations followed by replication analysis in a Croatian mainland population and we also combined results across all three analyzed populations. The analyses included 2596 individuals. A total of 7,411,206 variants, imputed using the 1000 Genomes reference panel, were analysed for the association. In addition, a sex-specific GWAS meta-analyses were performed.

Results

Polymorphisms with the lowest P-values were located on chromosome 4 approximately 84 kb of the 5′ of RASGEF1B gene. The most significant SNP was rs11099476 (P = 1.15 × 10⁻⁸). Sex-specific analysis identified genome-wide significant association of the variant rs77178854, located within DPP10 gene in females only (P = 2.21 × 10^− 9). There were no genome-wide significant findings in the meta-analysis of males.

Conclusions

We identified two biologically plausible novel loci associated with PTH levels, providing us with further insights into the genetics of this complex trait.

Electronic supplementary material

The online version of this article (10.1186/s10020-018-0018-5) contains supplementary material, which is available to authorized users.

Using Full Genomic Information to Predict Disease: Breaking Down the Barriers Between Complex and Mendelian Diseases

While sequence-based genetic tests have long been available for specific loci, especially for Mendelian disease, the rapidly falling costs of genome-wide genotyping arrays, whole-exome sequencing, and whole-genome sequencing are moving us toward a future where full genomic information might inform the prognosis and treatment of a variety of diseases, including complex disease. Similarly, the availability of large populations with full genomic information has enabled new insights about the etiology and genetic architecture of complex disease. Insights from the latest generation of genomic studies suggest that our categorization of diseases as complex may conceal a wide spectrum of genetic architectures and causal mechanisms that ranges from Mendelian forms of complex disease to complex regulatory structures underlying Mendelian disease. Here, we review these insights, along with advances in the prediction of disease risk and outcomes from full genomic information.

A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests

Motivation

Increasing amounts of whole exome or genome sequencing data present the challenge of analysing rare variants with extremely small minor allele frequencies. Various statistical tests have been proposed, which are specifically configured to increase power for rare variants by conducting the test within a certain bin, such as a gene or a pathway. However, a gene may contain from several to thousands of markers, and not all of them are related to the phenotype. Combining functional and non-functional variants in an arbitrary genomic region could impair the testing power.

Results

We propose a Zoom-Focus algorithm (ZFA) to locate the optimal testing region within a given genomic region. It can be applied as a wrapper function in existing rare variant association tests to increase testing power. The algorithm consists of two steps. In the first step, Zooming, a given genomic region is partitioned by an order of two, and the best partition is located. In the second step, Focusing, the boundaries of the zoomed region are refined. Simulation studies showed that ZFA substantially increased the statistical power of rare variants' tests, including the SKAT, SKAT-O, burden test and the W-test. The algorithm was applied on real exome sequencing data of hypertensive disorder, and identified biologically relevant genetic markers to metabolic disorders that were undetectable by a gene-based method. The proposed algorithm is an efficient and powerful tool to enhance the power of association study for whole exome or genome sequencing data.

Availability and Implementation

The ZFA software is available at: http://www2.ccrb.cuhk.edu.hk/statgene/software.html.

Contact

maggiew@cuhk.edu.hk or bzee@cuhk.edu.hk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Haplotype Heritability Mapping Method Uncovers Missing Heritability of Complex Traits

We propose a novel approach to analyze genomic data that incorporates haplotype information for detecting rare variants within a regional heritability mapping framework. The performance of our approach was tested in a simulation study based on human genotypes. The phenotypes were simulated by generating regional variance using either SNP(s) or haplotype(s). Regional genomic relationship matrices, constructed with either a SNP-based or a haplotype-based estimator, were employed to estimate the regional variance. The results from the study show that haplotype heritability mapping captures the regional effect, with its relative performance decreasing with increasing analysis window size. The SNP-based regional mapping approach often misses the effect of causal haplotype(s); however, it has a greater power to detect simulated SNP-based-variants. Heritability estimates suggest that the haplotype heritability mapping estimates the simulated regional heritability accurately for all phenotypes and analysis windows. However, the SNP-based analysis overestimates the regional heritability and performs less well than our haplotype-based approach for the simulated rare haplotype-based-variant. We conclude that haplotype heritability mapping is a useful tool to capture the effect of rare variants, and explain a proportion of the missing heritability.

Genetic Contributions to The Association Between Adult Height and Head and Neck Cancer: A Mendelian Randomization Analysis

With the aim to dissect the effect of adult height on head and neck cancer (HNC), we use the Mendelian randomization (MR) approach to test the association between genetic instruments for height and the risk of HNC. 599 single nucleotide polymorphisms (SNPs) were identified as genetic instruments for height, accounting for 16% of the phenotypic variation. Genetic data concerning HNC cases and controls were obtained from a genome-wide association study. Summary statistics for genetic association were used in complementary MR approaches: the weighted genetic risk score (GRS) and the inverse-variance weighted (IVW). MR-Egger regression was used for sensitivity analysis and pleiotropy evaluation. From the GRS analysis, one standard deviation (SD) higher height (6.9 cm; due to genetic predisposition across 599 SNPs) raised the risk for HNC (Odds ratio (OR), 1.14; 95% Confidence Interval (95%CI), 0.99-1.32). The association analyses with potential confounders revealed that the GRS was associated with tobacco smoking (OR = 0.80, 95% CI (0.69-0.93)). MR-Egger regression did not provide evidence of overall directional pleiotropy. Our study indicates that height is potentially associated with HNC risk. However, the reported risk could be underestimated since, at the genetic level, height emerged to be inversely associated with smoking.

Multiple genetic variations confer risks for obesity and type 2 diabetes mellitus in arab descendants from UAE

BACKGROUND

The United Arab Emirates (UAE) is one of the countries most threatened with obesity. Here we investigated associations between hundreds of single-nucleotide polymorphisms (SNPs) and the following obesity indicators: body mass index (BMI), waist circumference (WC), and height. We also investigated the associations between obesity-related genes with type 2 diabetes mellitus (T2DM).

METHODS

We tested 87, 58, and 586 SNPs in a previous genome-wide significance level for associations with BMI (n = 880), WC (n = 455), and height (n = 897), respectively. For each trait, we used normally transformed Z scores and tested them with SNPs using linear regression models that incorporated age and gender as covariates. The weighted polygenic risk scores for significant SNPs for each trait were tested with the corresponding Z scores using linear regression models with the same covariates. We further tested 145 obesity loci with T2DM (464 cases, 415 controls) using a logistic regression model including age, gender, and BMI Z scores as covariates.

RESULTS

The Mean BMI was 29.39 kg/m², and mean WC was 103.66 cm. Hypertension and dyslipidemia were common obesity comorbidities (>60%). The best associations for BMI was in FTO, LOC284260 and USP37, and for WC in RFX7 and MYEOV. For height, the best association was in NSD1 followed by MFAP2 and seven other loci. The polygenic scores revealed stronger associations for each trait than individual SNPs; although they could only explain <1% of the traits' Z scores variations. For T2DM, the strongest associations were with the TCF7L2 and MC4R loci (P < 0.01, OR ~1.70), with novel associations detected with KCNK3 and RARB.

CONCLUSIONS

In this first study of Arab descendants, we confirmed several known obesity (FTO, USP37, and RFX7), height (NSD1, MFAP2), and T2DM (TCF7L2, MC4R) associations; and report novel associations, like KCNK3 and RARB for T2DM.

Microfibril-associated glycoproteins MAGP-1 and MAGP-2 in disease

Microfibril-associated glycoproteins 1 and 2 (MAGP-1, MAGP-2) are protein components of extracellular matrix microfibrils. These proteins interact with fibrillin, the core component of microfibrils, and impart unique biological properties that influence microfibril function in vertebrates. MAGPs bind active forms of TGFβ and BMPs and are capable of modulating Notch signaling. Mutations in MAGP-1 or MAGP-2 have been linked to thoracic aneurysms and metabolic disease in humans. MAGP-2 has also been shown to be an important biomarker in several human cancers. Mice lacking MAGP-1 or MAGP-2 have defects in multiple organ systems, which reflects the widespread distribution of microfibrils in vertebrate tissues. This review summarizes our current understanding of the function of the MAGPs and their relationship to human disease.

Genetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation

BACKGROUND

The long-term probability of developing atrial fibrillation (AF) considering genetic predisposition and clinical risk factor burden is unknown.

METHODS

We estimated the lifetime risk of AF in individuals from the community-based Framingham Heart Study. Polygenic risk for AF was derived using a score of ≈1000 AF-associated single-nucleotide polymorphisms. Clinical risk factor burden was calculated for each individual using a validated risk score for incident AF comprised of height, weight, systolic and diastolic blood pressure, current smoking status, antihypertensive medication use, diabetes mellitus, history of myocardial infarction, and history of heart failure. We estimated the lifetime risk of AF within tertiles of polygenic and clinical risk.

RESULTS

Among 4606 participants without AF at 55 years of age, 580 developed incident AF (median follow-up, 9.4 years; 25th-75th percentile, 4.4-14.3 years). The lifetime risk of AF >55 years of age was 37.1% and was substantially influenced by both polygenic and clinical risk factor burden. Among individuals free of AF at 55 years of age, those in low-polygenic and clinical risk tertiles had a lifetime risk of AF of 22.3% (95% confidence interval, 15.4-9.1), whereas those in high-risk tertiles had a risk of 48.2% (95% confidence interval, 41.3-55.1). A lower clinical risk factor burden was associated with later AF onset after adjusting for genetic predisposition (P<0.001).

CONCLUSIONS

In our community-based cohort, the lifetime risk of AF was 37%. Estimation of polygenic AF risk is feasible and together with clinical risk factor burden explains a substantial gradient in long-term AF risk.

Comparative genomic evidence for the involvement of schizophrenia risk genes in antipsychotic effects

Genome-wide association studies (GWAS) for schizophrenia have identified over 100 loci encoding >500 genes. It is unclear whether any of these genes, other than dopamine receptor D₂, are immediately relevant to antipsychotic effects or represent novel antipsychotic targets. We applied an in vivo molecular approach to this question by performing RNA sequencing of brain tissue from mice chronically treated with the antipsychotic haloperidol or vehicle. We observed significant enrichments of haloperidol-regulated genes in schizophrenia GWAS loci and in schizophrenia-associated biological pathways. Our findings provide empirical support for overlap between genetic variation underlying the pathophysiology of schizophrenia and the molecular effects of a prototypical antipsychotic.

Largest GWAS of PTSD (N=20 070) yields genetic overlap with schizophrenia and sex differences in heritability

The Psychiatric Genomics Consortium-Posttraumatic Stress Disorder group (PGC-PTSD) combined genome-wide case-control molecular genetic data across 11 multiethnic studies to quantify PTSD heritability, to examine potential shared genetic risk with schizophrenia, bipolar disorder, and major depressive disorder and to identify risk loci for PTSD. Examining 20 730 individuals, we report a molecular genetics-based heritability estimate (h2SNP) for European-American females of 29% that is similar to h2SNP for schizophrenia and is substantially higher than h2SNP in European-American males (estimate not distinguishable from zero). We found strong evidence of overlapping genetic risk between PTSD and schizophrenia along with more modest evidence of overlap with bipolar and major depressive disorder. No single-nucleotide polymorphisms (SNPs) exceeded genome-wide significance in the transethnic (overall) meta-analysis and we do not replicate previously reported associations. Still, SNP-level summary statistics made available here afford the best-available molecular genetic index of PTSD-for both European- and African-American individuals-and can be used in polygenic risk prediction and genetic correlation studies of diverse phenotypes. Publication of summary statistics for ∼10 000 African Americans contributes to the broader goal of increased ancestral diversity in genomic data resources. In sum, the results demonstrate genetic influences on the development of PTSD, identify shared genetic risk between PTSD and other psychiatric disorders and highlight the importance of multiethnic/racial samples. As has been the case with schizophrenia and other complex genetic disorders, larger sample sizes are needed to identify specific risk loci.

Anthropometric factors and cutaneous melanoma: Prospective data from the population-based Janus Cohort

The aim of the present study was to prospectively examine risk of cutaneous melanoma (CM) according to measured anthropometric factors, adjusted for exposure to ultraviolet radiation (UVR), in a large population-based cohort in Norway. The Janus Cohort, including 292,851 Norwegians recruited 1972-2003, was linked to the Cancer Registry of Norway and followed for CM through 2014. Cox regression was used to estimate hazard ratios (HRs) of CM with 95% confidence intervals (CIs). Restricted cubic splines were incorporated into the Cox models to assess possible non-linear relationships. All analyses were adjusted for attained age, indicators of UVR exposure, education, and smoking status. During a mean follow-up of 27 years, 3,000 incident CM cases were identified. In men, CM risk was positively associated with body mass index, body surface area (BSA), height and weight (all ptrends  < 0.001), and the exposure-response curves indicated an exponential increase in risk for all anthropometric factors. Weight loss of more than 2 kg in men was associated with a 53% lower risk (HR 0.47, 95% CI: 0.39, 0.57). In women, CM risk increased with increasing BSA (ptrend  = 0.002) and height (ptrend  < 0.001). The shape of the height-CM risk curve indicated an exponential increase. Our study suggests that large body size, in general, is a CM risk factor in men, and is the first to report that weight loss may reduce the risk of CM among men.

The Growth Hormone Receptor: Mechanism of Receptor Activation, Cell Signaling, and Physiological Aspects

The growth hormone receptor (GHR), although most well known for regulating growth, has many other important biological functions including regulating metabolism and controlling physiological processes related to the hepatobiliary, cardiovascular, renal, gastrointestinal, and reproductive systems. In addition, growth hormone signaling is an important regulator of aging and plays a significant role in cancer development. Growth hormone activates the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway, and recent studies have provided a new understanding of the mechanism of JAK2 activation by growth hormone binding to its receptor. JAK2 activation is required for growth hormone-mediated activation of STAT1, STAT3, and STAT5, and the negative regulation of JAK-STAT signaling comprises an important step in the control of this signaling pathway. The GHR also activates the Src family kinase signaling pathway independent of JAK2. This review covers the molecular mechanisms of GHR activation and signal transduction as well as the physiological consequences of growth hormone signaling.

Adult height, coronary heart disease and stroke: a multi-locus Mendelian randomization meta-analysis

Background: We investigated causal effect of completed growth, measured by adult height, on coronary heart disease (CHD), stroke and cardiovascular traits, using instrumental variable (IV) Mendelian randomization meta-analysis.

Methods: We developed an allele score based on 69 single nucleotide polymorphisms (SNPs) associated with adult height, identified by the IBCCardioChip, and used it for IV analysis against cardiovascular risk factors and events in 21 studies and 60 028 participants. IV analysis on CHD was supplemented by summary data from 180 height-SNPs from the GIANT consortium and their corresponding CHD estimates derived from CARDIoGRAMplusC4D.

Results: IV estimates from IBCCardioChip and GIANT-CARDIoGRAMplusC4D showed that a 6.5-cm increase in height reduced the odds of CHD by 10% [odds ratios 0.90; 95% confidence intervals (CIs): 0.78 to 1.03 and 0.85 to 0.95, respectively],which agrees with the estimate from the Emerging Risk Factors Collaboration (hazard ratio 0.93; 95% CI: 0.91 to 0.94). IV analysis revealed no association with stroke (odds ratio 0.97; 95% CI: 0.79 to 1.19). IV analysis showed that a 6.5-cm increase in height resulted in lower levels of body mass index (P < 0.001), triglycerides (P < 0.001), non high-density (non-HDL) cholesterol (P < 0.001), C-reactive protein (P = 0.042), and systolic blood pressure (P = 0.064) and higher levels of forced expiratory volume in 1 s and forced vital capacity (P < 0.001 for both).

Conclusions: Taller individuals have a lower risk of CHD with potential explanations being that taller people have a better lung function and lower levels of body mass index, cholesterol and blood pressure.

New genetic tools in the diagnosis of growth defects

Growth is a complex biological process governed by thousands of genes. Genetic defects in a wide array of genes can cause severe growth disorders. Genomic technologies including chromosomal microarrays and whole exome sequencing have revolutionized our ability to diagnose growth disorders. In this brief review, we will discuss each of these technologies and how they have been applied in the field of growth disorders.

Predictive accuracy of combined genetic and environmental risk scores

The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores.

Genetic polymorphisms of GPR126 are functionally associated with PUMC classifications of adolescent idiopathic scoliosis in a Northern Han population

GPR126 has been identified to be associated with AIS (Adolescent Idiopathic Scoliosis) in different populations, but data on the northern Chinese population are unavailable. Additionally, it is important to know the exact clinical phenotypes associated with specific genetic polymorphisms. Fourteen SNP (single nucleotide polymorphism) loci in GPR126 were genotyped in 480 northern Chinese Han AIS patients and 841 controls. These patients were classified into three types based on the PUMC classification system. Luciferase assays were used to investigate their regulation of GPR126 transcription activity. Combined and stratified genotype-phenotype association analyses were conducted. The alleles rs225694, rs7774095 and rs2294773 were significantly associated with AIS (P = 0.021, 0.048 and 0.023, respectively). rs225694 and rs7774095 potentially have regulatory functions for the GRP126 gene. Correlation analysis revealed that allele A of rs225694 was a risk allele only for PUMC type II AIS (P = 0.036) and allele G of rs2294773 was a risk allele only for PUMC type I AIS (P = 0.018). In summary, rs225694, rs7774095 and rs2294773 are significantly associated with disease in northern Chinese Han AIS patients. The SNPs rs225694 and rs2294773 are associated with different AIS PUMC classifications.

Enriched expression of genes associated with autism spectrum disorders in human inhibitory neurons

Autism spectrum disorder (ASD) is highly heritable but genetically heterogeneous. The affected neural circuits and cell types remain unclear and may vary at different developmental stages. By analyzing multiple sets of human single cell transcriptome profiles, we found that ASD candidates showed relatively enriched gene expression in neurons, especially in inhibitory neurons. ASD candidates were also more likely to be the hubs of the co-expression gene module that is highly expressed in inhibitory neurons, a feature not detected for excitatory neurons. In addition, we found that upregulated genes in multiple ASD cortex samples were enriched with genes highly expressed in inhibitory neurons, suggesting a potential increase of inhibitory neurons and an imbalance in the ratio between excitatory and inhibitory neurons in ASD brains. Furthermore, the downstream targets of several ASD candidates, such as CHD8, EHMT1 and SATB2, also displayed enriched expression in inhibitory neurons. Taken together, our analyses of single cell transcriptomic data suggest that inhibitory neurons may be a major neuron subtype affected by the disruption of ASD gene networks, providing single cell functional evidence to support the excitatory/inhibitory (E/I) imbalance hypothesis.

Charting the genotype-phenotype map: lessons from the Drosophila melanogaster Genetic Reference Panel

Understanding the genetic architecture (causal molecular variants, their effects, and frequencies) of quantitative traits is important for precision agriculture and medicine and predicting adaptive evolution, but is challenging in most species. The Drosophila melanogaster Genetic Reference Panel (DGRP) is a collection of 205 inbred strains with whole genome sequences derived from a single wild population in Raleigh, NC, USA. The large amount of quantitative genetic variation, lack of population structure, and rapid local decay of linkage disequilibrium in the DGRP and outbred populations derived from DGRP lines present a favorable scenario for performing genome-wide association (GWA) mapping analyses to identify candidate causal genes, polymorphisms, and pathways affecting quantitative traits. The many GWA studies utilizing the DGRP have revealed substantial natural genetic variation for all reported traits, little evidence for variants with large effects but enrichment for variants with low P-values, and a tendency for lower frequency variants to have larger effects than more common variants. The variants detected in the GWA analyses rarely overlap those discovered using mutagenesis, and often are the first functional annotations of computationally predicted genes. Variants implicated in GWA analyses typically have sex-specific and genetic background-specific (epistatic) effects, as well as pleiotropic effects on other quantitative traits. Studies in the DGRP reveal substantial genetic control of environmental variation. Taking account of genetic architecture can greatly improve genomic prediction in the DGRP. These features of the genetic architecture of quantitative traits are likely to apply to other species, including humans. WIREs Dev Biol 2018, 7:e289. doi: 10.1002/wdev.289 This article is categorized under: Invertebrate Organogenesis > Flies.

Genome-Wide Study of Subcutaneous and Visceral Adipose Tissue Reveals Novel Sex-Specific Adiposity Loci in Mexican Americans

OBJECTIVE

This study aimed to explore the genetic mechanisms of regional fat deposition, which is a strong risk factor for metabolic diseases beyond total adiposity.

METHODS

A genome-wide association study of 7,757,139 single-nucleotide polymorphisms (SNPs) in 983 Mexican Americans (n_male  = 403; n_female  = 580) from the Insulin Resistance Atherosclerosis Family Study was performed. Association analyses were performed with and without sex stratification for subcutaneous adipose tissue, visceral adipose tissue (VAT), and visceral-subcutaneous ratio (VSR) obtained from computed tomography.

RESULTS

The strongest signal identified was SNP rs2185405 (minor allele frequencies [MAF] = 40%; P_VAT = 1.98 × 10^-8 ) with VAT. It is an intronic variant of the GLIS family zinc finger 3 gene (GLIS3). In addition, SNP rs12657394 (MAF = 19%) was associated with VAT in males (P_male = 2.39×10^-8 ; P_female = 2.5 × 10^-3 ). It is located intronically in the serum response factor binding protein 1 gene (SRFBP1). On average, male carriers of the variant had 24.6 cm² increased VAT compared with noncarriers. Subsequently, genome-wide SNP-sex interaction analysis was performed. SNP rs10913233 (MAF = 14%; P_int = 3.07 × 10^-8 ) in PAPPA2 and rs10923724 (MAF = 38%; P_int = 2.89 × 10^-8 ) upstream of TBX15 were strongly associated with the interaction effect for VSR.

CONCLUSIONS

Six loci were identified with genome-wide significant associations with fat deposition and interactive effects. These results provided genetic evidence for a differential basis of fat deposition between genders.

Charting the genotype-phenotype map: lessons from the Drosophila melanogaster Genetic Reference Panel

Understanding the genetic architecture (causal molecular variants, their effects, and frequencies) of quantitative traits is important for precision agriculture and medicine and predicting adaptive evolution, but is challenging in most species. The Drosophila melanogaster Genetic Reference Panel (DGRP) is a collection of 205 inbred strains with whole genome sequences derived from a single wild population in Raleigh, NC, USA. The large amount of quantitative genetic variation, lack of population structure, and rapid local decay of linkage disequilibrium in the DGRP and outbred populations derived from DGRP lines present a favorable scenario for performing genome-wide association (GWA) mapping analyses to identify candidate causal genes, polymorphisms, and pathways affecting quantitative traits. The many GWA studies utilizing the DGRP have revealed substantial natural genetic variation for all reported traits, little evidence for variants with large effects but enrichment for variants with low P-values, and a tendency for lower frequency variants to have larger effects than more common variants. The variants detected in the GWA analyses rarely overlap those discovered using mutagenesis, and often are the first functional annotations of computationally predicted genes. Variants implicated in GWA analyses typically have sex-specific and genetic background-specific (epistatic) effects, as well as pleiotropic effects on other quantitative traits. Studies in the DGRP reveal substantial genetic control of environmental variation. Taking account of genetic architecture can greatly improve genomic prediction in the DGRP. These features of the genetic architecture of quantitative traits are likely to apply to other species, including humans. WIREs Dev Biol 2018, 7:e289. doi: 10.1002/wdev.289 This article is categorized under: Invertebrate Organogenesis > Flies.

Translating Human Genetics into Novel Drug Targets

The most important promise of the human genome sequencing project is the identification of the genetic cause of devastating human diseases and the subsequent deliver of novel drug therapies to treat these diseases with high unmet medical need. In the last 10 years we have successfully identified hundreds of genetic loci associated with many traits and diseases. The translation of these findings into novel therapies is not straightforward and poses challenges that are usually overlooked in traditional gene mapping. This chapter describes some of the most common challenges and opportunities to use human genetics to identify and validate novel drug targets.

Growth plate expression profiling: Large and small breed dogs provide new insights in endochondral bone formation

The difference in the adult height of mammals, and hence in endochondral bone formation, is not yet fully understood and may serve to identify targets for bone and cartilage regeneration. In line with this hypothesis, the intra-species disparity between the adult height of Great Danes and Miniature Poodles was investigated at a transcriptional level. Microarray analysis of the growth plate of five Great Danes and five Miniature Poodles revealed 2,981 unique genes that were differentially expressed, including many genes with an unknown role in skeletal development. A signaling pathway impact analysis indicated activation of the cell cycle, extracellular matrix receptor interaction and the tight junction pathway, and inhibition of pathways associated with inflammation and the complement cascade. In additional validation steps, the gene expression profile of the separate growth plate zones for both dog breeds were determined. Given that the BMP signaling is known for its crucial role in skeletal development and fracture healing, and BMP-2 is used in orthopaedic and spine procedures for bone augmentation, further investigations concentrated on the BMP pathway.The canonical BMP-2 and BMP-6 signaling pathway was activated in the Great Danes compared to Miniature Poodles. In conclusion, investigating the differential expression of genes involved in endochondral bone formation in small and large breed dogs, could be a game changing strategy to provide new insights in growth plate development and identify new targets for bone and cartilage regeneration. © 2017 The Authors. Journal of Orthopaedic Research® published by Wiley Periodicals, Inc. on behalf of the Orthopaedic Research Society. J Orthop Res 36:138-148, 2018.

Werner syndrome (WRN) gene variants and their association with altered function and age-associated diseases

Werner syndrome (WS) is a heritable autosomal recessive human disorder characterized by the premature onset of several age-associated pathologies including cancer. The protein defective in WS patients, WRN, is encoded by a member of the human RECQ gene family that contains both a DNA exonuclease and a helicase domain. WRN has been shown to participate in several DNA metabolic pathways including DNA replication, recombination and repair, as well as telomere maintenance and transcription modulation. Here we review base pair-level genetic variation that has been documented in WRN, with an emphasis on non-synonymous coding single nucleotide polymorphisms (SNPs) and their associations with anthropomorphic features, longevity and disease risk. These associations have been challenging to identify, as many reported WRN SNP associations appear to be further conditioned upon ethnic, age, gender or other environmental co-variables. The WRN variant phenotypic associations identified to date are intriguing, and several are of clear clinical import. Consequently, it will be important to extend these initial associations and to identify the mechanisms and conditions under which specific WRN variants may compromise WRN function to drive cellular and organismal phenotypes as well as disease risk.

Charting the genotype-phenotype map: lessons from the Drosophila melanogaster Genetic Reference Panel

Understanding the genetic architecture (causal molecular variants, their effects, and frequencies) of quantitative traits is important for precision agriculture and medicine and predicting adaptive evolution, but is challenging in most species. The Drosophila melanogaster Genetic Reference Panel (DGRP) is a collection of 205 inbred strains with whole genome sequences derived from a single wild population in Raleigh, NC, USA. The large amount of quantitative genetic variation, lack of population structure, and rapid local decay of linkage disequilibrium in the DGRP and outbred populations derived from DGRP lines present a favorable scenario for performing genome-wide association (GWA) mapping analyses to identify candidate causal genes, polymorphisms, and pathways affecting quantitative traits. The many GWA studies utilizing the DGRP have revealed substantial natural genetic variation for all reported traits, little evidence for variants with large effects but enrichment for variants with low P-values, and a tendency for lower frequency variants to have larger effects than more common variants. The variants detected in the GWA analyses rarely overlap those discovered using mutagenesis, and often are the first functional annotations of computationally predicted genes. Variants implicated in GWA analyses typically have sex-specific and genetic background-specific (epistatic) effects, as well as pleiotropic effects on other quantitative traits. Studies in the DGRP reveal substantial genetic control of environmental variation. Taking account of genetic architecture can greatly improve genomic prediction in the DGRP. These features of the genetic architecture of quantitative traits are likely to apply to other species, including humans. WIREs Dev Biol 2018, 7:e289. doi: 10.1002/wdev.289 This article is categorized under: Invertebrate Organogenesis > Flies.

Novel Modulators of the Growth Hormone - Insulin-Like Growth Factor Axis: Pregnancy-Associated Plasma Protein-A2 and Stanniocalcin-2

Growth hormone (GH) and its mediator, insulin-like growth factor-1 (IGF-1), play a critical role in human growth. In circulation, IGF-1 is found in a ternary complex with IGF binding proteins (IGFBPs) and acid labile subunit (ALS) but little attention has been paid to the regulation of IGF-1 bioavailability. Recently, pregnancy-associated plasma protein-A2 (PAPP-A2) and stanniocalcin-2 (STC2) were identified as novel modulators of IGF-I bioavailability. PAPP-A2 is a protease which cleaves IGFBP-3 and -5, while STC2 inhibits PAPP-A and PAPP-A2 activity. In collaboration with a group in Madrid, we reported the first human cases carrying mutations in the PAPPA2 gene who presented with short stature, elevated total IGF-1, IGFBP-3, IGFBP-5 and ALS, but low free IGF-1. Additionally, the patients demonstrated insulin resistance and below average bone mineral density (BMD). The PAPP-A2 deficient patients were treated with recombinant human IGF-1, resulting in improvements in growth velocity, insulin resistance, and BMD. These findings suggested that the bioactive, free IGF-1 liberated from IGFBPs by PAPP-A2 is important for human growth. Mouse models of PAPP-A2 and STC2 provide further insights into their roles in growth physiology. This review will summarize new insights into PAPP-A2 and STC2 and their role in the GH-IGF axis, thereby highlighting the importance of the regulation of IGF-1 bioavailability in human health and disease.

Cognition, serum BDNF levels, and BDNF Val66Met polymorphism in type 2 diabetes patients and healthy controls

Background and aims

Type 2 diabetes (T2DM) is associated with cognitive deficits. However, their pathophysiological mechanisms are still unknown. Recent study suggests that brain-derived neurotrophic factor (BDNF) is correlated with cognitive deficits in T2DM patients. This study was to determine whether altered serum BDNF levels and cognitive deficits depended on the BDNF Val66Met polymorphism in T2DM.

Results

The BDNF Val66Met polymorphism may not contribute directly to the susceptibility to T2DM. The total and nearly all index scores (all p < 0.01) except for the attention and visuospatial/constructional indexes (both p > 0.05) of RBANS were markedly decreased in T2DM compared with healthy controls. Serum BDNF levels were significantly lower in patients than that in controls (p < 0.001), and BDNF was positively associated with delayed memory in patients (p < 0.05). The Met variant was associated with worse delayed memory performance among T2DM patients but not among normal controls. Moreover, serum BDNF was positively associated with delayed memory among Met homozygote patients (β = 0.29, t = 2.21, p = 0.033), while serum BDNF was negatively associated the RBANS total score (β = –0.92, t = –3.40, p = 0.002) and language index (β = −1.17, t = –3.54, p = 0.001) among Val homozygote T2DM patients.

Conclusions

BDNF gene Val66Met variation may be associated with cognitive deficits in T2DM, especially with delayed memory. The association between lower BDNF serum levels and cognitive impairment in T2DM is dependent on the BDNF Val66Met polymorphism.

Methods

We recruited 311 T2DM patients and 346 healthy controls and compared them on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), serum BDNF levels, and the BDNF Val66Met polymorphism.

Epigenetic profiling of growth plate chondrocytes sheds insight into regulatory genetic variation influencing height

GWAS have identified hundreds of height-associated loci. However, determining causal mechanisms is challenging, especially since height-relevant tissues (e.g. growth plates) are difficult to study. To uncover mechanisms by which height GWAS variants function, we performed epigenetic profiling of murine femoral growth plates. The profiled open chromatin regions recapitulate known chondrocyte and skeletal biology, are enriched at height GWAS loci, particularly near differentially expressed growth plate genes, and enriched for binding motifs of transcription factors with roles in chondrocyte biology. At specific loci, our analyses identified compelling mechanisms for GWAS variants. For example, at CHSY1, we identified a candidate causal variant (rs9920291) overlapping an open chromatin region. Reporter assays demonstrated that rs9920291 shows allelic regulatory activity, and CRISPR/Cas9 targeting of human chondrocytes demonstrates that the region regulates CHSY1 expression. Thus, integrating biologically relevant epigenetic information (here, from growth plates) with genetic association results can identify biological mechanisms important for human growth.

Heritability of Atrial Fibrillation

BACKGROUND

Previous reports have implicated multiple genetic loci associated with AF, but the contributions of genome-wide variation to AF susceptibility have not been quantified.

METHODS AND RESULTS

We assessed the contribution of genome-wide single-nucleotide polymorphism variation to AF risk (single-nucleotide polymorphism heritability, h2g ) using data from 120 286 unrelated individuals of European ancestry (2987 with AF) in the population-based UK Biobank. We ascertained AF based on self-report, medical record billing codes, procedure codes, and death records. We estimated h2g using a variance components method with variants having a minor allele frequency ≥1%. We evaluated h2g in age, sex, and genomic strata of interest. The h2g for AF was 22.1% (95% confidence interval, 15.6%-28.5%) and was similar for early- versus older-onset AF (≤65 versus >65 years of age), as well as for men and women. The proportion of AF variance explained by genetic variation was mainly accounted for by common (minor allele frequency, ≥5%) variants (20.4%; 95% confidence interval, 15.1%-25.6%). Only 6.4% (95% confidence interval, 5.1%-7.7%) of AF variance was attributed to variation within known AF susceptibility, cardiac arrhythmia, and cardiomyopathy gene regions.

CONCLUSIONS

Genetic variation contributes substantially to AF risk. The risk for AF conferred by genomic variation is similar to that observed for several other cardiovascular diseases. Established AF loci only explain a moderate proportion of disease risk, suggesting that further genetic discovery, with an emphasis on common variation, is warranted to understand the causal genetic basis of AF.

A UNIFIED FRAMEWORK FOR VARIANCE COMPONENT ESTIMATION WITH SUMMARY STATISTICS IN GENOME-WIDE ASSOCIATION STUDIES

Linear mixed models (LMMs) are among the most commonly used tools for genetic association studies. However, the standard method for estimating variance components in LMMs-the restricted maximum likelihood estimation method (REML)-suffers from several important drawbacks: REML requires individual-level genotypes and phenotypes from all samples in the study, is computationally slow, and produces downward-biased estimates in case control studies. To remedy these drawbacks, we present an alternative framework for variance component estimation, which we refer to as MQS. MQS is based on the method of moments (MoM) and the minimal norm quadratic unbiased estimation (MINQUE) criterion, and brings two seemingly unrelated methods-the renowned Haseman-Elston (HE) regression and the recent LD score regression (LDSC)-into the same unified statistical framework. With this new framework, we provide an alternative but mathematically equivalent form of HE that allows for the use of summary statistics. We provide an exact estimation form of LDSC to yield unbiased and statistically more efficient estimates. A key feature of our method is its ability to pair marginal z-scores computed using all samples with SNP correlation information computed using a small random subset of individuals (or individuals from a proper reference panel), while capable of producing estimates that can be almost as accurate as if both quantities are computed using the full data. As a result, our method produces unbiased and statistically efficient estimates, and makes use of summary statistics, while it is computationally efficient for large data sets. Using simulations and applications to 37 phenotypes from 8 real data sets, we illustrate the benefits of our method for estimating and partitioning SNP heritability in population studies as well as for heritability estimation in family studies. Our method is implemented in the GEMMA software package, freely available at www.xzlab.org/software.html.

Height associated variants demonstrate assortative mating in human populations

Understanding human mating patterns, which can affect population genetic structure, is important for correctly modeling populations and performing genetic association studies. Prior studies of assortative mating in humans focused on trait similarity among spouses and relatives via phenotypic correlations. Limited research has quantified the genetic consequences of assortative mating. The degree to which the non-random mating influences genetic architecture remains unclear. Here, we studied genetic variants associated with human height to assess the degree of height-related assortative mating in European-American and African-American populations. We compared the inbreeding coefficient estimated using known height associated variants with that calculated from frequency matched sets of random variants. We observed significantly higher inbreeding coefficients for the height associated variants than from frequency matched random variants (P < 0.05), demonstrating height-related assortative mating in both populations.

High-resolution analysis of selection sweeps identified between fine-wool Merino and coarse-wool Churra sheep breeds

BACKGROUND

With the aim of identifying selection signals in three Merino sheep lines that are highly specialized for fine wool production (Australian Industry Merino, Australian Merino and Australian Poll Merino) and considering that these lines have been subjected to selection not only for wool traits but also for growth and carcass traits and parasite resistance, we contrasted the OvineSNP50 BeadChip (50 K-chip) pooled genotypes of these Merino lines with the genotypes of a coarse-wool breed, phylogenetically related breed, Spanish Churra dairy sheep. Genome re-sequencing datasets of the two breeds were analyzed to further explore the genetic variation of the regions initially identified as putative selection signals.

RESULTS

Based on the 50 K-chip genotypes, we used the overlapping selection signals (SS) identified by four selection sweep mapping analyses (that detect genetic differentiation, reduced heterozygosity and patterns of haplotype diversity) to define 18 convergence candidate regions (CCR), five associated with positive selection in Australian Merino and the remainder indicating positive selection in Churra. Subsequent analysis of whole-genome sequences from 15 Churra and 13 Merino samples identified 142,400 genetic variants (139,745 bi-allelic SNPs and 2655 indels) within the 18 defined CCR. Annotation of 1291 variants that were significantly associated with breed identity between Churra and Merino samples identified 257 intragenic variants that caused 296 functional annotation variants, 275 of which were located across 31 coding genes. Among these, four synonymous and four missense variants (NPR2_His847Arg, NCAPG_Ser585Phe, LCORL_Asp1214Glu and LCORL_Ile1441Leu) were included.

CONCLUSIONS

Here, we report the mapping and genetic variation of 18 selection signatures that were identified between Australian Merino and Spanish Churra sheep breeds, which were validated by an additional contrast between Spanish Merino and Churra genotypes. Analysis of whole-genome sequencing datasets allowed us to identify divergent variants that may be viewed as candidates involved in the phenotypic differences for wool, growth and meat production/quality traits between the breeds analyzed. The four missense variants located in the NPR2, NCAPG and LCORL genes may be related to selection sweep regions previously identified and various QTL reported in sheep in relation to growth traits and carcass composition.

On meta- and mega-analyses for gene-environment interactions

Gene-by-environment (G × E) interactions are important in explaining the missing heritability and understanding the causation of complex diseases, but a single, moderately sized study often has limited statistical power to detect such interactions. With the increasing need for integrating data and reporting results from multiple collaborative studies or sites, debate over choice between mega- versus meta-analysis continues. In principle, data from different sites can be integrated at the individual level into a "mega" data set, which can be fit by a joint "mega-analysis." Alternatively, analyses can be done at each site, and results across sites can be combined through a "meta-analysis" procedure without integrating individual level data across sites. Although mega-analysis has been advocated in several recent initiatives, meta-analysis has the advantages of simplicity and feasibility, and has recently led to several important findings in identifying main genetic effects. In this paper, we conducted empirical and simulation studies, using data from a G × E study of lung cancer, to compare the mega- and meta-analyses in four commonly used G × E analyses under the scenario that the number of studies is small and sample sizes of individual studies are relatively large. We compared the two data integration approaches in the context of fixed effect models and random effects models separately. Our investigations provide valuable insights in understanding the differences between mega- and meta-analyses in practice of combining small number of studies in identifying G × E interactions.

Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia

We conducted a genome-wide association study (GWAS) with replication in 36,180 Chinese individuals and performed further transancestry meta-analyses with data from the Psychiatry Genomics Consortium (PGC2). Approximately 95% of the genome-wide significant (GWS) index alleles (or their proxies) from the PGC2 study were overrepresented in Chinese schizophrenia cases, including ∼50% that achieved nominal significance and ∼75% that continued to be GWS in the transancestry analysis. The Chinese-only analysis identified seven GWS loci; three of these also were GWS in the transancestry analyses, which identified 109 GWS loci, thus yielding a total of 113 GWS loci (30 novel) in at least one of these analyses. We observed improvements in the fine-mapping resolution at many susceptibility loci. Our results provide several lines of evidence supporting candidate genes at many loci and highlight some pathways for further research. Together, our findings provide novel insight into the genetic architecture and biological etiology of schizophrenia.

Human genetics contributes to the understanding of disease pathophysiology and drug discovery

BACKGROUND

Today, sequencing technology has markedly reduced the cost and time needed to read the human genome than ever before. Genome-wide association studies have successfully identified a number of disease risk genes.

CONTRIBUTION TO UNDERSTANDING OF DISEASE PATHOPHYSIOLOGY

Recent advancements in genomic technology have substantially furthered our understanding of the pathophysiology of many diseases, such as rheumatoid arthritis.

TOWARD DRUG DISCOVERY AND FUTURE DIRECTION

Accumulating genomic information is now expected to accelerate the discovery of novel drugs. Rapidly growing multi-dimensional information in life sciences would make human genetics significantly important in the near future.

Phenome-wide association study using research participants' self-reported data provides insight into the Th17 and IL-17 pathway

A phenome-wide association study of variants in genes in the Th17 and IL-17 pathway was performed using self-reported phenotypes and genetic data from 521,000 research participants of 23andMe. Results replicated known associations with similar effect sizes for autoimmune traits illustrating self-reported traits can be a surrogate for clinically assessed conditions. Novel associations controlling for a false discovery rate of 5% included the association of the variant encoding p.Ile684Ser in TYK2 with increased risk of tonsillectomy, strep throat occurrences and teen acne, the variant encoding p.Arg381Gln in IL23R with a decrease in dandruff frequency, the variant encoding p.Asp10Asn in TRAF3IP2 with risk of male-pattern balding, and the RORC regulatory variant (rs4845604) with protection from allergies. This approach enabled rapid assessment of association with a wide variety of traits and investigation of traits with limited reported associations to overlay meaningful phenotypic context on the range of conditions being considered for drugs targeting this pathway.

Consensus Genome-Wide Expression Quantitative Trait Loci and Their Relationship with Human Complex Trait Disease

Most of the risk loci identified from genome-wide association (GWA) studies do not provide direct information on the biological basis of a disease or on the underlying mechanisms. Recent expression quantitative trait locus (eQTL) association studies have provided information on genetic factors associated with gene expression variation. These eQTLs might contribute to phenotype diversity and disease susceptibility, but interpretation is handicapped by low reproducibility of the expression results. To address this issue, we have generated a set of consensus eQTLs by integrating publicly available data for specific human populations and cell types. Overall, we find over 4000 genes that are involved in high-confidence eQTL relationships. To elucidate the role that eQTLs play in human common diseases, we matched the high-confidence eQTLs to a set of 335 disease risk loci identified from the Wellcome Trust Case Control Consortium GWA study and follow-up studies for 7 human complex trait diseases-bipolar disorder (BD), coronary artery disease (CAD), Crohn's disease (CD), hypertension (HT), rheumatoid arthritis (RA), type 1 diabetes (T1D), and type 2 diabetes (T2D). The results show that the data are consistent with ∼50% of these disease loci arising from an underlying expression change mechanism.

Commentary: Fisher's infinitesimal model: A story for the ages

Mendel (1866) suggested that if many heritable "factors" contribute to a trait, near-continuous variation could result. Fisher (1918) clarified the connection between Mendelian inheritance and continuous trait variation by assuming many loci, each with small effect, and by informally invoking the central limit theorem. Barton et al. (2017) rigorously analyze the approach to a multivariate Gaussian distribution of the genetic effects for descendants of parents who may be related. This commentary distinguishes three nested approximations, referred to as "infinitesimal genetics," "Gaussian descendants" and "Gaussian population," each plausibly called "the infinitesimal model." The first and most basic is Fisher's "infinitesimal" approximation of the underlying genetics - namely, many loci, each making a small contribution to the total variance. As Barton et al. (2017) show, in the limit as the number of loci increases (with enough additivity), the distribution of genotypic values for descendants approaches a multivariate Gaussian, whose variance-covariance structure depends only on the relatedness, not the phenotypes, of the parents (or whether their population experiences selection or other processes such as mutation and migration). Barton et al. (2017) call this rigorously defensible "Gaussian descendants" approximation "the infinitesimal model." However, it is widely assumed that Fisher's genetic assumptions yield another Gaussian approximation, in which the distribution of breeding values in a population follows a Gaussian - even if the population is subject to non-Gaussian selection. This third "Gaussian population" approximation, is also described as the "infinitesimal model." Unlike the "Gaussian descendants" approximation, this third approximation cannot be rigorously justified, except in a weak-selection limit, even for a purely additive model. Nevertheless, it underlies the two most widely used descriptions of selection-induced changes in trait means and genetic variances, the "breeder's equation" and the "Bulmer effect." Future generations may understand why the "infinitesimal model" provides such useful approximations in the face of epistasis, linkage, linkage disequilibrium and strong selection.