scholarly journals The Genetic Basis of Delayed Puberty

2017 ◽  
Vol 106 (3) ◽  
pp. 283-291 ◽  
Author(s):  
Sasha R. Howard ◽  
Leo Dunkel
Keyword(s):  
Genetic Control ◽  
Large Scale ◽  
Pubertal Timing ◽  
Association Studies ◽  
Genetic Regulation ◽  
Psychosocial Health ◽  
Delayed Puberty ◽  
Genome Wide Association Studies ◽  
Fetal Life ◽  
Mineral Density

The genetic control of puberty remains an important but mostly unanswered question. Late pubertal timing affects over 2% of adolescents and is associated with adverse health outcomes including short stature, reduced bone mineral density, and compromised psychosocial health. Self-limited delayed puberty (DP) is a highly heritable trait, which often segregates in an autosomal dominant pattern; however, its neuroendocrine pathophysiology and genetic regulation remain unclear. Some insights into the genetic mutations that lead to familial DP have come from sequencing genes known to cause gonadotropin-releasing hormone (GnRH) deficiency, most recently via next-generation sequencing, and others from large-scale genome-wide association studies in the general population. Investigation of the genetic control of DP is complicated by the fact that this trait is not rare and that the phenotype is likely to represent a final common pathway, with a variety of different pathogenic mechanisms affecting the release of the puberty “brake.” These include abnormalities of GnRH neuronal development and function, GnRH receptor and luteinizing hormone/follicle-stimulating hormone abnormalities, metabolic and energy homeostatic derangements, and transcriptional regulation of the hypothalamic-pituitary-gonadal axis. Thus, genetic control of pubertal timing can range from early fetal life via development of the GnRH network to those factors directly influencing the puberty brake during mid-childhood.

scholarly journals Identification of known and novel long non-coding RNAs potentially responsible for the effects of BMD GWAS loci

2021 ◽  
Author(s):  
Abdullah Abood ◽  
Larry Mesner ◽  
Will Rosenow ◽  
Basel M Al-Barghouthi ◽  
Nina Horwitz ◽  
...  
Keyword(s):  
Complex Disease ◽  
Bone Cells ◽  
Association Studies ◽  
Genetic Regulation ◽  
Tissue Expression ◽  
Genome Wide Association Studies ◽  
Mineral Density ◽  
Common Complex Disease ◽  
Genetics Of Osteoporosis ◽  
Non Coding Rnas

Osteoporosis, characterized by low bone mineral density (BMD), is the most common complex disease affecting bone and constitutes a major societal health problem. Genome-wide association studies (GWASs) have identified over 1100 associations influencing BMD. It has been shown that perturbations to long non-coding RNAs (lncRNAs) influence BMD and the activities of bone cells; however, the extent to which lncRNAs are involved in the genetic regulation of BMD is unknown. Here, we combined the analysis of allelic imbalance (AI) in human acetabular bone fragments with a transcriptome-wide association study (TWAS) and expression quantitative trait loci (eQTL) colocalization analysis using data from the Genotype-Tissue Expression (GTEx) project to identify lncRNAs potentially responsible for GWAS associations. We identified 27 lncRNAs in bone that are located in proximity to a BMD GWAS association and harbor SNPs demonstrating AI. Using GTEx data we identified an additional 31 lncRNAs whose expression was associated (FDR correction<0.05) with BMD through TWAS and had a colocalizing eQTL (regional colocalization probability (RCP)>0.1). The 58 lncRNAs are located in 43 BMD associations. To further support a causal role for the identified lncRNAs, we show that 23 of the 58 lncRNAs are differentially expressed as a function of osteoblast differentiation. Our approach identifies lncRNAs that are potentially responsible for BMD GWAS associations and suggest that lncRNAs play a role in the genetics of osteoporosis.

scholarly journals Heritability of pubertal timing: detailed evaluation of specific milestones in healthy boys and girls

2020 ◽  
Vol 183 (1) ◽  
pp. 13-20
Author(s):  
Alexander S Busch ◽  
Casper P Hagen ◽  
Anders Juul
Keyword(s):  
Genetic Heterogeneity ◽  
Large Scale ◽  
Pubertal Timing ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genetic Data ◽  
Genome Wide Association Studies ◽  
Facial Hair ◽  
Timing Of Menarche ◽  
Relative Category

Objective Pubertal timing is highly heritable. Observational studies were inconclusive concerning a potential sex-specific difference in the parental contribution, while genome-wide association studies highlighted a heterogeneity in the genetic architecture of pubertal timing between sexes. Our objectives were to evaluate the association of timing of pubertal milestones in offspring with parental pubertal timing and to identify the genetic basis of the heterogeneity. Design (1.) Population-based mixed cross-sectional/longitudinal cohort (2006–2014, COPENHAGEN Puberty Study) comprising 1381 healthy Danish children including their parents. (2.) UK Biobank-based summary statistics of genetic data on timing of menarche (n = 188 644), voice-break (n = 154 459) and facial hair (n = 161 470). Methods (1.) Participants underwent clinical examination(s) including blood sampling. Parental pubertal timing was obtained by questionnaire. Timing of milestones were analyzed using SAS-lifereg. (2.) Genetic correlations between pubertal outcomes were estimated using LD Score regression. Genetic heterogeneity was analyzed using METAL. Results We observed significant associations of relative parental pubertal timing with timing of pubertal milestones in offspring of concordant sex, that is, fathers/sons (e.g. testicular enlargement: P = 0.004, β = 0.34 years per relative category) and mothers/daughters (e.g. thelarche: P < 0.001, β = 0.45 years per relative category). Fewer milestones were associated with relative parental pubertal timing in offspring of discordant sex compared to concordant sex. Large-scale genetic data highlight both moderate to strong genetic correlations between timing of menarche, voice-break and facial hair. Out of 434 lead single-nucleotide polymorphisms significantly associated with at least one outcome, 39 exhibited a significant genetic heterogeneity between sexes (P < 1.15 × 10−4). Conclusion Our results highlight a distinct genetic heterogeneity of pubertal timing between sexes.

scholarly journals Genome-Wide Association Studies Based on Equine Joint Angle Measurements Reveal New QTL Affecting the Conformation of Horses

Genes ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 370 ◽  
Author(s):  
Annik Imogen Gmel ◽  
Thomas Druml ◽  
Rudolf von Niederhäusern ◽  
Tosso Leeb ◽  
Markus Neuditschko
Keyword(s):  
Joint Angle ◽  
Association Studies ◽  
Genome Wide Association ◽  
Cranial Morphology ◽  
Genome Wide Association Studies ◽  
Joint Angles ◽  
Mineral Density ◽  
Mixed Effect ◽  
Stifle Joint ◽  
Genome Wide

The evaluation of conformation traits is an important part of selection for breeding stallions and mares. Some of these judged conformation traits involve joint angles that are associated with performance, health, and longevity. To improve our understanding of the genetic background of joint angles in horses, we have objectively measured the angles of the poll, elbow, carpal, fetlock (front and hind), hip, stifle, and hock joints based on one photograph of each of the 300 Franches-Montagnes (FM) and 224 Lipizzan (LIP) horses. After quality control, genome-wide association studies (GWASs) for these traits were performed on 495 horses, using 374,070 genome-wide single nucleotide polymorphisms (SNPs) in a mixed-effect model. We identified two significant quantitative trait loci (QTL) for the poll angle on ECA28 (p = 1.36 × 10−7), 50 kb downstream of the ALX1 gene, involved in cranial morphology, and for the elbow joint on ECA29 (p = 1.69 × 10−7), 49 kb downstream of the RSU1 gene, and 75 kb upstream of the PTER gene. Both genes are associated with bone mineral density in humans. Furthermore, we identified other suggestive QTL associated with the stifle joint on ECA8 (p = 3.10 × 10−7); the poll on ECA1 (p = 6.83 × 10−7); the fetlock joint of the hind limb on ECA27 (p = 5.42 × 10−7); and the carpal joint angle on ECA3 (p = 6.24 × 10−7), ECA4 (p = 6.07 × 10−7), and ECA7 (p = 8.83 × 10−7). The application of angular measurements in genetic studies may increase our understanding of the underlying genetic effects of important traits in equine breeding.

scholarly journals Optimized permutation testing for information theoretic measures of multi-gene interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
James M. Kunert-Graf ◽  
Nikita A. Sakhanenko ◽  
David J. Galas
Keyword(s):  
Large Scale ◽  
Permutation Test ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Permutation Testing ◽  
Exact Test ◽  
Information Theoretic ◽  
Information Theoretic Measures ◽  
Full Analysis ◽  
Computational Bottleneck

Abstract Background Permutation testing is often considered the “gold standard” for multi-test significance analysis, as it is an exact test requiring few assumptions about the distribution being computed. However, it can be computationally very expensive, particularly in its naive form in which the full analysis pipeline is re-run after permuting the phenotype labels. This can become intractable in multi-locus genome-wide association studies (GWAS), in which the number of potential interactions to be tested is combinatorially large. Results In this paper, we develop an approach for permutation testing in multi-locus GWAS, specifically focusing on SNP–SNP-phenotype interactions using multivariable measures that can be computed from frequency count tables, such as those based in Information Theory. We find that the computational bottleneck in this process is the construction of the count tables themselves, and that this step can be eliminated at each iteration of the permutation testing by transforming the count tables directly. This leads to a speed-up by a factor of over 103 for a typical permutation test compared to the naive approach. Additionally, this approach is insensitive to the number of samples making it suitable for datasets with large number of samples. Conclusions The proliferation of large-scale datasets with genotype data for hundreds of thousands of individuals enables new and more powerful approaches for the detection of multi-locus genotype-phenotype interactions. Our approach significantly improves the computational tractability of permutation testing for these studies. Moreover, our approach is insensitive to the large number of samples in these modern datasets. The code for performing these computations and replicating the figures in this paper is freely available at https://github.com/kunert/permute-counts.

scholarly journals Twelve years of GWAS discoveries for osteoporosis and related traits: advances, challenges and applications

Bone Research ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Xiaowei Zhu ◽  
Weiyang Bai ◽  
Houfeng Zheng
Keyword(s):  
Drug Targets ◽  
Complex Disease ◽  
Association Studies ◽  
Osteoporotic Fractures ◽  
Disease Prediction ◽  
Genome Wide Association Studies ◽  
Mineral Density ◽  
Small Effect Size ◽  
Endocrine Status ◽  
Meta Analyses

AbstractOsteoporosis is a common skeletal disease, affecting ~200 million people around the world. As a complex disease, osteoporosis is influenced by many factors, including diet (e.g. calcium and protein intake), physical activity, endocrine status, coexisting diseases and genetic factors. In this review, we first summarize the discovery from genome-wide association studies (GWASs) in the bone field in the last 12 years. To date, GWASs and meta-analyses have discovered hundreds of loci that are associated with bone mineral density (BMD), osteoporosis, and osteoporotic fractures. However, the GWAS approach has sometimes been criticized because of the small effect size of the discovered variants and the mystery of missing heritability, these two questions could be partially explained by the newly raised conceptual models, such as omnigenic model and natural selection. Finally, we introduce the clinical use of GWAS findings in the bone field, such as the identification of causal clinical risk factors, the development of drug targets and disease prediction. Despite the fruitful GWAS discoveries in the bone field, most of these GWAS participants were of European descent, and more genetic studies should be carried out in other ethnic populations to benefit disease prediction in the corresponding population.

scholarly journals Systems genetics in diversity outbred mice inform BMD GWAS and identify determinants of bone strength

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Basel M. Al-Barghouthi ◽  
Larry D. Mesner ◽  
Gina M. Calabrese ◽  
Daniel Brooks ◽  
Steven M. Tommasini ◽  
...  
Keyword(s):  
Cortical Bone ◽  
Bone Strength ◽  
Human Genetics ◽  
Association Studies ◽  
Systems Genetics ◽  
Genome Wide Association Studies ◽  
Mineral Density ◽  
Diversity Outbred ◽  
Wide Range ◽  
Genetics Of Osteoporosis

AbstractGenome-wide association studies (GWASs) for osteoporotic traits have identified over 1000 associations; however, their impact has been limited by the difficulties of causal gene identification and a strict focus on bone mineral density (BMD). Here, we use Diversity Outbred (DO) mice to directly address these limitations by performing a systems genetics analysis of 55 complex skeletal phenotypes. We apply a network approach to cortical bone RNA-seq data to discover 66 genes likely to be causal for human BMD GWAS associations, including the genes SERTAD4 and GLT8D2. We also perform GWAS in the DO for a wide-range of bone traits and identify Qsox1 as a gene influencing cortical bone accrual and bone strength. In this work, we advance our understanding of the genetics of osteoporosis and highlight the ability of the mouse to inform human genetics.

Power comparison of Cochran-Armitage trend test against allelic and genotypic tests in large-scale case-control genetic association studies

2016 ◽  
Vol 27 (9) ◽  
pp. 2657-2673 ◽  
Author(s):  
Mathieu Emily
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Disease Model ◽  
Trend Test ◽  
Genome Wide Association Studies ◽  
Power Functions ◽  
Power Comparison ◽  
Powerful Test ◽  
Armitage Trend Test ◽  
Mode Of Inheritance

The Cochran-Armitage trend test (CA) has become a standard procedure for association testing in large-scale genome-wide association studies (GWAS). However, when the disease model is unknown, there is no consensus on the most powerful test to be used between CA, allelic, and genotypic tests. In this article, we tackle the question of whether CA is best suited to single-locus scanning in GWAS and propose a power comparison of CA against allelic and genotypic tests. Our approach relies on the evaluation of the Taylor decompositions of non-centrality parameters, thus allowing an analytical comparison of the power functions of the tests. Compared to simulation-based comparison, our approach offers the advantage of simultaneously accounting for the multidimensionality of the set of features involved in power functions. Although power for CA depends on the sample size, the case-to-control ratio and the minor allelic frequency (MAF), our results first show that it is largely influenced by the mode of inheritance and a deviation from Hardy–Weinberg Equilibrium (HWE). Furthermore, when compared to other tests, CA is shown to be the most powerful test under a multiplicative disease model or when the single-nucleotide polymorphism largely deviates from HWE. In all other situations, CA lacks in power and differences can be substantial, especially for the recessive mode of inheritance. Finally, our results are illustrated by the comparison of the performances of the statistics in two genome scans.

scholarly journals GWASpro: a high-performance genome-wide association analysis server

2018 ◽  
Vol 35 (14) ◽  
pp. 2512-2514 ◽  
Author(s):  
Bongsong Kim ◽  
Xinbin Dai ◽  
Wenchao Zhang ◽  
Zhaohong Zhuang ◽  
Darlene L Sanchez ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Learning Curves ◽  
Experimental Designs ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Summary We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

2012 ◽  
Vol 215 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Georg Homuth ◽  
Alexander Teumer ◽  
Uwe Völker ◽  
Matthias Nauck
Keyword(s):  
Blood Cells ◽  
Large Scale ◽  
Genetic Factors ◽  
Association Studies ◽  
Transcriptional Profiling ◽  
Genome Wide Association Studies ◽  
Protein Levels ◽  
Future Developments ◽  
Genome Wide ◽  
Metabolome Data

The metabolome, defined as the reflection of metabolic dynamics derived from parameters measured primarily in easily accessible body fluids such as serum, plasma, and urine, can be considered as the omics data pool that is closest to the phenotype because it integrates genetic influences as well as nongenetic factors. Metabolic traits can be related to genetic polymorphisms in genome-wide association studies, enabling the identification of underlying genetic factors, as well as to specific phenotypes, resulting in the identification of metabolome signatures primarily caused by nongenetic factors. Similarly, correlation of metabolome data with transcriptional or/and proteome profiles of blood cells also produces valuable data, by revealing associations between metabolic changes and mRNA and protein levels. In the last years, the progress in correlating genetic variation and metabolome profiles was most impressive. This review will therefore try to summarize the most important of these studies and give an outlook on future developments.

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