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

Development of multivariable models to predict change in Body Mass Index within a clinical trial population of psychotic individuals

Many antipsychotics promote weight gain, which can lead to non-compliance and relapse of psychosis. By developing models that accurately identify individuals at greater risk of weight gain, clinicians can make informed treatment decisions and target intervention measures. We examined clinical, genetic and expression data for 284 individuals with psychosis derived from a previously published randomised controlled trial (IMPACT). These data were used to develop regression and classification models predicting change in Body Mass Index (BMI) over one year. Clinical predictors included demographics, anthropometrics, cardiac and blood measures, diet and exercise, physical and mental health, medication and BMI outcome measures. We included genetic polygenic risk scores (PRS) for schizophrenia, bipolar disorder, BMI, waist-hip-ratio, insulin resistance and height, as well as gene co-expression modules generated by Weighted Gene Co-expression Network Analysis (WGCNA). The best performing predictive models for BMI and BMI gain after one year used clinical data only, which suggests expression and genetic data do not improve prediction in this cohort.

Inferring Relevant Cell Types for Complex Traits by Using Single-Cell Gene Expression

Previous studies have prioritized trait-relevant cell types by looking for an enrichment of genome-wide association study (GWAS) signal within functional regions. However, these studies are limited in cell resolution by the lack of functional annotations from difficult-to-characterize or rare cell populations. Measurement of single-cell gene expression has become a popular method for characterizing novel cell types, and yet limited work has linked single-cell RNA sequencing (RNA-seq) to phenotypes of interest. To address this deficiency, we present RolyPoly, a regression-based polygenic model that can prioritize trait-relevant cell types and genes from GWAS summary statistics and gene expression data. RolyPoly is designed to use expression data from either bulk tissue or single-cell RNA-seq. In this study, we demonstrated RolyPoly's accuracy through simulation and validated previously known tissue-trait associations. We discovered a significant association between microglia and late-onset Alzheimer disease and an association between schizophrenia and oligodendrocytes and replicating fetal cortical cells. Additionally, RolyPoly computes a trait-relevance score for each gene to reflect the importance of expression specific to a cell type. We found that differentially expressed genes in the prefrontal cortex of individuals with Alzheimer disease were significantly enriched with genes ranked highly by RolyPoly gene scores. Overall, our method represents a powerful framework for understanding the effect of common variants on cell types contributing to complex traits.

Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits

Although genetic correlations between complex traits provide valuable insights into epidemiological and etiological studies, a precise quantification of which genomic regions disproportionately contribute to the genome-wide correlation is currently lacking. Here, we introduce ρ-HESS, a technique to quantify the correlation between pairs of traits due to genetic variation at a small region in the genome. Our approach requires GWAS summary data only and makes no distributional assumption on the causal variant effect sizes while accounting for linkage disequilibrium (LD) and overlapping GWAS samples. We analyzed large-scale GWAS summary data across 36 quantitative traits, and identified 25 genomic regions that contribute significantly to the genetic correlation among these traits. Notably, we find 6 genomic regions that contribute to the genetic correlation of 10 pairs of traits that show negligible genome-wide correlation, further showcasing the power of local genetic correlation analyses. Finally, we report the distribution of local genetic correlations across the genome for 55 pairs of traits that show putative causal relationships.

Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning

Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology's near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.

Cognitive genomics: Searching for the genetic roots of neuropsychological functioning

OBJECTIVE

Human cognition has long been known to be under substantial genetic control. With the complete mapping of the human genome, genome-wide association studies for many complex traits have proliferated; however, the highly polygenic nature of intelligence has made the identification of the precise genes that influence both global and specific cognitive abilities more difficult than anticipated.

METHOD

Here, we review the latest developments in the genomics of cognition, including a discussion of methodological advances in the genetic analysis of complex traits, and shared genetic contributions to cognitive abilities and neuropsychiatric disorders.

RESULTS

A wealth of twin and family studies have provided compelling evidence for a strong heritable component of both global and specific cognitive abilities, and for the existence of "generalist genes" responsible for a large portion of the variance in diverse cognitive abilities. Increasingly sophisticated analytic tools and ever-larger sample sizes are now facilitating the identification of specific genetic and molecular underpinnings of cognitive abilities, leading to optimism regarding possibilities for novel treatments for illnesses related to cognitive function.

CONCLUSIONS

We conclude with a set of future directions for the field, which will further accelerate discoveries regarding the biological pathways relevant to cognitive abilities. These, in turn, may be further interrogated in order to link biological mechanisms to behavior. (PsycINFO Database Record

The genetic underpinnings of body fat distribution

Obesity, defined as a body mass index (BMI) ≥ 30 kg/m², has reached epidemic proportions; people who are overweight (BMI > 25 kg/m²) or obese now comprise more than 25% of the world's population. Obese individuals have a higher risk of comorbidity development including type 2 diabetes, cardiovascular disease, cancer, and fertility complications. Areas covered: The study of monogenic and syndromic forms of obesity have revealed a small number of genes key to metabolic perturbations. Further, obesity and body shape in the general population are highly heritable phenotypes. Study of obesity at the population level, through genome-wide association studies of BMI and waist-to-hip ratio (WHR), have revealed > 150 genomic loci that associate with these traits, and highlight the role of adipose tissue and the central nervous system in obesity-related traits. Studies in animal models and cell lines have helped further elucidate the potential biological mechanisms underlying obesity. In particular, these studies implicate adipogenesis and expansion of adipose tissue as key biological pathways in obesity and weight gain. Expert commentary: Further work, including a focus on integrating genetic and additional genomic data types, as well as modeling obesity-like features in vitro, will be crucial in translating genome-wide association signals to the causal mechanisms driving disease.

Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change

Mapping cis-acting expression quantitative trait loci (cis-eQTL) has become a popular approach for characterizing proximal genetic regulatory variants. In this paper, we describe and characterize log allelic fold change (aFC), the magnitude of expression change associated with a given genetic variant, as a biologically interpretable unit for quantifying the effect size of cis-eQTLs and a mathematically convenient approach for systematic modeling of cis-regulation. This measure is mathematically independent from expression level and allele frequency, additive, applicable to multiallelic variants, and generalizable to multiple independent variants. We provide efficient tools and guidelines for estimating aFC from both eQTL and allelic expression data sets and apply it to Genotype Tissue Expression (GTEx) data. We show that aFC estimates independently derived from eQTL and allelic expression data are highly consistent, and identify technical and biological correlates of eQTL effect size. We generalize aFC to analyze genes with two eQTLs in GTEx and show that in nearly all cases the two eQTLs act independently in regulating gene expression. In summary, aFC is a solid measure of cis-regulatory effect size that allows quantitative interpretation of cellular regulatory events from population data, and it is a valuable approach for investigating novel aspects of eQTL data sets.

The osteoarthritis and height GDF5 locus yields its secrets

A new study reports molecular characterization of the GDF5 locus, which is associated with osteoarthritis risk and adult height in humans. This study provides evidence of positive selection for short stature at GDF5 in modern humans, as well as in archaic Neandertals and Denisovans.

Genetic determinants of growth hormone and GH-related phenotypes

Background

Higher fasting Growth Hormone (GH) has been associated with increased cardiovascular morbidity and mortality. Our objective was to find genetic determinants of fasting GH in order to facilitate future efforts of analyzing the association between fasting growth hormone and cardiovascular disease. A genome-wide association study (GWAS) was performed in a discovery cohort of 4134 persons (58% females; age 46–68 yrs), linking SNPs to fasting hs-GH. Fifteen SNPs were replicated in an independent cohort of 5262 persons (28.9% females; age 56–85 yrs). The best performing SNP was analyzed vs GH-related variables in a third independent cohort (n = 24,047; 61% females; age 44–73 yrs). A candidate gene approach searched for significant SNPs in the genes GH1 and GHR in the discovery cohort and was replicated as previously described.

Results

In the GWAS, the minor allele of rs7208736 was associated with lower GH in the discovery cohort (p = 5.15*10^-6) and the replication cohort (p = 0.005). The GH reducing allele was associated with lower BMI (P = 0.026) and waist (P = 0.021) in males only. In the candidate gene approach rs13153388 in the GHR-gene was associated with elevated GH-levels (P = 0.003) in the discovery cohort only and reduced height (P = 0.003).

Conclusion

In the first GWAS ever for GH, we identify a novel locus on chromosome 17 associated with fasting GH levels, suggesting novel biological mechanisms behind GH secretion and GH-related traits. The candidate gene approach identified a genetic variant in the GHR, which was associated with an elevation of fasting hs-GH and lower height suggesting reduced GHR ligand sensitivity. Our findings need further replication.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4219-z) contains supplementary material, which is available to authorized users.

Predicting causal variants affecting expression by using whole-genome sequencing and RNA-seq from multiple human tissues

Genetic association mapping produces statistical links between phenotypes and genomic regions, but identifying causal variants remains difficult. Whole-genome sequencing (WGS) can help by providing complete knowledge of all genetic variants, but it is financially prohibitive for well-powered GWAS studies. We performed mapping of expression quantitative trait loci (eQTLs) with WGS and RNA-seq, and found that lead eQTL variants called with WGS were more likely to be causal. Through simulations, we derived properties of causal variants and used them to develop a method for identifying likely causal SNPs. We estimated that 25-70% of causal variants were located in open-chromatin regions, depending on the tissue and experiment. Finally, we identified a set of high-confidence causal variants and showed that these were more enriched in GWAS associations than other eQTLs. Of those, we found 65 associations with GWAS traits and provide examples in which genes implicated by expression are functionally validated as being relevant for complex traits.

Clinical relevance of systematic phenotyping and exome sequencing in patients with short stature

Purpose

Short stature is a common condition of great concern to patients and their families. Mostly genetic in origin, the underlying cause often remains elusive due to clinical and genetic heterogeneity.

Methods

We systematically phenotyped 565 patients where common nongenetic causes of short stature were excluded, selected 200 representative patients for whole-exome sequencing, and analyzed the identified variants for pathogenicity and the affected genes regarding their functional relevance for growth.

Results

By standard targeted diagnostic and phenotype assessment, we identified a known disease cause in only 13.6% of the 565 patients. Whole-exome sequencing in 200 patients identified additional mutations in known short-stature genes in 16.5% of these patients who manifested only part of the symptomatology. In 15.5% of the 200 patients our findings were of significant clinical relevance. Heterozygous carriers of recessive skeletal dysplasia alleles represented 3.5% of the cases.

Conclusion

A combined approach of systematic phenotyping, targeted genetic testing, and whole-exome sequencing allows the identification of the underlying cause of short stature in at least 33% of cases, enabling physicians to improve diagnosis, treatment, and genetic counseling. Exome sequencing significantly increases the diagnostic yield and consequently care in patients with short stature.

[Common and rare variants, polygenic traits and missing heritability]

Recently, a systematic (but limited) search for rare variants implicated in adult height, a highly polygenic trait, has uncovered a number of new variants for which the effect size is inversely correlated with the minor allele frequency. This opens interesting perspectives on the genetic architecture of complex traits and on the vexing problem of "missing heritability".

BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a "Regression with Summary Statistics" (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss.

Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies

During the past few years, various novel statistical methods have been developed for fine-mapping with the use of summary statistics from genome-wide association studies (GWASs). Although these approaches require information about the linkage disequilibrium (LD) between variants, there has not been a comprehensive evaluation of how estimation of the LD structure from reference genotype panels performs in comparison with that from the original individual-level GWAS data. Using population genotype data from Finland and the UK Biobank, we show here that a reference panel of 1,000 individuals from the target population is adequate for a GWAS cohort of up to 10,000 individuals, whereas smaller panels, such as those from the 1000 Genomes Project, should be avoided. We also show, both theoretically and empirically, that the size of the reference panel needs to scale with the GWAS sample size; this has important consequences for the application of these methods in ongoing GWAS meta-analyses and large biobank studies. We conclude by providing software tools and by recommending practices for sharing LD information to more efficiently exploit summary statistics in genetics research.

A machine-learning heuristic to improve gene score prediction of polygenic traits

Machine-learning techniques have helped solve a broad range of prediction problems, yet are not widely used to build polygenic risk scores for the prediction of complex traits. We propose a novel heuristic based on machine-learning techniques (GraBLD) to boost the predictive performance of polygenic risk scores. Gradient boosted regression trees were first used to optimize the weights of SNPs included in the score, followed by a novel regional adjustment for linkage disequilibrium. A calibration set with sample size of ~200 individuals was sufficient for optimal performance. GraBLD yielded prediction R² of 0.239 and 0.082 using GIANT summary association statistics for height and BMI in the UK Biobank study (N = 130 K; 1.98 M SNPs), explaining 46.9% and 32.7% of the overall polygenic variance, respectively. For diabetes status, the area under the receiver operating characteristic curve was 0.602 in the UK Biobank study using summary-level association statistics from the DIAGRAM consortium. GraBLD outperformed other polygenic score heuristics for the prediction of height (p < 2.2 × 10⁻¹⁶) and BMI (p < 1.57 × 10⁻⁴), and was equivalent to LDpred for diabetes. Results were independently validated in the Health and Retirement Study (N = 8,292; 688,398 SNPs). Our report demonstrates the use of machine-learning techniques, coupled with summary-level data from large genome-wide meta-analyses to improve the prediction of polygenic traits.

Short stature homeobox-containing gene duplications in 3.7% of girls with tall stature and normal karyotypes

AIM

The short stature homeobox-containing gene (SHOX) plays an important role in short stature, but has not been explored in detail in a tall stature population before. This study explored the prevalence of SHOX aberrations in girls diagnosed with idiopathic tall stature with a normal karyotype.

METHODS

We studied SHOX aberrations in 81 girls with a median age of 10.43 (7.17-12.73) years diagnosed with tall stature who were referred to our clinic at Copenhagen University Hospital, Denmark, between 2003 and 2013. SHOX copy variations were analysed by quantitative polymerase chain reaction, and aberrations were confirmed by multiplex ligation probe-dependent amplification.

RESULTS

One extra SHOX copy was found in three (3.7%) of the 81 girls with tall stature, and their heights were 2.87, 3.71 and 3.98 standard deviation scores (SDS) and above the median height SDS of the girls with two SHOX copies. Their sitting height/height ratios (-3.08, -2.00 and -2.18 SDS) were all lower than the population mean. Despite these SHOX duplications, the three girls were clinically and biochemically comparable to the 78 girls with two SHOX copies.

CONCLUSION

This study was the first to demonstrate SHOX duplications in three girls with tall stature and normal karyotypes.

Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis

Osteoporosis is a common disease diagnosed primarily by measurement of bone mineral density (BMD). We undertook a genome-wide association study (GWAS) in 142,487 individuals from the UK Biobank to identify loci associated with BMD as estimated by quantitative ultrasound of the heel. We identified 307 conditionally independent single-nucleotide polymorphisms (SNPs) that attained genome-wide significance at 203 loci, explaining approximately 12% of the phenotypic variance. These included 153 previously unreported loci, and several rare variants with large effect sizes. To investigate the underlying mechanisms, we undertook (1) bioinformatic, functional genomic annotation and human osteoblast expression studies; (2) gene-function prediction; (3) skeletal phenotyping of 120 knockout mice with deletions of genes adjacent to lead independent SNPs; and (4) analysis of gene expression in mouse osteoblasts, osteocytes and osteoclasts. The results implicate GPC6 as a novel determinant of BMD, and also identify abnormal skeletal phenotypes in knockout mice associated with a further 100 prioritized genes.

New Blood Pressure-Associated Loci Identified in Meta-Analyses of 475 000 Individuals

BACKGROUND

Genome-wide association studies have recently identified >400 loci that harbor DNA sequence variants that influence blood pressure (BP). Our earlier studies identified and validated 56 single nucleotide variants (SNVs) associated with BP from meta-analyses of exome chip genotype data. An additional 100 variants yielded suggestive evidence of association.

METHODS AND RESULTS

Here, we augment the sample with 140 886 European individuals from the UK Biobank, in whom 77 of the 100 suggestive SNVs were available for association analysis with systolic BP or diastolic BP or pulse pressure. We performed 2 meta-analyses, one in individuals of European, South Asian, African, and Hispanic descent (pan-ancestry, ≈475 000), and the other in the subset of individuals of European descent (≈423 000). Twenty-one SNVs were genome-wide significant (P<5×10-8) for BP, of which 4 are new BP loci: rs9678851 (missense, SLC4A1AP), rs7437940 (AFAP1), rs13303 (missense, STAB1), and rs1055144 (7p15.2). In addition, we identified a potentially independent novel BP-associated SNV, rs3416322 (missense, SYNPO2L) at a known locus, uncorrelated with the previously reported SNVs. Two SNVs are associated with expression levels of nearby genes, and SNVs at 3 loci are associated with other traits. One SNV with a minor allele frequency <0.01, (rs3025380 at DBH) was genome-wide significant.

CONCLUSIONS

We report 4 novel loci associated with BP regulation, and 1 independent variant at an established BP locus. This analysis highlights several candidate genes with variation that alter protein function or gene expression for potential follow-up.

The RhoGAP Myo9b Promotes Bone Growth by Mediating Osteoblastic Responsiveness to IGF-1

The Ras homolog A (RhoA) subfamily of Rho guanosine triphosphatases (GTPases) regulates actin-based cellular functions in bone such as differentiation, migration, and mechanotransduction. Polymorphisms or genetic ablation of RHOA and some of its regulatory guanine exchange factors (GEFs) have been linked to poor bone health in humans and mice, but the effects of RhoA-specific GTPase-activating proteins (GAPs) on bone quality have not yet been identified. Therefore, we examined the consequences of RhoGAP Myo9b gene knockout on bone growth, phenotype, and cellular activity. Male and female mice lacking both alleles demonstrated growth retardation and decreased bone formation rates during early puberty. These mice had smaller, weaker bones by 4 weeks of age, but only female KOs had altered cellular numbers, with fewer osteoblasts and more osteoclasts. By 12 weeks of age, bone quality in KOs worsened. In contrast, 4-week-old heterozygotes demonstrated bone defects that resolved by 12 weeks of age. Throughout, Myo9b ablation affected females more than males. Osteoclast activity appeared unaffected. In primary osteogenic cells, Myo9b was distributed in stress fibers and focal adhesions, and its absence resulted in poor spreading and eventual detachment from culture dishes. Similarly, MC3T3-E1 preosteoblasts with transiently suppressed Myo9b levels spread poorly and contained decreased numbers of focal adhesions. These cells also demonstrated reduced ability to undergo IGF-1-induced spreading or chemotaxis toward IGF-1, though responses to PDGF and BMP-2 were unaffected. IGF-1 receptor (IGF1R) activation was normal in cells with diminished Myo9b levels, but the activated receptor was redistributed from stress fibers and focal adhesions into nuclei, potentially affecting receptor accessibility and gene expression. These results demonstrate that Myo9b regulates a subset of RhoA-activated processes necessary for IGF-1 responsiveness in osteogenic cells, and is critical for normal bone formation in growing mice. © 2017 American Society for Bone and Mineral Research.

CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits

There are few examples of robust associations between rare copy number variants (CNVs) and complex continuous human traits. Here we present a large-scale CNV association meta-analysis on anthropometric traits in up to 191,161 adult samples from 26 cohorts. The study reveals five CNV associations at 1q21.1, 3q29, 7q11.23, 11p14.2, and 18q21.32 and confirms two known loci at 16p11.2 and 22q11.21, implicating at least one anthropometric trait. The discovered CNVs are recurrent and rare (0.01–0.2%), with large effects on height (>2.4 cm), weight (>5 kg), and body mass index (BMI) (>3.5 kg/m²). Burden analysis shows a 0.41 cm decrease in height, a 0.003 increase in waist-to-hip ratio and increase in BMI by 0.14 kg/m² for each Mb of total deletion burden (P = 2.5 × 10⁻¹⁰, 6.0 × 10⁻⁵, and 2.9 × 10⁻³). Our study provides evidence that the same genes (e.g., MC4R, FIBIN, and FMO5) harbor both common and rare variants affecting body size and that anthropometric traits share genetic loci with developmental and psychiatric disorders.

Individual SNPs have small effects on anthropometric traits, yet the impact of CNVs has remained largely unknown. Here, Kutalik and co-workers perform a large-scale genome-wide meta-analysis of structural variation and find rare CNVs associated with height, weight and BMI with large effect sizes.

Concepts, estimation and interpretation of SNP-based heritability

Narrow-sense heritability (h²) is an important genetic parameter that quantifies the proportion of phenotypic variance in a trait attributable to the additive genetic variation generated by all causal variants. Estimation of h² previously relied on closely related individuals, but recent developments allow estimation of the variance explained by all SNPs used in a genome-wide association study (GWAS) in conventionally unrelated individuals, that is, the SNP-based heritability (). In this Perspective, we discuss recently developed methods to estimate for a complex trait (and genetic correlation between traits) using individual-level or summary GWAS data. We discuss issues that could influence the accuracy of , definitions, assumptions and interpretations of the models, and pitfalls of misusing the methods and misinterpreting the models and results.

Detecting gene subnetworks under selection in biological pathways

Advances in high throughput sequencing technologies have created a gap between data production and functional data analysis. Indeed, phenotypes result from interactions between numerous genes, but traditional methods treat loci independently, missing important knowledge brought by network-level emerging properties. Therefore, detecting selection acting on multiple genes affecting the evolution of complex traits remains challenging. In this context, gene network analysis provides a powerful framework to study the evolution of adaptive traits and facilitates the interpretation of genome-wide data. We developed a method to analyse gene networks that is suitable to evidence polygenic selection. The general idea is to search biological pathways for subnetworks of genes that directly interact with each other and that present unusual evolutionary features. Subnetwork search is a typical combinatorial optimization problem that we solve using a simulated annealing approach. We have applied our methodology to find signals of adaptation to high-altitude in human populations. We show that this adaptation has a clear polygenic basis and is influenced by many genetic components. Our approach, implemented in the R package signet, improves on gene-level classical tests for selection by identifying both new candidate genes and new biological processes involved in adaptation to altitude.

Identification of individuals by trait prediction using whole-genome sequencing data

Prediction of human physical traits and demographic information from genomic data challenges privacy and data deidentification in personalized medicine. To explore the current capabilities of phenotype-based genomic identification, we applied whole-genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a cohort of 1,061 participants of diverse ancestry. Individually, for a large fraction of the traits, their predictive accuracy beyond ancestry and demographic information is limited. However, we have developed a maximum entropy algorithm that integrates multiple predictions to determine which genomic samples and phenotype measurements originate from the same person. Using this algorithm, we have reidentified an average of >8 of 10 held-out individuals in an ethnically mixed cohort and an average of 5 of either 10 African Americans or 10 Europeans. This work challenges current conceptions of personal privacy and may have far-reaching ethical and legal implications.

Genome-wide compound heterozygote analysis highlights alleles associated with adult height in Europeans

Adult height is the most widely genetically studied common trait in humans; however, the trait variance explainable by currently known height-associated single nucleotide polymorphisms (SNPs) identified from the previous genome-wide association studies (GWAS) is yet far from complete given the high heritability of this complex trait. To exam if compound heterozygotes (CH) may explain extra height variance, we conducted a genome-wide analysis to screen for CH in association with adult height in 10,631 Dutch Europeans enriched with extremely tall people, using our recently developed method implemented in the software package CollapsABEL. The analysis identified six regions (3q23, 5q35.1, 6p21.31, 6p21.33, 7q21.2, and 9p24.3), where multiple pairs of SNPs as CH showed genome-wide significant association with height (P < 1.67 × 10⁻¹⁰). Of those, 9p24.3 represents a novel region influencing adult height, whereas the others have been highlighted in the previous GWAS on height based on analysis of individual SNPs. A replication analysis in 4080 Australians of European ancestry confirmed the significant CH-like association at 9p24.3 (P < 0.05). Together, the collapsed genotypes at these six loci explained 2.51% of the height variance (after adjusting for sex and age), compared with 3.23% explained by the 14 top-associated SNPs at 14 loci identified by traditional GWAS in the same data set (P < 5 × 10⁻⁸). Overall, our study empirically demonstrates that CH plays an important role in adult height and may explain a proportion of its “missing heritability”. Moreover, our findings raise promising expectations for other highly polygenic complex traits to explain missing heritability identifiable through CH-like associations.

The relative contribution of DNA methylation and genetic variants on protein biomarkers for human diseases

Associations between epigenetic alterations and disease status have been identified for many diseases. However, there is no strong evidence that epigenetic alterations are directly causal for disease pathogenesis. In this study, we combined SNP and DNA methylation data with measurements of protein biomarkers for cancer, inflammation or cardiovascular disease, to investigate the relative contribution of genetic and epigenetic variation on biomarker levels. A total of 121 protein biomarkers were measured and analyzed in relation to DNA methylation at 470,000 genomic positions and to over 10 million SNPs. We performed epigenome-wide association study (EWAS) and genome-wide association study (GWAS) analyses, and integrated biomarker, DNA methylation and SNP data using between 698 and 1033 samples depending on data availability for the different analyses. We identified 124 and 45 loci (Bonferroni adjusted P < 0.05) with effect sizes up to 0.22 standard units' change per 1% change in DNA methylation levels and up to four standard units' change per copy of the effective allele in the EWAS and GWAS respectively. Most GWAS loci were cis-regulatory whereas most EWAS loci were located in trans. Eleven EWAS loci were associated with multiple biomarkers, including one in NLRC5 associated with CXCL11, CXCL9, IL-12, and IL-18 levels. All EWAS signals that overlapped with a GWAS locus were driven by underlying genetic variants and three EWAS signals were confounded by smoking. While some cis-regulatory SNPs for biomarkers appeared to have an effect also on DNA methylation levels, cis-regulatory SNPs for DNA methylation were not observed to affect biomarker levels. We present associations between protein biomarker and DNA methylation levels at numerous loci in the genome. The associations are likely to reflect the underlying pattern of genetic variants, specific environmental exposures, or represent secondary effects to the pathogenesis of disease.

Knowledge-driven computational modeling in Alzheimer's disease research: Current state and future trends

Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be required for further progress in understanding and treating AD.

Pubertal growth and adult height in relation to breast cancer risk in African American women

Adult height has been positively associated with breast cancer risk. The timing of pubertal growth-as measured by age at menarche and age at attained height-may also influence risk. We evaluated associations of adult height, age at attained height, and age at menarche with incidence of invasive breast cancer in 55,687 African American women in the prospective Black Women's Health Study. Over 20 years, 1,826 invasive breast cancers [1,015 estrogen receptor (ER) positive; 542 ER negative] accrued. We used multivariable Cox proportional hazards regression to estimate hazards ratios (HRs) and 95% confidence intervals (CIs) for associations with breast cancer overall and by ER status, mutually adjusted for the three factors of interest. Adult height was associated with increased risk of ER+ breast cancer (HR for ≥70 inches vs ≤63 inches: 1.44; 95% CI: 1.09, 1.89) but not ER- (corresponding HR: 1.16; 95% CI: 0.78, 1.71) (p heterogeneity = 0.34). HRs for attained height before age 13 versus age >17 were 1.30 (95% CI: 0.96, 1.76) for ER+ and 1.25 (95% CI: 0.80, 1.96) for ER- breast cancer. Results for age at menarche (≤11 vs ≥14 years) were similar for ER+ and ER- breast cancer (HR for breast cancer overall: 1.30; 95% CI: 1.12, 1.50). We confirmed height as a strong risk factor for ER+ breast cancer in African American women and identified early age at attained height as a risk factor for both ER+ and ER- breast cancer, albeit without statistical significance of the latter associations. While adult height and timing of pubertal growth are inter-related, our findings suggest that they may be independent risk factors for breast cancer.

Mutations in pregnancy-associated plasma protein A2 cause short stature due to low IGF-I availability

Mutations in multiple genes of the growth hormone/IGF‐I axis have been identified in syndromes marked by growth failure. However, no pathogenic human mutations have been reported in the six high‐affinity IGF‐binding proteins (IGFBPs) or their regulators, such as the metalloproteinase pregnancy‐associated plasma protein A2 (PAPP‐A2) that is hypothesized to increase IGF‐I bioactivity by specific proteolytic cleavage of IGFBP‐3 and ‐5. Multiple members of two unrelated families presented with progressive growth failure, moderate microcephaly, thin long bones, mildly decreased bone density and elevated circulating total IGF‐I, IGFBP‐3, and ‐5, acid labile subunit, and IGF‐II concentrations. Two different homozygous mutations in PAPPA2, p.D643fs25* and p.Ala1033Val, were associated with this novel syndrome of growth failure. In vitro analysis of IGFBP cleavage demonstrated that both mutations cause a complete absence of PAPP‐A2 proteolytic activity. Size‐exclusion chromatography showed a significant increase in IGF‐I bound in its ternary complex. Free IGF‐I concentrations were decreased. These patients provide important insights into the regulation of longitudinal growth in humans, documenting the critical role of PAPP‐A2 in releasing IGF‐I from its BPs.

Omega-3 fatty acids correlate with gut microbiome diversity and production of N-carbamylglutamate in middle aged and elderly women

Omega-3 fatty acids may influence human physiological parameters in part by affecting the gut microbiome. The aim of this study was to investigate the links between omega-3 fatty acids, gut microbiome diversity and composition and faecal metabolomic profiles in middle aged and elderly women. We analysed data from 876 twins with 16S microbiome data and DHA, total omega-3, and other circulating fatty acids. Estimated food intake of omega-3 fatty acids were obtained from food frequency questionnaires. Both total omega-3and DHA serum levels were significantly correlated with microbiome alpha diversity (Shannon index) after adjusting for confounders (DHA Beta(SE) = 0.13(0.04), P = 0.0006 total omega-3: 0.13(0.04), P = 0.001). These associations remained significant after adjusting for dietary fibre intake. We found even stronger associations between DHA and 38 operational taxonomic units (OTUs), the strongest ones being with OTUs from the Lachnospiraceae family (Beta(SE) = 0.13(0.03), P = 8 × 10-7). Some of the associations with gut bacterial OTUs appear to be mediated by the abundance of the faecal metabolite N-carbamylglutamate. Our data indicate a link between omega-3 circulating levels/intake and microbiome composition independent of dietary fibre intake, particularly with bacteria of the Lachnospiraceae family. These data suggest the potential use of omega-3 supplementation to improve the microbiome composition.

Will Big Data Close the Missing Heritability Gap?

Modern biobanks that collect genotype-phenotype information from hundreds of thousands of individuals bring unprecedented opportunities for genomic...

Despite the important discoveries reported by genome-wide association (GWA) studies, for most traits and diseases the prediction R-squared (R-sq.) achieved with genetic scores remains considerably lower than the trait heritability. Modern biobanks will soon deliver unprecedentedly large biomedical data sets: Will the advent of big data close the gap between the trait heritability and the proportion of variance that can be explained by a genomic predictor? We addressed this question using Bayesian methods and a data analysis approach that produces a surface response relating prediction R-sq. with sample size and model complexity (e.g., number of SNPs). We applied the methodology to data from the interim release of the UK Biobank. Focusing on human height as a model trait and using 80,000 records for model training, we achieved a prediction R-sq. in testing (n = 22,221) of 0.24 (95% C.I.: 0.23–0.25). Our estimates show that prediction R-sq. increases with sample size, reaching an estimated plateau at values that ranged from 0.1 to 0.37 for models using 500 and 50,000 (GWA-selected) SNPs, respectively. Soon much larger data sets will become available. Using the estimated surface response, we forecast that larger sample sizes will lead to further improvements in prediction R-sq. We conclude that big data will lead to a substantial reduction of the gap between trait heritability and the proportion of interindividual differences that can be explained with a genomic predictor. However, even with the power of big data, for complex traits we anticipate that the gap between prediction R-sq. and trait heritability will not be fully closed.

Expression quantitative trait loci (eQTLs) in human placentas suggest developmental origins of complex diseases

Epidemiologic studies support that at least part of the risk of chronic diseases in childhood and even adulthood may have an in utero origin, and the placenta is a key organ that plays a pivotal role in fetal growth and development. The transcriptomes of 159 human placenta tissues were profiled by genome-wide RNA sequencing (Illumina High-Seq 2500), and linked to fetal genotypes assessed by a high density single nucleotide polymorphism (SNP) genotyping array (Illumina MegaEx). Expression quantitative trait loci (eQTLs) across all annotated transcripts were mapped and examined for enrichment for disease susceptibility loci annotated in the genome-wide association studies (GWAS) catalog. We discovered 3218 cis- and 35 trans-eQTLs at ≤10% false discovery rate in human placentas. Among the 16 439 known disease loci of genome-wide significance, 835 were placental eSNPs (enrichment fold = 1.68, P = 7.41e-42). Stronger effect sizes were observed between GWAS SNPs and gene expression in placentas than what has been reported in other tissues, such as the correlation between asthma risk allele, rs7216389-T and Gasdermin-B (GSDMB) in placenta (r2=27%) versus lung (r2=6%). Finally, our results suggest the placental eQTLs may mediate the function of GWAS loci on postnatal disease susceptibility. Results suggest that transcripts in placenta are under tight genetic control, and that placental gene networks may influence postnatal risk of multiple human diseases lending support for the Developmental Origins of Health and Disease.

Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease

UK Biobank is among the world's largest repositories for phenotypic and genotypic information in individuals of European ancestry. We performed a genome-wide association study in UK Biobank testing ∼9 million DNA sequence variants for association with coronary artery disease (4,831 cases and 115,455 controls) and carried out meta-analysis with previously published results. We identified 15 new loci, bringing the total number of loci associated with coronary artery disease to 95 at the time of analysis. Phenome-wide association scanning showed that CCDC92 likely affects coronary artery disease through insulin resistance pathways, whereas experimental analysis suggests that ARHGEF26 influences the transendothelial migration of leukocytes.

The Role of Obesity, Type 2 Diabetes, and Metabolic Factors in Pancreatic Cancer: A Mendelian Randomization Study

Background

Risk factors for pancreatic cancer include a cluster of metabolic conditions such as obesity, hypertension, dyslipidemia, insulin resistance, and type 2 diabetes. Given that these risk factors are correlated, separating out causal from confounded effects is challenging. Mendelian randomization (MR), or the use of genetic instrumental variables, may facilitate the identification of the metabolic drivers of pancreatic cancer.

Methods

We identified genetic instruments for obesity, body shape, dyslipidemia, insulin resistance, and type 2 diabetes in order to evaluate their causal role in pancreatic cancer etiology. These instruments were analyzed in relation to risk using a likelihood-based MR approach within a series of 7110 pancreatic cancer patients and 7264 control subjects using genome-wide data from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Potential unknown pleiotropic effects were assessed using a weighted median approach and MR-Egger sensitivity analyses.

Results

Results indicated a robust causal association of increasing body mass index (BMI) with pancreatic cancer risk (odds ratio [OR] = 1.34, 95% confidence interval [CI] = 1.09 to 1.65, for each standard deviation increase in BMI [4.6 kg/m2]). There was also evidence that genetically increased fasting insulin levels were causally associated with an increased risk of pancreatic cancer (OR = 1.66, 95% CI = 1.05 to 2.63, per SD [44.4 pmol/L]). Notably, no evidence of a causal relationship was observed for type 2 diabetes, nor for dyslipidemia. Sensitivity analyses did not indicate that pleiotropy was an important source of bias.

Conclusions

Our results suggest a causal role of BMI and fasting insulin in pancreatic cancer etiology.

Education and coronary heart disease: mendelian randomisation study

Objective To determine whether educational attainment is a causal risk factor in the development of coronary heart disease.Design Mendelian randomisation study, using genetic data as proxies for education to minimise confounding.Setting The main analysis used genetic data from two large consortia (CARDIoGRAMplusC4D and SSGAC), comprising 112 studies from predominantly high income countries. Findings from mendelian randomisation analyses were then compared against results from traditional observational studies (164 170 participants). Finally, genetic data from six additional consortia were analysed to investigate whether longer education can causally alter the common cardiovascular risk factors.Participants The main analysis was of 543 733 men and women (from CARDIoGRAMplusC4D and SSGAC), predominantly of European origin.Exposure A one standard deviation increase in the genetic predisposition towards higher education (3.6 years of additional schooling), measured by 162 genetic variants that have been previously associated with education.Main outcome measure Combined fatal and non-fatal coronary heart disease (63 746 events in CARDIoGRAMplusC4D).Results Genetic predisposition towards 3.6 years of additional education was associated with a one third lower risk of coronary heart disease (odds ratio 0.67, 95% confidence interval 0.59 to 0.77; P=3×10^-8). This was comparable to findings from traditional observational studies (prevalence odds ratio 0.73, 0.68 to 0.78; incidence odds ratio 0.80, 0.76 to 0.83). Sensitivity analyses were consistent with a causal interpretation in which major bias from genetic pleiotropy was unlikely, although this remains an untestable possibility. Genetic predisposition towards longer education was additionally associated with less smoking, lower body mass index, and a favourable blood lipid profile.Conclusions This mendelian randomisation study found support for the hypothesis that low education is a causal risk factor in the development of coronary heart disease. Potential mechanisms could include smoking, body mass index, and blood lipids. In conjunction with the results from studies with other designs, these findings suggest that increasing education may result in substantial health benefits.

Mendelian randomization incorporating uncertainty about pleiotropy

Mendelian randomization (MR) requires strong assumptions about the genetic instruments, of which the most difficult to justify relate to pleiotropy. In a two-sample MR, different methods of analysis are available if we are able to assume, M₁ : no pleiotropy (fixed effects meta-analysis), M₂ : that there may be pleiotropy but that the average pleiotropic effect is zero (random effects meta-analysis), and M₃ : that the average pleiotropic effect is nonzero (MR-Egger). In the latter 2 cases, we also require that the size of the pleiotropy is independent of the size of the effect on the exposure. Selecting one of these models without good reason would run the risk of misrepresenting the evidence for causality. The most conservative strategy would be to use M₃ in all analyses as this makes the weakest assumptions, but such an analysis gives much less precise estimates and so should be avoided whenever stronger assumptions are credible. We consider the situation of a two-sample design when we are unsure which of these 3 pleiotropy models is appropriate. The analysis is placed within a Bayesian framework and Bayesian model averaging is used. We demonstrate that even large samples of the scale used in genome-wide meta-analysis may be insufficient to distinguish the pleiotropy models based on the data alone. Our simulations show that Bayesian model averaging provides a reasonable trade-off between bias and precision. Bayesian model averaging is recommended whenever there is uncertainty about the nature of the pleiotropy.

Heritability Estimation using a Regularized Regression Approach (HERRA): Applicable to continuous, dichotomous or age-at-onset outcome

The popular Genome-wide Complex Trait Analysis (GCTA) software uses the random-effects models for estimating the narrow-sense heritability based on GWAS data of unrelated individuals without knowing and identifying the causal loci. Many methods have since extended this approach to various situations. However, since the proportion of causal loci among the variants is typically very small and GCTA uses all variants to calculate the similarities among individuals, the estimation of heritability may be unstable, resulting in a large variance of the estimates. Moreover, if the causal SNPs are not genotyped, GCTA sometimes greatly underestimates the true heritability. We present a novel narrow-sense heritability estimator, named HERRA, using well-developed ultra-high dimensional machine-learning methods, applicable to continuous or dichotomous outcomes, as other existing methods. Additionally, HERRA is applicable to time-to-event or age-at-onset outcome, which, to our knowledge, no existing method can handle. Compared to GCTA and LDAK for continuous and binary outcomes, HERRA often has a smaller variance, and when causal SNPs are not genotyped, HERRA has a much smaller empirical bias. We applied GCTA, LDAK and HERRA to a large colorectal cancer dataset using dichotomous outcome (4,312 cases, 4,356 controls, genotyped using Illumina 300K), the respective heritability estimates of GCTA, LDAK and HERRA are 0.068 (SE = 0.017), 0.072 (SE = 0.021) and 0.110 (SE = 5.19 x 10⁻³). HERRA yields over 50% increase in heritability estimate compared to GCTA or LDAK.

Childhood body mass index and height in relation to site-specific risks of colorectal cancers in adult life

As colorectal cancers have a long latency period, their origins may lie early in life. Therefore childhood body mass index (BMI; kg/m²) and height may be associated with adult colorectal cancer. Using a cohort design, 257,623 children from The Copenhagen School Health Records Register born from 1930 to 1972 with measured heights and weights at ages 7 to 13 years were followed for adult colon and rectal adenocarcinomas by linkage to the Danish Cancer Registry. Hazard ratios (HRs) with 95% confidence intervals (CI) were estimated by Cox proportional hazard regressions. During follow-up, 2676 colon and 1681 rectal adenocarcinomas were diagnosed. No sex differences were observed in the associations between child BMI or height and adult colon or rectal cancers. Childhood BMI and height were positively associated with colon cancer; at age 13 years the HRs were 1.09 (95% CI 1.04-1.14) and 1.14 (95% CI 1.09-1.19) per z-score, respectively. Children who were persistently taller or heavier than average, had increased risk of colon cancer. Similarly, growing taller or gaining more weight than average was positively associated with colon cancer. No associations were observed between BMI or height and rectal cancer. Childhood BMI and height, along with above average change during childhood are significantly and positively associated with adult colon cancers, but not with rectal cancer, suggesting different etiologies.

Constraints on eQTL Fine Mapping in the Presence of Multisite Local Regulation of Gene Expression

Expression quantitative trait locus (eQTL) detection has emerged as an important tool for unraveling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and the magnitude of effects. We describe a series of simulation studies designed to evaluate the impact of linkage disequilibrium (LD) on the fine mapping of causal variants with typical eQTL effect sizes. In the presence of multisite regulation, even though between 80 and 90% of modeled eSNPs associate with normally distributed traits, up to 10% of all secondary signals could be statistical artifacts, and at least 5% but up to one-quarter of credible intervals of SNPs within r² > 0.8 of the peak may not even include a causal site. The Bayesian methods eCAVIAR and DAP (Deterministic Approximation of Posteriors) provide only modest improvement in resolution. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine mapping of causal variants needs to be adjusted for multisite influences, as conditional estimates can be highly biased by interference among linked sites, but ultimately experimental verification of individual effects is needed. Presumably similar conclusions apply not just to eQTL mapping, but to multisite influences on fine mapping of most types of quantitative trait.

Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations

An essential component of precision medicine is the ability to predict an individual's risk of disease based on genetic and non-genetic factors. For complex traits and diseases, assessing the risk due to genetic factors is challenging because it requires knowledge of both the identity of variants that influence the trait and their corresponding allelic effects. Although the set of risk variants and their allelic effects may vary between populations, a large proportion of these variants were identified based on studies in populations of European descent. Heterogeneity in genetic architecture underlying complex traits and diseases, while broadly acknowledged, remains poorly characterized. Ignoring such heterogeneity likely reduces predictive accuracy for minority individuals. In this study, we propose an approach, called XP-BLUP, which ameliorates this ethnic disparity by combining trans-ethnic and ethnic-specific information. We build a polygenic model for complex traits that distinguishes candidate trait-relevant variants from the rest of the genome. The set of candidate variants are selected based on studies in any human population, yet the allelic effects are evaluated in a population-specific fashion. Simulation studies and real data analyses demonstrate that XP-BLUP adaptively utilizes trans-ethnic information and can substantially improve predictive accuracy in minority populations. At the same time, our study highlights the importance of the continued expansion of minority cohorts.

Mortality selection in a genetic sample and implications for association studies

Background

Mortality selection occurs when a non-random subset of a population of interest has died before data collection and is unobserved in the data. Mortality selection is of general concern in the social and health sciences, but has received little attention in genetic epidemiology. We tested the hypothesis that mortality selection may bias genetic association estimates, using data from the US-based Health and Retirement Study (HRS).

Methods

We tested mortality selection into the HRS genetic database by comparing HRS respondents who survive until genetic data collection in 2006 with those who do not. We next modelled mortality selection on demographic, health and social characteristics to calculate mortality selection probability weights. We analysed polygenic score associations with several traits before and after applying inverse-probability weighting to account for mortality selection. We tested simple associations and time-varying genetic associations (i.e. gene-by-cohort interactions).

Results

We observed mortality selection into the HRS genetic database on demographic, health and social characteristics. Correction for mortality selection using inverse probability weighting methods did not change simple association estimates. However, using these methods did change estimates of gene-by-cohort interaction effects. Correction for mortality selection changed gene-by-cohort interaction estimates in the opposite direction from increased mortality selection based on analysis of HRS respondents surviving through 2012.

Conclusions

Mortality selection may bias estimates of gene-by-cohort interaction effects. Analyses of HRS data can adjust for mortality selection associated with observables by including probability weights. Mortality selection is a potential confounder of genetic association studies, but the magnitude of confounding varies by trait.

Growth patterns in early childhood: Better trajectories in Afro-Ecuadorians independent of sex and socioeconomic factors

The first years of life are the most dynamic period for childhood growth. There are limited data available on growth patterns of infants and children living in rural Latin America. The aim of this study was to describe the growth patterns from birth to 5years in children living in a rural District of tropical coastal Ecuador using data from a birth cohort of 2404 neonates. We hypothesize that there would be growth differences according to ethnicity and sex. Evaluations were conducted at birth or until 2weeks of age and at 7, 13, 24, 36 and 60months during clinic and home visits. Individual growth trajectories for weight-for-age, height-for-age and weight/height-for-age Z-scores were estimated using multilevel models. Girls were lighter and shorter than boys at birth. However, Afro-Ecuadorian children (versus mestizo or indigenous) were longer/taller and heavier throughout the first 5years of life and had greater mean trajectories for HAZ and WAZ independent of sex and socioeconomic factors. Our data indicate that ethnicity is a determinant of growth trajectories during the first 5years of life independent of socioeconomic factors in a birth cohort conducted in a rural region of Latin America.

Body height and mortality - mortality follow-up of four Swiss surveys

BACKGROUND

Adult body height is largely determined by genetics, but also by dietary factors, which in turn depend on socioeconomic status and lifestyle. We examined the association between adult body height and mortality in Switzerland, a country with three main language regions with different cultural background.

METHODS

We included 16,831 men and 18,654 women, who participated in Swiss population-based health surveys conducted 1977-1993 and who were followed up until end of 2008. Multivariable Cox proportional hazards models were computed to examine the association of body height with overall, cardiovascular, and cancer mortality.

RESULTS

We observed a positive association between adult body height and all-cause mortality in women (HR=1.34, 95% CI 1.10-1.62, tallest vs. average women). In men, mortality risk decreased with increasing height, with shortest men tending to have higher (1.06, 0.94-1.19) and tallest men a lower (0.94, 0.77-1.14) risk compared with men of average height (p-trend 0.0001). Body height was associated with cancer mortality in women, such that tallest women had a higher risk of dying from cancer than women of average height (1.37, 1.02-1.84), but there was no such association in men (0.95, 0.69-1.30). In both sexes, height was not associated with cardiovascular mortality in a statistically significant manner.

CONCLUSION

Our study does not support an inverse association of body height with all-cause mortality. On the contrary, our data suggests a higher overall risk in taller women, mainly driven by a positive association between body height and cancer mortality.

Review: Genetic research on alcohol use outcomes in African American populations: A review of the literature, associated challenges, and implications

BACKGROUND AND OBJECTIVES

There have been remarkable advances in understanding genetic influences on complex traits; however, individuals of African descent have been underrepresented in genetic research.

METHODS

We review the limitations of existing genetic research on alcohol phenotypes in African Americans (AA) including both twin and gene identification studies, possible reasons for underrepresentation of AAs in genetic research, the implications of the lack of racially diverse samples, and special considerations regarding conducting genetic research in AA populations.

RESULTS

There is a marked absence of large-scale AA twin studies so little is known about the genetic epidemiology of alcohol use and problems among AAs. Individuals of African descent have also been underrepresented in gene identification efforts; however, there have been recent efforts to enhance representation. It remains unknown the extent to which genetic variants associated with alcohol use outcomes in individuals of European and African descent will be shared. Efforts to increase representation must be accompanied by careful attention to the ethical, legal, and social implications of genetic research. This is particularly true for AAs due to the history of abuse by the biomedical community and the persistent racial discrimination targeting this population.

CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE

Lack of representation in genetic studies limits our understanding of the etiological factors that contribute to substance use and psychiatric outcomes in populations of African descent and has the potential to further perpetuate health disparities. Involving individuals of diverse ancestry in discussions about genetic research will be critical to ensure that all populations benefit equally from genetic advances. (Am J Addict 2017;26:486-493).

Adult height, dietary patterns, and healthy aging

Background: Adult height has shown directionally diverse associations with several age-related disorders, including cardiovascular disease, cancer, decline in cognitive function, and mortality.Objective: We investigated the associations of adult height with healthy aging measured by a full spectrum of health outcomes, including incidence of chronic diseases, memory, physical functioning, and mental health, among populations who have survived to older age, and whether lifestyle factors modified such relations.Design: We included 52,135 women (mean age: 44.2 y) from the Nurses' Health Study without chronic diseases in 1980 and whose health status was available in 2012. Healthy aging was defined as being free of 11 major chronic diseases and having no reported impairment of subjective memory, physical impairment, or mental health limitations.Results: Of all eligible study participants, 6877 (13.2%) were classified as healthy agers. After adjustment for demographic and lifestyle factors, we observed an 8% (95% CI: 6%, 11%) decrease in the odds of healthy aging per SD (0.062 m) increase in height. Compared with the lowest category of height (≤1.57 m), the OR of achieving healthy aging in the highest category (≥1.70 m) was 0.80 (95% CI: 0.73, 0.87; P-trend < 0.001). In addition, we found a significant interaction of height with a prudent dietary pattern in relation to healthy aging (P-interaction = 0.005), and among the individual dietary factors characterizing the prudent dietary pattern, fruit and vegetable intake showed the strongest effect modification (P-interaction = 0.01). The association of greater height with reduced odds of healthy aging appeared to be more evident among women with higher adherence to the prudent dietary pattern rich in vegetable and fruit intake.Conclusions: Greater height was associated with a modest decrease in the likelihood of healthy aging. A prudent diet rich in fruit and vegetables might modify the relation.

The population genomics of archaeological transition in west Iberia: Investigation of ancient substructure using imputation and haplotype-based methods

We analyse new genomic data (0.05–2.95x) from 14 ancient individuals from Portugal distributed from the Middle Neolithic (4200–3500 BC) to the Middle Bronze Age (1740–1430 BC) and impute genomewide diploid genotypes in these together with published ancient Eurasians. While discontinuity is evident in the transition to agriculture across the region, sensitive haplotype-based analyses suggest a significant degree of local hunter-gatherer contribution to later Iberian Neolithic populations. A more subtle genetic influx is also apparent in the Bronze Age, detectable from analyses including haplotype sharing with both ancient and modern genomes, D-statistics and Y-chromosome lineages. However, the limited nature of this introgression contrasts with the major Steppe migration turnovers within third Millennium northern Europe and echoes the survival of non-Indo-European language in Iberia. Changes in genomic estimates of individual height across Europe are also associated with these major cultural transitions, and ancestral components continue to correlate with modern differences in stature.

Recent ancient DNA work has demonstrated the significant genetic impact of mass migrations from the Steppe into Central and Northern Europe during the transition from the Neolithic to the Bronze Age. In Iberia, archaeological change at the level of material culture and funerary rituals has been reported during this period, however, the genetic impact associated with this cultural transformation has not yet been estimated. In order to investigate this, we sequence Neolithic and Bronze Age samples from Portugal, which we compare to other ancient and present-day individuals. Genome-wide imputation of a large dataset of ancient samples enabled sensitive methods for detecting population structure and selection in ancient samples. We revealed subtle genetic differentiation between the Portuguese Neolithic and Bronze Age samples suggesting a markedly reduced influx in Iberia compared to other European regions. Furthermore, we predict individual height in ancients, suggesting that stature was reduced in the Neolithic and affected by subsequent admixtures. Lastly, we examine signatures of strong selection in important traits and the timing of their origins.

Detection and quantification of inbreeding depression for complex traits from SNP data

Quantifying the effects of inbreeding is critical to characterizing the genetic architecture of complex traits. This study highlights through theory and simulations the strengths and shortcomings of three SNP-based inbreeding measures commonly used to estimate inbreeding depression (ID). We demonstrate that heterogeneity in linkage disequilibrium (LD) between causal variants and SNPs biases ID estimates, and we develop an approach to correct this bias using LD and minor allele frequency stratified inference (LDMS). We quantified ID in 25 traits measured in [Formula: see text] participants of the UK Biobank, using LDMS, and confirmed previously published ID for 4 traits. We find unique evidence of ID for handgrip strength, waist/hip ratio, and visual and auditory acuity (ID between -2.3 and -5.2 phenotypic SDs for complete inbreeding; [Formula: see text]). Our results illustrate that a careful choice of the measure of inbreeding combined with LDMS stratification improves both detection and quantification of ID using SNP data.

Association of human height-related genetic variants with familial short stature in Han Chinese in Taiwan

Human height can be described as a classical and inherited trait model. Genome-wide association studies (GWAS) have revealed susceptible loci and provided insights into the polygenic nature of human height. Familial short stature (FSS) represents a suitable trait for investigating short stature genetics because disease associations with short stature have been ruled out in this case. In addition, FSS is caused only by genetically inherited factors. In this study, we explored the correlations of FSS risk with the genetic loci associated with human height in previous GWAS, alone and cumulatively. We systematically evaluated 34 known human height single nucleotide polymorphisms (SNPs) in relation to FSS in the additive model (p < 0.00005). A cumulative effect was observed: the odds ratios gradually increased with increasing genetic risk score quartiles (p < 0.001; Cochran-Armitage trend test). Six affected genes-ZBTB38, ZNF638, LCORL, CABLES1, CDK10, and TSEN15-are located in the nucleus and have been implicated in embryonic, organismal, and tissue development. In conclusion, our study suggests that 13 human height GWAS-identified SNPs are associated with FSS risk both alone and cumulatively.