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

Nutrition, Other Environmental Influences, and Genetics in the Determination of Human Stature

Linear growth during three distinct stages of life determines attained stature in adulthood: namely, in utero, early postnatal life, and puberty and the adolescent period. Individual host factors, genetics, and the environment, including nutrition, influence attained human stature. Each period of physical growth has its specific biological and environmental considerations. Recent epidemiologic investigations reveal a strong influence of prenatal factors on linear size at birth that in turn influence the postnatal growth trajectory. Although average population height changes have been documented in high-income regions, stature as a complex human trait is not well understood or easily modified. This review summarizes the biology of linear growth and its major drivers, including nutrition from a life-course perspective, the genetics of programmed growth patterns or height, and gene-environment interactions that determine human stature in toto over the life span. Implications for public health interventions and knowledge gaps are discussed.

Genetic Associations of Anhedonia: Insights into Overlap of Mental and Somatic Disorders

BACKGROUND

Anhedonia is characterized by a reduced ability to anticipate, experience, and/or learn about pleasure. This phenomenon has a transdiagnostic nature and is one of the key symptoms of mood disorders, schizophrenia, addictions, and somatic conditions.

AIM

To evaluate the genetic architecture of anhedonia and its overlap with other mental disorders and somatic conditions.

METHODS

We performed a genome-wide association study of anhedonia on a sample of 4,520 individuals from a Russian non-clinical population. Using the available summary statistics, we calculated polygenic risk scores (PRS) to investigate the genetic relationship between anhedonia and other psychiatric or somatic phenotypes.

RESULTS

No variants with a genome-wide significant association were identified. PRS for major depression, bipolar disorder, and schizophrenia were significantly associated with anhedonia. Conversely, no significant associations were found between PRS for anxiety and anhedonia, which aligns well with existing clinical evidence. None of the PRS for somatic phenotypes attained a significance level after correction for multiple comparisons. A nominal significance for the anhedonia association was determined for omega-3 fatty acids, type 2 diabetes mellitus, and Crohn's disease.

CONCLUSION

Anhedonia has a complex polygenic architecture, and its presence in somatic diseases or normal conditions may be due to a genetic predisposition to mood disorders or schizophrenia.

Multiomic profiling of transcription factor binding and function in human brain

Transcription factors (TFs) orchestrate gene expression programs crucial for brain function, but we lack detailed information about TF binding in human brain tissue. We generated a multiomic resource (ChIP-seq, ATAC-seq, RNA-seq, DNA methylation) on bulk tissues and sorted nuclei from several postmortem brain regions, including binding maps for more than 100 TFs. We demonstrate improved measurements of TF activity, including motif recognition and gene expression modeling, upon identification and removal of high TF occupancy regions. Further, predictive TF binding models demonstrate a bias for these high-occupancy sites. Neuronal TFs SATB2 and TBR1 bind unique regions depleted for such sites and promote neuronal gene expression. Binding sites for TFs, including TBR1 and PKNOX1, are enriched for risk variants associated with neuropsychiatric disorders, predominantly in neurons. This work, titled BrainTF, is a powerful resource for future studies seeking to understand the roles of specific TFs in regulating gene expression in the human brain.

Genomic prediction and genome-wide association study using combined genotypic data from different genotyping systems: application to apple fruit quality traits

With advances in next-generation sequencing technologies, various marker genotyping systems have been developed for genomics-based approaches such as genomic selection (GS) and genome-wide association study (GWAS). As new genotyping platforms are developed, data from different genotyping platforms must be combined. However, the potential use of combined data for GS and GWAS has not yet been clarified. In this study, the accuracy of genomic prediction (GP) and the detection power of GWAS increased for most fruit quality traits of apples when using combined data from different genotyping systems, Illumina Infinium single-nucleotide polymorphism array and genotyping by random amplicon sequencing-direct (GRAS-Di) systems. In addition, the GP model, which considered the inbreeding effect, further improved the accuracy of the seven fruit traits. Runs of homozygosity (ROH) islands overlapped with the significantly associated regions detected by the GWAS for several fruit traits. Breeders may have exploited these regions to select promising apples by breeders, increasing homozygosity. These results suggest that combining genotypic data from different genotyping platforms benefits the GS and GWAS of fruit quality traits in apples. Information on inbreeding could be beneficial for improving the accuracy of GS for fruit traits of apples; however, further analysis is required to elucidate the relationship between the fruit traits and inbreeding depression (e.g. decreased vigor).

Mendelian randomization analyses reveal causal relationships between brain functional networks and risk of psychiatric disorders

Dysfunction of brain resting-state functional networks has been widely reported in psychiatric disorders. However, the causal relationships between brain resting-state functional networks and psychiatric disorders remain largely unclear. Here we perform bidirectional two-sample Mendelian randomization (MR) analyses to investigate the causalities between 191 resting-state functional magnetic resonance imaging (rsfMRI) phenotypes (n = 34,691 individuals) and 12 psychiatric disorders (n = 14,307 to 698,672 individuals). Forward MR identified 8 rsfMRI phenotypes causally associated with the risk of psychiatric disorders. For example, the increase in the connectivity of motor, subcortical-cerebellum and limbic network was associated with lower risk of autism spectrum disorder. In adddition, increased connectivity in the default mode and central executive network was associated with lower risk of post-traumatic stress disorder and depression. Reverse MR analysis revealed significant associations between 4 psychiatric disorders and 6 rsfMRI phenotypes. For instance, the risk of attention-deficit/hyperactivity disorder increases the connectivity of the attention, salience, motor and subcortical-cerebellum network. The risk of schizophrenia mainly increases the connectivity of the default mode and central executive network and decreases the connectivity of the attention network. In summary, our findings reveal causal relationships between brain functional networks and psychiatric disorders, providing important interventional and therapeutic targets for psychiatric disorders at the brain functional network level.

Cephalometric Evaluation of Children with Short Stature of Genetic Etiology: A Review

Introduction: A plethora of biological molecules regulate chondrogenesis in the epiphyseal growth plate. Disruptions of the quantity and function of these molecules can manifest clinically as stature abnormalities of various etiologies. Traditionally, the growth hormone/insulin-like growth factor 1 (IGF1) axis represents the etiological centre of final stature attainment. Of note, little is known about the molecular events that dominate the growth of the craniofacial complex and its correlation with somatic stature. Aim: Given the paucity of relevant data, this review discusses available information regarding potential applications of lateral cephalometric radiography as a potential clinical indicator of genetic short stature in children. Materials and Methods: A literature search was conducted in the PubMed electronic database using the keywords: cephalometric analysis and short stature; cephalometric analysis and achondroplasia; cephalometric analysis and hypochondroplasia; cephalometric analysis and skeletal abnormalities; cephalometr* and SHOX; cephalometr* and CNP; cephalometr* and ACAN; cephalometr* and CNVs; cephalometr* and IHH; cephalometr* and FGFR3; cephalometr* and Noonan syndrome; cephalometr* and "Turner syndrome"; cephalometr* and achondroplasia. Results: In individuals with genetic syndromes causing short stature, linear growth of the craniofacial complex is confined, following the pattern of somatic short stature regardless of its aetiology. The angular and linear cephalometric measurements differ from the measurements of the average normal individuals and are suggestive of a posterior placement of the jaws and a vertical growth pattern of the face. Conclusions: The greater part of the existing literature regarding cephalometric measurements in short-statured children with genetic syndromes provides qualitative data. Furthermore, cephalometric data for individuals affected with specific rare genetic conditions causing short stature should be the focus of future studies. These quantitative data are required to potentially establish cut-off values for reference for genetic testing based on craniofacial phenotypes.

Testing for differences in polygenic scores in the presence of confounding

Polygenic scores have become an important tool in human genetics, enabling the prediction of individuals' phenotypes from their genotypes. Understanding how the pattern of differences in polygenic score predictions across individuals intersects with variation in ancestry can provide insights into the evolutionary forces acting on the trait in question, and is important for understanding health disparities. However, because most polygenic scores are computed using effect estimates from population samples, they are susceptible to confounding by both genetic and environmental effects that are correlated with ancestry. The extent to which this confounding drives patterns in the distribution of polygenic scores depends on patterns of population structure in both the original estimation panel and in the prediction/test panel. Here, we use theory from population and statistical genetics, together with simulations, to study the procedure of testing for an association between polygenic scores and axes of ancestry variation in the presence of confounding. We use a general model of genetic relatedness to describe how confounding in the estimation panel biases the distribution of polygenic scores in a way that depends on the degree of overlap in population structure between panels. We then show how this confounding can bias tests for associations between polygenic scores and important axes of ancestry variation in the test panel. Specifically, for any given test, there exists a single axis of population structure in the GWAS panel that needs to be controlled for in order to protect the test. Based on this result, we propose a new approach for directly estimating this axis of population structure in the GWAS panel. We then use simulations to compare the performance of this approach to the standard approach in which the principal components of the GWAS panel genotypes are used to control for stratification.

Approach to the Peripubertal Patient With Short Stature

CONTEXT

The assessment and treatment of children with growth retardation is increasingly complex, and due to availability of targeted genetic sequencing, an ever-expanding number of conditions impeding growth are being identified. Among endocrine-related etiologies of short stature amenable to hormonal treatment, defects in the growth hormone (GH)-insulin-like growth factor I axis remain pre-eminent, with a multiplicity of disorders causing decreased secretion or insensitivity to GH action. Sex steroids in puberty increase epiphyseal senescence and eventual growth plate closure. This is mediated mostly via estrogen receptor (ER)α in males and females, effects that can greatly limit time available for growth.

EVIDENCE ACQUISITION

Extensive literature review through PubMed and other search engines.

EVIDENCE SYNTHESIS

Therapeutic strategies to be considered in peripubertal and pubertal children with disordered growth are here discussed, including daily and weekly GH, low-dose sex steroids, gonadotropin hormone releasing hormone (GnRH) analogues in combination with GH, aromatase inhibitors (AIs) alone and in combination with GH in boys. When used for at least 2 to 3 years, GnRH analogues combined with GH can result in meaningful increases in height. AIs used with GH permit puberty to progress in boys without hindrance, selectively decreasing estrogen, and resulting in taller height. With more than 20 years of cumulative experience in clinical use of these medications, we discuss the safety profile of these treatments.

CONCLUSION

The approach of growth retardation in the peripubertal and pubertal years must consider the sex steroid milieu and the tempo of bone acceleration. Treatment of affected children in this period must be individualized.

Clinical profiles and molecular genetic analyses of 98 Chinese children with short statures

Background

Short stature is one of the most prevalent endocrine disorders in children, and its genetic basis is a complex and actively researched subject. Currently, there is limited genetic research on exome sequencing for short stature, and more large-scale studies are necessary for further exploration.

Methods

The retrospective study entailed investigation of 98 Chinese children with short statures (height SDS ≤ -2.5) of unknown etiologies recruited between 2017 and 2021. Whole-exome sequencing (WES) was performed on these patients to identify the potential genetic etiologies. The clinical data were reviewed retrospectively to assess the pathogenicity of the identified mutations. Additionally, 31 patients consented to and received recombinant human growth hormone (rhGH) therapy for 12 months. The short-term effects of rhGH treatment were evaluated across different etiologies of patients with short statures.

Results

The WES results were used to identify 31 different variants in 18 genes among 24 (24.5%) patients. Individuals with more severe short statures were more likely to have underlying genetic etiologies. Short stature accompanied by other phenotypes had significantly higher diagnostic yields than simple severe short stature. The rhGH therapy demonstrated efficacy in most children. Nevertheless, the treatment response was suboptimal in a boy diagnosed with 3M syndrome.

Conclusion

WES is an important approach for confirming genetic disorders in patients with severe short statures of unknown etiologies, suggesting that it could be used as a primary diagnostic strategy. The administration of rhGH may not be suitable for all children with short statures, and the identification of the genetic cause of short stature by WES has significant guidance value for rhGH treatment.

shaPRS: Leveraging shared genetic effects across traits or ancestries improves accuracy of polygenic scores

We present shaPRS, a method that leverages widespread pleiotropy between traits or shared genetic effects across ancestries, to improve the accuracy of polygenic scores. The method uses genome-wide summary statistics from two diseases or ancestries to improve the genetic effect estimate and standard error at SNPs where there is homogeneity of effect between the two datasets. When there is significant evidence of heterogeneity, the genetic effect from the disease or population closest to the target population is maintained. We show via simulation and a series of real-world examples that shaPRS substantially enhances the accuracy of polygenic risk scores (PRSs) for complex diseases and greatly improves PRS performance across ancestries. shaPRS is a PRS pre-processing method that is agnostic to the actual PRS generation method, and as a result, it can be integrated into existing PRS generation pipelines and continue to be applied as more performant PRS methods are developed over time.

Causal relationships between anthropometric traits, bone mineral density, osteoarthritis and spinal stenosis: A Mendelian randomisation investigation

OBJECTIVE

Spinal stenosis is a common condition among older individuals, with significant morbidity attached. Little is known about its risk factors but degenerative conditions, such as osteoarthritis (OA) have been identified for their mechanistic role. This study aims to explore causal relationships between anthropometric risk factors, OA, and spinal stenosis using Mendelian randomisation (MR) techniques.

DESIGN

We applied two-sample MR to investigate the causal relationships between genetic liability for select risk factors and spinal stenosis. Next, we examined the genetic relationship between OA and spinal stenosis with linkage disequilibrium score regression and Causal Analysis Using Summary Effect estimates MR method. Finally, we used multivariable MR (MVMR) to explore whether OA and body mass index (BMI) mediate the causal pathways identified.

RESULTS

Our analysis revealed strong evidence for the effect of higher BMI (odds ratio [OR] = 1.54, 95%CI: 1.41-1.69, p-value = 2.7 × 10-21), waist (OR = 1.43, 95%CI: 1.15-1.79, p-value = 1.5 × 10-3) and hip (OR = 1.50, 95%CI: 1.27-1.78, p-value = 3.3 × 10-6) circumference on spinal stenosis. Strong evidence of causality was also observed for higher bone mineral density (BMD): total body (OR = 1.21, 95%CI: 1.12-1.29, p-value = 1.6 × 10-7), femoral neck (OR = 1.35, 95%CI: 1.09-1.37, p-value = 7.5×10-7), and lumbar spine (OR = 1.38, 95%CI: 1.25-1.52, p-value = 4.4 × 10-11). We detected high genetic correlations between spinal stenosis and OA (rg range: 0.47-0.66), with Causal Analysis Using Summary Effect estimates results supporting a causal effect of OA on spinal stenosis (ORallOA = 1.6, 95%CI: 1.41-1.79). Direct effects of BMI, BMD on spinal stenosis remained after adjusting for OA in the MVMR.

CONCLUSIONS

Genetic susceptibility to anthropometric risk factors, particularly higher BMI and BMD can increase the risk of spinal stenosis, independent of OA status. These results may inform preventative strategies and treatments.

Dynamic influence of maternal education on height among Chinese children aged 0-18 years

Background

Maternal education is one of key factors affecting nurturing environment which significantly impacts children's height levels throughout their developmental stages. However, the influence of maternal education on children's height is less studied. This study aims to investigate the dynamic influence of maternal education on children's height among Chinese children aged 0-18 years.

Methods

Children undergoing health examinations from January 2021 to September 2023 were included in this study. Clinical information including height, weight, maternal pregnancy history, blood specimens for bone metabolism-related indicators and maternal education level was collected. Children's height was categorized into 14 groups based on age and gender percentiles, following WHO 2006 growth standards. One-way analysis of variance (ANOVA), linear regression, chi-square test and Fisher's exact test were applied for data analysis.

Results

A total of 6269 samples were collected, including 3654 males and 2615 females, with an average age of 8.38 (3.97) for males and 7.89 (3.55) for females. Significant correlations between maternal education level, birth weight, birth order, weight percentile, vitamin D, serum phosphorus, alkaline phosphatase levels, and children's height were identified. Birth weight's influence on height varied across age groups. Compared with normal birth weight children, low birth weight children exhibited catch-up growth within the first 6 years and a subsequent gradual widening of the height gap from 6 to 18 years old. Remarkably, the impact of maternal education on height became more pronounced among children above 3-6 years old, which can mitigate the effect of low birth weight on height.

Conclusion

We found that weight percentile, birth weight, birth order, bone marker levels, and maternal education level have significant effect on height. Maternal education attenuates the impact of low birth weight on height. The findings indicated that maternal education plays a consistent and critical role in promoting robust and healthy growth.

Rapid response of fly populations to gene dosage across development and generations

Although the effects of genetic and environmental perturbations on multicellular organisms are rarely restricted to single phenotypic layers, our current understanding of how developmental programs react to these challenges remains limited. Here, we have examined the phenotypic consequences of disturbing the bicoid regulatory network in early Drosophila embryos. We generated flies with two extra copies of bicoid, which causes a posterior shift of the network's regulatory outputs and a decrease in fitness. We subjected these flies to EMS mutagenesis, followed by experimental evolution. After only 8-15 generations, experimental populations have normalized patterns of gene expression and increased survival. Using a phenomics approach, we find that populations were normalized through rapid increases in embryo size driven by maternal changes in metabolism and ovariole development. We extend our results to additional populations of flies, demonstrating predictability. Together, our results necessitate a broader view of regulatory network evolution at the systems level.

Early prediction of growth patterns after pediatric kidney transplantation based on height-related single-nucleotide polymorphisms

BACKGROUND

Growth retardation is a common complication of chronic kidney disease in children, which can be partially relieved after renal transplantation. This study aimed to develop and validate a predictive model for growth patterns of children with end-stage renal disease (ESRD) after kidney transplantation using machine learning algorithms based on genomic and clinical variables.

METHODS

A retrospective cohort of 110 children who received kidney transplants between May 2013 and September 2021 at the First Affiliated Hospital of Zhengzhou University were recruited for whole-exome sequencing (WES), and another 39 children who underwent transplant from October 2021 to March 2022 were enrolled for external validation. Based on previous studies, we comprehensively collected 729 height-related single-nucleotide polymorphisms (SNPs) in exon regions. Seven machine learning algorithms and 10-fold cross-validation analysis were employed for model construction.

RESULTS

The 110 children were divided into two groups according to change in height-for-age Z -score. After univariate analysis, age and 19 SNPs were incorporated into the model and validated. The random forest model showed the best prediction efficacy with an accuracy of 0.8125 and an area under curve (AUC) of 0.924, and also performed well in the external validation cohort (accuracy, 0.7949; AUC, 0.796).

CONCLUSIONS

A model with good performance for predicting post-transplant growth patterns in children based on SNPs and clinical variables was constructed and validated using machine learning algorithms. The model is expected to guide clinicians in the management of children after renal transplantation, including the use of growth hormone, glucocorticoid withdrawal, and nutritional supplementation, to alleviate growth retardation in children with ESRD.

Defining a Haplotype Encompassing the LCORL-NCAPG Locus Associated with Increased Lean Growth in Beef Cattle

Numerous studies have shown genetic variation at the LCORL-NCAPG locus is strongly associated with growth traits in beef cattle. However, a causative molecular variant has yet to be identified. To define all possible candidate variants, 34 Charolais-sired calves were whole-genome sequenced, including 17 homozygous for a long-range haplotype associated with increased growth (QQ) and 17 homozygous for potential ancestral haplotypes for this region (qq). The Q haplotype was refined to an 814 kb region between chr6:37,199,897-38,014,080 and contained 218 variants not found in qq individuals. These variants include an insertion in an intron of NCAPG, a previously documented mutation in NCAPG (rs109570900), two coding sequence mutations in LCORL (rs109696064 and rs384548488), and 15 variants located within ATAC peaks that were predicted to affect transcription factor binding. Notably, rs384548488 is a frameshift variant likely resulting in loss of function for long isoforms of LCORL. To test the association of the coding sequence variants of LCORL with phenotype, 405 cattle from five populations were genotyped. The two variants were in complete linkage disequilibrium. Statistical analysis of the three populations that contained QQ animals revealed significant (p < 0.05) associations with genotype and birth weight, live weight, carcass weight, hip height, and average daily gain. These findings affirm the link between this locus and growth in beef cattle and describe DNA variants that define the haplotype. However, further studies will be required to define the true causative mutation.

The association between DNA methylation and human height and a prospective model of DNA methylation-based height prediction

As a vital anthropometric characteristic, human height information not only helps to understand overall developmental status and genetic risk factors, but is also important for forensic DNA phenotyping. We utilized linear regression analysis to test the association between each CpG probe and the height phenotype. Next, we designed a methylation sequencing panel targeting 959 CpGs and subsequent height inference models were constructed for the Chinese population. A total of 11,730 height-associated sites were identified. By employing KPCA and deep neural networks, a prediction model was developed, of which the cross-validation RMSE, MAE and R2 were 5.62 cm, 4.45 cm and 0.64, respectively. Genetic factors could explain 39.4% of the methylation level variance of sites used in the height inference models. Collectively, we demonstrated an association between height and DNA methylation status through an EWAS analysis. Targeted methylation sequencing of only 959 CpGs combined with deep learning techniques could provide a model to estimate human height with higher accuracy than SNP-based prediction models.

Risk factors for prostate cancer: An umbrella review of prospective observational studies and mendelian randomization analyses

BACKGROUND

The incidence of prostate cancer is increasing in older males globally. Age, ethnicity, and family history are identified as the well-known risk factors for prostate cancer, but few modifiable factors have been firmly established. The objective of this study was to identify and evaluate various factors modifying the risk of prostate cancer reported in meta-analyses of prospective observational studies and mendelian randomization (MR) analyses.

METHODS AND FINDINGS

We searched PubMed, Embase, and Web of Science from the inception to January 10, 2022, updated on September 9, 2023, to identify meta-analyses and MR studies on prostate cancer. Eligibility criteria for meta-analyses were (1) meta-analyses including prospective observational studies or studies that declared outcome-free at baseline; (2) evaluating the factors of any category associated with prostate cancer incidence; and (3) providing effect estimates for further data synthesis. Similar criteria were applied to MR studies. Meta-analysis was repeated using the random-effects inverse-variance model with DerSimonian-Laird method. Quality assessment was then conducted for included meta-analyses using AMSTAR-2 tool and for MR studies using STROBE-MR and assumption evaluation. Subsequent evidence grading criteria for significant associations in meta-analyses contained sample size, P values and 95% confidence intervals, 95% prediction intervals, heterogeneity, and publication bias, assigning 4 evidence grades (convincing, highly suggestive, suggestive, or weak). Significant associations in MR studies were graded as robust, probable, suggestive, or insufficient considering P values and concordance of effect directions. Finally, 92 selected from 411 meta-analyses and 64 selected from 118 MR studies were included after excluding the overlapping and outdated studies which were published earlier and contained fewer participants or fewer instrument variables for the same exposure. In total, 123 observational associations (45 significant and 78 null) and 145 causal associations (55 significant and 90 null) were categorized into lifestyle; diet and nutrition; anthropometric indices; biomarkers; clinical variables, diseases, and treatments; and environmental factors. Concerning evidence grading on significant associations, there were 5 highly suggestive, 36 suggestive, and 4 weak associations in meta-analyses, and 10 robust, 24 probable, 4 suggestive, and 17 insufficient causal associations in MR studies. Twenty-six overlapping factors between meta-analyses and MR studies were identified, with consistent significant effects found for physical activity (PA) (occupational PA in meta: OR = 0.87, 95% CI: 0.80, 0.94; accelerator-measured PA in MR: OR = 0.49, 95% CI: 0.33, 0.72), height (meta: OR = 1.09, 95% CI: 1.06, 1.12; MR: OR = 1.07, 95% CI: 1.01, 1.15, for aggressive prostate cancer), and smoking (current smoking in meta: OR = 0.74, 95% CI: 0.68, 0.80; smoking initiation in MR: OR = 0.91, 95% CI: 0.86, 0.97). Methodological limitation is that the evidence grading criteria could be expanded by considering more indices.

CONCLUSIONS

In this large-scale study, we summarized the associations of various factors with prostate cancer risk and provided comparisons between observational associations by meta-analysis and genetically estimated causality by MR analyses. In the absence of convincing overlapping evidence based on the existing literature, no robust associations were identified, but some effects were observed for height, physical activity, and smoking.

Maternal Age at Menarche Genes Determines Fetal Growth Restriction Risk

We aimed to explore the potential link of maternal age at menarche (mAAM) gene polymorphisms with risk of the fetal growth restriction (FGR). This case (FGR)-control (FGR free) study included 904 women (273 FGR and 631 control) in the third trimester of gestation examined/treated in the Departments of Obstetrics. For single nucleotide polymorphism (SNP) multiplex genotyping, 50 candidate loci of mAAM were chosen. The relationship of mAAM SNPs and FGR was appreciated by regression procedures (logistic/model-based multifactor dimensionality reduction [MB-MDR]) with subsequent in silico assessment of the assumed functionality pithy of FGR-related loci. Three mAAM-appertain loci were FGR-linked to genes such as KISS1 (rs7538038) (effect allele G-odds ratio (OR)allelic = 0.63/pperm = 0.0003; ORadditive = 0.61/pperm = 0.001; ORdominant = 0.56/pperm = 0.001), NKX2-1 (rs999460) (effect allele A-ORallelic = 1.37/pperm = 0.003; ORadditive = 1.45/pperm = 0.002; ORrecessive = 2.41/pperm = 0.0002), GPRC5B (rs12444979) (effect allele T-ORallelic = 1.67/pperm = 0.0003; ORdominant = 1.59/pperm = 0.011; ORadditive = 1.56/pperm = 0.009). The haplotype ACA FSHB gene (rs555621*rs11031010*rs1782507) was FRG-correlated (OR = 0.71/pperm = 0.05). Ten FGR-implicated interworking models were founded for 13 SNPs (pperm ≤ 0.001). The rs999460 NKX2-1 and rs12444979 GPRC5B interplays significantly influenced the FGR risk (these SNPs were present in 50% of models). FGR-related mAAM-appertain 15 polymorphic variants and 350 linked SNPs were functionally momentous in relation to 39 genes participating in the regulation of hormone levels, the ovulation cycle process, male gonad development and vitamin D metabolism. Thus, this study showed, for the first time, that the mAAM-appertain genes determine FGR risk.

Skeletal growth is enhanced by a shared role for SOX8 and SOX9 in promoting reserve chondrocyte commitment to columnar proliferation

SOX8 was linked in a genome-wide association study to human height heritability, but roles in chondrocytes for this close relative of the master chondrogenic transcription factor SOX9 remain unknown. We undertook here to fill this knowledge gap. High-throughput assays demonstrate expression of human SOX8 and mouse Sox8 in growth plate cartilage. In situ assays show that Sox8 is expressed at a similar level as Sox9 in reserve and early columnar chondrocytes and turned off when Sox9 expression peaks in late columnar and prehypertrophic chondrocytes. Sox8-/- mice and Sox8fl/flPrx1Cre and Sox9fl/+Prx1Cre mice (inactivation in limb skeletal cells) have a normal or near normal skeletal size. In contrast, juvenile and adult Sox8fl/flSox9fl/+Prx1Cre compound mutants exhibit a 15 to 20% shortening of long bones. Their growth plate reserve chondrocytes progress slowly toward the columnar stage, as witnessed by a delay in down-regulating Pthlh expression, in packing in columns and in elevating their proliferation rate. SOX8 or SOX9 overexpression in chondrocytes reveals not only that SOX8 can promote growth plate cell proliferation and differentiation, even upon inactivation of endogenous Sox9, but also that it is more efficient than SOX9, possibly due to greater protein stability. Altogether, these findings uncover a major role for SOX8 and SOX9 in promoting skeletal growth by stimulating commitment of growth plate reserve chondrocytes to actively proliferating columnar cells. Further, by showing that SOX8 is more chondrogenic than SOX9, they suggest that SOX8 could be preferred over SOX9 in therapies to promote cartilage formation or regeneration in developmental and degenerative cartilage diseases.

Growth disorders caused by variants in epigenetic regulators: progress and prospects

Epigenetic modifications play an important role in regulation of transcription and gene expression. The molecular machinery governing epigenetic modifications, also known as epigenetic regulators, include non-coding RNA, chromatin remodelers, and enzymes or proteins responsible for binding, reading, writing and erasing DNA and histone modifications. Recent advancement in human genetics and high throughput sequencing technology have allowed the identification of causative variants, many of which are epigenetic regulators, for a wide variety of childhood growth disorders that include skeletal dysplasias, idiopathic short stature, and generalized overgrowth syndromes. In this review, we highlight the connection between epigenetic modifications, genetic variants in epigenetic regulators and childhood growth disorders being established over the past decade, discuss their insights into skeletal biology, and the potential of epidrugs as a new type of therapeutic intervention.

Genome-wide association studies and polygenic risk score phenome-wide association studies across complex phenotypes in the human phenotype project

BACKGROUND

Genome-wide association studies (GWASs) associate phenotypes and genetic variants across a study cohort. GWASs require large-scale cohorts with both phenotype and genetic sequencing data, limiting studied phenotypes. The Human Phenotype Project is a longitudinal study that has measured a wide range of clinical and biomolecular features from a self-assignment cohort over 5 years. The phenotypes collected are quantitative traits, providing higher-resolution insights into the genetics of complex phenotypes.

METHODS

We present the results of GWASs and polygenic risk score phenome-wide association studies with 729 clinical phenotypes and 4,043 molecular features from the Human Phenotype Project. This includes clinical traits that have not been previously associated with genetics, including measures from continuous sleep monitoring, continuous glucose monitoring, liver ultrasound, hormonal status, and fundus imaging.

FINDINGS

In GWAS of 8,706 individuals, we found significant associations between 169 clinical traits and 1,184 single-nucleotide polymorphisms. We found genes associated with both glycemic control and mental disorders, and we quantify the strength of genetic signals in serum metabolites. In polygenic risk score phenome-wide association studies for clinical traits, we found 16,047 significant associations.

CONCLUSIONS

The entire set of findings, which we disseminate publicly, provides newfound resolution into the genetic architecture of complex human phenotypes.

FUNDING

E.S. is supported by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation.

A rapid and reference-free imputation method for low-cost genotyping platforms

Most current genotype imputation methods are reference-based, which posed several challenges to users, such as high computational costs and reference panel inaccessibility. Thus, deep learning models are expected to create reference-free imputation methods performing with higher accuracy and shortening the running time. We proposed a imputation method using recurrent neural networks integrating with an additional discriminator network, namely GRUD. This method was applied to datasets from genotyping chips and Low-Pass Whole Genome Sequencing (LP-WGS) with the reference panels from The 1000 Genomes Project (1KGP) phase 3, the dataset of 4810 Singaporeans (SG10K), and The 1000 Vietnamese Genome Project (VN1K). Our model performed more accurately than other existing methods on multiple datasets, especially with common variants with large minor allele frequency, and shrank running time and memory usage. In summary, these results indicated that GRUD can be implemented in genomic analyses to improve the accuracy and running-time of genotype imputation.

Leveraging genetic correlations and multiple populations to improve genetic risk prediction for non-European populations

The disparity in genetic risk prediction accuracy between European and non-European individuals highlights a critical challenge in health inequality. To bridge this gap, we introduce JointPRS, a novel method that models multiple populations jointly to improve genetic risk predictions for non-European individuals. JointPRS has three key features. First, it encompasses all diverse populations to improve prediction accuracy, rather than relying solely on the target population with a singular auxiliary European group. Second, it autonomously estimates and leverages chromosome-wise cross-population genetic correlations to infer the effect sizes of genetic variants. Lastly, it provides an auto version that has comparable performance to the tuning version to accommodate the situation with no validation dataset. Through extensive simulations and real data applications to 22 quantitative traits and four binary traits in East Asian populations, nine quantitative traits and one binary trait in African populations, and four quantitative traits in South Asian populations, we demonstrate that JointPRS outperforms state-of-art methods, improving the prediction accuracy for both quantitative and binary traits in non-European populations.

Selection on the promoter regions plays an important role in complex traits during duck domestication

BACKGROUND

Identifying the key factors that underlie complex traits during domestication is a great challenge for evolutionary and biological studies. In addition to the protein-coding region differences caused by variants, a large number of variants are located in the noncoding regions containing multiple types of regulatory elements. However, the roles of accumulated variants in gene regulatory elements during duck domestication and economic trait improvement are poorly understood.

RESULTS

We constructed a genomics, transcriptomics, and epigenomics map of the duck genome and assessed the evolutionary forces that have been in play across the whole genome during domestication. In total, 304 (42.94%) gene promoters have been specifically selected in Pekin duck among all selected genes. Joint multi-omics analysis reveals that 218 genes (72.01%) with selected promoters are located in open and active chromatin, and 267 genes (87.83%) with selected promoters were highly and differentially expressed in domestic trait-related tissues. One important candidate gene ELOVL3, with a strong signature of differentiation on the core promoter region, is known to regulate fatty acid elongation. Functional experiments showed that the nearly fixed variants in the top selected ELOVL3 promoter in Pekin duck decreased binding ability with HLF and increased gene expression, with the overexpression of ELOVL3 able to increase lipid deposition and unsaturated fatty acid enrichment.

CONCLUSIONS

This study presents genome resequencing, RNA-Seq, Hi-C, and ATAC-Seq data of mallard and Pekin duck, showing that selection of the gene promoter region plays an important role in gene expression and phenotypic changes during domestication and highlights that the variants of the ELOVL3 promoter may have multiple effects on fat and long-chain fatty acid content in ducks.

Chargaff's second parity rule lies at the origin of additive genetic interactions in quantitative traits to make omnigenic selection possible

Background

Francis Crick's central dogma provides a residue-by-residue mechanistic explanation of the flow of genetic information in living systems. However, this principle may not be sufficient for explaining how random mutations cause continuous variation of quantitative highly polygenic complex traits. Chargaff's second parity rule (CSPR), also referred to as intrastrand DNA symmetry, defined as near-exact equalities G ≈ C and A ≈ T within a single DNA strand, is a statistical property of cellular genomes. The phenomenon of intrastrand DNA symmetry was discovered more than 50 years ago; at present, it remains unclear what its biological role is, what the mechanisms are that force cellular genomes to comply strictly with CSPR, and why genomes of certain noncellular organisms have broken intrastrand DNA symmetry. The present work is aimed at studying a possible link between intrastrand DNA symmetry and the origin of genetic interactions in quantitative traits.

Methods

Computational analysis of single-nucleotide polymorphisms in human and mouse populations and of nucleotide composition biases at different codon positions in bacterial and human proteomes.

Results

The analysis of mutation spectra inferred from single-nucleotide polymorphisms observed in murine and human populations revealed near-exact equalities of numbers of reverse complementary mutations, indicating that random genetic variations obey CSPR. Furthermore, nucleotide compositions of coding sequences proved to be statistically interwoven via CSPR because pyrimidine bias at the 3rd codon position compensates purine bias at the 1st and 2nd positions.

Conclusions

According to Fisher's infinitesimal model, we propose that accumulation of reverse complementary mutations results in a continuous phenotypic variation due to small additive effects of statistically interwoven genetic variations. Therefore, additive genetic interactions can be inferred as a statistical entanglement of nucleotide compositions of separate genetic loci. CSPR challenges the neutral theory of molecular evolution-because all random mutations participate in variation of a trait-and provides an alternative solution to Haldane's dilemma by making a gene function diffuse. We propose that CSPR is symmetry of Fisher's infinitesimal model and that genetic information can be transferred in an implicit contactless manner.

A meta-analysis of genetic and phenotypic diversity of European local pig breeds reveals genomic regions associated with breed differentiation for production traits

BACKGROUND

Intense selection of modern pig breeds has resulted in genetic improvement of production traits while the performance of local pig breeds has remained lower. As local pig breeds have been bred in extensive systems, they have adapted to specific environmental conditions, resulting in a rich genotypic and phenotypic diversity. This study is based on European local pig breeds that have been genetically characterized using DNA-pool sequencing data and phenotypically characterized using breed level phenotypes related to stature, fatness, growth, and reproductive performance traits. These data were analyzed using a dedicated approach to detect signatures of selection linked to phenotypic traits in order to uncover potential candidate genes that may underlie adaptation to specific environments.

RESULTS

Analysis of the genetic data of European pig breeds revealed four main axes of genetic variation represented by the Iberian and three modern breeds (i.e. Large White, Landrace, and Duroc). In addition, breeds clustered according to their geographical origin, for example French Gascon and Basque breeds, Italian Apulo Calabrese and Casertana breeds, Spanish Iberian, and Portuguese Alentejano breeds. Principal component analysis of the phenotypic data distinguished the larger and leaner breeds with better growth potential and reproductive performance from the smaller and fatter breeds with low growth and reproductive efficiency. Linking the signatures of selection with phenotype identified 16 significant genomic regions associated with stature, 24 with fatness, 2 with growth, and 192 with reproduction. Among them, several regions contained candidate genes with possible biological effects on stature, fatness, growth, and reproductive performance traits. For example, strong associations were found for stature in two regions containing, respectively, the ANXA4 and ANTXR1 genes, for fatness in a region containing the DNMT3A and POMC genes and for reproductive performance in a region containing the HSD17B7 gene.

CONCLUSIONS

In this study on European local pig breeds, we used a dedicated approach for detecting signatures of selection that were supported by phenotypic data at the breed level to identify potential candidate genes that may have adapted to different living environments and production systems.

The Big (Genetic) Sort? A Research Note on Migration Patterns and Their Genetic Imprint in the United Kingdom

This research note reinvestigates Abdellaoui et al.'s (2019) findings that genetically selective migration may lead to persistent and accumulating socioeconomic and health inequalities between types (coal mining or non-coal mining) of places in the United Kingdom. Their migration measure classified migrants who moved to the same type of place (coal mining to coal mining or non-coal mining to non-coal mining) into "stay" categories, preventing them from distinguishing migrants from nonmigrants. We reinvestigate the question of genetically selective migration by examining migration patterns between places rather than place types and find genetic selectivity in whether people migrate and where. For example, we find evidence of positive selection: people with genetic variants correlated with better education moved from non-coal mining to coal mining places with our measure of migration. Such findings were obscured in earlier work that could not distinguish nonmigrants from migrants.

Risk and resilience in Syrian refugee children: A multisystem analysis

Refugee children are often exposed to substantial trauma, placing them at increased risk for mental illness. However, this risk can be mitigated by a capacity for resilience, conferred from multiple ecological systems (e.g., family, community), including at an individual biological level. We examined the ability of hair cortisol concentrations and polygenic scores for mental health to predict risk and resilience in a sample of Syrian refugee children (n = 1359). Children were categorized as either at-risk or resilient depending on clinical thresholds for posttraumatic stress disorder, depression, and externalizing behavior problems. Logistic regression was used to examine main and interacting effects while controlling for covariates. Elevated hair cortisol concentrations were significantly associated with reduced resilience (odds ratio (OR)=0.58, 95%CI [0.40, 0.83]) while controlling for levels of war exposure. Polygenic scores for depression, self-harm, and neuroticism were not found to have any significant main effects. However, a significant interaction emerged between hair cortisol and polygenic scores for depression (OR=0.04, 95%CI [0.003 0.47]), suggesting that children predisposed to depression were more at risk for mental health problems when hair cortisol concentrations were high. Our results suggest that biomarkers (separately and in combination) might support early identification of refugee children at risk for mental health problems.

Leveraging genetic data for predicting consumer choices of alcoholic products

Purpose

Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

Design/methodology/approach

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

Findings

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

Originality/value

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.

Cohort profile: Genetic data in the German Socio-Economic Panel Innovation Sample (SOEP-G)

The German Socio-Economic Panel (SOEP) serves a global research community by providing representative annual longitudinal data of respondents living in private households in Germany. The dataset offers a valuable life course panorama, encompassing living conditions, socioeconomic status, familial connections, personality traits, values, preferences, health, and well-being. To amplify research opportunities further, we have extended the SOEP Innovation Sample (SOEP-IS) by collecting genetic data from 2,598 participants, yielding the first genotyped dataset for Germany based on a representative population sample (SOEP-G). The sample includes 107 full-sibling pairs, 501 parent-offspring pairs, and 152 triads, which overlap with the parent-offspring pairs. Leveraging the results from well-powered genome-wide association studies, we created a repository comprising 66 polygenic indices (PGIs) in the SOEP-G sample. We show that the PGIs for height, BMI, and educational attainment capture 22∼24%, 12∼13%, and 9% of the variance in the respective phenotypes. Using the PGIs for height and BMI, we demonstrate that the considerable increase in average height and the decrease in average BMI in more recent birth cohorts cannot be attributed to genetic shifts within the German population or to age effects alone. These findings suggest an important role of improved environmental conditions in driving these changes. Furthermore, we show that higher values in the PGIs for educational attainment and the highest math class are associated with better self-rated health, illustrating complex relationships between genetics, cognition, behavior, socio-economic status, and health. In summary, the SOEP-G data and the PGI repository we created provide a valuable resource for studying individual differences, inequalities, life-course development, health, and interactions between genetic predispositions and the environment.

A Bayesian Approach to Correcting the Attenuation Bias of Regression Using Polygenic Risk Score

Polygenic risk score (PRS) has become increasingly popular for predicting the value of complex traits. In many settings, PRS is used as a covariate in regression analysis to study the association between different phenotypes. However, measurement error in PRS causes attenuation bias in the estimation of regression coefficients. In this paper, we employ a Bayesian approach to accounting for the measurement error of PRS and correcting the attenuation bias in linear and logistic regression. Through simulation, we show that our approach is able to obtain approximately unbiased estimation of coefficients and credible intervals with correct coverage probability. We also empirically compare our Bayesian measurement error model to the conventional regression model by analyzing real traits in the UK Biobank. The results demonstrate the effectiveness of our approach as it significantly reduces the error in coefficient estimates.

Sequenced-based GWAS for linear classification traits in Belgian Blue beef cattle reveals new coding variants in genes regulating body size in mammals

BACKGROUND

Cohorts of individuals that have been genotyped and phenotyped for genomic selection programs offer the opportunity to better understand genetic variation associated with complex traits. Here, we performed an association study for traits related to body size and muscular development in intensively selected beef cattle. We leveraged multiple trait information to refine and interpret the significant associations.

RESULTS

After a multiple-step genotype imputation to the sequence-level for 14,762 Belgian Blue beef (BBB) cows, we performed a genome-wide association study (GWAS) for 11 traits related to muscular development and body size. The 37 identified genome-wide significant quantitative trait loci (QTL) could be condensed in 11 unique QTL regions based on their position. Evidence for pleiotropic effects was found in most of these regions (e.g., correlated association signals, overlap between credible sets (CS) of candidate variants). Thus, we applied a multiple-trait approach to combine information from different traits to refine the CS. In several QTL regions, we identified strong candidate genes known to be related to growth and height in other species such as LCORL-NCAPG or CCND2. For some of these genes, relevant candidate variants were identified in the CS, including three new missense variants in EZH2, PAPPA2 and ADAM12, possibly two additional coding variants in LCORL, and candidate regulatory variants linked to CCND2 and ARMC12. Strikingly, four other QTL regions associated with dimension or muscular development traits were related to five (recessive) deleterious coding variants previously identified.

CONCLUSIONS

Our study further supports that a set of common genes controls body size across mammalian species. In particular, we added new genes to the list of those associated with height in both humans and cattle. We also identified new strong candidate causal variants in some of these genes, strengthening the evidence of their causality. Several breed-specific recessive deleterious variants were identified in our QTL regions, probably as a result of the extreme selection for muscular development in BBB cattle.

Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value

Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 17 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies.

GWAS of Dizygotic Twinning in an Enlarged Australian Sample of Mothers of DZ Twins

Female fertility is a complex trait with age-specific changes in spontaneous dizygotic (DZ) twinning and fertility. To elucidate factors regulating female fertility and infertility, we conducted a genome-wide association study (GWAS) on mothers of spontaneous DZ twins (MoDZT) versus controls (3273 cases, 24,009 controls). This is a follow-up study to the Australia/New Zealand (ANZ) component of that previously reported (Mbarek et al., 2016), with a sample size almost twice that of the entire discovery sample meta-analysed in the previous article (and five times the ANZ contribution to that), resulting from newly available additional genotyping and representing a significant increase in power. We compare analyses with and without male controls and show unequivocally that it is better to include male controls who have been screened for recent family history, than to use only female controls. Results from the SNP based GWAS identified four genomewide significant signals, including one novel region, ZFPM1 (Zinc Finger Protein, FOG Family Member 1), on chromosome 16. Previous signals near FSHB (Follicle Stimulating Hormone beta subunit) and SMAD3 (SMAD Family Member 3) were also replicated (Mbarek et al., 2016). We also ran the GWAS with a dominance model that identified a further locus ADRB2 on chr 5. These results have been contributed to the International Twinning Genetics Consortium for inclusion in the next GWAS meta-analysis (Mbarek et al., in press).

A scalable approach to characterize pleiotropy across thousands of human diseases and complex traits using GWAS summary statistics

Genome-wide association studies (GWASs) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra-large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N = 420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (p = 2.58E-10) and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest shared etiologies between rheumatoid arthritis and periodontal condition in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWASs.

An atlas of genetic determinants of forearm fracture

Osteoporotic fracture is among the most common and costly of diseases. While reasonably heritable, its genetic determinants have remained elusive. Forearm fractures are the most common clinically recognized osteoporotic fractures with a relatively high heritability. To establish an atlas of the genetic determinants of forearm fractures, we performed genome-wide association analyses including 100,026 forearm fracture cases. We identified 43 loci, including 26 new fracture loci. Although most fracture loci associated with bone mineral density, we also identified loci that primarily regulate bone quality parameters. Functional studies of one such locus, at TAC4, revealed that Tac4-/- mice have reduced mechanical bone strength. The strongest forearm fracture signal, at WNT16, displayed remarkable bone-site-specificity with no association with hip fractures. Tall stature and low body mass index were identified as new causal risk factors for fractures. The insights from this atlas may improve fracture prediction and enable therapeutic development to prevent fractures.

Genome-wide association study on pharmacological outcomes of musculoskeletal pain in UK Biobank

The pharmacological management of musculoskeletal pain starts with NSAIDs, followed by weak or strong opioids until the pain is under control. However, the treatment outcome is usually unsatisfying due to inter-individual differences. To investigate the genetic component of treatment outcome differences, we performed a genome-wide association study (GWAS) in ~23,000 participants with musculoskeletal pain from the UK Biobank. NSAID vs. opioid users were compared as a reflection of the treatment outcome of NSAIDs. We identified one genome-wide significant hit in chromosome 4 (rs549224715, P = 3.88 × 10-8). Suggestive significant (P < 1 × 10-6) loci were functionally annotated to 18 target genes, including four genes linked to neuropathic pain processes or musculoskeletal development. Pathway and network analyses identified immunity-related processes and a (putative) central role of EGFR. However, this study should be viewed as a first step to elucidate the genetic background of musculoskeletal pain treatment.

Structure-informed clustering for population stratification in association studies

BACKGROUND

Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants.

RESULTS

To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans.

CONCLUSIONS

CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

Genomic and transcriptomic analyses reveal polygenic architecture for ecologically important traits in aspen (Populus tremuloides Michx.)

Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single-locus genome-wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this "missing heritability" phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

Boosting the power of genome-wide association studies within and across ancestries by using polygenic scores

Genome-wide association studies (GWASs) have been mostly conducted in populations of European ancestry, which currently limits the transferability of their findings to other populations. Here, we show, through theory, simulations and applications to real data, that adjustment of GWAS analyses for polygenic scores (PGSs) increases the statistical power for discovery across all ancestries. We applied this method to analyze seven traits available in three large biobanks with participants of East Asian ancestry (n = 340,000 in total) and report 139 additional associations across traits. We also present a two-stage meta-analysis strategy whereby, in contributing cohorts, a PGS-adjusted GWAS is rerun using PGSs derived from a first round of a standard meta-analysis. On average, across traits, this approach yields a 1.26-fold increase in the number of detected associations (range 1.07- to 1.76-fold increase). Altogether, our study demonstrates the value of using PGSs to increase the power of GWASs in underrepresented populations and promotes such an analytical strategy for future GWAS meta-analyses.

Analysis of children with familial short stature: who should be indicated for genetic testing?

Familial short stature (FSS) describes vertically transmitted growth disorders. Traditionally, polygenic inheritance is presumed, but monogenic inheritance seems to occur more frequently than expected. Clinical predictors of monogenic FSS have not been elucidated. The aim of the study was to identify the monogenic etiology and its clinical predictors in FSS children. Of 747 patients treated with growth hormone (GH) in our center, 95 with FSS met the inclusion criteria (pretreatment height ≤-2 SD in child and his/her shorter parent); secondary short stature and Turner/Prader-Willi syndrome were excluded criteria. Genetic etiology was known in 11/95 children before the study, remaining 84 were examined by next-generation sequencing. The results were evaluated by American College of Medical Genetics and Genomics (ACMG) guidelines. Nonparametric tests evaluated differences between monogenic and non-monogenic FSS, an ROC curve estimated quantitative cutoffs for the predictors. Monogenic FSS was confirmed in 36/95 (38%) children. Of these, 29 (81%) carried a causative genetic variant affecting the growth plate, 4 (11%) a variant affecting GH-insulin-like growth factor 1 (IGF1) axis and 3 (8%) a variant in miscellaneous genes. Lower shorter parent's height (P = 0.015) and less delayed bone age (BA) before GH treatment (P = 0.026) predicted monogenic FSS. In children with BA delayed less than 0.4 years and with shorter parent's heights ≤-2.4 SD, monogenic FSS was revealed in 13/16 (81%) cases. To conclude, in FSS children treated with GH, a monogenic etiology is frequent, and gene variants affecting the growth plate are the most common. Shorter parent's height and BA are clinical predictors of monogenic FSS.

Identification of Missense Variants Affecting Carcass Traits for Hanwoo Precision Breeding

This study aimed to identify causal variants associated with important carcass traits such as weight and meat quality in Hanwoo cattle. We analyzed missense mutations extracted from imputed sequence data (ARS-UCD1.2) and performed an exon-specific association test on the carcass traits of 16,970 commercial Hanwoo. We found 33, 2, 1, and 3 significant SNPs associated with carcass weight (CW), backfat thickness (BFT), eye muscle area (EMA), and marbling score (MS), respectively. In CW and EMA, the most significant missense SNP was identified at 19,524,263 on BTA14 and involved the PRKDC. A missense SNP in the ZFAND2B, located at 107,160,304 on BTA2 was identified as being involved in BFT. For MS, missense SNP in the ACVR2B gene, located at 11,849,704 in BTA22 was identified as the most significant marker. The contribution of the most significant missense SNPs to genetic variance was confirmed to be 8.47%, 2.08%, 1.73%, and 1.19% in CW, BFT, EMA, and MS, respectively. We generated favorable and unfavorable haplotype combinations based on the significant SNPs for CW. Significant differences in GEBV (Genomic Estimated Breeding Values) were observed between groups with each favorable and unfavorable haplotype combination. In particular, the missense SNPs in PRKDC, MRPL9, and ANKFN1 appear to significantly affect the protein's function and structure, making them strong candidates as causal mutations. These missense SNPs have the potential to serve as valuable markers for improving carcass traits in Hanwoo commercial farms.

Identification and analysis of individuals who deviate from their genetically-predicted phenotype

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol (LDL-C). We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL-C and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions.

TMCO3, a Putative K+ :Proton Antiporter at the Golgi Apparatus, Is Important for Longitudinal Growth in Mice and Humans

Isolated short stature, defined as short stature without any other abnormalities, is a common heterogeneous condition in children. Exome sequencing identified the homozygous nonsense variant c.1832G>A/p.(Trp611*) in TMCO3 in two sisters with isolated short stature. Radiological studies, biochemical measurements, assessment of the skeletal status, and three-dimensional bone microarchitecture revealed no relevant skeletal and bone abnormalities in both sisters. The homozygous TMCO3 variant segregated with short stature in the family. TMCO3 transcript levels were reduced by ~50% in leukocyte-derived RNA of both sisters compared with controls, likely due to nonsense-mediated mRNA decay. In primary urinary cells of heterozygous family members, we detected significantly reduced TMCO3 protein levels. TMCO3 is functionally uncharacterized. We ectopically expressed wild-type TMCO3 in HeLa and ATDC5 chondrogenic cells and detected TMCO3 predominantly at the Golgi apparatus, whereas the TMCO3W611* mutant did not reach the Golgi. Coordinated co-expression of TMCO3W611* -HA and EGFP in HeLa cells confirmed intrinsic instability and/or degradation of the mutant. Tmco3 is expressed in all relevant mouse skeletal cell types. Highest abundance of Tmco3 was found in chondrocytes of the prehypertrophic zone in mouse and minipig growth plates where it co-localizes with a Golgi marker. Knockdown of Tmco3 in differentiated ATDC5 cells caused reduced and increased expression of Pthlh and Ihh, respectively. Measurement of long bones in Tmco3tm1b(KOMP)Wtsi knockout mice revealed significant shortening of forelimbs and hindlimbs. TMCO3 is a potential member of the monovalent cation:proton antiporter 2 (CPA2) family. By in silico tools and homology modeling, TMCO3 is predicted to have an N-terminal secretory signal peptide, forms a dimer localized to the membrane, and is organized in a dimerization and a core domain. The core domain contains the CPA2 motif essential for K+ binding and selectivity. Collectively, our data demonstrate that loss of TMCO3 causes growth defects in both humans and mice. © 2023 American Society for Bone and Mineral Research (ASBMR).

Mendelian randomization analysis revealed potential metabolic causal factors for breast cancer

Observational studies showed that metabolic phenotypes were associated with the risk of developing breast cancer (BC). However, those results are inconsistent regarding the magnitude of the association, particularly by subtypes of breast cancer. Furthermore, the mechanisms of the association remain unclear. We performed two-sample Mendelian randomization (MR) analyses to evaluate the causal effect of metabolic risk factors on breast cancer in the European population. Assessed individually using MR, body mass index (BMI) (odds ratio [OR] 0.94, 95% Confidence interval [CI] 0.90-0.98, P = 0.007), high-density lipoprotein cholesterol (HDL-C) (OR 1.10, 95% CI 1.07-1.13, P = 6.10 × 10-11) and triglycerides (TG) (OR 0.92, 95% CI 0.90-0.96, P = 1.58 × 10-6) were causally related to breast cancer risk. In multivariable MR, only HDL-C (OR 1.08; 95% CI 1.02-1.14; P = 0.02) retained a robust effect, suggesting that the genetic association between BMI, HDL-C and TG with breast cancer risk in univariable analysis was explained via HDL-C. These findings suggest a possible causal role of HDL-C in breast cancer etiology.

DNA methylation at the suppressor of cytokine signaling 3 (SOCS3) gene influences height in childhood

Human height is strongly influenced by genetics but the contribution of modifiable epigenetic factors is under-explored, particularly in low and middle-income countries (LMIC). We investigate links between blood DNA methylation and child height in four LMIC cohorts (n = 1927) and identify a robust association at three CpGs in the suppressor of cytokine signaling 3 (SOCS3) gene which replicates in a high-income country cohort (n = 879). SOCS3 methylation (SOCS3m)-height associations are independent of genetic effects. Mendelian randomization analysis confirms a causal effect of SOCS3m on height. In longitudinal analysis, SOCS3m explains a maximum 9.5% of height variance in mid-childhood while the variance explained by height polygenic risk score increases from birth to 21 years. Children's SOCS3m is associated with prenatal maternal folate and socio-economic status. In-vitro characterization confirms a regulatory effect of SOCS3m on gene expression. Our findings suggest epigenetic modifications may play an important role in driving child height in LMIC.

Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants

Genome-wide association studies (GWAS) revolutionized our understanding of common genetic variation and its impact on common human disease and traits. Developed and adopted in the mid-2000s, GWAS led to searchable genotype-phenotype catalogs and genome-wide datasets available for further data mining and analysis for the eventual development of translational applications. The GWAS revolution was swift and specific, including almost exclusively populations of European descent, to the neglect of the majority of the world's genetic diversity. In this narrative review, we recount the GWAS landscape of the early years that established a genotype-phenotype catalog that is now universally understood to be inadequate for a complete understanding of complex human genetics. We then describe approaches taken to augment the genotype-phenotype catalog, including the study populations, collaborative consortia, and study design approaches aimed to generalize and then ultimately discover genome-wide associations in non-European descent populations. The collaborations and data resources established in the efforts to diversify genomic findings undoubtedly provide the foundations of the next chapters of genetic association studies with the advent of budget-friendly whole-genome sequencing.

Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes

Molecular quantitative trait loci (xQTLs) are often harnessed to prioritize genes or functional elements underpinning variant-trait associations identified from genome-wide association studies (GWASs). Here, we introduce OPERA, a method that jointly analyzes GWAS and multi-omics xQTL summary statistics to enhance the identification of molecular phenotypes associated with complex traits through shared causal variants. Applying OPERA to summary-level GWAS data for 50 complex traits (n = 20,833-766,345) and xQTL data from seven omics layers (n = 100-31,684) reveals that 50% of the GWAS signals are shared with at least one molecular phenotype. GWAS signals shared with multiple molecular phenotypes, such as those at the MSMB locus for prostate cancer, are particularly informative for understanding the genetic regulatory mechanisms underlying complex traits. Future studies with more molecular phenotypes, measured considering spatiotemporal effects in larger samples, are required to obtain a more saturated map linking molecular intermediates to GWAS signals.

Genetic determinants of thyroid function in children

OBJECTIVE

Genome-wide association studies in adults have identified 42 loci associated with thyroid stimulating hormone (TSH) and 21 loci associated with free thyroxine (FT4) concentrations. While biologically plausible, age-dependent effects have not been assessed. We aimed to study the association of previously identified genetic determinants of TSH and FT4 with TSH and FT4 concentrations in newborns and (pre)school children.

METHODS

We selected participants from three population-based prospective cohorts with data on genetic variants and thyroid function: Generation R (N = 2169 children, mean age 6 years; N = 2388 neonates, the Netherlands), the Avon Longitudinal Study of Parents and Children (ALSPAC; N = 3382, age 7.5 years, United Kingdom), and the Brisbane Longitudinal Twin Study (BLTS; N = 1680, age 12.1 years, Australia). The association of single nucleotide polymorphisms (SNPs) with TSH and FT4 concentrations was studied with multivariable linear regression models. Weighted polygenic risk scores (PRSs) were defined to combine SNP effects.

RESULTS

In childhood, 30/60 SNPs were associated with TSH and 11/31 SNPs with FT4 after multiple testing correction. The effect sizes for AADAT, GLIS3, TM4SF4, and VEGFA were notably larger than in adults. The TSH PRS explained 5.3%-8.4% of the variability in TSH concentrations; the FT4 PRS explained 1.5%-4.2% of the variability in FT4 concentrations. Five TSH SNPs and no FT4 SNPs were associated with thyroid function in neonates.

CONCLUSIONS

The effects of many known thyroid function SNPs are already apparent in childhood and some might be notably larger in children as compared to adults. These findings provide new knowledge about genetic regulation of thyroid function in early life.