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

Large meta-analysis of genome-wide association studies identifies five loci for lean body mass

Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p < 5 × 10-8) or suggestively genome wide (p < 2.3 × 10-6). Replication in 63,475 (47,227 of European ancestry) individuals from 33 cohorts for whole body lean body mass and in 45,090 (42,360 of European ancestry) subjects from 25 cohorts for appendicular lean body mass was successful for five single-nucleotide polymorphisms in/near HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO for total lean body mass and for three single-nucleotide polymorphisms in/near VCAN, ADAMTSL3, and IRS1 for appendicular lean body mass. Our findings provide new insight into the genetics of lean body mass.Lean body mass is a highly heritable trait and is associated with various health conditions. Here, Kiel and colleagues perform a meta-analysis of genome-wide association studies for whole body lean body mass and find five novel genetic loci to be significantly associated.

Genotype-covariate interaction effects and the heritability of adult body mass index

Obesity is a worldwide epidemic, with major health and economic costs. Here we estimate heritability for body mass index (BMI) in 172,000 sibling pairs and 150,832 unrelated individuals and explore the contribution of genotype-covariate interaction effects at common SNP loci. We find evidence for genotype-age interaction (likelihood ratio test (LRT) = 73.58, degrees of freedom (df) = 1, P = 4.83 × 10^-18), which contributed 8.1% (1.4% s.e.) to BMI variation. Across eight self-reported lifestyle factors, including diet and exercise, we find genotype-environment interaction only for smoking behavior (LRT = 19.70, P = 5.03 × 10^-5 and LRT = 30.80, P = 1.42 × 10^-8), which contributed 4.0% (0.8% s.e.) to BMI variation. Bayesian association analysis suggests that BMI is highly polygenic, with 75% of the SNP heritability attributable to loci that each explain <0.01% of the phenotypic variance. Our findings imply that substantially larger sample sizes across ages and lifestyles are required to understand the full genetic architecture of BMI.

Quantitative genetics of circulating Hyaluronic Acid (HA) and its correlation with hand osteoarthritis and obesity-related phenotypes in a community-based sample

BACKGROUND

One of the potential molecular biomarkers of osteoarthritis (OA) is hyaluronic acid (HA). HA levels may be related to the severity and progression of OA. However, little is known about the contribution of major risk factors for osteoarthritis, e.g. obesity-related phenotypes and genetics to HA variation.

AIM

To clarify the quantitative effect of these factors on HA.

SUBJECTS AND METHODS

An ethnically homogeneous sample of 911 apparently healthy European-derived individuals, assessed for radiographic hand osteoarthritis (RHOA), HA, leptin, adiponectin, and several anthropometrical measures of obesity-related phenotypes was studied. Model-based quantitative genetic analysis was used to reveal genetic and shared environmental factors affecting the variation of the study's phenotypes.

RESULTS

The HA levels significantly correlated with the age, RHOA, adiponectin, obesity-related phenotypes, and the waist-to-hip ratio. The putative genetic effects contributed significantly to the variation of HA (66.2 ± 9.3%) and they were also significant factors in the variations of all the other studied phenotypes, with the heritability estimate ranging between 0.122 ± 4.4% (WHR) and 45.7 ± 2.2% (joint space narrowing).

CONCLUSIONS

This is the first study to report heritability estimates of HA variation and its correlation with obesity-related phenotypes, ADP and RHOA. However, the nature of genetic effects on HA and its correlation with other study phenotypes require further clarification.

10 Years of GWAS Discovery: Biology, Function, and Translation

Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.

Putative enhancer sites in the bovine genome are enriched with variants affecting complex traits

Background

Enhancers are non-coding DNA sequences, which when they are bound by specific proteins increase the level of gene transcription. Enhancers activate unique gene expression patterns within cells of different types or under different conditions. Enhancers are key contributors to gene regulation, and causative variants that affect quantitative traits in humans and mice have been located in enhancer regions. However, in the bovine genome, enhancers as well as other regulatory elements are not yet well defined. In this paper, we sought to improve the annotation of bovine enhancer regions by using publicly available mammalian enhancer information. To test if the identified putative bovine enhancer regions are enriched with functional variants that affect milk production traits, we performed genome-wide association studies using imputed whole-genome sequence data followed by meta-analysis and enrichment analysis.

Results

We produced a library of candidate bovine enhancer regions by using publicly available bovine ChIP-Seq enhancer data in combination with enhancer data that were identified based on sequence homology with human and mouse enhancer databases. We found that imputed whole-genome sequence variants associated with milk production traits in 16,581 dairy cattle were enriched with enhancer regions that were marked by bovine-liver H3K4me3 and H3K27ac histone modifications from both permutation tests and gene set enrichment analysis. Enhancer regions that were identified based on sequence homology with human and mouse enhancer regions were not as strongly enriched with trait-associated sequence variants as the bovine ChIP-Seq candidate enhancer regions. The bovine ChIP-Seq enriched enhancer regions were located near genes and quantitative trait loci that are associated with pregnancy, growth, disease resistance, meat quality and quantity, and milk quality and quantity traits in dairy and beef cattle.

Conclusions

Our results suggest that sequence variants within enhancer regions that are located in bovine non-coding genomic regions contribute to the variation in complex traits. The level of enrichment was higher in bovine-specific enhancer regions that were identified by detecting histone modifications H3K4me3 and H3K27ac in bovine liver tissues than in enhancer regions identified by sequence homology with human and mouse data. These results highlight the need to use bovine-specific experimental data for the identification of enhancer regions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-017-0331-4) contains supplementary material, which is available to authorized users.

Genome-Wide Polygenic Scores Predict Reading Performance Throughout the School Years

It is now possible to create individual-specific genetic scores, called genome-wide polygenic scores (GPS). We used a GPS for years of education (EduYears) to predict reading performance assessed at UK National Curriculum Key Stages 1 (age 7), 2 (age 12) and 3 (age 14) and on reading tests administered at ages 7 and 12 in a UK sample of 5,825 unrelated individuals. EduYears GPS accounts for up to 5% of the variance in reading performance at age 14. GPS predictions remained significant after accounting for general cognitive ability and family socioeconomic status. Reading performance of children in the lowest and highest 12.5% of the EduYears GPS distribution differed by a mean growth in reading ability of approximately two school years. It seems certain that polygenic scores will be used to predict strengths and weaknesses in education.

The potential of shifting recombination hotspots to increase genetic gain in livestock breeding

Background

This study uses simulation to explore and quantify the potential effect of shifting recombination hotspots on genetic gain in livestock breeding programs.

Methods

We simulated three scenarios that differed in the locations of quantitative trait nucleotides (QTN) and recombination hotspots in the genome. In scenario 1, QTN were randomly distributed along the chromosomes and recombination was restricted to occur within specific genomic regions (i.e. recombination hotspots). In the other two scenarios, both QTN and recombination hotspots were located in specific regions, but differed in whether the QTN occurred outside of (scenario 2) or inside (scenario 3) recombination hotspots. We split each chromosome into 250, 500 or 1000 regions per chromosome of which 10% were recombination hotspots and/or contained QTN. The breeding program was run for 21 generations of selection, after which recombination hotspot regions were kept the same or were shifted to adjacent regions for a further 80 generations of selection. We evaluated the effect of shifting recombination hotspots on genetic gain, genetic variance and genic variance.

Results

Our results show that shifting recombination hotspots reduced the decline of genetic and genic variance by releasing standing allelic variation in the form of new allele combinations. This in turn resulted in larger increases in genetic gain. However, the benefit of shifting recombination hotspots for increased genetic gain was only observed when QTN were initially outside recombination hotspots. If QTN were initially inside recombination hotspots then shifting them decreased genetic gain.

Discussion

Shifting recombination hotspots to regions of the genome where recombination had not occurred for 21 generations of selection (i.e. recombination deserts) released more of the standing allelic variation available in each generation and thus increased genetic gain. However, whether and how much increase in genetic gain was achieved by shifting recombination hotspots depended on the distribution of QTN in the genome, the number of recombination hotspots and whether QTN were initially inside or outside recombination hotspots.

Conclusions

Our findings show future scope for targeted modification of recombination hotspots e.g. through changes in zinc-finger motifs of the PRDM9 protein to increase genetic gain in production species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-017-0330-5) contains supplementary material, which is available to authorized users.

Ancient selection for derived alleles at a GDF5 enhancer influencing human growth and osteoarthritis risk

Variants in GDF5 are associated with human arthritis and decreased height, but the causal mutations are still unknown. We surveyed the Gdf5 locus for regulatory regions in transgenic mice and fine-mapped separate enhancers controlling expression in joints versus growing ends of long bones. A large downstream regulatory region contains a novel growth enhancer (GROW1), which is required for normal Gdf5 expression at ends of developing bones and for normal bone lengths in vivo. Human GROW1 contains a common base-pair change that decreases enhancer activity and colocalizes with peaks of positive selection in humans. The derived allele is rare in Africa but common in Eurasia and is found in Neandertals and Denisovans. Our results suggest that an ancient regulatory variant in GROW1 has been repeatedly selected in northern environments and that past selection on growth phenotypes explains the high frequency of a GDF5 haplotype that also increases arthritis susceptibility in many human populations.

Strong epistatic and additive effects of linked candidate SNPs for Drosophila pigmentation have implications for analysis of genome-wide association studies results

Background

The mapping resolution of genome-wide association studies (GWAS) is limited by historic recombination events and effects are often assigned to haplotype blocks rather than individual SNPs. It is not clear how many of the SNPs in the block, and which ones, are causative. Drosophila pigmentation is a powerful model to dissect the genetic basis of intra-specific and inter-specific phenotypic variation. Three tightly linked SNPs in the t-MSE enhancer have been identified in three D. melanogaster populations as major contributors to female abdominal pigmentation. This enhancer controls the expression of the pigmentation gene tan (t) in the abdominal epidermis. Two of the three SNPs were confirmed in an independent study using the D. melanogaster Genetic Reference Panel established from a North American population.

Results

We determined the functional impact of SNP1, SNP2, and SNP3 using transgenic lines to test all possible haplotypes in vivo. We show that all three candidate SNPs contribute to female Drosophila abdominal pigmentation. Interestingly, only two SNPs agree with the effect predicted by GWAS; the third one goes in the opposite direction because of linkage disequilibrium between multiple functional SNPs. Our experimental design uncovered strong additive effects for the three SNPs, but we also found significant epistatic effects explaining up to 11% of the total variation.

Conclusions

Our results suggest that linked causal variants are important for the interpretation of GWAS and functional validation is needed to understand the genetic architecture of traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1262-7) contains supplementary material, which is available to authorized users.

Taller height as a risk factor for venous thromboembolism: a Mendelian randomization meta-analysis

Essentials Observational data suggest taller people have a higher risk of venous thromboembolism (VTE). We used Mendelian randomization techniques to further explore this association in three studies. Risk of VTE increased by 30-40% for each 10 cm increment in height. Height was more strongly associated with deep vein thrombosis than with pulmonary embolism.

SUMMARY

Background Taller height is associated with a greater risk of venous thromboembolism (VTE). Objectives To use instrumental variable (IV) techniques (Mendelian randomization) to further explore this relationship. Methods Participants of European ancestry were included from two cohort studies (Atherosclerosis Risk in Communities [ARIC] study and Cardiovascular Health Study [CHS]) and one case-control study (Mayo Clinic VTE Study [Mayo]). We created two weighted genetic risk scores (GRSs) for height; the full GRS included 668 single-nucleotide polymorphisms (SNPs) from a previously published meta-analysis, and the restricted GRS included a subset of 362 SNPs not associated with weight independently of height. Standard logistic regression and IV models were used to estimate odds ratios (ORs) for VTE per 10-cm increment in height. ORs were pooled across the three studies by the use of inverse variance-weighted random effects meta-analysis. Results Among 9143 ARIC and 3180 CHS participants free of VTE at baseline, there were 367 and 109 incident VTE events. There were 1143 VTE cases and 1292 controls included from Mayo. The pooled ORs from non-IV models and models using the full and restricted GRSs as IVs were 1.27 (95% confidence interval [CI] 1.11-1.46), 1.34 (95% CI 1.04-1.73) and 1.45 (95% CI 1.04-2.01) per 10-cm greater height, respectively. Conclusions Taller height is associated with an increased risk of VTE in adults of European ancestry. Possible explanations for this association, including taller people having a greater venous surface area, a higher number of venous valves, or greater hydrostatic pressure, need to be explored further.

Personalized medicine: Genetic risk prediction of drug response

Pharmacogenomics (PGx), a substantial component of "personalized medicine", seeks to understand each individual's genetic composition to optimize drug therapy -- maximizing beneficial drug response, while minimizing adverse drug reactions (ADRs). Drug responses are highly variable because innumerable factors contribute to ultimate phenotypic outcomes. Recent genome-wide PGx studies have provided some insight into genetic basis of variability in drug response. These can be grouped into three categories. [a] Monogenic (Mendelian) traits include early examples mostly of inherited disorders, and some severe (idiosyncratic) ADRs typically influenced by single rare coding variants. [b] Predominantly oligogenic traits represent variation largely influenced by a small number of major pharmacokinetic or pharmacodynamic genes. [c] Complex PGx traits resemble most multifactorial quantitative traits -- influenced by numerous small-effect variants, together with epigenetic effects and environmental factors. Prediction of monogenic drug responses is relatively simple, involving detection of underlying mutations; due to rarity of these events and incomplete penetrance, however, prospective tests based on genotype will have high false-positive rates, plus pharmacoeconomics will require justification. Prediction of predominantly oligogenic traits is slowly improving. Although a substantial fraction of variation can be explained by limited numbers of large-effect genetic variants, uncertainty in successful predictions and overall cost-benefit ratios will make such tests elusive for everyday clinical use. Prediction of complex PGx traits is almost impossible in the foreseeable future. Genome-wide association studies of large cohorts will continue to discover relevant genetic variants; however, these small-effect variants, combined, explain only a small fraction of phenotypic variance -- thus having limited predictive power and clinical utility.

Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease

Continuing efforts from large international consortia have made genome-wide epigenomic and transcriptomic annotation data publicly available for a variety of cell and tissue types. However, synthesis of these datasets into effective summary metrics to characterize the functional non-coding genome remains a challenge. Here, we present GenoSkyline-Plus, an extension of our previous work through integration of an expanded set of epigenomic and transcriptomic annotations to produce high-resolution, single tissue annotations. After validating our annotations with a catalog of tissue-specific non-coding elements previously identified in the literature, we apply our method using data from 127 different cell and tissue types to present an atlas of heritability enrichment across 45 different GWAS traits. We show that broader organ system categories (e.g. immune system) increase statistical power in identifying biologically relevant tissue types for complex diseases while annotations of individual cell types (e.g. monocytes or B-cells) provide deeper insights into disease etiology. Additionally, we use our GenoSkyline-Plus annotations in an in-depth case study of late-onset Alzheimer's disease (LOAD). Our analyses suggest a strong connection between LOAD heritability and genetic variants contained in regions of the genome functional in monocytes. Furthermore, we show that LOAD shares a similar localization of SNPs to monocyte-functional regions with Parkinson's disease. Overall, we demonstrate that integrated genome annotations at the single tissue level provide a valuable tool for understanding the etiology of complex human diseases. Our GenoSkyline-Plus annotations are freely available at http://genocanyon.med.yale.edu/GenoSkyline.

Susceptibility to Childhood Pneumonia: A Genome-Wide Analysis

Previous studies have indicated that in adult smokers, a history of childhood pneumonia is associated with reduced lung function and chronic obstructive pulmonary disease. There have been few previous investigations using genome-wide association studies to investigate genetic predisposition to pneumonia. This study aims to identify the genetic variants associated with the development of pneumonia during childhood and over the course of the lifetime. Study subjects included current and former smokers with and without chronic obstructive pulmonary disease participating in the COPDGene Study. Pneumonia was defined by subject self-report, with childhood pneumonia categorized as having the first episode at <16 years. Genome-wide association studies for childhood pneumonia (843 cases, 9,091 control subjects) and lifetime pneumonia (3,766 cases, 5,659 control subjects) were performed separately in non-Hispanic whites and African Americans. Non-Hispanic white and African American populations were combined in the meta-analysis. Top genetic variants from childhood pneumonia were assessed in network analysis. No single-nucleotide polymorphisms reached genome-wide significance, although we identified potential regions of interest. In the childhood pneumonia analysis, this included variants in NGR1 (P = 6.3 × 10^-8), PAK6 (P = 3.3 × 10^-7), and near MATN1 (P = 2.8 × 10^-7). In the lifetime pneumonia analysis, this included variants in LOC339862 (P = 8.7 × 10^-7), RAPGEF2 (P = 8.4 × 10^-7), PHACTR1 (P = 6.1 × 10^-7), near PRR27 (P = 4.3 × 10^-7), and near MCPH1 (P = 2.7 × 10^-7). Network analysis of the genes associated with childhood pneumonia included top networks related to development, blood vessel morphogenesis, muscle contraction, WNT signaling, DNA damage, apoptosis, inflammation, and immune response (P ≤ 0.05). We have identified genes potentially associated with the risk of pneumonia. Further research will be required to confirm these associations and to determine biological mechanisms.

CLINICAL TRIAL REGISTRATION

NCT00608764.

Mutations in C-natriuretic peptide (NPPC): a novel cause of autosomal dominant short stature

PurposeC-type natriuretic peptide (CNP) and its principal receptor, natriuretic peptide receptor B (NPR-B), have been shown to be important in skeletal development. CNP and NPR-B are encoded by natriuretic peptide precursor-C (NPPC) and natriuretic peptide receptor 2 (NPR2) genes, respectively. While NPR2 mutations have been described in patients with skeletal dysplasias and idiopathic short stature (ISS), and several Npr2 and Nppc skeletal dysplasia mouse models exist, no mutations in NPPC have been described in patients to date.MethodsNPPC was screened in 668 patients (357 with disproportionate short stature and 311 with autosomal dominant ISS) and 29 additional ISS families in an ongoing whole-exome sequencing study.ResultsTwo heterozygous NPPC mutations, located in the highly conserved CNP ring, were identified. Both showed significant reductions in cyclic guanosine monophosphate synthesis, confirming their pathogenicity. Interestingly, one has been previously linked to skeletal abnormalities in the spontaneous Nppc mouse long-bone abnormality (lbab) mutant.ConclusionsOur results demonstrate, for the first time, that NPPC mutations cause autosomal dominant short stature in humans. The NPPC mutations cosegregated with a short stature and small hands phenotype. A CNP analog, which is currently in clinical trials for the treatment of achondroplasia, seems a promising therapeutic approach, since it directly replaces the defective protein.

O-GlcNAcylation mediates the control of cytosolic phosphoenolpyruvate carboxykinase activity via Pgc1α

PGC1α is a coactivator of many transcription factors and cytosolic phosphoenolpyruvate carboxykinase (PCK1) is a key enzyme for gluconeogenesis. PGC1α interacts with the transcription factor PPARγ to stimulate PCK1 expression and thus de novo glucose synthesis. These proteins are not only important for central energy metabolism but also for supplying intermediates for other metabolic pathways, including lipidogenesis and protein synthesis and might therefore be important factors in the ethiopathogenesis of metabolic disorders like diabetes but also in other pathologies like cancer. Since polymorphisms in these proteins have been related to some phenotypic traits in animals like pigs and PGC1α G482S polymorphism increases fat deposition in humans, we have investigated the molecular basis of such effects focusing on a commonly studied polymorphism in pig Pgc1α, which changes a cysteine at position 430 (WT) of the protein to a serine (C430S). Biochemical analyses show that Pgc1α WT stimulates higher expression of human PCK1 in HEK293T and HepG2 cells. Paradoxically, Pgc1α WT is less stable than Pgc1α p.C430S in HEK293T cells. However, the study of different post-translational modifications shows a higher O-GlcNAcylation level of Pgc1α p.C430S. This higher O-GlcNAcylation level significantly decreases the interaction between Pgc1α and PPARγ demonstrating the importance of post-translational glycosylation of PGC1α in the regulation of PCK1 activity. This, furthermore, could explain at least in part the observed epistatic effects between PGC1α and PCK1 in pigs.

Differential expression analysis for RNAseq using Poisson mixed models

Identifying differentially expressed (DE) genes from RNA sequencing (RNAseq) studies is among the most common analyses in genomics. However, RNAseq DE analysis presents several statistical and computational challenges, including over-dispersed read counts and, in some settings, sample non-independence. Previous count-based methods rely on simple hierarchical Poisson models (e.g. negative binomial) to model independent over-dispersion, but do not account for sample non-independence due to relatedness, population structure and/or hidden confounders. Here, we present a Poisson mixed model with two random effects terms that account for both independent over-dispersion and sample non-independence. We also develop a scalable sampling-based inference algorithm using a latent variable representation of the Poisson distribution. With simulations, we show that our method properly controls for type I error and is generally more powerful than other widely used approaches, except in small samples (n <15) with other unfavorable properties (e.g. small effect sizes). We also apply our method to three real datasets that contain related individuals, population stratification or hidden confounders. Our results show that our method increases power in all three data compared to other approaches, though the power gain is smallest in the smallest sample (n = 6). Our method is implemented in MACAU, freely available at www.xzlab.org/software.html.

Puzzles in modern biology. V. Why are genomes overwired?

Many factors affect eukaryotic gene expression. Transcription factors, histone codes, DNA folding, and noncoding RNA modulate expression. Those factors interact in large, broadly connected regulatory control networks. An engineer following classical principles of control theory would design a simpler regulatory network. Why are genomes overwired? Neutrality or enhanced robustness may lead to the accumulation of additional factors that complicate network architecture. Dynamics progresses like a ratchet. New factors get added. Genomes adapt to the additional complexity. The newly added factors can no longer be removed without significant loss of fitness. Alternatively, highly wired genomes may be more malleable. In large networks, most genomic variants tend to have a relatively small effect on gene expression and trait values. Many small effects lead to a smooth gradient, in which traits may change steadily with respect to underlying regulatory changes. A smooth gradient may provide a continuous path from a starting point up to the highest peak of performance. A potential path of increasing performance promotes adaptability and learning. Genomes gain by the inductive process of natural selection, a trial and error learning algorithm that discovers general solutions for adapting to environmental challenge. Similarly, deeply and densely connected computational networks gain by various inductive trial and error learning procedures, in which the networks learn to reduce the errors in sequential trials. Overwiring alters the geometry of induction by smoothing the gradient along the inductive pathways of improving performance. Those overwiring benefits for induction apply to both natural biological networks and artificial deep learning networks.

An Expanded View of Complex Traits: From Polygenic to Omnigenic

A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome-including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an "omnigenic" model.

An Expanded View of Complex Traits: From Polygenic to Omnigenic

A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome-including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an "omnigenic" model.

Pleiotropic Effects of Trait-Associated Genetic Variation on DNA Methylation: Utility for Refining GWAS Loci

Most genetic variants identified in genome-wide association studies (GWASs) of complex traits are thought to act by affecting gene regulation rather than directly altering the protein product. As a consequence, the actual genes involved in disease are not necessarily the most proximal to the associated variants. By integrating data from GWAS analyses with those from genetic studies of regulatory variation, it is possible to identify variants pleiotropically associated with both a complex trait and measures of gene regulation. In this study, we used summary-data-based Mendelian randomization (SMR), a method developed to identify variants pleiotropically associated with both complex traits and gene expression, to identify variants associated with complex traits and DNA methylation. We used large DNA methylation quantitative trait locus (mQTL) datasets generated from two different tissues (blood and fetal brain) to prioritize genes for >40 complex traits with robust GWAS data and found considerable overlap with the results of SMR analyses performed with expression QTL (eQTL) data. We identified multiple examples of variable DNA methylation associated with GWAS variants for a range of complex traits, demonstrating the utility of this approach for refining genetic association signals.

Genetics of Short Stature

Short stature is a common and heterogeneous condition that is often genetic in etiology. For most children with genetic short stature, the specific molecular causes remain unknown; but with advances in exome/genome sequencing and bioinformatics approaches, new genetic causes of growth disorders have been identified, contributing to the understanding of the underlying molecular mechanisms of longitudinal bone growth and growth failure. Identifying new genetic causes of growth disorders has the potential to improve diagnosis, prognostic accuracy, and individualized management, and help avoid unnecessary testing for endocrine and other disorders.

Increased detection of genetic loci associated with risk predictors of osteoporotic fracture using a pleiotropic cFDR method

Although GWAS have been successful in identifying some osteoporosis associated loci, the findings explain only a small fraction of the total genetic variance. In this study we use a recently developed novel pleiotropic conditional false discovery rate (cFDR) method to identify novel genetic loci associated with two risk traits for osteoporotic fracture (the clinical outcome and end result of osteoporosis), Height (HT) and Femoral Neck (FNK) BMD. The cFDR method allows us to improve the detection of associated variants by incorporating any potentially shared genetic mechanisms between the two associated traits. We analyzed the summary statistics from two GWAS meta-analyses for single nucleotide polymorphisms (SNPs) that are associated with HT and FNK BMD. Using the cFDR method, we show enrichment in the identification of SNPs associated with each trait conditioned on their strength of association with the second trait. The findings revealed 18 SNPs that are associated with both HT and FNK BMD, 4 of which had not previously been reported to play a role in bone health. The novel SNPs located at KIF1B and the intergenic region between FERD3L and TWISTNB are noteworthy as these genes may be associated with processes that are functionally important in bone metabolism. By leveraging GWAS results from related phenotypes we identified several novel loci that may contribute to the proportion of variability explained for each trait, although we cannot speculate about these potential contributions to heritability based on this analysis alone.

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.

MANAGEMENT OF ENDOCRINE DISEASE: Diagnostic and therapeutic approach of tall stature

Tall stature is defined as a height of more than 2 standard deviations (s.d.) above average for same sex and age. Tall individuals are usually referred to endocrinologists so that hormonal disorders leading to abnormal growth are excluded. However, the majority of these patients have familial tall stature or constitutional advance of growth (generally associated with obesity), both of which are diagnoses of exclusion. It is necessary to have familiarity with a large number of rarer overgrowth syndromes, especially because some of them may have severe complications such as aortic aneurysm, thromboembolism and tumor predisposition and demand-specific follow-up approaches. Additionally, endocrine disorders associated with tall stature have specific treatments and for this reason their recognition is mandatory. With this review, we intend to provide an up-to-date summary of the genetic conditions associated with overgrowth to emphasize a practical diagnostic approach of patients with tall stature and to discuss the limitations of current growth interruption treatment options.

Cognitive ability and physical health: a Mendelian randomization study

Causes of the association between cognitive ability and health remain unknown, but may reflect a shared genetic aetiology. This study examines the causal genetic associations between cognitive ability and physical health. We carried out two-sample Mendelian randomization analyses using the inverse-variance weighted method to test for causality between later life cognitive ability, educational attainment (as a proxy for cognitive ability in youth), BMI, height, systolic blood pressure, coronary artery disease, and type 2 diabetes using data from six independent GWAS consortia and the UK Biobank sample (N = 112 151). BMI, systolic blood pressure, coronary artery disease and type 2 diabetes showed negative associations with cognitive ability; height was positively associated with cognitive ability. The analyses provided no evidence for casual associations from health to cognitive ability. In the other direction, higher educational attainment predicted lower BMI, systolic blood pressure, coronary artery disease, type 2 diabetes, and taller stature. The analyses indicated no causal association from educational attainment to physical health. The lack of evidence for causal associations between cognitive ability, educational attainment, and physical health could be explained by weak instrumental variables, poorly measured outcomes, or the small number of disease cases.

Genetic variations at the human growth hormone receptor (GHR) gene locus are associated with idiopathic short stature

GH plays an essential role in the growing child by binding to the growth hormone receptor (GHR) on target cells and regulating multiple growth promoting and metabolic effects. Mutations in the GHR gene coding regions result in GH insensitivity (dwarfism) due to a dysfunctional receptor protein. However, children with idiopathic short stature (ISS) show growth impairment without GH or GHR defects. We hypothesized that decreased expression of the GHR gene may be involved. To test this, we investigated whether common genetic variants (microsatellites, SNPs) in regulatory regions of the GHR gene region were associated with the ISS phenotype. Genotyping of a GT‐repeat microsatellite in the GHR 5′UTR in a Montreal ISS cohort (n = 37 ISS, n = 105 controls) revealed that the incidence of the long/short (L/S) genotype was 3.3× higher in ISS children than controls (P = 0.04, OR = 3.85). In an Italian replication cohort (n = 143 ISS, n = 282 controls), the medium/short (M/S) genotype was 1.9× more frequent in the male ISS than controls (P = 0.017, OR = 2.26). In both ISS cohorts, logistic regression analysis of 27 SNPs showed an association of ISS with rs4292454, while haplotype analysis revealed specific risk haplotypes in the 3′ haploblocks. In contrast, there were no differences in GT genotype frequencies in a cohort of short stature (SS) adults versus controls (CARTaGENE: n = 168 SS, n = 207 controls) and the risk haplotype in the SS cohort was located in the most 5′ haploblock. These data suggest that the variants identified are potentially genetic markers specifically associated with the ISS phenotype.

Whole genome sequencing and imputation in isolated populations identify genetic associations with medically-relevant complex traits

Next-generation association studies can be empowered by sequence-based imputation and by studying founder populations. Here we report ∼9.5 million variants from whole-genome sequencing (WGS) of a Cretan-isolated population, and show enrichment of rare and low-frequency variants with predicted functional consequences. We use a WGS-based imputation approach utilizing 10,422 reference haplotypes to perform genome-wide association analyses and observe 17 genome-wide significant, independent signals, including replicating evidence for association at eight novel low-frequency variant signals. Two novel cardiometabolic associations are at lead variants unique to the founder population sequences: chr16:70790626 (high-density lipoprotein levels beta −1.71 (SE 0.25), P=1.57 × 10⁻¹¹, effect allele frequency (EAF) 0.006); and rs145556679 (triglycerides levels beta −1.13 (SE 0.17), P=2.53 × 10⁻¹¹, EAF 0.013). Our findings add empirical support to the contribution of low-frequency variants in complex traits, demonstrate the advantage of including population-specific sequences in imputation panels and exemplify the power gains afforded by population isolates.

Isolated populations can provide useful information on low-frequency variants for dissecting genetic architecture of complex traits. Here, Zeggini and colleagues show enrichment of rare and low-frequency variants and 8 novel low-frequency variant signals for cardiometabolic traits in two Greek isolated populations

Identification of the gene-regulatory landscape in skeletal development and potential links to skeletal regeneration

A class of gene-regulatory elements called enhancers are the main mediators controlling quantitative, temporal and spatial gene expressions. In the course of evolution, the enhancer landscape of higher organisms such as mammals has become quite complex, exerting biological functions precisely and coordinately. In mammalian skeletal development, the master transcription factors Sox9, Runx2 and Sp7/Osterix function primarily through enhancers on the genome to achieve specification and differentiation of skeletal cells. Recently developed genome-scale analyses have shed light on multiple layers of gene regulations, uncovering not only the primary mode of actions of these transcription factors on skeletal enhancers, but also the relation of the epigenetic landscape to three-dimensional chromatin architecture. Here, we review findings on the emerging framework of gene-regulatory networks involved in skeletal development. We further discuss the power of genome-scale analyses to provide new insights into genetic diseases and regenerative medicine in skeletal tissues.

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Skeletal development is coordinated by master transcription factors.

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ChIP-seq analyses for the skeletal regulators identified their modes of actions.

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Analyses of epigenetic landscape features distinct cell types in skeletal tissues.

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Integrated analyses of the gene regulatory networks link to skeletal regeneration.

Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data

Background

Understanding the mapping precision of genome-wide association studies (GWAS), that is the physical distances between the top associated single-nucleotide polymorphisms (SNPs) and the causal variants, is essential to design fine-mapping experiments for complex traits and diseases.

Results

Using simulations based on whole-genome sequencing (WGS) data from 3642 unrelated individuals of European descent, we show that the association signals at rare causal variants (minor allele frequency ≤ 0.01) are very unlikely to be mapped to common variants in GWAS using either WGS data or imputed data and vice versa. We predict that at least 80% of the common variants identified from published GWAS using imputed data are within 33.5 Kbp of the causal variants, a resolution that is comparable with that using WGS data. Mapping precision at these loci will improve with increasing sample sizes of GWAS in the future. For rare variants, the mapping precision of GWAS using WGS data is extremely high, suggesting WGS is an efficient strategy to detect and fine-map rare variants simultaneously. We further assess the mapping precision by linkage disequilibrium between GWAS hits and causal variants and develop an online tool (gwasMP) to query our results with different thresholds of physical distance and/or linkage disequilibrium (http://cnsgenomics.com/shiny/gwasMP).

Conclusions

Our findings provide a benchmark to inform future design and development of fine-mapping experiments and technologies to pinpoint the causal variants at GWAS loci.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1216-0) contains supplementary material, which is available to authorized users.

Quantifying the extent to which index event biases influence large genetic association studies

As genetic association studies increase in size to 100 000s of individuals, subtle biases may influence conclusions. One possible bias is 'index event bias' (IEB) that appears due to the stratification by, or enrichment for, disease status when testing associations between genetic variants and a disease-associated trait. We aimed to test the extent to which IEB influences some known trait associations in a range of study designs and provide a statistical framework for assessing future associations. Analyzing data from 113 203 non-diabetic UK Biobank participants, we observed three (near TCF7L2, CDKN2AB and CDKAL1) overestimated (body mass index (BMI) decreasing) and one (near MTNR1B) underestimated (BMI increasing) associations among 11 type 2 diabetes risk alleles (at P  <  0.05). IEB became even stronger when we tested a type 2 diabetes genetic risk score composed of these 11 variants (-0.010 standard deviations BMI per allele, P  =  5 × 10- 4), which was confirmed in four additional independent studies. Similar results emerged when examining the effect of blood pressure increasing alleles on BMI in normotensive UK Biobank samples. Furthermore, we demonstrated that, under realistic scenarios, common disease alleles would become associated at P <  5 × 10- 8 with disease-related traits through IEB alone, if disease prevalence in the sample differs appreciably from the background population prevalence. For example, some hypertension and type 2 diabetes alleles will be associated with BMI in sample sizes of  >500 000 if the prevalence of those diseases differs by >10% from the background population. In conclusion, IEB may result in false positive or negative genetic associations in very large studies stratified or strongly enriched for/against disease cases.

Towards precision medicine

There is great potential for genome sequencing to enhance patient care through improved diagnostic sensitivity and more precise therapeutic targeting. To maximize this potential, genomics strategies that have been developed for genetic discovery - including DNA-sequencing technologies and analysis algorithms - need to be adapted to fit clinical needs. This will require the optimization of alignment algorithms, attention to quality-coverage metrics, tailored solutions for paralogous or low-complexity areas of the genome, and the adoption of consensus standards for variant calling and interpretation. Global sharing of this more accurate genotypic and phenotypic data will accelerate the determination of causality for novel genes or variants. Thus, a deeper understanding of disease will be realized that will allow its targeting with much greater therapeutic precision.

Prediction of gene expression with cis-SNPs using mixed models and regularization methods

Background

It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases.

Methods

We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data.

Results

The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R² of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R² ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R² ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks.

Conclusions

Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.

The Interaction of Genetic Background and Mutational Effects in Regulation of Mouse Craniofacial Shape

Inbred genetic background significantly influences the expression of phenotypes associated with known genetic perturbations and can underlie variation in disease severity between individuals with the same mutation. However, the effect of epistatic interactions on the development of complex traits, such as craniofacial morphology, is poorly understood. Here, we investigated the effect of three inbred backgrounds (129X1/SvJ, C57BL/6J, and FVB/NJ) on the expression of craniofacial dysmorphology in mice (Mus musculus) with loss of function in three members of the Sprouty family of growth factor negative regulators (Spry1, Spry2, or Spry4) in order to explore the impact of epistatic interactions on skull morphology. We found that the interaction of inbred background and the Sprouty genotype explains as much craniofacial shape variation as the Sprouty genotype alone. The most severely affected genotypes display a relatively short and wide skull, a rounded cranial vault, and a more highly angled inferior profile. Our results suggest that the FVB background is more resilient to Sprouty loss of function than either C57 or 129, and that Spry4 loss is generally less severe than loss of Spry1 or Spry2 While the specific modifier genes responsible for these significant background effects remain unknown, our results highlight the value of intercrossing mice of multiple inbred backgrounds to identify the genes and developmental interactions that modulate the severity of craniofacial dysmorphology. Our quantitative results represent an important first step toward elucidating genetic interactions underlying variation in robustness to known genetic perturbations in mice.

Mutations in Epigenetic Regulation Genes Are a Major Cause of Overgrowth with Intellectual Disability

To explore the genetic architecture of human overgrowth syndromes and human growth control, we performed experimental and bioinformatic analyses of 710 individuals with overgrowth (height and/or head circumference ≥+2 SD) and intellectual disability (OGID). We identified a causal mutation in 1 of 14 genes in 50% (353/710). This includes HIST1H1E, encoding histone H1.4, which has not been associated with a developmental disorder previously. The pathogenic HIST1H1E mutations are predicted to result in a product that is less effective in neutralizing negatively charged linker DNA because it has a reduced net charge, and in DNA binding and protein-protein interactions because key residues are truncated. Functional network analyses demonstrated that epigenetic regulation is a prominent biological process dysregulated in individuals with OGID. Mutations in six epigenetic regulation genes—NSD1, EZH2, DNMT3A, CHD8, HIST1H1E, and EED—accounted for 44% of individuals (311/710). There was significant overlap between the 14 genes involved in OGID and 611 genes in regions identified in GWASs to be associated with height (p = 6.84 × 10⁻⁸), suggesting that a common variation impacting function of genes involved in OGID influences height at a population level. Increased cellular growth is a hallmark of cancer and there was striking overlap between the genes involved in OGID and 260 somatically mutated cancer driver genes (p = 1.75 × 10⁻¹⁴). However, the mutation spectra of genes involved in OGID and cancer differ, suggesting complex genotype-phenotype relationships. These data reveal insights into the genetic control of human growth and demonstrate that exome sequencing in OGID has a high diagnostic yield.

Child height and intergenerational transmission of health: Evidence from ethnic Indians in England

A large literature documents a widespread prevalence of small stature among Indian children as well as adults. We show that a height gap relative to a richer population such as whites in England also exists, although substantially reduced, among adult immigrants of Indian ethnicity in England. This is despite positive height selection into migration, demonstrated by ethnic Indian adults in England being on average 6-7cm taller than in India. However, the difference between natives and ethnic Indians in England disappears among their younger sons and daughters, although it re-appears among adolescents. We estimate that, conditional on age, gender and parental height, ethnic Indian children of age 2-4 in England are 6-8% taller than in India. Such degree of catch up in one generation is remarkable, also because in England children of ethnic Indians have much smaller birthweight than whites, by about 0.4kg on average.

Genetics of primary sclerosing cholangitis and pathophysiological implications

Primary sclerosing cholangitis (PSC) is a chronic disease leading to fibrotic scarring of the intrahepatic and extrahepatic bile ducts, causing considerable morbidity and mortality via the development of cholestatic liver cirrhosis, concurrent IBD and a high risk of bile duct cancer. Expectations have been high that genetic studies would determine key factors in PSC pathogenesis to support the development of effective medical therapies. Through the application of genome-wide association studies, a large number of disease susceptibility genes have been identified. The overall genetic architecture of PSC shares features with both autoimmune diseases and IBD. Strong human leukocyte antigen gene associations, along with several susceptibility genes that are critically involved in T-cell function, support the involvement of adaptive immune responses in disease pathogenesis, and position PSC as an autoimmune disease. In this Review, we survey the developments that have led to these gene discoveries. We also elaborate relevant interpretations of individual gene findings in the context of established disease models in PSC, and propose relevant translational research efforts to pursue novel insights.

Combined GWAS and 'guilt by association'-based prioritization analysis identifies functional candidate genes for body size in sheep

Background

Body size in sheep is an important indicator of productivity, growth and health as well as of environmental adaptation. It is a composite quantitative trait that has been studied with high-throughput genomic methods, i.e. genome-wide association studies (GWAS) in various mammalian species. Several genomic markers have been associated with body size traits and genes have been identified as causative candidates in humans, dog and cattle. A limited number of related GWAS have been performed in various sheep breeds and have identified genomic regions and candidate genes that partly account for body size variability. Here, we conducted a GWAS in Frizarta dairy sheep with phenotypic data from 10 body size measurements and genotypic data (from Illumina ovineSNP50 BeadChip) for 459 ewes.

Results

The 10 body size measurements were subjected to principal component analysis and three independent principal components (PC) were constructed, interpretable as width, height and length dimensions, respectively. The GWAS performed for each PC identified 11 significant SNPs, at the chromosome level, one on each of the chromosomes 3, 8, 9, 10, 11, 12, 19, 20, 23 and two on chromosome 25. Nine out of the 11 SNPs were located on previously identified quantitative trait loci for sheep meat, production or reproduction. One hundred and ninety-seven positional candidate genes within a 1-Mb distance from each significant SNP were found. A guilt-by-association-based (GBA) prioritization analysis (PA) was performed to identify the most plausible functional candidate genes. GBA-based PA identified 39 genes that were significantly associated with gene networks relevant to body size traits. Prioritized genes were identified in the vicinity of all significant SNPs except for those on chromosomes 10 and 12. The top five ranking genes were TP53, BMPR1A, PIK3R5, RPL26 and PRKDC.

Conclusions

The results of this GWAS provide evidence for 39 causative candidate genes across nine chromosomal regions for body size traits, some of which are novel and some are previously identified candidates from other studies (e.g. TP53, NTN1 and ZNF521). GBA-based PA has proved to be a useful tool to identify genes with increased biological relevance but it is subjected to certain limitations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-017-0316-3) contains supplementary material, which is available to authorized users.

The impact of rare and low-frequency genetic variants in common disease

Despite thousands of genetic loci identified to date, a large proportion of genetic variation predisposing to complex disease and traits remains unaccounted for. Advances in sequencing technology enable focused explorations on the contribution of low-frequency and rare variants to human traits. Here we review experimental approaches and current knowledge on the contribution of these genetic variants in complex disease and discuss challenges and opportunities for personalised medicine.

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.

Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium

Genome-wide association studies (GWAS) have identified >300 loci associated with measures of adiposity including body mass index (BMI) and waist-to-hip ratio (adjusted for BMI, WHR_adjBMI), but few have been identified through screening of the African ancestry genomes. We performed large scale meta-analyses and replications in up to 52,895 individuals for BMI and up to 23,095 individuals for WHR_adjBMI from the African Ancestry Anthropometry Genetics Consortium (AAAGC) using 1000 Genomes phase 1 imputed GWAS to improve coverage of both common and low frequency variants in the low linkage disequilibrium African ancestry genomes. In the sex-combined analyses, we identified one novel locus (TCF7L2/HABP2) for WHR_adjBMI and eight previously established loci at P < 5×10⁻⁸: seven for BMI, and one for WHR_adjBMI in African ancestry individuals. An additional novel locus (SPRYD7/DLEU2) was identified for WHR_adjBMI when combined with European GWAS. In the sex-stratified analyses, we identified three novel loci for BMI (INTS10/LPL and MLC1 in men, IRX4/IRX2 in women) and four for WHR_adjBMI (SSX2IP, CASC8, PDE3B and ZDHHC1/HSD11B2 in women) in individuals of African ancestry or both African and European ancestry. For four of the novel variants, the minor allele frequency was low (<5%). In the trans-ethnic fine mapping of 47 BMI loci and 27 WHR_adjBMI loci that were locus-wide significant (P < 0.05 adjusted for effective number of variants per locus) from the African ancestry sex-combined and sex-stratified analyses, 26 BMI loci and 17 WHR_adjBMI loci contained ≤ 20 variants in the credible sets that jointly account for 99% posterior probability of driving the associations. The lead variants in 13 of these loci had a high probability of being causal. As compared to our previous HapMap imputed GWAS for BMI and WHR_adjBMI including up to 71,412 and 27,350 African ancestry individuals, respectively, our results suggest that 1000 Genomes imputation showed modest improvement in identifying GWAS loci including low frequency variants. Trans-ethnic meta-analyses further improved fine mapping of putative causal variants in loci shared between the African and European ancestry populations.

Genome-wide association studies (GWAS) have identified >300 genetic regions that influence body size and shape as measured by body mass index (BMI) and waist-to-hip ratio (WHR), respectively, but few have been identified in populations of African ancestry. We conducted large scale high coverage GWAS and replication of these traits in 52,895 and 23,095 individuals of African ancestry, respectively, followed by additional replication in European populations. We identified 10 genome-wide significant loci in all individuals, and an additional seven loci by analyzing men and women separately. We combined African and European ancestry GWAS and were able to narrow down 43 out of 74 African ancestry associated genetic regions to contain small number of putative causal variants. Our results highlight the improvement of applying high density genome coverage and combining multiple ancestries in the identification and refinement of location of genetic regions associated with adiposity traits.

Integration of Immune Cell Populations, mRNA-Seq, and CpG Methylation to Better Predict Humoral Immunity to Influenza Vaccination: Dependence of mRNA-Seq/CpG Methylation on Immune Cell Populations

The development of a humoral immune response to influenza vaccines occurs on a multisystems level. Due to the orchestration required for robust immune responses when multiple genes and their regulatory components across multiple cell types are involved, we examined an influenza vaccination cohort using multiple high-throughput technologies. In this study, we sought a more thorough understanding of how immune cell composition and gene expression relate to each other and contribute to interindividual variation in response to influenza vaccination. We first hypothesized that many of the differentially expressed (DE) genes observed after influenza vaccination result from changes in the composition of participants' peripheral blood mononuclear cells (PBMCs), which were assessed using flow cytometry. We demonstrated that DE genes in our study are correlated with changes in PBMC composition. We gathered DE genes from 128 other publically available PBMC-based vaccine studies and identified that an average of 57% correlated with specific cell subset levels in our study (permutation used to control false discovery), suggesting that the associations we have identified are likely general features of PBMC-based transcriptomics. Second, we hypothesized that more robust models of vaccine response could be generated by accounting for the interplay between PBMC composition, gene expression, and gene regulation. We employed machine learning to generate predictive models of B-cell ELISPOT response outcomes and hemagglutination inhibition (HAI) antibody titers. The top HAI and B-cell ELISPOT model achieved an area under the receiver operating curve (AUC) of 0.64 and 0.79, respectively, with linear model coefficients of determination of 0.08 and 0.28. For the B-cell ELISPOT outcomes, CpG methylation had the greatest predictive ability, highlighting potentially novel regulatory features important for immune response. B-cell ELISOT models using only PBMC composition had lower performance (AUC = 0.67), but highlighted well-known mechanisms. Our analysis demonstrated that each of the three data sets (cell composition, mRNA-Seq, and DNA methylation) may provide distinct information for the prediction of humoral immune response outcomes. We believe that these findings are important for the interpretation of current omics-based studies and set the stage for a more thorough understanding of interindividual immune responses to influenza vaccination.

Past, Present, and Future in the Relationship between Growth Retardation and the IGF System: Excerpts from the Cesar Bergada Lecture Given during the SLEP 2015 Annual Meeting

This mini review presents a personal view about the past, the present and the future of the relationship between growth retardation and the IGF system. Looking back, it is pertinent to include a brief look at the evolution of the somatomedin hypothesis, the use of IGF-I determinations in the clinic, and a review of the literature beginning in the late 1980s with the description of mutations in the Growth Hormone Receptor (GHR) gene. The present possibly started in the mid-1990s with the description of mutations in the IGF-I gene, followed in 2003 by reports of mutations in the genes coding for the IGF-I receptor and in the signal transducer and activator of transcription 5b (STAT5b). Finally, in 2004, mutations in the IGFALS gene were described. A diffuse limit between the present and the future might have been reached (the author's arbitrary decision) with the clinical applications of whole exome sequencing, which rapidly showed mutations in genes coding for STAT3, PAPP-A2 (pregnancy-associated plasma protein A2), and IGF-II.

More than 25 years of genetic studies of clozapine-induced agranulocytosis

Clozapine is one of the most effective atypical antipsychotic drugs prescribed to patients with treatment-resistant schizophrenia. Approximately 1% of patients experience potential life-threatening adverse effects in the form of agranulocytosis, greatly hindering its applicability in clinical practice. The etiology of clozapine-induced agranulocytosis (CIA) remains unclear, but is thought to be a heritable trait. We reviewed the genetic studies of CIA published thus far. One recurrent finding from early candidate gene study to more recent genome-wide analysis is that of the involvement of human leukocyte antigen locus. We conclude that CIA is most likely a complex, polygenic trait, which may hamper efforts to the development of a genetic predictor test with clinical relevance. To decipher the genetic architecture of CIA, it is necessary to apply more rigorous standards of phenotyping and study much larger sample sizes.

Missing heritability: is the gap closing? An analysis of 32 complex traits in the Lifelines Cohort Study

Despite the recent explosive rise in number of genetic markers for complex disease traits identified in genome-wide association studies, there is still a large gap between the known heritability of these traits and the part explained by these markers. To gauge whether this 'heritability gap' is closing, we first identified genome-wide significant SNPs from the literature and performed replication analyses for 32 highly relevant traits from five broad disease areas in 13 436 subjects of the Lifelines Cohort. Next, we calculated the variance explained by multi-SNP genetic risk scores (GRSs) for each trait, and compared it to their broad- and narrow-sense heritabilities captured by all common SNPs. The majority of all previously-associated SNPs (median=75%) were significantly associated with their respective traits. All GRSs were significant, with unweighted GRSs generally explaining less phenotypic variance than weighted GRSs, for which the explained variance was highest for height (15.5%) and varied between 0.02 and 6.7% for the other traits. Broad-sense common-SNP heritability estimates were significant for all traits, with the additive effect of common SNPs explaining 48.9% of the variance for height and between 5.6 and 39.2% for the other traits. Dominance effects were uniformly small (0-1.5%) and not significant. On average, the variance explained by the weighted GRSs accounted for only 10.7% of the common-SNP heritability of the 32 traits. These results indicate that GRSs may not yet be ready for accurate personalized prediction of complex disease traits limiting widespread adoption in clinical practice.

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.