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

Meta-analysis of genome-wide association studies identifies 8 novel loci involved in shape variation of human head hair

Shape variation of human head hair shows striking variation within and between human populations, while its genetic basis is far from being understood. We performed a series of genome-wide association studies (GWASs) and replication studies in a total of 28 964 subjects from 9 cohorts from multiple geographic origins. A meta-analysis of three European GWASs identified 8 novel loci (1p36.23 ERRFI1/SLC45A1, 1p36.22 PEX14, 1p36.13 PADI3, 2p13.3 TGFA, 11p14.1 LGR4, 12q13.13 HOXC13, 17q21.2 KRTAP, and 20q13.33 PTK6), and confirmed 4 previously known ones (1q21.3 TCHH/TCHHL1/LCE3E, 2q35 WNT10A, 4q21.21 FRAS1, and 10p14 LINC00708/GATA3), all showing genome-wide significant association with hair shape (P < 5e-8). All except one (1p36.22 PEX14) were replicated with nominal significance in at least one of the 6 additional cohorts of European, Native American and East Asian origins. Three additional previously known genes (EDAR, OFCC1, and PRSS53) were confirmed at the nominal significance level. A multivariable regression model revealed that 14 SNPs from different genes significantly and independently contribute to hair shape variation, reaching a cross-validated AUC value of 0.66 (95% CI: 0.62–0.70) and an AUC value of 0.64 in an independent validation cohort, providing an improved accuracy compared with a previous model. Prediction outcomes of 2504 individuals from a multiethnic sample were largely consistent with general knowledge on the global distribution of hair shape variation. Our study thus delivers target genes and DNA variants for future functional studies to further evaluate the molecular basis of hair shape in humans.

The daunting polygenicity of mental illness: making a new map

An epochal opportunity to elucidate the pathogenic mechanisms of psychiatric disorders has emerged from advances in genomic technology, new computational tools and the growth of international consortia committed to data sharing. The resulting large-scale, unbiased genetic studies have begun to yield new biological insights and with them the hope that a half century of stasis in psychiatric therapeutics will come to an end. Yet a sobering picture is coming into view; it reveals daunting genetic and phenotypic complexity portending enormous challenges for neurobiology. Successful exploitation of results from genetics will require eschewal of long-successful reductionist approaches to investigation of gene function, a commitment to supplanting much research now conducted in model organisms with human biology, and development of new experimental systems and computational models to analyse polygenic causal influences. In short, psychiatric neuroscience must develop a new scientific map to guide investigation through a polygenic terra incognita.

This article is part of a discussion meeting issue ‘Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists’.

Reduced signal for polygenic adaptation of height in UK Biobank

Several recent papers have reported strong signals of selection on European polygenic height scores. These analyses used height effect estimates from the GIANT consortium and replication studies. Here, we describe a new analysis based on the the UK Biobank (UKB), a large, independent dataset. We find that the signals of selection using UKB effect estimates are strongly attenuated or absent. We also provide evidence that previous analyses were confounded by population stratification. Therefore, the conclusion of strong polygenic adaptation now lacks support. Moreover, these discrepancies highlight (1) that methods for correcting for population stratification in GWAS may not always be sufficient for polygenic trait analyses, and (2) that claims of differences in polygenic scores between populations should be treated with caution until these issues are better understood.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Genetics of Growth Disorders-Which Patients Require Genetic Testing?

The second 360° European Meeting on Growth Hormone Disorders, held in Barcelona, Spain, in June 2017, included a session entitled Pragmatism vs. Curiosity in Genetic Diagnosis of Growth Disorders, which examined current concepts of genetics and growth in the clinical setting, in terms of both growth failure and overgrowth. For patients with short stature, multiple genes have been identified that result in GH deficiency, which may be isolated or associated with additional pituitary hormone deficiencies, or in growth hormone resistance, primary insulin-like growth factor (IGF) acid-labile subunit deficiency, IGF-I deficiency, IGF-II deficiency, IGF-I resistance, and primary PAPP-A2 deficiency. While genetic causes of short stature were previously thought to primarily be associated with the GH-IGF-I axis, it is now established that multiple genetic anomalies not associated with the GH-IGF-I axis can result in short stature. A number of genetic anomalies have also been shown to be associated with overgrowth, some of which involve the GH-IGF-I axis. In patients with overgrowth in combination with an intellectual disability, two predominant gene families, the epigenetic regulator genes, and PI3K/AKT pathway genes, have now been identified. Specific processes should be followed for decisions on which patients require genetic testing and which genes should be examined for anomalies. The decision to carry out genetic testing should be directed by the clinical process, not merely for research purposes. The intention of genetic testing should be to direct the clinical options for management of the growth disorder.

Schizophrenia Polygenic Risk Score as a Predictor of Antipsychotic Efficacy in First-Episode Psychosis

OBJECTIVE

Pharmacogenomic studies of antipsychotics have typically examined effects of individual polymorphisms. By contrast, polygenic risk scores (PRSs) derived from genome-wide association studies (GWAS) can quantify the influence of thousands of common alleles of small effect in a single measure. The authors examined whether PRSs for schizophrenia were predictive of antipsychotic efficacy in four independent cohorts of patients with first-episode psychosis (total N=510).

METHOD

All study subjects received initial treatment with antipsychotic medication for first-episode psychosis, and all were genotyped on standard single-nucleotide polymorphism (SNP) arrays imputed to the 1000 Genomes Project reference panel. PRS was computed based on the results of the large-scale schizophrenia GWAS reported by the Psychiatric Genomics Consortium. Symptoms were measured by using total symptom rating scales at baseline and at week 12 or at the last follow-up visit before dropout.

RESULTS

In the discovery cohort, higher PRS significantly predicted higher symptom scores at the 12-week follow-up (controlling for baseline symptoms, sex, age, and ethnicity). The PRS threshold set at a p value <0.01 gave the strongest result in the discovery cohort and was used to replicate the findings in the other three cohorts. Higher PRS significantly predicted greater posttreatment symptoms in the combined replication analysis and was individually significant in two of the three replication cohorts. Across the four cohorts, PRS was significantly predictive of adjusted 12-week symptom scores (pooled partial r=0.18; 3.24% of variance explained). Patients with low PRS were more likely to be treatment responders than patients with high PRS (odds ratio=1.91 in the two Caucasian samples).

CONCLUSIONS

Patients with higher PRS for schizophrenia tended to have less improvement with antipsychotic drug treatment. PRS burden may have potential utility as a prognostic biomarker.

Pseudoacromegaly

Individuals with acromegaloid physical appearance or tall stature may be referred to endocrinologists to exclude growth hormone (GH) excess. While some of these subjects could be healthy individuals with normal variants of growth or physical traits, others will have acromegaly or pituitary gigantism, which are, in general, straightforward diagnoses upon assessment of the GH/IGF-1 axis. However, some patients with physical features resembling acromegaly - usually affecting the face and extremities -, or gigantism - accelerated growth/tall stature - will have no abnormalities in the GH axis. This scenario is termed pseudoacromegaly, and its correct diagnosis can be challenging due to the rarity and variability of these conditions, as well as due to significant overlap in their characteristics. In this review we aim to provide a comprehensive overview of pseudoacromegaly conditions, highlighting their similarities and differences with acromegaly and pituitary gigantism, to aid physicians with the diagnosis of patients with pseudoacromegaly.

Targeted Searches of the Electronic Health Record and Genomics Identify an Etiology in Three Patients with Short Stature and High IGF-I Levels

INTRODUCTION

Short stature is one of the most common reasons for referral to a pediatric endocrinologist and can result from many etiologies. However, many patients with short stature do not receive a definitive diagnosis.

OBJECTIVE

To ascertain whether integrating targeted bioinformatics searches of electronic health records (EHRs) combined with genomic studies could identify patients with previously undiagnosed rare genetic etiologies of short stature. We focused on a specific rare phenotypic subgroup: patients with short stature and elevated IGF-I levels.

METHODS

We performed a cross-sectional cohort study at three large academic pediatric healthcare networks. Eligible subjects included children with heights below -2 SD, IGF-I levels >90th percentile, and no known etiology for short stature. We performed a search of the EHRs to identify eligible patients. Patients were then recruited for phenotyping followed by exome sequencing and in vitro assays of IGF1R function.

RESULTS

A total of 234 patients were identified by the bioinformatics algorithm with 39 deemed eligible after manual review (17%). Of those, 9 were successfully recruited. A genetic etiology was identified in 3 of the 9 patients including 2 novel variants in IGF1R and a de novo variant in CHD2. In vitro studies supported the pathogenicity of the IGF1R variants.

CONCLUSIONS

This study provides proof of principle that patients with rare phenotypic subgroups can be identified based on discrete data elements in the EHRs. Although limitations exist to fully automating this approach, these searches may help find patients with previously unidentified rare genetic disorders.

Genetic architecture of human thinness compared to severe obesity

The variation in weight within a shared environment is largely attributable to genetic factors. Whilst many genes/loci confer susceptibility to obesity, little is known about the genetic architecture of healthy thinness. Here, we characterise the heritability of thinness which we found was comparable to that of severe obesity (h2 = 28.07 vs 32.33% respectively), although with incomplete genetic overlap (r = -0.49, 95% CI [-0.17, -0.82], p = 0.003). In a genome-wide association analysis of thinness (n = 1,471) vs severe obesity (n = 1,456), we identified 10 loci previously associated with obesity, and demonstrate enrichment for established BMI-associated loci (pbinomial = 3.05x10-5). Simulation analyses showed that different association results between the extremes were likely in agreement with additive effects across the BMI distribution, suggesting different effects on thinness and obesity could be due to their different degrees of extremeness. In further analyses, we detected a novel obesity and BMI-associated locus at PKHD1 (rs2784243, obese vs. thin p = 5.99x10-6, obese vs. controls p = 2.13x10-6 pBMI = 2.3x10-13), associations at loci recently discovered with much larger sample sizes (e.g. FAM150B and PRDM6-CEP120), and novel variants driving associations at previously established signals (e.g. rs205262 at the SNRPC/C6orf106 locus and rs112446794 at the PRDM6-CEP120 locus). Our ability to replicate loci found with much larger sample sizes demonstrates the value of clinical extremes and suggest that characterisation of the genetics of thinness may provide a more nuanced understanding of the genetic architecture of body weight regulation and may inform the identification of potential anti-obesity targets.

Exposing the Causal Effect of C-Reactive Protein on the Risk of Type 2 Diabetes Mellitus: A Mendelian Randomization Study

As a biomarker of inflammation, C-reactive protein (CRP) has attracted much attention due to its role in the incidence of type 2 diabetes mellitus (T2DM). Prospective studies have observed a positive correlation between the level of serum CRP and the incidence of T2DM. Recently, studies have reported that drugs for curing T2DM can also decrease the level of serum CRP. However, it is not yet clear whether high CRP levels cause T2DM. To evaluate this, we conducted a Mendelian randomization (MR) analysis using genetic variations as instrumental variables (IVs). Significantly associated single nucleotide polymorphisms (SNPs) of CRP were obtained from a genome-wide study and a replication study. Therein, 17,967 participants were utilized for the genome-wide association study (GWAS), and another 14,747 participants were utilized for the replication of identifying SNPs associated with CRP levels. The associations between SNPs and T2DM were from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium. After removing SNPs in linkage disequilibrium (LD) and T2DM-related SNPs, the four remaining CRP-related SNPs were deemed as IVs. To evaluate the pooled influence of these IVs on the risk of developing T2DM through CRP, the penalized robust inverse-variance weighted (IVW) method was carried out. The combined result (OR 1.114048; 95% CI 1.058656 to 1.172338; P = 0.024) showed that high levels of CRP significantly increase the risk of T2DM. In the subsequent analysis of the relationship between CRP and type 1 diabetes mellitus (T1DM), the pooled result (OR 1.017145; 95% CI 0.9066489 to 1.14225; P = 0.909) supported that CRP levels cannot determine the risk of developing T1DM.

The causes and consequences of pituitary gigantism

In the general population, height is determined by a complex interplay between genetic and environmental factors. Pituitary gigantism is a rare but very important subgroup of patients with excessive height, as it has an identifiable and clinically treatable cause. The disease is caused by chronic growth hormone and insulin-like growth factor 1 secretion from a pituitary somatotrope adenoma that forms before the closure of the epiphyses. If not controlled effectively, this hormonal hypersecretion could lead to extremely elevated final adult height. The past 10 years have seen marked advances in the understanding of pituitary gigantism, including the identification of genetic causes in ~50% of cases, such as mutations in the AIP gene or chromosome Xq26.3 duplications in X-linked acrogigantism syndrome. Pituitary gigantism has a male preponderance, and patients usually have large pituitary adenomas. The large tumour size, together with the young age of patients and frequent resistance to medical therapy, makes the management of pituitary gigantism complex. Early diagnosis and rapid referral for effective therapy appear to improve outcomes in patients with pituitary gigantism; therefore, a high level of clinical suspicion and efficient use of diagnostic resources is key to controlling overgrowth and preventing patients from reaching very elevated final adult heights.

SHOX gene deletion screening by FISH in children with short stature and Madelung deformity and their characteristics

Background The short stature homeobox-containing (SHOX) gene strongly affects height. Therefore, a better understanding of SHOX haploinsufficiency could be advantageous to early diagnosis and treatment. We investigated the rate of SHOX haploinsufficiency in patients of short stature and documented their anthropometric measurements. Methods Between 2010 and 2017, we evaluated 86 patients (70 females, 16 males; age 4.3-18 years) with clinical diagnoses of short stature and Madelung deformity (MD). Clinical abnormalities are presented for patients with MD with and without SHOX haploinsufficiency as determined by fluorescence in situ hybridisation (FISH). Results According to our inclusion criteria, 78 of 86 patients (70 females, 16 males) had short stature (height <-2.5 standard deviation [SD]) and a family history suggestive of short stature. Eight patients had short stature, a family history suggestive of short stature and MD. MD was obvious in eight children in radiographic examinations. Although five of these had no deletion of SHOX, three had deletion of this gene. The deletion detection rate was 37.5% in the individuals with short stature and MD, i.e. Leri-Weill dyschondrosteosis syndrome (LWS), whilst no deletions were detected in the individuals with only short stature. One individual responded well to growth hormone (GH) treatment for the first 2 years but then developed an intolerance with persistently elevated insulin-like growth factor-1 (IGF-1) levels. Conclusions As we likely missed cases due to our methodology, the routine analysis for SHOX screening should be firstly multiplex ligation-dependent probe amplification (MLPA). The incidence of MD may have been higher in the cohort if X-rays were performed in all individuals. GH treatment was not well tolerated in one case due to persistently elevated IGF-1 levels, and long-term evaluations of patients with SHOX deficiency are required.

Association of SHMT1, MAZ, ERG, and L3MBTL3 Gene Polymorphisms with Susceptibility to Multiple Sclerosis

Multiple sclerosis (MS) is the most common inflammatory and chronic disease of the central nervous system (CNS). A complex interaction between genetic, environmental, and epigenetic factors is involved in the pathogenesis of MS. With the advancement of GWAS, various variants associated with MS have been identified. This study aimed to evaluate the association of single-nucleotide polymorphisms (SNPs) rs4925166 and rs1979277 in the SHMT1, MAZ rs34286592, ERG rs2836425, and L3MBTL3 rs4364506 with MS. In this case-control study, the association of five SNPs in SHMT1, MAZ, ERG, and L3MBTL3 genes with relapsing-remitting MS (RR-MS) was investigated in 190 patients and 200 healthy individuals. Four SNPs including SHMT1 rs4925166, SHMT1 rs1979277, MAZ rs34286592, and L3MBTL3 rs4364506 were genotyped using PCR-RFLP and genotyping of ERG rs2836425 was performed by tetra-primer ARMS PCR. Our findings showed a significant difference in the allelic frequencies for the four SNPs of SHMT1 rs4925166, SHMT1 rs1979277, MAZ rs34286592, and ERG rs2836425, while there were no differences in the allele and genotype frequencies for L3MBTL3 rs4364506. These significant associations were observed for the following genotypes: TT and GG genotypes of SHMT1 rs4925166 (OR 0.47 and 1.90, respectively) genotype GG of SHMT1 rs1979277 (OR 0.63), genotype GG of MAZ rs34286592 (OR 0.61), TC and CC genotypes of ERG rs2836425 (OR 1.89 and 0.50, respectively). Our study highlighted that people who are carrying genotypes including GG (SHMT1 rs4925166) and TC (ERG rs2836425) have the highest susceptibility chance for MS, respectively. However, genotypes TT (SHMT1 rs4925166), CC (ERG rs2836425), GG (MAZ rs34286592), and GG (SHMT1 rs1979277) had the highest negative association (protective effect) with MS, respectively. L3MBTL3 rs4364506 was found neither as a predisposing nor a protective variant.

Detecting significant genotype-phenotype association rules in bipolar disorder: market research meets complex genetics

BACKGROUND

Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted.

RESULTS

Two of these rules-one associated with eating disorder and the other with anxiety-remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings.

CONCLUSION

Our approach detected novel specific genotype-phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.

Comparing Genome-Wide Association Study Results from Different Measurements of an Underlying Phenotype

Increasing popularity of high-throughput phenotyping technologies, such as image-based phenotyping, offer novel ways for quantifying plant growth and morphology. These new methods can be more or less accurate and precise than traditional, manual measurements. Many large-scale phenotyping efforts are conducted to enable genome-wide association studies (GWAS), but it is unclear exactly how alternative methods of phenotyping will affect GWAS results. In this study we simulate phenotypes that are controlled by the same set of causal loci but have differing heritability, similar to two different measurements of the same morphological character. We then perform GWAS with the simulated traits and create receiver operating characteristic (ROC) curves from the results. The areas under the ROC curves (AUCs) provide a metric that allows direct comparisons of GWAS results from different simulated traits. We use this framework to evaluate the effects of heritability and the number of causative loci on the AUCs of simulated traits; we also test the differences between AUCs of traits with differing heritability. We find that both increasing the number of causative loci and decreasing the heritability reduce a trait’s AUC. We also find that when two traits are controlled by a greater number of causative loci, they are more likely to have significantly different AUCs as the difference between their heritabilities increases. When simulation results are applied to measures of tassel morphology, we find no significant difference between AUCs from GWAS using manual and image-based measurements of typical maize tassel characters. This finding indicates that both measurement methods have similar ability to identify genetic associations. These results provide a framework for deciding between competing phenotyping strategies when the ultimate goal is to generate and use phenotype-genotype associations from GWAS.

Genome-wide association meta-analysis of age at first cannabis use

BACKGROUND AND AIMS

Cannabis is one of the most commonly used substances among adolescents and young adults. Earlier age at cannabis initiation is linked to adverse life outcomes, including multi-substance use and dependence. This study estimated the heritability of age at first cannabis use and identified associations with genetic variants.

METHODS

A twin-based heritability analysis using 8055 twins from three cohorts was performed. We then carried out a genome-wide association meta-analysis of age at first cannabis use in a discovery sample of 24 953 individuals from nine European, North American and Australian cohorts, and a replication sample of 3735 individuals.

RESULTS

The twin-based heritability for age at first cannabis use was 38% [95% confidence interval (CI) = 19-60%]. Shared and unique environmental factors explained 39% (95% CI = 20-56%) and 22% (95% CI = 16-29%). The genome-wide association meta-analysis identified five single nucleotide polymorphisms (SNPs) on chromosome 16 within the calcium-transporting ATPase gene (ATP2C2) at P < 5E-08. All five SNPs are in high linkage disequilibrium (LD) (r2  > 0.8), with the strongest association at the intronic variant rs1574587 (P = 4.09E-09). Gene-based tests of association identified the ATP2C2 gene on 16q24.1 (P = 1.33e-06). Although the five SNPs and ATP2C2 did not replicate, ATP2C2 has been associated with cocaine dependence in a previous study. ATP2B2, which is a member of the same calcium signalling pathway, has been associated previously with opioid dependence. SNP-based heritability for age at first cannabis use was non-significant.

CONCLUSION

Age at cannabis initiation appears to be moderately heritable in western countries, and individual differences in onset can be explained by separate but correlated genetic liabilities. The significant association between age of initiation and ATP2C2 is consistent with the role of calcium signalling mechanisms in substance use disorders.

Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits

Although recent studies provide evidence for a common genetic basis between complex traits and Mendelian disorders, a thorough quantification of their overlap in a phenotype-specific manner remains elusive. Here, we have quantified the overlap of genes identified through large-scale genome-wide association studies (GWASs) for 62 complex traits and diseases with genes containing mutations known to cause 20 broad categories of Mendelian disorders. We identified a significant enrichment of genes linked to phenotypically matched Mendelian disorders in GWAS gene sets; of the total 1,240 comparisons, a higher proportion of phenotypically matched or related pairs (n = 50 of 92 [54%]) than phenotypically unmatched pairs (n = 27 of 1,148 [2%]) demonstrated significant overlap, confirming a phenotype-specific enrichment pattern. Further, we observed elevated GWAS effect sizes near genes linked to phenotypically matched Mendelian disorders. Finally, we report examples of GWAS variants localized at the transcription start site or physically interacting with the promoters of genes linked to phenotypically matched Mendelian disorders. Our results are consistent with the hypothesis that genes that are disrupted in Mendelian disorders are dysregulated by non-coding variants in complex traits and demonstrate how leveraging findings from related Mendelian disorders and functional genomic datasets can prioritize genes that are putatively dysregulated by local and distal non-coding GWAS variants.

Does education protect against depression? Evidence from the Young Finns Study using Mendelian randomization

Using participants (N = 1733) drawn from the nationally representative longitudinal Young Finns Study (YFS) we estimate the effect of education on depressive symptoms. In 2007, when the participants were between 30 and 45 years old, they reported their depressive symptoms using a revised version of Beck's Depression Inventory. Education was measured using register information on the highest completed level of education in 2007, which was converted to years of education. To identify a causal relationship between education and depressive symptoms we use an instrumental variables approach (Mendelian randomization, MR) with a genetic risk score as an instrument for years of education. The genetic risk score was based on 74 genetic variants, which were associated with years of education in a genome-wide association study (GWAS). Because the genetic variants are randomly assigned at conception, they induce exogenous variation in years of education and thus identify a causal effect if the assumptions of the MR approach are met. In Ordinary Least Squares (OLS) estimation years of education in 2007 were negatively associated with depressive symptoms in 2007 (b = -0.027, 95% Confidence Interval (CI) = -0.040, -0.015). However, the results based on Mendelian randomization suggested that the effect is not causal (b = 0.017; 95% CI = -0.144, 0.178). This indicates that omitted variables correlated with education and depression may bias the linear regression coefficients and exogenous variation in education caused by differences in genetic make-up does not seem to protect against depressive symptoms.

Analysis of porcine body size variation using re-sequencing data of miniature and large pigs

Background

Domestication has led to substantial phenotypic and genetic variation in domestic animals. In pigs, the size of so called minipigs differs by one order of magnitude compared to breeds of large body size. We used biallelic SNPs identified from re-sequencing data to compare various publicly available wild and domestic populations against two minipig breeds to gain better understanding of the genetic background of the extensive body size variation. We combined two complementary measures, expected heterozygosity and the composite likelihood ratio test implemented in “SweepFinder”, to identify signatures of selection in Minipigs. We intersected these sweep regions with a measure of differentiation, namely F_ST, to remove regions of low variation across pigs. An extraordinary large sweep between 52 and 61 Mb on chromosome X was separately analyzed based on SNP-array data of F₂ individuals from a cross of Goettingen Minipigs and large pigs.

Results

Selective sweep analysis identified putative sweep regions for growth and subsequent gene annotation provided a comprehensive set of putative candidate genes. A long swept haplotype on chromosome X, descending from the Goettingen Minipig founders was associated with a reduction of adult body length by 3% in F₂ cross-breds.

Conclusion

The resulting set of genes in putative sweep regions implies that the genetic background of body size variation in pigs is polygenic rather than mono- or oligogenic. Identified genes suggest alterations in metabolic functions and a possible insulin resistance to contribute to miniaturization. A size QTL located within the sweep on chromosome X, with an estimated effect of 3% on body length, is comparable to the largest known in pigs or other species. The androgen receptor AR, previously known to influence pig performance and carcass traits, is the most obvious potential candidate gene within this region.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5009-y) contains supplementary material, which is available to authorized users.

Genetic determinants of childhood and adult height associated with osteosarcoma risk

BACKGROUND

Although increased height has been associated with osteosarcoma risk in previous epidemiologic studies, to the authors' knowledge the relative contribution of stature during different developmental timepoints remains unclear. Furthermore, the question of how genetic determinants of height impact osteosarcoma etiology remains unexplored. Genetic variants associated with stature in previous genome-wide association studies may be biomarkers of osteosarcoma risk.

METHODS

The authors tested the associations between osteosarcoma risk and polygenic scores for adult height (416 variants), childhood height (6 variants), and birth length (5 variants) in 864 osteosarcoma cases and 1879 controls of European ancestry.

RESULTS

Each standard deviation increase in the polygenic score for adult height, corresponding to a 1.7-cm increase in stature, was found to be associated with a 1.10-fold increase in the risk of osteosarcoma (95% confidence interval [95% CI], 1.01-1.19; P =.027). Each standard deviation increase in the polygenic score for childhood height, corresponding to a 0.5-cm increase in stature, was associated with a 1.10-fold increase in the risk of osteosarcoma (95% CI, 1.01-1.20; P =.023). The polygenic score for birth length was not found to be associated with osteosarcoma risk (P =.11). When adult and childhood height scores were modeled together, they were found to be independently associated with osteosarcoma risk (P =.037 and P = .043, respectively). An expression quantitative trait locus for cartilage intermediate layer protein 2 (CILP2), rs8103992, was significantly associated with osteosarcoma risk after adjustment for multiple comparisons (odds ratio, 1.35; 95% CI, 1.16-1.56 [P = 7.93×10-5 and Padjusted =.034]).

CONCLUSIONS

A genetic propensity for taller adult and childhood height attainments contributed independently to osteosarcoma risk in the current study data. These results suggest that the biological pathways affecting normal bone growth may be involved in osteosarcoma etiology.

Detecting genetic regions associated with height in the native ponies of the British Isles by using high density SNP genotyping

Height is an important characteristic in the equine industry although little is known about its genetic control in native British breeds of ponies. This study aimed to map QTL data with the withers height in four pony breeds native to the British Isles, including two different sections within Welsh Cobs. In this study, a genome-wide analysis approach using the Illumina EquineSNP50 Infinium BeadChip was applied to 105 ponies and cobs. Analysis identified 222 highly significant height-associated SNPs (P ≤ 10^-5), among which three SNPs on ECA9 have also been previously reported elsewhere. The highest number of significant SNPs associated to height in the native British horses were located on ECA1, ECA8, and ECA16.

Insights and Implications of Genome-Wide Association Studies of Height

CONTEXT

In the last decade, genome-wide association studies (GWASs) have catalyzed our understanding of the genetics of height and have identified hundreds of regions of the genome associated with adult height and other height-related body measurements.

EVIDENCE ACQUISITION

GWASs related to height were identified via PubMed search and a review of the GWAS catalog.

EVIDENCE SYNTHESIS

The GWAS results demonstrate that height is highly polygenic: that is, many thousands of genetic variants distributed across the genome each contribute to an individual's height. These height-associated regions of the genome are enriched for genes in known biological pathways involved in growth, such as fibroblast growth factor signaling, as well as for genes expressed in relevant tissues, such as the growth plate. GWASs can also uncover previously unappreciated biological pathways, such as the STC2/PAPPA/IGFBP4 pathway. The genes implicated by GWASs are often the same genes that are the genetic causes of Mendelian growth disorders or skeletal dysplasias, and GWAS results can provide complementary information about these disorders.

CONCLUSIONS

Here, we review the rationale behind GWASs and what we have learned from GWASs for height, including how it has enhanced our understanding of the underlying biology of human growth. We also highlight the implications of GWASs in terms of prediction of adult height and our understanding of Mendelian growth disorders.

Epigenetic variance in dopamine D2 receptor: a marker of IQ malleability?

Genetic and environmental factors both contribute to cognitive test performance. A substantial increase in average intelligence test results in the second half of the previous century within one generation is unlikely to be explained by genetic changes. One possible explanation for the strong malleability of cognitive performance measure is that environmental factors modify gene expression via epigenetic mechanisms. Epigenetic factors may help to understand the recent observations of an association between dopamine-dependent encoding of reward prediction errors and cognitive capacity, which was modulated by adverse life events. The possible manifestation of malleable biomarkers contributing to variance in cognitive test performance, and thus possibly contributing to the "missing heritability" between estimates from twin studies and variance explained by genetic markers, is still unclear. Here we show in 1475 healthy adolescents from the IMaging and GENetics (IMAGEN) sample that general IQ (gIQ) is associated with (1) polygenic scores for intelligence, (2) epigenetic modification of DRD2 gene, (3) gray matter density in striatum, and (4) functional striatal activation elicited by temporarily surprising reward-predicting cues. Comparing the relative importance for the prediction of gIQ in an overlapping subsample, our results demonstrate neurobiological correlates of the malleability of gIQ and point to equal importance of genetic variance, epigenetic modification of DRD2 receptor gene, as well as functional striatal activation, known to influence dopamine neurotransmission. Peripheral epigenetic markers are in need of confirmation in the central nervous system and should be tested in longitudinal settings specifically assessing individual and environmental factors that modify epigenetic structure.

Enrichment of genetic markers of recent human evolution in educational and cognitive traits

Higher cognitive functions are regarded as one of the main distinctive traits of humans. Evidence for the cognitive evolution of human beings is mainly based on fossil records of an expanding cranium and an increasing complexity of material culture artefacts. However, the molecular genetic factors involved in the evolution are still relatively unexplored. Here, we investigated whether genomic regions that underwent positive selection in humans after divergence from Neanderthals are enriched for genetic association with phenotypes related to cognitive functions. We used genome wide association data from a study of college completion (N = 111,114), one of educational attainment (N = 293,623) and two different studies of general cognitive ability (N = 269,867 and 53,949). We found nominally significant polygenic enrichment of associations with college completion (p = 0.025), educational attainment (p = 0.043) and general cognitive ability (p = 0.015 and 0.025, respectively), suggesting that variants influencing these phenotypes are more prevalent in evolutionarily salient regions. The enrichment remained significant after controlling for other known genetic enrichment factors, and for affiliation to genes highly expressed in the brain. These findings support the notion that phenotypes related to higher order cognitive skills typical of humans have a recent genetic component that originated after the separation of the human and Neanderthal lineages.

Relevance of polymorphisms in MC4R and BDNF in short normal stature

Background

Variation in genes of the leptinergic-melanocortinergic system influence both body weight and height. Because short normal stature (SNS) is characterized by reduced body height, delayed maturation and leanness, allelic variation of genes in this pathway are hypothesized to affect this common condition.

Methods

We analyzed the coding regions of LEP, MC4R, MRAP2 and BDNF in 185 children with SNS (height < 5th percentile) to search for non-synonymous and frameshift variants. For association studies (two-sided χ²-tests) population-based data sets (ExAC, EVS and KORA) were used. Cyclic AMP accumulation, cell surface expression, central expression and MAP kinase activation were assayed in vitro to determine the functional implications of identified variants.

Results

We detected eleven variants predicted to be protein-altering, four in MC4R, four in BDNF, and three in MRAP2. No variants were found in LEP. In vitro analysis implied reduced function for the MC4R variant p.Met215Ile. Loss-of-function is contrary to expectations based on obesity studies, and thus does not support that this variant is relevant for SNS. The minor SNP alleles at MC4R p.Val103Ile and BDNF p.Val66Met were nominally associated with SNS.

Conclusion

Taken together, although genes of the leptinergic-melanocortinergic system are important for normal growth, our data do not support the involvement of rare mutations in LEP, MC4R, MRAP2 or BDNF in short normal stature.

Electronic supplementary material

The online version of this article (10.1186/s12887-018-1245-1) contains supplementary material, which is available to authorized users.

Bi-allelic Loss-of-Function Mutations in the NPR-C Receptor Result in Enhanced Growth and Connective Tissue Abnormalities

The natriuretic peptide signaling pathway has been implicated in many cellular processes, including endochondral ossification and bone growth. More precisely, different mutations in the NPR-B receptor and the CNP ligand have been identified in individuals with either short or tall stature. In this study we show that the NPR-C receptor (encoded by NPR3) is also important for the regulation of linear bone growth. We report four individuals, originating from three different families, with a phenotype characterized by tall stature, long digits, and extra epiphyses in the hands and feet. In addition, aortic dilatation was observed in two of these families. In each affected individual, we identified a bi-allelic loss-of-function mutation in NPR3. The missense mutations (c.442T>C [p.Ser148Pro] and c.1088A>T [p.Asp363Val]) resulted in intracellular retention of the NPR-C receptor and absent localization on the plasma membrane, whereas the nonsense mutation (c.1524delC [p.Tyr508^∗]) resulted in nonsense-mediated mRNA decay. Biochemical analysis of plasma from two affected and unrelated individuals revealed a reduced NTproNP/NP ratio for all ligands and also high cGMP levels. These data strongly suggest a reduced clearance of natriuretic peptides by the defective NPR-C receptor and consequently increased activity of the NPR-A/B receptors. In conclusion, this study demonstrates that loss-of-function mutations in NPR3 result in increased NPR-A/B signaling activity and cause a phenotype marked by enhanced bone growth and cardiovascular abnormalities.

Genetic causes of proportionate short stature

Human growth is a very complex phenomenon influenced by genetic, hormonal, nutritional and environmental factors, from fetal life to puberty. Although the GH-IGF axis has a central role with specific actions on growth, numerous genes are involved in the control of stature. Genome-wide association studies have identified >600 variants associated with human height, still explaining only a small fraction of phenotypic variation. Since short stature in childhood is a common reason for referral, pediatric endocrinologists must be aware of the multifactorial and polygenic contributions to height. Multiple disorders characterized by growth failure of prenatal and/or postnatal onset due to single gene defects have been described. Their early diagnosis, facilitated by advances in genomic technologies, is of upmost importance for their clinical management and to provide genetic counseling. Here we review the current clinical and genetic information regarding different syndromes and hormone abnormalities with proportionate short stature as the main feature, and provide an update of the approach for diagnosis and management.

An Evolutionary Genomic Perspective on the Breeding of Dwarf Chickens

The evolutionary history for dwarfism in chickens remains an enigma. Herein, we explore the evolution of the Serama, the smallest breed of chicken. Leveraging comparative population genomics, analyses identify several genes that are potentially associated with the growth and development of bones and muscles. These genes, and in particular both POU1F1 and IGF1, are under strong positive selection. Three allopatric dwarf bantams (Serama, Yuanbao, and Daweishan) with different breeding-histories, form distinct clusters and exhibit unique population structures. Parallel genetic mechanisms underlay their variation in body size. These findings provide insights into the multiple and complex pathways, depending on genomic variation, that chicken can take in response to aviculture selection for dwarfism.

A genome scan for genes underlying adult body size differences between Central African hunter-gatherers and farmers

The evolutionary and biological bases of the Central African "pygmy" phenotype, a characteristic of rainforest hunter-gatherers defined by reduced body size compared with neighboring farmers, remain largely unknown. Here, we perform a joint investigation in Central African hunter-gatherers and farmers of adult standing height, sitting height, leg length, and body mass index (BMI), considering 358 hunter-gatherers and 169 farmers with genotypes for 153,798 SNPs. In addition to reduced standing heights, hunter-gatherers have shorter sitting heights and leg lengths and higher sitting/standing height ratios than farmers and lower BMI for males. Standing height, sitting height, and leg length are strongly correlated with inferred levels of farmer genetic ancestry, whereas BMI is only weakly correlated, perhaps reflecting greater contributions of non-genetic factors to body weight than to height. Single- and multi-marker association tests identify one region and eight genes associated with hunter-gatherer/farmer status, and 24 genes associated with the height-related traits. Many of these genes have putative functions consistent with roles in determining their associated traits and the pygmy phenotype, and they include three associated with standing height in non-Africans (PRKG1, DSCAM, MAGI2). We find evidence that European height-associated SNPs or variants in linkage disequilibrium with them contribute to standing- and sitting-height determination in Central Africans, but not to the differential status of hunter-gatherers and farmers. These findings provide new insights into the biological basis of the pygmy phenotype, and they highlight the potential of cross-population studies for exploring the genetic basis of phenotypes that vary naturally across populations.

Genetics of pleiotropic effects of dexamethasone

OBJECTIVES

Glucocorticoids such as dexamethasone have pleiotropic effects, including desired antileukemic, anti-inflammatory, or immunosuppressive effects, and undesired metabolic or toxic effects. The most serious adverse effects of dexamethasone among patients with acute lymphoblastic leukemia are osteonecrosis and thrombosis. To identify inherited genomic variation involved in these severe adverse effects, we carried out genome-wide association studies (GWAS) by analyzing 14 pleiotropic glucocorticoid phenotypes in 391 patients with acute lymphoblastic leukemia.

PATIENTS AND METHODS

We used the Projection Onto the Most Interesting Statistical Evidence integrative analysis technique to identify genetic variants associated with pleiotropic dexamethasone phenotypes, stratifying for age, sex, race, and treatment, and compared the results with conventional single-phenotype GWAS. The phenotypes were osteonecrosis, central nervous system toxicity, hyperglycemia, hypokalemia, thrombosis, dexamethasone exposure, BMI, growth trajectory, and levels of cortisol, albumin, and asparaginase antibodies, and changes in cholesterol, triglycerides, and low-density lipoproteins after dexamethasone.

RESULTS

The integrative analysis identified more pleiotropic single nucleotide polymorphism variants (P=1.46×10(-215), and these variants were more likely to be in gene-regulatory regions (P=1.22×10(-6)) than traditional single-phenotype GWAS. The integrative analysis yielded genomic variants (rs2243057 and rs6453253) in F2RL1, a receptor that functions in hemostasis, thrombosis, and inflammation, which were associated with pleiotropic effects, including osteonecrosis and thrombosis, and were in regulatory gene regions.

CONCLUSION

The integrative pleiotropic analysis identified risk variants for osteonecrosis and thrombosis not identified by single-phenotype analysis that may have importance for patients with underlying sensitivity to multiple dexamethasone adverse effects.

A Review of Current Concepts of the Etiology and Treatment of Myopia

Myopia occurs in more than 50% of the population in many industrialized countries and is expected to increase; complications associated with axial elongation from myopia are the sixth leading cause of blindness. Thus, understanding its etiology, epidemiology, and the results of various treatment regiments may modify current care and result in a reduction in morbidity from progressive myopia. This rapid increase cannot be explained by genetics alone. Current animal and human research demonstrates that myopia development is a result of the interplay between genetic and the environmental factors. The prevalence of myopia is higher in individuals whose both parents are myopic, suggesting that genetic factors are clearly involved in myopia development. At the same time, population studies suggest that development of myopia is associated with education and the amount time spent doing near work; hence, activities increase the exposure to optical blur. Recently, there has been an increase in efforts to slow the progression of myopia because of its relationship to the development of serious pathological conditions such as macular degeneration, retinal detachments, glaucoma, and cataracts. We reviewed meta-analysis and other of current treatments that include: atropine, progressive addition spectacle lenses, orthokeratology, and multifocal contact lenses.

A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework

BACKGROUND

There is considerable evidence that many complex traits have a partially shared genetic basis, termed pleiotropy. It is therefore useful to consider integrating genome-wide association study (GWAS) data across several traits, usually at the summary statistic level. A major practical challenge arises when these GWAS have overlapping subjects. This is particularly an issue when estimating pleiotropy using methods that condition the significance of one trait on the signficance of a second, such as the covariate-modulated false discovery rate (cmfdr).

RESULTS

We propose a method for correcting for sample overlap at the summary statistic level. We quantify the expected amount of spurious correlation between the summary statistics from two GWAS due to sample overlap, and use this estimated correlation in a simple linear correction that adjusts the joint distribution of test statistics from the two GWAS. The correction is appropriate for GWAS with case-control or quantitative outcomes. Our simulations and data example show that without correcting for sample overlap, the cmfdr is not properly controlled, leading to an excessive number of false discoveries and an excessive false discovery proportion. Our correction for sample overlap is effective in that it restores proper control of the false discovery rate, at very little loss in power.

CONCLUSIONS

With our proposed correction, it is possible to integrate GWAS summary statistics with overlapping samples in a statistical framework that is dependent on the joint distribution of the two GWAS.

Weak effects of common genetic variation in oxytocin and vasopressin receptor genes on rhesus macaque social behavior

The neuropeptides oxytocin (OT) and arginine vasopressin (AVP) influence pair bonding, attachment, and sociality, as well as anxiety and stress responses in humans and other mammals. The effects of these peptides are mediated by genetic variability in their associated receptors, OXTR and the AVPR gene family. However, the role of these genes in regulating social behaviors in non-human primates is not well understood. To address this question, we examined whether genetic variation in the OT receptor gene OXTR and the AVP receptor genes AVPR1A and AVPR1B influence naturally-occurring social behavior in free-ranging rhesus macaques-gregarious primates that share many features of their biology and social behavior with humans. We assessed rates of social behavior across 3,250 hr of observational behavioral data from 201 free-ranging rhesus macaques on Cayo Santiago island in Puerto Rico, and used genetic sequence data to identify 25 OXTR, AVPR1A, and AVPR1B single-nucleotide variants (SNVs) in the population. We used an animal model to estimate the effects of 12 SNVs (n = 3 OXTR; n = 5 AVPR1A; n = 4 AVPR1B) on rates of grooming, approaches, passive contact, contact aggression, and non-contact aggression, given and received. Though we found evidence for modest heritability of these behaviors, estimates of effect sizes of the selected SNVs were close to zero, indicating that common OXTR and AVPR variation contributed little to social behavior in these animals. Our results are consistent with recent findings in human genetics that the effects of individual common genetic variants on complex phenotypes are generally small.

IGESS: a statistical approach to integrating individual-level genotype data and summary statistics in genome-wide association studies

Motivation

Results from genome-wide association studies (GWAS) suggest that a complex phenotype is often affected by many variants with small effects, known as 'polygenicity'. Tens of thousands of samples are often required to ensure statistical power of identifying these variants with small effects. However, it is often the case that a research group can only get approval for the access to individual-level genotype data with a limited sample size (e.g. a few hundreds or thousands). Meanwhile, summary statistics generated using single-variant-based analysis are becoming publicly available. The sample sizes associated with the summary statistics datasets are usually quite large. How to make the most efficient use of existing abundant data resources largely remains an open question.

Results

In this study, we propose a statistical approach, IGESS, to increasing statistical power of identifying risk variants and improving accuracy of risk prediction by i ntegrating individual level ge notype data and s ummary s tatistics. An efficient algorithm based on variational inference is developed to handle the genome-wide analysis. Through comprehensive simulation studies, we demonstrated the advantages of IGESS over the methods which take either individual-level data or summary statistics data as input. We applied IGESS to perform integrative analysis of Crohns Disease from WTCCC and summary statistics from other studies. IGESS was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.2% ( ±0.4% ) to 69.4% ( ±0.1% ) using about 240 000 variants.

Availability and implementation

The IGESS software is available at https://github.com/daviddaigithub/IGESS .

Contact

zbxu@xjtu.edu.cn or xwan@comp.hkbu.edu.hk or eeyang@hkbu.edu.hk.

Supplementary information

Supplementary data are available at Bioinformatics online.

The in vitro functional analysis of single-nucleotide polymorphisms associated with growth hormone (GH) response in children with GH deficiency

Response to recombinant human growth hormone (r-hGH) in the first year of therapy has been associated with single-nucleotide polymorphisms (SNPs) in children with GH deficiency (GHD). Associated SNPs were screened for regulatory function using a combination of in silico techniques. Four SNPs in regulatory sequences were selected for the analysis of in vitro transcriptional activity (TA). There was an additive effect of the alleles in the four genes associated with good growth response. For rs3110697 within IGFBP3, rs1045992 in CYP19A1 and rs2888586 in SOS1, the variant associated with better growth response showed higher TA with r-hGH treatment. For rs1024531 in GRB10, a negative regulator of IGF-I signalling and growth, the variant associated with better growth response had a significantly lower TA on r-hGH stimulation. These results indicate that specific SNP variants have effects on TA that provide a rationale for their clinical impact on growth response to r-hGH therapy.

Genome-wide association study identifies nine novel loci for 2D:4D finger ratio, a putative retrospective biomarker of testosterone exposure in utero

The ratio of the length of the index finger to that of the ring finger (2D:4D) is sexually dimorphic and is commonly used as a non-invasive biomarker of prenatal androgen exposure. Most association studies of 2D:4D ratio with a diverse range of sex-specific traits have typically involved small sample sizes and have been difficult to replicate, raising questions around the utility and precise meaning of the measure. In the largest genome-wide association meta-analysis of 2D:4D ratio to date (N = 15 661, with replication N = 75 821), we identified 11 loci (9 novel) explaining 3.8% of the variance in mean 2D:4D ratio. We also found weak evidence for association (β = 0.06; P = 0.02) between 2D:4D ratio and sensitivity to testosterone [length of the CAG microsatellite repeat in the androgen receptor (AR) gene] in females only. Furthermore, genetic variants associated with (adult) testosterone levels and/or sex hormone-binding globulin were not associated with 2D:4D ratio in our sample. Although we were unable to find strong evidence from our genetic study to support the hypothesis that 2D:4D ratio is a direct biomarker of prenatal exposure to androgens in healthy individuals, our findings do not explicitly exclude this possibility, and pathways involving testosterone may become apparent as the size of the discovery sample increases further. Our findings provide new insight into the underlying biology shaping 2D:4D variation in the general population.

Genome-wide association study identifies nine novel loci for 2D:4D finger ratio, a putative retrospective biomarker of testosterone exposure in utero

The ratio of the length of the index finger to that of the ring finger (2D:4D) is sexually dimorphic and is commonly used as a non-invasive biomarker of prenatal androgen exposure. Most association studies of 2D:4D ratio with a diverse range of sex-specific traits have typically involved small sample sizes and have been difficult to replicate, raising questions around the utility and precise meaning of the measure. In the largest genome-wide association meta-analysis of 2D:4D ratio to date (N = 15 661, with replication N = 75 821), we identified 11 loci (9 novel) explaining 3.8% of the variance in mean 2D:4D ratio. We also found weak evidence for association (β = 0.06; P = 0.02) between 2D:4D ratio and sensitivity to testosterone [length of the CAG microsatellite repeat in the androgen receptor (AR) gene] in females only. Furthermore, genetic variants associated with (adult) testosterone levels and/or sex hormone-binding globulin were not associated with 2D:4D ratio in our sample. Although we were unable to find strong evidence from our genetic study to support the hypothesis that 2D:4D ratio is a direct biomarker of prenatal exposure to androgens in healthy individuals, our findings do not explicitly exclude this possibility, and pathways involving testosterone may become apparent as the size of the discovery sample increases further. Our findings provide new insight into the underlying biology shaping 2D:4D variation in the general population.

Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals

Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.

PAPPA2 as a Therapeutic Modulator of IGF-I Bioavailability: in Vivo and in Vitro Evidence

Context

Pregnancy-associated plasma protein A2 (PAPPA2) is a protease that cleaves IGF-binding protein (IGFBP)-3 and IGFBP-5, liberating free IGF-I. Five patients from two families with genetic mutations in PAPPA2 presented with growth retardation, elevated total IGF-I, and IGFBP-3 but decreased free IGF-I.

Objective

To determine whether plasma transfusion or recombinant human (rh)PAPPA2 could increase free IGF-I in patients with PAPPA2 deficiency or idiopathic short stature (ISS).

Design

Single patient interventional study combined with in vitro experimentation.

Setting

Academic medical center.

Patients

Three siblings with PAPPA2 deficiency and four patients with ISS.

Interventions

An adult female with PAPPA2 deficiency received a 20 mL/kg plasma transfusion. PAPPA2, intact IGFBP-3, and free and total IGF-I levels were monitored during 2 weeks. rhPAPPA2 was added to serum from patients with PAPPA2 deficiency and ISS in vitro for 4 hours. Intact IGFBP-3 and free IGF-I levels were assayed via ELISA.

Main Outcome Measures

Free IGF-I concentrations.

Results

Plasma transfusion resulted in a 2.5-fold increase of free IGF-I levels on day 1 posttransfusion with a return to baseline during a 2-week period. In vitro studies demonstrated a dose-dependent increase in free IGF-I and decrease in intact IGFBP-3 after the addition of rhPAPPA2. The increase in free IGF-I was more pronounced in patients with PAPPA2 deficiency compared with those with ISS.

Conclusions

PAPPA2 plays a key role in regulation of IGF-I bioavailability. rhPAPPA2 is a promising therapy to increase free IGF-I levels both in patients with PAPPA2 deficiency as well as in patients with ISS.

Genetically determined height was associated with lung cancer risk in East Asian population

The association between adult height and risk of lung cancer has been investigated by epidemiology studies, but the results are inconsistent. Mendelian randomization (MR) analyses with individual‐level data from two genome‐wide association studies, including a total of 7127 lung cancer cases and 6818 controls, were carried out to explore whether adult height is causally associated with risk of lung cancer. A weighted genetic risk score (wGRS) was created based on genotypes of 101 known height‐associated genetic variants. Association between the wGRS and risk of lung cancer was analyzed by logistic regression for each study separately. The combined effect was calculated using fixed effect meta‐analysis. MR analyses showed that increased risk of lung cancer (OR = 1.19, 95%CI: 1.05‐1.35, P = 0.006) associated with taller genetically determined height. Compared with individuals in the lowest tertile of the height‐associated wGRS, those in the highest tertile had 1.10‐fold (95% CI: 1.01‐1.20) increased risk of developing lung cancer. Sensitivity analyses excluding BMI‐associated genetic variants demonstrated consistent association. Our study suggested that genetically taller height was associated with increased risk of lung cancer in East Asian population, indicating that increasing height may have a causal role in lung cancer carcinogenesis.

Genotype Imputation from Large Reference Panels

Genotype imputation has become a standard tool in genome-wide association studies because it enables researchers to inexpensively approximate whole-genome sequence data from genome-wide single-nucleotide polymorphism array data. Genotype imputation increases statistical power, facilitates fine mapping of causal variants, and plays a key role in meta-analyses of genome-wide association studies. Only variants that were previously observed in a reference panel of sequenced individuals can be imputed. However, the rapid increase in the number of deeply sequenced individuals will soon make it possible to assemble enormous reference panels that greatly increase the number of imputable variants. In this review, we present an overview of genotype imputation and describe the computational techniques that make it possible to impute genotypes from reference panels with millions of individuals.

Genes Whose Gain or Loss-Of-Function Increases Skeletal Muscle Mass in Mice: A Systematic Literature Review

Skeletal muscle mass differs greatly in mice and humans and this is partially inherited. To identify muscle hypertrophy candidate genes we conducted a systematic review to identify genes whose experimental loss or gain-of-function results in significant skeletal muscle hypertrophy in mice. We found 47 genes that meet our search criteria and cause muscle hypertrophy after gene manipulation. They are from high to small effect size: Ski, Fst, Acvr2b, Akt1, Mstn, Klf10, Rheb, Igf1, Pappa, Ppard, Ikbkb, Fstl3, Atgr1a, Ucn3, Mcu, Junb, Ncor1, Gprasp1, Grb10, Mmp9, Dgkz, Ppargc1a (specifically the Ppargc1a4 isoform), Smad4, Ltbp4, Bmpr1a, Crtc2, Xiap, Dgat1, Thra, Adrb2, Asb15, Cast, Eif2b5, Bdkrb2, Tpt1, Nr3c1, Nr4a1, Gnas, Pld1, Crym, Camkk1, Yap1, Inhba, Tp53inp2, Inhbb, Nol3, Esr1. Knock out, knock down, overexpression or a higher activity of these genes causes overall muscle hypertrophy as measured by an increased muscle weight or cross sectional area. The mean effect sizes range from 5 to 345% depending on the manipulated gene as well as the muscle size variable and muscle investigated. Bioinformatical analyses reveal that Asb15, Klf10, Tpt1 are most highly expressed hypertrophy genes in human skeletal muscle when compared to other tissues. Many of the muscle hypertrophy-regulating genes are involved in transcription and ubiquitination. Especially genes belonging to three signaling pathways are able to induce hypertrophy: (a) Igf1-Akt-mTOR pathway, (b) myostatin-Smad signaling, and (c) the angiotensin-bradykinin signaling pathway. The expression of several muscle hypertrophy-inducing genes and the phosphorylation of their protein products changes after human resistance and high intensity exercise, in maximally stimulated mouse muscle or in overloaded mouse plantaris.

A Bayesian Gene-Based Genome-Wide Association Study Analysis of Osteosarcoma Trio Data Using a Hierarchically Structured Prior

Osteosarcoma is considered to be the most common primary malignant bone cancer among children and young adults. Previous studies suggest growth spurts and height to be risk factors for osteosarcoma. However, studies on the genetic cause are still limited given the rare occurrence of the disease. In this study, we investigated in a family trio data set that is composed of 209 patients and their unaffected parents and conducted a genome-wide association study (GWAS) to identify genetic risk factors for osteosarcoma. We performed a Bayesian gene-based GWAS based on the single-nucleotide polymorphism (SNP)-level summary statistics obtained from a likelihood ratio test of the trio data, which uses a hierarchically structured prior that incorporates the SNP-gene hierarchical structure. The Bayesian approach has higher power than SNP-level GWAS analysis due to the reduced number of tests and is robust by accounting for the correlations between SNPs so that it borrows information across SNPs within a gene. We identified 217 genes that achieved genome-wide significance. Ingenuity pathway analysis of the gene set indicated that osteosarcoma is potentially related to TP53, estrogen receptor signaling, xenobiotic metabolism signaling, and RANK signaling in osteoclasts.

Method to estimate the approximate samples size that yield a certain number of significant GWAS signals in polygenic traits

To argue for increased sample collection for disorders without significant findings, researchers resorted to plotting, for multiple traits, the number of significant findings as a function of the sample size. However, for polygenic traits, the prevalence of the disorder confounds the relationship between the number of significant findings and the sample size. To adjust the number of significant findings for prevalence, we develop a method that uses the expected noncentrality of the contrast between liabilities of cases and controls. We empirically find that, when compared to the sample size, this measure is a better predictor of number of significant findings. Even more, we show that the sample size effect on the number of signals is explained by the noncentrality measure. Finally, we provide an R script to estimate the required sample size (noncentrality) needed to yield a prespecified number of significant findings, along with the converse.