Defining the role of common variation in the genomic and biological architecture of adult human height

Genetic variation in the exome: Associations with alcohol and tobacco co-use

Shared genetic factors represent one underlying mechanism thought to contribute to high rates of alcohol and tobacco co-use and dependence. Common variants identified by molecular genetic studies tend to confer only small disease risk, and rare protein-coding variants are posited to contribute to disease risk, as well. However, given that genotyping technologies allowing for their inclusion in association studies have only recently become available, the magnitude of their contribution is poorly understood. The current study examined genetic variation in protein-coding regions (i.e., the exome) for associations with measures of lifetime alcohol and tobacco co-use. Participants from the UCSF Family Alcoholism Study (N = 1,862) were genotyped using an exome-focused genotyping array, and assessed for DSM-IV diagnoses of alcohol and tobacco dependence and quantitative consumption measures using a modified version of the Semi-Structured Assessment for the Genetics of Alcoholism. Analyses included single variant, gene-based, and pathway-based tests of association. One EMR3 variant and a pathway related to genes upregulated in mesenchymal stem cells during the late phase of adipogenesis met criteria for statistical significance. Suggestive associations were consistent with previous findings from studies of substance use and dependence, including variants in the CHRNA5-CHRNA3-CHRNB4 gene cluster with cigarettes smoked per day. Further, several variants and genes demonstrated suggestive association across phenotypes, suggesting that shared genetic factors may underlie risk for increased levels of alcohol and tobacco use, as well as psychopathology more broadly, providing insight into our understanding of the genetic architecture underlying these traits. (PsycINFO Database Record

Automated Phenotyping Indicates Pupal Size in Drosophila Is a Highly Heritable Trait with an Apparent Polygenic Basis

The intense focus on studying human height has done more than any other genetic analysis to advance our understanding of the heritability of highly complex phenotypes. Here, we describe in detail the properties of a previously unexplored trait in Drosophila melanogaster that shares many salient properties with human height. The total length of the pupal case varies between 2.8 and 3.9 mm among natural variants, and we report that it is among the most heritable traits reported in this species. We have developed a simple semiautomatic phenotyping system with which a single operator can reliably score >5000 individuals in a day. The precision of the automated system is 0.042 mm (± 0.030 SD). All phenotyped individuals are available to be mated in subsequent generations or uniquely archived for future molecular work. We report both broad sense and narrow sense heritability estimates for two biologically distinct data sets. Narrow sense heritability (h²) ranged from 0.44 to 0.50, and broad sense heritability (H²) ranged from 0.58 to 0.61. We present results for mapping the trait in 195 recombinant inbred lines, which suggests that there are no loci with >10% effect size in this panel. We propose that pupal size genetics in Drosophila could represent a model complex trait amenable to deep genetic dissection using the automated system described.

Time-Variant Genetic Effects as a Cause for Preterm Birth: Insights from a Population of Maternal Cousins in Sweden

Preterm delivery (PTD) is the leading cause of neonatal mortality worldwide, yet its etiology remains largely unexplained. We propose that the genetic factors controlling this trait could act in a nonuniform manner during pregnancy, with each factor having a unique “window of sensitivity.” We test this hypothesis by modeling the distribution of gestational ages (GAs) observed in maternal cousins from the Swedish Medical Birth Register (MBR) (n = 35,541 pairs). The models were built using a time-to-event framework, with simulated genetic factors that increase the hazard of birth either uniformly across the pregnancy (constant effect) or only in particular windows (varying effect). By including various combinations of these factors, we obtained four models that were then optimized and compared. Best fit to the clinical data was observed when most of the factors had time-variant effects, independently of the number of loci simulated. Finally, power simulations were performed to assess the ability to discover varying-effect loci by usual methods for genome-wide association testing. We believe that the tools and concepts presented here should prove useful for the design of future studies of PTD and provide new insights into the genetic architecture determining human GA.

Assessing causality in associations between cannabis use and schizophrenia risk: a two-sample Mendelian randomization study

BACKGROUND

Observational associations between cannabis and schizophrenia are well documented, but ascertaining causation is more challenging. We used Mendelian randomization (MR), utilizing publicly available data as a method for ascertaining causation from observational data.

METHOD

We performed bi-directional two-sample MR using summary-level genome-wide data from the International Cannabis Consortium (ICC) and the Psychiatric Genomics Consortium (PGC2). Single nucleotide polymorphisms (SNPs) associated with cannabis initiation (p < 10-5) and schizophrenia (p < 5 × 10-8) were combined using an inverse-variance-weighted fixed-effects approach. We also used height and education genome-wide association study data, representing negative and positive control analyses.

RESULTS

There was some evidence consistent with a causal effect of cannabis initiation on risk of schizophrenia [odds ratio (OR) 1.04 per doubling odds of cannabis initiation, 95% confidence interval (CI) 1.01-1.07, p = 0.019]. There was strong evidence consistent with a causal effect of schizophrenia risk on likelihood of cannabis initiation (OR 1.10 per doubling of the odds of schizophrenia, 95% CI 1.05-1.14, p = 2.64 × 10-5). Findings were as predicted for the negative control (height: OR 1.00, 95% CI 0.99-1.01, p = 0.90) but weaker than predicted for the positive control (years in education: OR 0.99, 95% CI 0.97-1.00, p = 0.066) analyses.

CONCLUSIONS

Our results provide some that cannabis initiation increases the risk of schizophrenia, although the size of the causal estimate is small. We find stronger evidence that schizophrenia risk predicts cannabis initiation, possibly as genetic instruments for schizophrenia are stronger than for cannabis initiation.

Integrating Sequence-based GWAS and RNA-Seq Provides Novel Insights into the Genetic Basis of Mastitis and Milk Production in Dairy Cattle

Connecting genome-wide association study (GWAS) to biological mechanisms underlying complex traits is a major challenge. Mastitis resistance and milk production are complex traits of economic importance in the dairy sector and are associated with intra-mammary infection (IMI). Here, we integrated IMI-relevant RNA-Seq data from Holstein cattle and sequence-based GWAS data from three dairy cattle breeds (i.e., Holstein, Nordic red cattle, and Jersey) to explore the genetic basis of mastitis resistance and milk production using post-GWAS analyses and a genomic feature linear mixed model. At 24 h post-IMI, genes responsive to IMI in the mammary gland were preferentially enriched for genetic variants associated with mastitis resistance rather than milk production. Response genes in the liver were mainly enriched for variants associated with mastitis resistance at an early time point (3 h) post-IMI, whereas responsive genes at later stages were enriched for associated variants with milk production. The up- and down-regulated genes were enriched for associated variants with mastitis resistance and milk production, respectively. The patterns were consistent across breeds, indicating that different breeds shared similarities in the genetic basis of these traits. Our approaches provide a framework for integrating multiple layers of data to understand the genetic architecture underlying complex traits.

Genetic correlation between amyotrophic lateral sclerosis and schizophrenia

We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P=1 × 10-4) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P=8.4 × 10-7). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies.

Multivariate simulation framework reveals performance of multi-trait GWAS methods

Burgeoning availability of genome-wide association study (GWAS) results and national biobank data has led to growing interest in performing multi-trait genetic analyses. Numerous multi-trait GWAS methods that exploit either summary statistics or individual-level data have been developed, but their relative performance is unclear. Here we develop a simulation framework to model the complex networks underlying multivariate genetic epidemiology, enabling the vast model space of genetic effects on multiple correlated traits to be explored systematically. We perform a comprehensive comparison of the leading multi-trait GWAS methods, finding: (1) method performance is highly sensitive to the specific combination of genetic effects and phenotypic correlations, (2) most of the current multivariate methods have remarkably similar statistical power, and (3) multivariate methods may offer a substantial increase in the discovery of genetic variants over the standard univariate approach. We believe our findings offer the clearest picture to date of the relative performance of multi-trait GWAS methods and act as a guide for method selection. We provide a web application and open-source software program implementing our simulation framework, for: (i) further benchmarking of multivariate GWAS methods, (ii) power calculations for multivariate genetic studies, and (iii) generating data for testing any multivariate method in genetic epidemiology.

Genetic Dissection of a QTL Affecting Bone Geometry

Parameters of bone geometry such as width, length, and cross-sectional area are major determinants of bone strength. Although these traits are highly heritable, few genes influencing bone geometry have been identified. Here, we dissect a major quantitative trait locus (QTL) influencing femur size. This QTL was originally identified in an F2 cross between the C57BL/6J-hg/hg (HG) and CAST/EiJ strains and was referred to as femur length in high growth mice 2 (Feml2). Feml2 was located on chromosome (Chr.) 9 at ∼20 cM. Here, we show that the HG.CAST-(D9Mit249-D9Mit133)/Ucd congenic strain captures Feml2. In an F2 congenic cross, we fine-mapped the location of Feml2 to an ∼6 Mbp region extending from 57.3 to 63.3 Mbp on Chr. 9. We have identified candidates by mining the complete genome sequence of CAST/EiJ and through allele-specific expression (ASE) analysis of growth plates in C57BL/6J × CAST/EiJ F1 hybrids. Interestingly, we also find that the refined location of Feml2 overlaps a cluster of six independent genome-wide associations for human height. This work provides the foundation for the identification of novel genes affecting bone geometry.

Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization

We propose a novel statistical framework for integrating the result from molecular quantitative trait loci (QTL) mapping into genome-wide genetic association analysis of complex traits, with the primary objectives of quantitatively assessing the enrichment of the molecular QTLs in complex trait-associated genetic variants and the colocalizations of the two types of association signals. We introduce a natural Bayesian hierarchical model that treats the latent association status of molecular QTLs as SNP-level annotations for candidate SNPs of complex traits. We detail a computational procedure to seamlessly perform enrichment, fine-mapping and colocalization analyses, which is a distinct feature compared to the existing colocalization analysis procedures in the literature. The proposed approach is computationally efficient and requires only summary-level statistics. We evaluate and demonstrate the proposed computational approach through extensive simulation studies and analyses of blood lipid data and the whole blood eQTL data from the GTEx project. In addition, a useful utility from our proposed method enables the computation of expected colocalization signals using simple characteristics of the association data. Using this utility, we further illustrate the importance of enrichment analysis on the ability to discover colocalized signals and the potential limitations of currently available molecular QTL data. The software pipeline that implements the proposed computation procedures, enloc, is freely available at https://github.com/xqwen/integrative.

Genome-wide association studies (GWAS) have been tremendously successful in identifying genetic variants that impact complex diseases. However, the roles of such studies in disease etiology remain poorly understood, primarily because a large proportion of the GWAS findings are located in the non-coding region of the genome. Recent advancements in high-throughput sequencing technology enable the systematic investigation of molecular quantitative trait loci (QTLs), which are genetic variants that directly affect molecular phenotypes (e.g., gene expression, transcription factor binding and DNA methylation). Linking molecular QTLs to GWAS findings intuitively represents an important step for interpreting the biological and clinical relevance of the GWAS results. In this paper, we describe a rigorous and efficient computational approach that assesses the enrichment and overlap between the GWAS findings and molecular QTLs. Importantly, we illustrate that the accurate quantification of overlapping between molecular QTL and GWAS signals requires reliable enrichment estimation. Our proposed approach fully accounts for the intrinsic uncertainty embedded in the association analyses of GWAS and molecular QTL mapping, and it outperforms the existing state-of-the-art approaches. Applying the proposed approach to the GWAS data of blood lipid traits and the whole blood expression QTLs (eQTLs) yields some novel biological insights and also illustrates the potential limitations of the currently available molecular QTL data.

Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits

Although genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. Here, we introduce a method for estimating the local genetic correlation between gene expression and a complex trait and utilize it to estimate the genetic correlation due to predicted expression between pairs of traits. We integrated gene expression measurements from 45 expression panels with summary GWAS data to perform 30 multi-tissue transcriptome-wide association studies (TWASs). We identified 1,196 genes whose expression is associated with these traits; of these, 168 reside more than 0.5 Mb away from any previously reported GWAS significant variant. We then used our approach to find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, eight were not found through genetic correlation at the SNP level. Finally, we used bi-directional regression to find evidence that BMI causally influences triglyceride levels and that triglyceride levels causally influence low-density lipoprotein. Together, our results provide insight into the role of gene expression in the susceptibility of complex traits and diseases.

Archaic Adaptive Introgression in TBX15/WARS2

A recent study conducted the first genome-wide scan for selection in Inuit from Greenland using single nucleotide polymorphism chip data. Here, we report that selection in the region with the second most extreme signal of positive selection in Greenlandic Inuit favored a deeply divergent haplotype that is closely related to the sequence in the Denisovan genome, and was likely introgressed from an archaic population. The region contains two genes, WARS2 and TBX15, and has previously been associated with adipose tissue differentiation and body-fat distribution in humans. We show that the adaptively introgressed allele has been under selection in a much larger geographic region than just Greenland. Furthermore, it is associated with changes in expression of WARS2 and TBX15 in multiple tissues including the adrenal gland and subcutaneous adipose tissue, and with regional DNA methylation changes in TBX15.

Using GWAS to identify novel therapeutic targets for osteoporosis

Osteoporosis is a common, increasingly prevalent, global health burden characterized by low bone mineral density (BMD) and increased risk of fracture. Despite its significant impact on human health, there is currently a lack of highly effective treatments free of side effects for osteoporosis. Therefore, a major goal in the field is to identify new drug targets. Genetic discovery has been shown to be effective in the unbiased identification of novel drug targets and genome-wide association studies (GWASs) have begun to provide insight into genetic basis of osteoporosis. Over the last decade, GWASs have led to the identification of ∼100 loci associated with BMD and other bone traits related to risk of fracture. However, there have been limited efforts to identify the causal genes underlying the GWAS loci or the mechanisms by which GWAS loci alter bone physiology. In this review, we summarize the current state of the field and discuss strategies for causal gene discovery and the evidence that the novel genes underlying GWAS loci are likely to be a new source of drug targets.

Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis

Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. We performed a genetic association study in 15,256 cases and 47,936 controls, with replication of select top results (P < 5 × 10-6) in 9,498 cases and 9,748 controls. In the combined meta-analysis, we identified 22 loci associated at genome-wide significance, including 13 new associations with COPD. Nine of these 13 loci have been associated with lung function in general population samples, while 4 (EEFSEC, DSP, MTCL1, and SFTPD) are new. We noted two loci shared with pulmonary fibrosis (FAM13A and DSP) but that had opposite risk alleles for COPD. None of our loci overlapped with genome-wide associations for asthma, although one locus has been implicated in joint susceptibility to asthma and obesity. We also identified genetic correlation between COPD and asthma. Our findings highlight new loci associated with COPD, demonstrate the importance of specific loci associated with lung function to COPD, and identify potential regions of genetic overlap between COPD and other respiratory diseases.

Transcriptome sequencing implicates dorsal striatum-specific gene network, immune response and energy metabolism pathways in bipolar disorder

Bipolar disorder (BD) is a highly heritable and heterogeneous mental illness whose manifestations often include impulsive and risk-taking behavior. This particular phenotype suggests that abnormal striatal function could be involved in BD etiology, yet most transcriptomic studies of this disorder have concentrated on cortical brain regions. We believe we report the first transcriptome sequencing of the postmortem human dorsal striatum comparing bipolar (18) and control (17) subjects. Fourteen genes were detected as differentially expressed at a 5% false discovery rate, including a few immune response genes such as NLRC5, S100A12, LILRA4 and FCGBP, as well as an assortment of non-protein coding genes. Functional pathway analysis found an enrichment of upregulated genes across many immune/inflammation pathways and an enrichment of downregulated genes among oxidative phosphorylation pathways. Co-expression network analysis revealed 20 modules of highly interconnected genes; two of the modules were significantly enriched for BD susceptibility single-nucleotide polymorphisms deriving from a large genome-wide association study data set. Remarkably, the module with the highest genetic association signal for BD, which contained many genes from signaling pathways, was also enriched in markers characteristic of gene expression in dorsal striatum medium spiny neurons-unlike most other modules, which showed no such regional and neuronal specificity. These findings draw a link between BD etiology at the gene level and a specific brain region, and highlight striatal signaling pathways as potential targets for the development of novel treatments to manage BD.

GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium

The complex nature of human cognition has resulted in cognitive genomics lagging behind many other fields in terms of gene discovery using genome-wide association study (GWAS) methods. In an attempt to overcome these barriers, the current study utilized GWAS meta-analysis to examine the association of common genetic variation (~8M single-nucleotide polymorphisms (SNP) with minor allele frequency ⩾1%) to general cognitive function in a sample of 35 298 healthy individuals of European ancestry across 24 cohorts in the Cognitive Genomics Consortium (COGENT). In addition, we utilized individual SNP lookups and polygenic score analyses to identify genetic overlap with other relevant neurobehavioral phenotypes. Our primary GWAS meta-analysis identified two novel SNP loci (top SNPs: rs76114856 in the CENPO gene on chromosome 2 and rs6669072 near LOC105378853 on chromosome 1) associated with cognitive performance at the genome-wide significance level (P<5 × 10^-8). Gene-based analysis identified an additional three Bonferroni-corrected significant loci at chromosomes 17q21.31, 17p13.1 and 1p13.3. Altogether, common variation across the genome resulted in a conservatively estimated SNP heritability of 21.5% (s.e.=0.01%) for general cognitive function. Integration with prior GWAS of cognitive performance and educational attainment yielded several additional significant loci. Finally, we found robust polygenic correlations between cognitive performance and educational attainment, several psychiatric disorders, birth length/weight and smoking behavior, as well as a novel genetic association to the personality trait of openness. These data provide new insight into the genetics of neurocognitive function with relevance to understanding the pathophysiology of neuropsychiatric illness.

Sexual dimorphisms in genetic loci linked to body fat distribution

Obesity is a chronic condition associated with increased morbidity and mortality and is a risk factor for a number of other diseases including type 2 diabetes and cardiovascular disease. Obesity confers an enormous, costly burden on both individuals and public health more broadly. Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes. Body fat distribution is distinct from overall obesity in measurement, but studies of body fat distribution can yield insights into the risk factors for and causes of overall obesity. Sexual dimorphism in body fat distribution is present throughout life. Though sexual dimorphism is subtle in early stages of life, it is attenuated in puberty and during menopause. This phenomenon could be, at least in part, due to the influence of sex hormones on the trait. Findings from recent large genome-wide association studies (GWAS) for various measures of body fat distribution (including waist-to-hip ratio, hip or waist circumference, trunk fat percentage and the ratio of android and gynoid fat percentage) emphasize the strong sexual dimorphism in the genetic regulation of fat distribution traits. Importantly, sexual dimorphism is not observed for overall obesity (as assessed by body mass index or total fat percentage). Notably, the genetic loci associated with body fat distribution, which show sexual dimorphism, are located near genes that are expressed in adipose tissues and/or adipose cells. Considering the epidemiological and genetic evidence, sexual dimorphism is a prominent feature of body fat distribution. Research that specifically focuses on sexual dimorphism in fat distribution can provide novel insights into human physiology and into the development of obesity and its comorbidities, as well as yield biological clues that will aid in the improvement of disease prevention and treatment.

Diversity in non-repetitive human sequences not found in the reference genome

Genomes usually contain some non-repetitive sequences that are missing from the reference genome and occur only in a population subset. Such non-repetitive, non-reference (NRNR) sequences have remained largely unexplored in terms of their characterization and downstream analyses. Here we describe 3,791 breakpoint-resolved NRNR sequence variants called using PopIns from whole-genome sequence data of 15,219 Icelanders. We found that over 95% of the 244 NRNR sequences that are 200 bp or longer are present in chimpanzees, indicating that they are ancestral. Furthermore, 149 variant loci are in linkage disequilibrium (r² > 0.8) with a genome-wide association study (GWAS) catalog marker, suggesting disease relevance. Additionally, we report an association (P = 3.8 × 10^-8, odds ratio (OR) = 0.92) with myocardial infarction (23,360 cases, 300,771 controls) for a 766-bp NRNR sequence variant. Our results underline the importance of including variation of all complexity levels when searching for variants that associate with disease.

Impacts of Neanderthal-Introgressed Sequences on the Landscape of Human Gene Expression

Regulatory variation influencing gene expression is a key contributor to phenotypic diversity, both within and between species. Unfortunately, RNA degrades too rapidly to be recovered from fossil remains, limiting functional genomic insights about our extinct hominin relatives. Many Neanderthal sequences survive in modern humans due to ancient hybridization, providing an opportunity to assess their contributions to transcriptional variation and to test hypotheses about regulatory evolution. We developed a flexible Bayesian statistical approach to quantify allele-specific expression (ASE) in complex RNA-seq datasets. We identified widespread expression differences between Neanderthal and modern human alleles, indicating pervasive cis-regulatory impacts of introgression. Brain regions and testes exhibited significant downregulation of Neanderthal alleles relative to other tissues, consistent with natural selection influencing the tissue-specific regulatory landscape. Our study demonstrates that Neanderthal-inherited sequences are not silent remnants of ancient interbreeding but have measurable impacts on gene expression that contribute to variation in modern human phenotypes.

Sequence variant at 8q24.21 associates with sciatica caused by lumbar disc herniation

Lumbar disc herniation (LDH) is common and often debilitating. Microdiscectomy of herniated lumbar discs (LDHsurg) is performed on the most severe cases to resolve the resulting sciatica. Here we perform a genome-wide association study on 4,748 LDHsurg cases and 282,590 population controls and discover 37 highly correlated markers associating with LDHsurg at 8q24.21 (between CCDC26 and GSDMC), represented by rs6651255[C] (OR=0.81; P=5.6 × 10⁻¹²) with a stronger effect among younger patients than older. As rs6651255[C] also associates with height, we performed a Mendelian randomization analysis using height polygenic risk scores as instruments to estimate the effect of height on LDHsurg risk, and found that the marker's association with LDHsurg is much greater than predicted by its effect on height. In light of presented findings, we speculate that the effect of rs6651255 on LDHsurg is driven by susceptibility to developing severe and persistent sciatica upon LDH.

Lumbar disc herniation (LDH) can cause persistent sciatica, and in some cases surgery is required to relieve symptoms. Here, the authors carry out a genome-wide association study using microdiscectomy as an indicator of severe LDH, and find a locus on chromosome 8 associated with this condition.

A genome wide association study identifies a lncRna as risk factor for pathological inflammatory responses in leprosy

Leprosy Type-1 Reactions (T1Rs) are pathological inflammatory responses that afflict a sub-group of leprosy patients and result in peripheral nerve damage. Here, we employed a family-based GWAS in 221 families with 229 T1R-affect offspring with stepwise replication to identify risk factors for T1R. We discovered, replicated and validated T1R-specific associations with SNPs located in chromosome region 10p21.2. Combined analysis across the three independent samples resulted in strong evidence of association of rs1875147 with T1R (p = 4.5x10^-8; OR = 1.54, 95% CI = 1.32–1.80). The T1R-risk locus was restricted to a lncRNA-encoding genomic interval with rs1875147 being an eQTL for the lncRNA. Since a genetic overlap between leprosy and inflammatory bowel disease (IBD) has been detected, we evaluated if the shared genetic control could be traced to the T1R endophenotype. Employing the results of a recent IBD GWAS meta-analysis we found that 10.6% of IBD SNPs available in our dataset shared a common risk-allele with T1R (p = 2.4x10^-4). This finding points to a substantial overlap in the genetic control of clinically diverse inflammatory disorders.

Leprosy still affects approximately 200,000 new victims each year. A major challenge of leprosy control is the prevention of permanent disability due to nerve damage. Nerve damage occurs if leprosy remains undiagnosed for extended periods or when patients undergo pathological inflammatory responses termed Type-1 Reactions (T1R). T1R is a rare example where beneficial inflammatory responses are temporal separated from host pathological responses. There is strong experimental evidence that supports a role of host genetic factors in T1R susceptibility. Here, we employed a genome-wide association study (GWAS) to investigate susceptibility factors for T1R in Vietnamese families. We followed up the initial GWAS findings in independent population samples from Vietnam and Brazil and identified a set of cis-eQTL genetic variants for the ENSG00000235140 lncRNA as global risk factors for T1R. To test our proposal that T1R is a strong model for pathological inflammatory responses we evaluated if inflammatory bowel disease (IBD) genetic risk-factors were enriched among T1R risk factors. We observed that more than 10% of IBD-risk loci were nominally associated with risk for T1R suggesting a shared mechanism of excessive inflammatory response in the both disease etiologies.

graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. However, identification of risk variants associated with complex diseases remains challenging as they are often affected by many genetic variants with small or moderate effects. There has been accumulating evidence suggesting that different complex traits share common risk basis, namely pleiotropy. Recently, several statistical methods have been developed to improve statistical power to identify risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging pleiotropy. While these methods were shown to improve statistical power for association mapping compared to separate analyses, they are still limited in the number of phenotypes that can be integrated. In order to address this challenge, in this paper, we propose a novel statistical framework, graph-GPA, to integrate a large number of GWAS datasets for multiple phenotypes using a hidden Markov random field approach. Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to statistical methods based on smaller number of GWAS datasets. In addition, graph-GPA also promotes better understanding of genetic mechanisms shared among phenotypes, which can potentially be useful for the development of improved diagnosis and therapeutics. The R implementation of graph-GPA is currently available at https://dongjunchung.github.io/GGPA/.

Recently, there has been accumulating evidence suggesting pleiotropy, i.e., genetic components shared across multiple phenotypes. Incorporation of pleiotropy in genetic analysis might improve statistical power to identify risk associated genetic variants. Several statistical approaches have been proposed to utilize pleiotropy for association mapping but they are currently still limited to a relatively small number of phenotypes, e.g., a pair of phenotypes. This restricts potential gain in statistical power in association mapping and investigation of pleiotropic structure among a large number of phenotypes. In order to address this challenge, in this paper, we propose graph-GPA, a novel statistical framework to integrate a large number of phenotypes using a hidden Markov random field architecture. Application of the proposed statistical method to GWAS datasets for 12 phenotypes showed that graph-GPA does not only provide a parsimonious representation of genetic relationship among these phenotypes, but also identify significantly larger number of novel genetic variants that are potentially functional. We believe that this novel approach might help investigation of common etiology and improvement of diagnosis and therapeutics.

Rare and low-frequency coding variants alter human adult height

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.

A molecular census of arcuate hypothalamus and median eminence cell types

The hypothalamic arcuate-median eminence complex (Arc-ME) controls energy balance, fertility, and growth through molecularly distinct cell types, many of which remain unknown. To catalog cell types in an unbiased way, we profiled gene expression in 20,921 individual cells in and around the adult mouse Arc-ME using Drop-seq. We identify 50 transcriptionally distinct Arc-ME cell populations, including a rare tanycyte population at the Arc-ME diffusion barrier, a novel leptin-sensing neuronal population, multiple AgRP and POMC subtypes, and an orexigenic somatostatin neuronal population. We extended Drop-seq to detect dynamic expression changes across relevant physiological perturbations, revealing cell type-specific responses to energy status, including distinctly responsive subtypes of AgRP and POMC neurons. Finally, integrating our data with human GWAS data implicates two previously unknown neuronal subtypes in the genetic control of obesity. This resource will accelerate biological discovery by providing insights into molecular and cell type diversity from which function can be inferred.

Clinical Characterization of Patients With Autosomal Dominant Short Stature due to Aggrecan Mutations

CONTEXT

Heterozygous mutations in the aggrecan gene (ACAN) cause autosomal dominant short stature with accelerated skeletal maturation.

OBJECTIVE

We sought to characterize the phenotypic spectrum and response to growth-promoting therapies.

PATIENTS AND METHODS

One hundred three individuals (57 females, 46 males) from 20 families with autosomal dominant short stature and heterozygous ACAN mutations were identified and confirmed using whole-exome sequencing, targeted next-generation sequencing, and/or Sanger sequencing. Clinical information was collected from the medical records.

RESULTS

Identified ACAN variants showed perfect cosegregation with phenotype. Adult individuals had mildly disproportionate short stature [median height, -2.8 standard deviation score (SDS); range, -5.9 to -0.9] and a history of early growth cessation. The condition was frequently associated with early-onset osteoarthritis (12 families) and intervertebral disc disease (9 families). No apparent genotype-phenotype correlation was found between the type of ACAN mutation and the presence of joint complaints. Childhood height was less affected (median height, -2.0 SDS; range, -4.2 to -0.6). Most children with ACAN mutations had advanced bone age (bone age - chronologic age; median, +1.3 years; range, +0.0 to +3.7 years). Nineteen individuals had received growth hormone therapy with some evidence of increased growth velocity.

CONCLUSIONS

Heterozygous ACAN mutations result in a phenotypic spectrum ranging from mild and proportionate short stature to a mild skeletal dysplasia with disproportionate short stature and brachydactyly. Many affected individuals developed early-onset osteoarthritis and degenerative disc disease, suggesting dysfunction of the articular cartilage and intervertebral disc cartilage. Additional studies are needed to determine the optimal treatment strategy for these patients.

Stature and long-term labor market outcomes: Evidence using Mendelian randomization

We use the Young Finns Study (N=∼2000) on the measured height linked to register-based long-term labor market outcomes. The data contain six age cohorts (ages 3, 6, 9, 12, 15 and 18, in 1980) with the average age of 31.7, in 2001, and with the female share of 54.7. We find that taller people earn higher earnings according to the ordinary least squares (OLS) estimation. The OLS models show that 10cm of extra height is associated with 13% higher earnings. We use Mendelian randomization, with the genetic score as an instrumental variable (IV) for height to account for potential confounders that are related to socioeconomic background, early life conditions and parental investments, which are otherwise very difficult to fully account for when using covariates in observational studies. The IV point estimate is much lower and not statistically significant, suggesting that the OLS estimation provides an upward biased estimate for the height premium. Our results show the potential value of using genetic information to gain new insights into the determinants of long-term labor market success.

Growth in patients with type 1 diabetes

PURPOSE OF REVIEW

As the incidence of type 1 diabetes (T1DM) continues to rise, complications including impairment of childhood growth remain a major concern. This review provides an overview of alterations in growth patterns before and after the onset of T1DM.

RECENT FINDINGS

Recent advances in this field include several prospective investigations of height and weight trajectories in children leading up to the development of islet autoimmunity and T1DM as well as evaluations of larger cohorts of T1DM patients to better assess predictors of altered growth. In addition, genetic and metabolic investigations have improved our understanding of the more rare severe growth impairment of Mauriac Syndrome.

SUMMARY

Despite advances in medical care of children with T1DM, growth remains suboptimal in this population and likely reflects ongoing metabolic derangement linked with classic microvascular diabetic complications.

Genetics and educational attainment

We explore how advances in our understanding of the genetics of complex traits such as educational attainment could constructively be leveraged to advance research on education and learning. We discuss concepts and misconceptions about genetic findings with regard to causes, consequences, and policy. Our main thesis is that educational attainment as a measure that varies between individuals in a population can be subject to exactly the same experimental biological designs as other outcomes, for example, those studied in epidemiology and medical sciences, and the same caveats about interpretation and implication apply.

Dissecting the genetics of complex traits using summary association statistics

During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.

Why were Sardinians the shortest Europeans? A journey through genes, infections, nutrition, and sex

Since ancient times the Mediterranean island of Sardinia has been known for harboring a population with an average body height shorter than almost every other ethnic group in Europe. After over a century of investigations, the cause(s) at the origin of this uniqueness are not yet clear. The shorter stature of Sardinians appears to have been documented since prehistoric times, as revealed by the analysis of skeletal remains discovered in archaeological sites on the island. Recently, a number of genetic, hormonal, environmental, infective and nutritional factors have been put forward to explain this unique anthropometric feature, which persisted for a long time, even when environmental and living conditions improved around 1960. Although some of the putative factors are supported by sound empirical evidence, weaker support is available for others. The recent advent of whole genome analysis techniques shed new light on specific variants at the origin of this short stature. However, the marked geographical variability of stature across time and space within the island, and the well-known presence of pockets of short height in the population of the southern districts, are still puzzling findings that have attracted the interest of anthropologists and geneticists. The purpose of this review is to focus on the state-of-the-art research on stature, as well as the factors that made Sardinians the shortest among Europeans.

Genome-wide association study of prostate-specific antigen levels identifies novel loci independent of prostate cancer

Prostate-specific antigen (PSA) levels have been used for detection and surveillance of prostate cancer (PCa). However, factors other than PCa—such as genetics—can impact PSA. Here we present findings from a genome-wide association study (GWAS) of PSA in 28,503 Kaiser Permanente whites and 17,428 men from replication cohorts. We detect 40 genome-wide significant (P<5 × 10⁻⁸) single-nucleotide polymorphisms (SNPs): 19 novel, 15 previously identified for PSA (14 of which were also PCa-associated), and 6 previously identified for PCa only. Further analysis incorporating PCa cases suggests that at least half of the 40 SNPs are PSA-associated independent of PCa. The 40 SNPs explain 9.5% of PSA variation in non-Hispanic whites, and the remaining GWAS SNPs explain an additional 31.7%; this percentage is higher in younger men, supporting the genetic basis of PSA levels. These findings provide important information about genetic markers for PSA that may improve PCa screening, thereby reducing over-diagnosis and over-treatment.

Prostate-specific antigen is used as a biomarker of prostate cancer, but levels can be affected by other factors not related to cancer. Here, the authors find genes associated with prostate specific antigen levels in healthy men, which could be used to reduce over-diagnosis and over-treatment.

The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases

The Metabolic Syndrome in Men (METSIM) study is a population-based study including 10,197 Finnish men examined in 2005-2010. The aim of the study is to investigate nongenetic and genetic factors associated with the risk of T2D and CVD, and with cardiovascular risk factors. The protocol includes a detailed phenotyping of the participants, an oral glucose tolerance test, fasting laboratory measurements including proton NMR measurements, mass spectometry metabolomics, adipose tissue biopsies from 1,400 participants, and a stool sample. In our ongoing follow-up study, we have, to date, reexamined 6,496 participants. Extensive genotyping and exome sequencing have been performed for essentially all METSIM participants, and >2,000 METSIM participants have been whole-genome sequenced. We have identified several nongenetic markers associated with the development of diabetes and cardiovascular events, and participated in several genetic association studies to identify gene variants associated with diabetes, hyperglycemia, and cardiovascular risk factors. The generation of a phenotype and genotype resource in the METSIM study allows us to proceed toward a "systems genetics" approach, which includes statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein, or metabolite levels, to provide a global view of the molecular architecture of complex traits.

Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study

An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.

MixFit: Methodology for Computing Ancestry-Related Genetic Scores at the Individual Level and Its Application to the Estonian and Finnish Population Studies

Ancestry information at the individual level can be a valuable resource for personalized medicine, medical, demographical and history research, as well as for tracing back personal history. We report a new method for quantitatively determining personal genetic ancestry based on genome-wide data. Numerical ancestry component scores are assigned to individuals based on comparisons with reference populations. These comparisons are conducted with an existing analytical pipeline making use of genotype phasing, similarity matrix computation and our addition-multidimensional best fitting by MixFit. The method is demonstrated by studying Estonian and Finnish populations in geographical context. We show the main differences in the genetic composition of these otherwise close European populations and how they have influenced each other. The components of our analytical pipeline are freely available computer programs and scripts one of which was developed in house (available at: www.geenivaramu.ee/en/tools/mixfit).

Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies

Large-scale genome-wide association results are typically obtained from a fixed-effects meta-analysis of GWAS summary statistics from multiple studies spanning different regions and/or time periods. This approach averages the estimated effects of genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called ‘missing heritability’. Here, we describe the online Meta-GWAS Accuracy and Power (MetaGAP) calculator (available at www.devlaming.eu) which quantifies this attenuation based on a novel multi-study framework. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy provided by this calculator are accurate. We compare the predictions from the MetaGAP calculator with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood to estimate the SNP heritability and cross-study genetic correlation of height, BMI, years of education, and self-rated health in three large samples. These estimates are used as input parameters for the MetaGAP calculator. Results from the calculator suggest that cross-study heterogeneity has led to attenuation of statistical power and predictive accuracy in recent large-scale GWAS efforts on these traits (e.g., for years of education, we estimate a relative loss of 51–62% in the number of genome-wide significant loci and a relative loss in polygenic score R² of 36–38%). Hence, cross-study heterogeneity contributes to the missing heritability.

Large-scale genome-wide association studies are uncovering the genetic architecture of traits which are affected by many genetic variants. In such efforts, one typically meta-analyzes association results from multiple studies spanning different regions and/or time periods. Results from such efforts do not yet capture a large share of the heritability. The origins of this so-called ‘missing heritability’ have been strongly debated. One factor exacerbating the missing heritability is heterogeneity in the effects of genetic variants across studies. The effect of this type of heterogeneity on statistical power to detect associated genetic variants and the accuracy of polygenic predictions is poorly understood. In the current study, we derive the precise effects of heterogeneity in genetic effects across studies on both the statistical power to detect associated genetic variants as well as the accuracy of polygenic predictions. We present an online calculator, available at www.devlaming.eu, which accounts for these effects. By means of this calculator, we show that imperfect genetic correlations between studies substantially decrease statistical power and predictive accuracy and, thereby, contribute to the missing heritability. The MetaGAP calculator helps researchers to gauge how sensitive their results will be to heterogeneity in genetic effects across studies. If strong heterogeneity is expected, random-effects meta-analysis methods should be used instead of fixed-effects methods.

ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide

In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) - a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date - of schizophrenia and major depression - ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens - now numbering over 30,000 MRI scans - have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants - and genetic variants in general - may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures - from tens of thousands of people - that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.

The transcriptional architecture of phenotypic dimorphism

The profound differences in gene expression between the sexes are increasingly used to study the molecular basis of sexual dimorphism, sexual selection and sexual conflict. Studies of transcriptional architecture, based on comparisons of gene expression, have also been implemented for a wide variety of other intra-specific polymorphisms. These efforts are based on key assumptions regarding the relationship between transcriptional architecture, phenotypic variation and the target of selection. Some of these assumptions are better supported by available evidence than others. In all cases, the evidence is largely circumstantial, leaving considerable gaps in our understanding of the relationship between transcriptional and phenotypic dimorphism.

Relationships of Measured and Genetically Determined Height With the Cardiac Conduction System in Healthy Adults

Background—

Increasing height is an independent risk factor for atrial fibrillation, but the underlying mechanisms are unknown. We hypothesized that height-related differences in electric conduction could be potential mediators of this relationship.

Methods and Results—

We enrolled 2149 adults aged 25 to 41 years from the general population. Height was directly measured, and a resting 12-lead ECG obtained under standardized conditions. Multivariable linear regression models were used to evaluate the association between measured height and ECG parameters. Mendelian randomization analyses were then performed using 655 independent height-associated genetic variants previously identified in the GIANT consortium. Median age was 37 years, and median height was 1.71 m. Median PR interval, QRS duration, and QTc interval were 156, 88, and 402 ms, respectively. After multivariable adjustment, β-coefficients (95% confidence intervals) per 10 cm increase in measured height were 4.17 (2.65–5.69; P <0.0001) for PR interval and 2.06 (1.54–2.58; P <0.0001) for QRS duration. Height was not associated with QTc interval or the Sokolow–Lyon index. An increase of 10 cm in genetically determined height was associated with increases of 4.33 ms (0.76–7.96; P =0.02) in PR interval and 2.57 ms (1.33–3.83; P <0.0001) in QRS duration but was not related to QTc interval or Sokolow–Lyon index.

Conclusions—

In this large population-based study, we found significant associations of measured and genetically determined height with PR interval and QRS duration. Our findings suggest that adult height is a marker of altered cardiac conduction and that these relationships may be causal.

A Weighted SNP Correlation Network Method for Estimating Polygenic Risk Scores

Polygenic scores are useful for examining the joint associations of genetic markers. However, because traditional methods involve summing weighted allele counts, they may fail to capture the complex nature of biology. Here we describe a network-based method, which we call weighted SNP correlation network analysis (WSCNA), and demonstrate how it could be used to generate meaningful polygenic scores. Using data on human height in a US population of non-Hispanic whites, we illustrate how this method can be used to identify SNP networks from GWAS data, create network-specific polygenic scores, examine network topology to identify hub SNPs, and gain biological insights into complex traits. In our example, we show that this method explains a larger proportion of the variance in human height than traditional polygenic score methods. We also identify hub genes and pathways that have previously been identified as influencing human height. In moving forward, this method may be useful for generating genetic susceptibility measures for other health related traits, examining genetic pleiotropy, identifying at-risk individuals, examining gene score by environmental effects, and gaining a deeper understanding of the underlying biology of complex traits.

Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance

Insulin resistance is a key mediator of obesity-related cardiometabolic disease, yet the mechanisms underlying this link remain obscure. Using an integrative genomic approach, we identify 53 genomic regions associated with insulin resistance phenotypes (higher fasting insulin levels adjusted for BMI, lower HDL cholesterol levels and higher triglyceride levels) and provide evidence that their link with higher cardiometabolic risk is underpinned by an association with lower adipose mass in peripheral compartments. Using these 53 loci, we show a polygenic contribution to familial partial lipodystrophy type 1, a severe form of insulin resistance, and highlight shared molecular mechanisms in common/mild and rare/severe insulin resistance. Population-level genetic analyses combined with experiments in cellular models implicate CCDC92, DNAH10 and L3MBTL3 as previously unrecognized molecules influencing adipocyte differentiation. Our findings support the notion that limited storage capacity of peripheral adipose tissue is an important etiological component in insulin-resistant cardiometabolic disease and highlight genes and mechanisms underpinning this link.

HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics

MOTIVATION

Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients ([Formula: see text]) of the variants. However, haplotypes rather than pairwise [Formula: see text], are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel.

RESULTS

Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well with a small training sample size (N < 2000) while other methods become suboptimal. Moreover, HAPRAP's performance is not affected substantially by single nucleotide polymorphisms (SNPs) with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization).

AVAILABILITY AND IMPLEMENTATION

The HAPRAP package and documentation are available at http://apps.biocompute.org.uk/haprap/ CONTACT: : jie.zheng@bristol.ac.uk or tom.gaunt@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Endophenotype best practices

This review examines the current state of electrophysiological endophenotype research and recommends best practices that are based on knowledge gleaned from the last decade of molecular genetic research with complex traits. Endophenotype research is being oversold for its potential to help discover psychopathology relevant genes using the types of small samples feasible for electrophysiological research. This is largely because the genetic architecture of endophenotypes appears to be very much like that of behavioral traits and disorders: they are complex, influenced by many variants (e.g., tens of thousands) within many genes, each contributing a very small effect. Out of over 40 electrophysiological endophenotypes covered by our review, only resting heart, a measure that has received scant advocacy as an endophenotype, emerges as an electrophysiological variable with verified associations with molecular genetic variants. To move the field forward, investigations designed to discover novel variants associated with endophenotypes will need extremely large samples best obtained by forming consortia and sharing data obtained from genome wide arrays. In addition, endophenotype research can benefit from successful molecular genetic studies of psychopathology by examining the degree to which these verified psychopathology-relevant variants are also associated with an endophenotype, and by using knowledge about the functional significance of these variants to generate new endophenotypes. Even without molecular genetic associations, endophenotypes still have value in studying the development of disorders in unaffected individuals at high genetic risk, constructing animal models, and gaining insight into neural mechanisms that are relevant to clinical disorder.

Estrogens and Androgens in Skeletal Physiology and Pathophysiology

Estrogens and androgens influence the growth and maintenance of the mammalian skeleton and are responsible for its sexual dimorphism. Estrogen deficiency at menopause or loss of both estrogens and androgens in elderly men contribute to the development of osteoporosis, one of the most common and impactful metabolic diseases of old age. In the last 20 years, basic and clinical research advances, genetic insights from humans and rodents, and newer imaging technologies have changed considerably the landscape of our understanding of bone biology as well as the relationship between sex steroids and the physiology and pathophysiology of bone metabolism. Together with the appreciation of the side effects of estrogen-related therapies on breast cancer and cardiovascular diseases, these advances have also drastically altered the treatment of osteoporosis. In this article, we provide a comprehensive review of the molecular and cellular mechanisms of action of estrogens and androgens on bone, their influences on skeletal homeostasis during growth and adulthood, the pathogenetic mechanisms of the adverse effects of their deficiency on the female and male skeleton, as well as the role of natural and synthetic estrogenic or androgenic compounds in the pharmacotherapy of osteoporosis. We highlight latest advances on the crosstalk between hormonal and mechanical signals, the relevance of the antioxidant properties of estrogens and androgens, the difference of their cellular targets in different bone envelopes, the role of estrogen deficiency in male osteoporosis, and the contribution of estrogen or androgen deficiency to the monomorphic effects of aging on skeletal involution.

The Effect of the Exon-3-Deleted Growth Hormone Receptor on Pegvisomant-Treated Acromegaly: A Systematic Review and Meta-Analysis

BACKGROUND

The common exon 3 deletion polymorphism of the growth hormone receptor (d3-GHR) is associated with disease severity in acromegaly patients. The GHR antagonist pegvisomant (PEGV) is highly effective in treating severe acromegaly. Response to PEGV treatment seems to be influenced by d3-GHR and appears to be more responsive to PEGV, although available results remain conflicting.

OBJECTIVE

To assess the influence of d3-GHR on the responsiveness of acromegaly patients to PEGV by compiling the evidence derived from the largest available studies.

DESIGN

A systematic review of the literature identified three published studies and one conference abstract. Acromegaly patients (n = 324, 49.7% d3-GHR carriers) were treated with either PEGV monotherapy or PEGV combined with long-acting somatostatin analogues and/or cabergoline. A meta-analysis of raw data from these studies was performed.

RESULTS

No significant effect of the d3-GHR was observed while bringing insulin-like growth factor I (IGF-I) levels below the upper limit of normal with PEGV, which was defined as the lowest IGF-I level during PEGV treatment (mean difference: -2.3%; 95% CI: -6.5 to 1.8, p = 0.270). The PEGV dose required to achieve the lowest IGF-I levels was also not significantly influenced by individuals carrying d3-GHR (mean difference: 4.1 mg weekly; 95% CI: -5.1 to 13.2, p = 0.385). For both outcomes, separate analysis of PEGV monotherapy and combination treatment gave similar results.

CONCLUSION

Our findings suggest that the d3-GHR polymorphism has no effect on biochemical disease control in acromegaly, as it is not of added value for either the prediction of PEGV responsiveness or the determination of the required PEGV dose.