Intergenic GWAS SNPs are key components of the spatial and regulatory network for human growth

Whole-Genome Resequencing Revealed Selective Signatures for Growth Traits in Hu and Gangba Sheep

A genomic study was conducted to uncover the selection signatures in sheep that show extremely significant differences in growth traits under the same breed, age in months, nutrition level, and management practices. Hu sheep from Gansu Province and Gangba sheep from the Tibet Autonomous Region in China were selected. We collected whole-genome data from 40 sheep individuals (24 Hu sheep and 16 Gangba sheep), through whole-genome sequencing. Selection signals were analyzed using parameters such as FST, π ratio, and Tajima's D. We have identified several candidate genes that have undergone strong selection, particularly those associated with growth traits. Specifically, five growth-related genes were identified in both the Hu sheep group (HDAC1, MYH7B, LCK, ACVR1, GNAI2) and the Gangba sheep group (RBBP8, ACSL3, FBXW11, PLAT, CRB1). Additionally, in a genomic region strongly selected in both the Hu and Gangba sheep groups (Chr 22: 51,425,001-51,500,000), the growth-associated gene CYP2E1 was identified, further highlighting the genetic factors influencing growth characteristics in these breeds. This study analyzes the genetic basis for significant differences in sheep phenotypes, identifies candidate genes related to sheep growth traits, lays the foundation for molecular genetic breeding in sheep, and accelerates the genetic improvement in livestock.

Whole-genome resequencing facilitates the development of a 50K single nucleotide polymorphism genotyping array for Scots pine (Pinus sylvestris L.) and its transferability to other pine species

Scots pine (Pinus sylvestris L.) is one of the most widespread and economically important conifer species in the world. Applications like genomic selection and association studies, which could help accelerate breeding cycles, are challenging in Scots pine because of its large and repetitive genome. For this reason, genotyping tools for conifer species, and in particular for Scots pine, are commonly based on transcribed regions of the genome. In this article, we present the Axiom Psyl50K array, the first single nucleotide polymorphism (SNP) genotyping array for Scots pine based on whole-genome resequencing, that represents both genic and intergenic regions. This array was designed following a two-step procedure: first, 192 trees were sequenced, and a 430K SNP screening array was constructed. Then, 480 samples, including haploid megagametophytes, full-sib family trios, breeding population, and range-wide individuals from across Eurasia were genotyped with the screening array. The best 50K SNPs were selected based on quality, replicability, distribution across the draft genome assembly, balance between genic and intergenic regions, and genotype-environment and genotype-phenotype associations. Of the final 49 877 probes tiled in the array, 20 372 (40.84%) occur inside gene models, while the rest lie in intergenic regions. We also show that the Psyl50K array can yield enough high-confidence SNPs for genetic studies in pine species from North America and Eurasia. This new genotyping tool will be a valuable resource for high-throughput fundamental and applied research of Scots pine and other pine species.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Intergenic variants, rs1200114 and rs1200108 are genetically associated along with a decreased ATP1B1 expression in Fuchs Endothelial Corneal Dystrophy

Fuchs endothelial corneal dystrophy (FECD) is an age-related, bilateral corneal condition, characterized by apoptosis of the terminally differentiated endothelial cells. A genome-wide association study (GWAS) conducted in the European population in 2017, identified a new single nucleotide polymorphism (SNP), rs1200114 in the intergenic region between long intergenic non-protein coding RNA 970 (LINC00970) and ATPase Na+/K+ transporting subunit beta 1 (ATP1B1). The major focus of the current study is to understand the genetic association of this intergenic variant, rs1200114 with FECD in the Indian population. Sanger sequencing followed by statistical analysis indicated a significant difference in the allelic frequency between controls and cases (P = 0.01) with the minor allele 'G' of rs1200114 imparting a 1.64 fold increased risk for the disease. Luciferase reporter assay revealed no significant difference in the luciferase activity between allele 'A' and 'G' of rs1200114. However, quantitative RT-PCR assay revealed lower expression of ATP1B1 in FECD subjects compared with controls (P = 0.007). Therefore, to find whether another nearby SNP imparts regulatory effect, tag SNP association analysis was carried out; which revealed a significant association of another SNP, rs1200108, present in the intergenic region between LINC00970 and ATP1B1 with FECD (P = 0.009). The protective allele 'A' of rs1200108 displayed reduced reporter activity as opposed to the risk allele 'G' (P = 0.014). Furthermore, haplotype 'A-A' of rs1200108 - rs1200114 was present at a higher frequency in control subjects, suggesting it as a protective haplotype. Altogether, this study inferred the genetic association of rs1200114 and rs1200108 along with the decreased expression of ATP1B1 related to FECD pathogenesis in the Indian population.

Why does the X chromosome lag behind autosomes in GWAS findings?

The X-chromosome is among the largest human chromosomes. It differs from autosomes by a number of important features including hemizygosity in males, an almost complete inactivation of one copy in females, and unique patterns of recombination. We used data from the Catalog of Published Genome Wide Association Studies to compare densities of the GWAS-detected SNPs on the X-chromosome and autosomes. The density of GWAS-detected SNPs on the X-chromosome is 6-fold lower compared to the density of the GWAS-detected SNPs on autosomes. Differences between the X-chromosome and autosomes cannot be explained by differences in the overall SNP density, lower X-chromosome coverage by genotyping platforms or low call rate of X-chromosomal SNPs. Similar differences in the density of GWAS-detected SNPs were found in female-only GWASs (e.g. ovarian cancer GWASs). We hypothesized that the lower density of GWAS-detected SNPs on the X-chromosome compared to autosomes is not a result of a methodological bias, e.g. differences in coverage or call rates, but has a real underlying biological reason-a lower density of functional SNPs on the X-chromosome versus autosomes. This hypothesis is supported by the observation that (i) the overall SNP density of X-chromosome is lower compared to the SNP density on autosomes and that (ii) the density of genic SNPs on the X-chromosome is lower compared to autosomes while densities of intergenic SNPs are similar.

Integration of eQTL Analysis and GWAS Highlights Regulation Networks in Cotton under Stress Condition

The genus Gossypium is one of the most economically important crops in the world. Here, we used RNA-seq to quantify gene expression in a collection of G. arboreum seedlings and performed eGWAS on 28,382 expressed genes. We identified a total of 30,089 eQTLs in 10,485 genes, of which >90% were trans-regulate target genes. Using luciferase assays, we confirmed that different cis-eQTL haplotypes could affect promoter activity. We found ~6600 genes associated with ~1300 eQTL hotspots. Moreover, hotspot 309 regulates the expression of 325 genes with roles in stem length, fresh weight, seed germination rate, and genes related to cell wall biosynthesis and salt stress. Transcriptome-wide association study (TWAS) identified 19 candidate genes associated with the cotton growth and salt stress response. The variation in gene expression across the population played an essential role in population differentiation. Only a small number of the differentially expressed genes between South China, the Yangtze River region, and the Yellow River region sites were located in different chromosomal regions. The eQTLs found across the duplicated gene pairs showed conservative cis- or trans- regulation and that the expression levels of gene pairs were correlated. This study provides new insights into the evolution of gene expression regulation in cotton, and identifies eQTLs in stress-related genes for use in breeding improved cotton varieties.

A Final Frontier in Environment-Genome Interactions? Integrated, Multi-Omic Approaches to Predictions of Non-Communicable Disease Risk

Epidemiological and associative research from humans and animals identifies correlations between the environment and health impacts. The environment—health inter-relationship is effected through an individual’s underlying genetic variation and mediated by mechanisms that include the changes to gene regulation that are associated with the diversity of phenotypes we exhibit. However, the causal relationships have yet to be established, in part because the associations are reduced to individual interactions and the combinatorial effects are rarely studied. This problem is exacerbated by the fact that our genomes are highly dynamic; they integrate information across multiple levels (from linear sequence, to structural organisation, to temporal variation) each of which is open to and responds to environmental influence. To unravel the complexities of the genomic basis of human disease, and in particular non-communicable diseases that are also influenced by the environment (e.g., obesity, type II diabetes, cancer, multiple sclerosis, some neurodegenerative diseases, inflammatory bowel disease, rheumatoid arthritis) it is imperative that we fully integrate multiple layers of genomic data. Here we review current progress in integrated genomic data analysis, and discuss cases where data integration would lead to significant advances in our ability to predict how the environment may impact on our health. We also outline limitations which should form the basis of future research questions. In so doing, this review will lay the foundations for future research into the impact of the environment on our health.

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

Perspective of the GEMSTONE Consortium on Current and Future Approaches to Functional Validation for Skeletal Genetic Disease Using Cellular, Molecular and Animal-Modeling Techniques

The availability of large human datasets for genome-wide association studies (GWAS) and the advancement of sequencing technologies have boosted the identification of genetic variants in complex and rare diseases in the skeletal field. Yet, interpreting results from human association studies remains a challenge. To bridge the gap between genetic association and causality, a systematic functional investigation is necessary. Multiple unknowns exist for putative causal genes, including cellular localization of the molecular function. Intermediate traits ("endophenotypes"), e.g. molecular quantitative trait loci (molQTLs), are needed to identify mechanisms of underlying associations. Furthermore, index variants often reside in non-coding regions of the genome, therefore challenging for interpretation. Knowledge of non-coding variance (e.g. ncRNAs), repetitive sequences, and regulatory interactions between enhancers and their target genes is central for understanding causal genes in skeletal conditions. Animal models with deep skeletal phenotyping and cell culture models have already facilitated fine mapping of some association signals, elucidated gene mechanisms, and revealed disease-relevant biology. However, to accelerate research towards bridging the current gap between association and causality in skeletal diseases, alternative in vivo platforms need to be used and developed in parallel with the current -omics and traditional in vivo resources. Therefore, we argue that as a field we need to establish resource-sharing standards to collectively address complex research questions. These standards will promote data integration from various -omics technologies and functional dissection of human complex traits. In this mission statement, we review the current available resources and as a group propose a consensus to facilitate resource sharing using existing and future resources. Such coordination efforts will maximize the acquisition of knowledge from different approaches and thus reduce redundancy and duplication of resources. These measures will help to understand the pathogenesis of osteoporosis and other skeletal diseases towards defining new and more efficient therapeutic targets.

Genomic dissection of 43 serum urate-associated loci provides multiple insights into molecular mechanisms of urate control

High serum urate is a prerequisite for gout and associated with metabolic disease. Genome-wide association studies (GWAS) have reported dozens of loci associated with serum urate control; however, there has been little progress in understanding the molecular basis of the associated loci. Here, we employed trans-ancestral meta-analysis using data from European and East Asian populations to identify 10 new loci for serum urate levels. Genome-wide colocalization with cis-expression quantitative trait loci (eQTL) identified a further five new candidate loci. By cis- and trans-eQTL colocalization analysis, we identified 34 and 20 genes, respectively, where the causal eQTL variant has a high likelihood that it is shared with the serum urate-associated locus. One new locus identified was SLC22A9 that encodes organic anion transporter 7 (OAT7). We demonstrate that OAT7 is a very weak urate-butyrate exchanger. Newly implicated genes identified in the eQTL analysis include those encoding proteins that make up the dystrophin complex, a scaffold for signaling proteins and transporters at the cell membrane; MLXIP that, with the previously identified MLXIPL, is a transcription factor that may regulate serum urate via the pentose-phosphate pathway and MRPS7 and IDH2 that encode proteins necessary for mitochondrial function. Functional fine mapping identified six loci (RREB1, INHBC, HLF, UBE2Q2, SFMBT1 and HNF4G) with colocalized eQTL containing putative causal SNPs. This systematic analysis of serum urate GWAS loci identified candidate causal genes at 24 loci and a network of previously unidentified genes likely involved in control of serum urate levels, further illuminating the molecular mechanisms of urate control.

The Identification of the SARS-CoV-2 Whole Genome: Nine Cases Among Patients in Banten Province, Indonesia

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the strain of virus that causes coronavirus disease 2019 (COVID-19), the respiratory illness responsible for the current pandemic. Viral genome sequencing has been widely applied during outbreaks to study the relatedness of this virus to other viruses, its transmission mode, pace, evolution and geographical spread, and also its adaptation to human hosts. To date, more than 90,000 SARS-CoV-2 genome sequences have been uploaded to the GISAID database. The availability of sequencing data along with clinical and geographical data may be useful for epidemiological investigations. In this study, we aimed to analyse the genetic background of SARS-CoV-2 from patients in Indonesia by whole genome sequencing. We examined nine samples from COVID-19 patients with RT-PCR cycle threshold (Ct) of less than 25 using ARTIC Network protocols for Oxford Nanopore’s Gridi On sequencer. The analytical methods were based on the ARTIC multiplex PCR sequencing protocol for COVID-19. In this study, we found that several genetic variants within the nine COVID-19 patient samples. We identified a mutation at position 614 P323L mutation in the ORF1ab gene often found in our severe patient samples. The number of SNPs and their location within the SARS-CoV-2 genome seems to vary. This diversity might be responsible for the virulence of the virus and its clinical manifestation.

Development of a highly efficient 50K single nucleotide polymorphism genotyping array for the large and complex genome of Norway spruce (Picea abies L. Karst) by whole genome resequencing and its transferability to other spruce species

Norway spruce (Picea abies L. Karst) is one of the most important forest tree species with significant economic and ecological impact in Europe. For decades, genomic and genetic studies on Norway spruce have been challenging due to the large and repetitive genome (19.6 Gb with more than 70% being repetitive). To accelerate genomic studies, including population genetics, genome‐wide association studies (GWAS) and genomic selection (GS), in Norway spruce and related species, we here report on the design and performance of a 50K single nucleotide polymorphism (SNP) genotyping array for Norway spruce. The array is developed based on whole genome resequencing (WGS), making it the first WGS‐based SNP array in any conifer species so far. After identifying SNPs using genome resequencing data from 29 trees collected in northern Europe, we adopted a two‐step approach to design the array. First, we built a 450K screening array and used this to genotype a population of 480 trees sampled from both natural and breeding populations across the Norway spruce distribution range. These samples were then used to select high‐confidence probes that were put on the final 50K array. The SNPs selected are distributed over 45,552 scaffolds from the P. abies version 1.0 genome assembly and target 19,954 unique gene models with an even coverage of the 12 linkage groups in Norway spruce. We show that the array has a 99.5% probe specificity, >98% Mendelian allelic inheritance concordance, an average sample call rate of 96.30% and an SNP call rate of 98.90% in family trios and haploid tissues. We also observed that 23,797 probes (50%) could be identified with high confidence in three other spruce species (white spruce [Picea glauca], black spruce [P. mariana] and Sitka spruce [P. sitchensis]). The high‐quality genotyping array will be a valuable resource for genetic and genomic studies in Norway spruce as well as in other conifer species of the same genus.

Influence of Genetic Variance on Biomarker Levels After Occupational Exposure to 1,6-Hexamethylene Diisocyanate Monomer and 1,6-Hexamethylene Diisocyanate Isocyanurate

We evaluated the impact of genetic variance on biomarker levels in a population of workers in the automotive repair and refinishing industry who were exposed to respiratory sensitizers 1,6-hexamethylene diisocyanate (HDI) monomer and one of its trimers, HDI isocyanurate. The exposures and respective urine and plasma biomarkers 1,6-diaminohexane (HDA) and trisaminohexyl isocyanurate (TAHI) were measured in 33 workers; and genome-wide microarrays (Affymetrix 6.0) were used to genotype the workers' single-nucleotide polymorphisms (SNPs). Linear mixed model analyses have indicated that interindividual variations in both inhalation and skin exposures influenced these biomarker levels. Using exposure values as covariates and a false discovery rate < 0.10 to assess statistical significance, we observed that seven SNPs were associated with HDA in plasma, five were associated with HDA in urine, none reached significance for TAHI in plasma, and eight were associated with TAHI levels in urine. The different genotypes for the 20 significant SNPs accounted for 4- to 16-fold changes observed in biomarker levels. Associated gene functions include transcription regulation, calcium ion transport, vascular morphogenesis, and transforming growth factor beta signaling pathway, which may impact toxicokinetics indirectly by altering inflammation levels. Additionally, in an expanded analysis using a minor allele cutoff of 0.05 instead of 0.10, there were biomarker-associated SNPs within three genes that have been associated with isocyanate-induced asthma: ALK, DOCK2, and LHPP. We demonstrate that genetic variance impacts the biomarker levels in workers exposed to HDI monomer and HDI isocyanurate and that genetics can be used to refine exposure predictions in small cohorts when quantitative personal exposure and biomarker measurements are included in the models.

Genetic variants associated with alcohol dependence co-ordinate regulation of ADH genes in gastrointestinal and adipose tissues

GWAS studies have identified genetic variants associated with Alcohol Dependence (AD), but how they link to genes, their regulation and disease traits, remains largely unexplored. Here we integrated information on the 3D genome organization with expression quantitative loci (eQTLs) analysis, using CoDeS3D, to identify the functional impacts of single nucleotide polymorphisms associated with AD (p < 1 × 10-6). We report that 42% of the 285 significant tissue-specific regulatory interactions we identify were associated with four genes encoding Alcohol Dehydrogenase - ADH1A, ADH1B, ADH1C and ADH4. Identified eQTLs produced a co-ordinated regulatory action between ADH genes, especially between ADH1A and ADH1C within the subcutaneous adipose and gastrointestinal tissues. Five eQTLs were associated with regulatory motif alterations and tissue-specific histone marks consistent with these variants falling in enhancer and promoter regions. By contrast, few regulatory connections were identified in the stomach and liver. This suggests that changes in gene regulation associated with AD are linked to changes in tissues other than the primary sites of alcohol absorption and metabolism. Future work to functionally characterise the putative regulatory regions we have identified and their links to metabolic and regulatory changes in genes will improve our mechanistic understanding of AD disease development and progression.

Reconstructing the blood metabolome and genotype using long-range chromatin interactions

BACKGROUND

-Maintenance of tight controls on circulating blood metabolites is crucial to normal, healthy tissue and organismal function. A number of single nucleotide polymorphisms (SNPs) have been associated with changes in the levels of blood metabolites. However, the impacts of the metabolite-associated SNPs are largely unknown because they fall within non-coding regions of the genome.

OBJECTIVE

-We aimed to identify genes and tissues that are linked to changes in circulating blood metabolites by characterizing genome-wide spatial regulatory interactions involving blood metabolite-associated SNPs.

METHOD

-We systematically integrated chromatin interaction (Hi-C), expression quantitative trait loci (eQTL), gene ontology, drug interaction, and literature-supported connections to deconvolute the genetic regulatory influences of 145 blood metabolite-associated SNPs.

FINDINGS

-We identified 577 genes that are regulated by 130 distal and proximal metabolite-associated SNPs across 48 different human tissues. The affected genes are enriched in categories that include metabolism, enzymes, plasma proteins, disease development, and potential drug targets. Our results suggest that regulatory interactions in other tissues contribute to the modulation of blood metabolites.

CONCLUSIONS

-The spatial SNP-gene-metabolite associations identified in this study expand on the list of genes and tissues that are influenced by metabolic-associated SNPs and improves our understanding of the molecular mechanisms underlying pathologic blood metabolite levels.

Genomic regions associated with muscularity in beef cattle differ in five contrasting cattle breeds

BACKGROUND

Linear type traits, which reflect the muscular characteristics of an animal, could provide insight into how, in some cases, morphologically very different animals can yield the same carcass weight. Such variability may contribute to differences in the overall value of the carcass since primal cuts vary greatly in price; such variability may also hinder successful genome-based association studies. Therefore, the objective of our study was to identify genomic regions that are associated with five muscularity linear type traits and to determine if these significant regions are common across five different breeds. Analyses were carried out using linear mixed models on imputed whole-genome sequence data in each of the five breeds, separately. Then, the results of the within-breed analyses were used to conduct an across-breed meta-analysis per trait.

RESULTS

We identified many quantitative trait loci (QTL) that are located across the whole genome and associated with each trait in each breed. The only commonality among the breeds and traits was a large-effect pleiotropic QTL on BTA2 that contained the MSTN gene, which was associated with all traits in the Charolais and Limousin breeds. Other plausible candidate genes were identified for muscularity traits including PDE1A, PPP1R1C and multiple collagen and HOXD genes. In addition, associated (gene ontology) GO terms and KEGG pathways tended to differ between breeds and between traits especially in the numerically smaller populations of Angus, Hereford, and Simmental breeds. Most of the SNPs that were associated with any of the traits were intergenic or intronic SNPs located within regulatory regions of the genome.

CONCLUSIONS

The commonality between the Charolais and Limousin breeds indicates that the genetic architecture of the muscularity traits may be similar in these breeds due to their similar origins. Conversely, there were vast differences in the QTL associated with muscularity in Angus, Hereford, and Simmental. Knowledge of these differences in genetic architecture between breeds is useful to develop accurate genomic prediction equations that can operate effectively across breeds. Overall, the associated QTL differed according to trait, which suggests that breeding for a morphologically different (e.g. longer and wider versus shorter and smaller) more efficient animal may become possible in the future.

A regression framework to uncover pleiotropy in large-scale electronic health record data

OBJECTIVE

Pleiotropy, where 1 genetic locus affects multiple phenotypes, can offer significant insights in understanding the complex genotype-phenotype relationship. Although individual genotype-phenotype associations have been thoroughly explored, seemingly unrelated phenotypes can be connected genetically through common pleiotropic loci or genes. However, current analyses of pleiotropy have been challenged by both methodologic limitations and a lack of available suitable data sources.

MATERIALS AND METHODS

In this study, we propose to utilize a new regression framework, reduced rank regression, to simultaneously analyze multiple phenotypes and genotypes to detect pleiotropic effects. We used a large-scale biobank linked electronic health record data from the Penn Medicine BioBank to select 5 cardiovascular diseases (hypertension, cardiac dysrhythmias, ischemic heart disease, congestive heart failure, and heart valve disorders) and 5 mental disorders (mood disorders; anxiety, phobic and dissociative disorders; alcohol-related disorders; neurological disorders; and delirium dementia) to validate our framework.

RESULTS

Compared with existing methods, reduced rank regression showed a higher power to distinguish known associated single-nucleotide polymorphisms from random single-nucleotide polymorphisms. In addition, genome-wide gene-based investigation of pleiotropy showed that reduced rank regression was able to identify candidate genetic variants with novel pleiotropic effects compared to existing methods.

CONCLUSION

The proposed regression framework offers a new approach to account for the phenotype and genotype correlations when identifying pleiotropic effects. By jointly modeling multiple phenotypes and genotypes together, the method has the potential to distinguish confounding from causal genotype and phenotype associations.

Nonclassical GH Insensitivity: Characterization of Mild Abnormalities of GH Action

GH insensitivity (GHI) presents in childhood with growth failure and in its severe form is associated with extreme short stature and dysmorphic and metabolic abnormalities. In recent years, the clinical, biochemical, and genetic characteristics of GHI and other overlapping short stature syndromes have rapidly expanded. This can be attributed to advancing genetic techniques and a greater awareness of this group of disorders. We review this important spectrum of defects, which present with phenotypes at the milder end of the GHI continuum. We discuss their clinical, biochemical, and genetic characteristics. The objective of this review is to clarify the definition, identification, and investigation of this clinically relevant group of growth defects. We also review the therapeutic challenges of mild GHI.

Machine Learning SNP Based Prediction for Precision Medicine

In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalized medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. Despite recent improvements, the results of polygenic risk scoring remain limited due to the approaches that are currently used. By contrast, machine learning algorithms have increased predictive abilities for complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenic risk scoring and machine learning in complex disease risk prediction. We highlight recent machine learning application developments and describe how machine learning approaches can lead to improved complex disease prediction, which will help to incorporate genetic features into future personalized healthcare. Finally, we discuss how the future application of machine learning prediction models might help manage complex disease by providing tissue-specific targets for customized, preventive interventions.

Networking in Biology: The Hybrid Rat Diversity Panel

One of the most fruitful resources for systems genetic studies of nonhuman mammals is a panel of inbred strains that exhibits significant genetic diversity between strains but genetic stability (isogenicity) within strains. These characteristics allow for fine mapping of complex phenotypes (QTLs) and provide statistical power to identify loci which contribute nominally to the phenotype. This type of resource also allows the planning and performance of investigations using the same genetic backgrounds over several generations of the test animals. Often, rats are preferred over mice for physiologic and behavioral studies because of their larger size and more distinguishable anatomy (particularly for their central nervous system). The Hybrid Rat Diversity Panel (HRDP) is a panel of inbred rat strains, which combines two recombinant inbred panels (the HXB/BXH, 30 strains; the LEXF/FXLE, 34 strains and 35 more strains of inbred rats which were selected for genetic diversity, based on their fully sequenced genomes and/or thorough genotyping). The genetic diversity and statistical power of this panel for mapping studies rivals or surpasses currently available panels in mouse. The genetic stability of this panel makes it particularly suitable for collection of high-throughput omics data as relevant technology becomes available for engaging in truly integrative systems biology. The PhenoGen website ( http://phenogen.org ) is the repository for the initial transcriptome data, making the raw data, the processed data, and the analysis results, e.g., organ-specific protein coding and noncoding transcripts, isoform analysis, expression quantitative trait loci, and co-expression networks, available to the research public. The data sets and tools being developed will complement current efforts to analyze the human transcriptome and its genetic controls (the Genotype-Tissue Expression Project (GTEx)) and allow for dissection of genetic networks that predispose to particular phenotypes and gene-by-environment interactions that are difficult or even impossible to study in humans. The HRDP is an essential population for exploring truly integrative systems genetics.

Chromatin interactions and expression quantitative trait loci reveal genetic drivers of multimorbidities

Clinical studies of non-communicable diseases identify multimorbidities that suggest a common set of predisposing factors. Despite the fact that humans have ~24,000 genes, we do not understand the genetic pathways that contribute to the development of multimorbid non-communicable disease. Here we create a multimorbidity atlas of traits based on pleiotropy of spatially regulated genes. Using chromatin interaction and expression Quantitative Trait Loci (eQTL) data, we analyse 20,782 variants (p < 5 × 10-6) associated with 1351 phenotypes to identify 16,248 putative spatial eQTL-eGene pairs that are involved in 76,013 short- and long-range regulatory interactions (FDR < 0.05) in different human tissues. Convex biclustering of spatial eGenes that are shared among phenotypes identifies complex interrelationships between nominally different phenotype-associated SNPs. Our approach enables the simultaneous elucidation of variant interactions with target genes that are drivers of multimorbidity, and those that contribute to unique phenotype associated characteristics.

The genetic architecture of type 1 diabetes mellitus

Type 1 diabetes mellitus (T1D) is a complex autoimmune disorder characterised by loss of the insulin-producing pancreatic beta cells in genetically predisposed individuals, ultimately resulting in insulin deficiency and hyperglycaemia. T1D is most common among children and young adults, and the incidence is on the rise across the world. The aetiology of T1D is hypothesized to involve genetic and environmental factors that result in the T-cell mediated destruction of pancreatic beta cells. There is a strong genetic risk to T1D; with genome-wide association studies (GWAS) identifying over 60 susceptibility regions within the human genome which are marked by single nucleotide polymorphisms (SNPs). Here, we review what is currently known about the genetics of T1D. We argue that advancing our understanding of the aetiology and pathogenesis of T1D will require the integration of genome biology (omics-data) with GWAS data, thereby making it possible to elucidate the putative gene regulatory networks modulated by disease-associated SNPs. This approach has a potential to revolutionize clinical management of T1D in an era of precision medicine.

Type 1 Diabetes Mellitus-Associated Genetic Variants Contribute to Overlapping Immune Regulatory Networks

Type 1 diabetes (T1D) is a chronic metabolic disorder characterized by the autoimmune destruction of insulin-producing pancreatic islet beta cells in genetically predisposed individuals. Genome-wide association studies (GWAS) have identified over 60 risk regions across the human genome, marked by single nucleotide polymorphisms (SNPs), which confer genetic predisposition to T1D. There is increasing evidence that disease-associated SNPs can alter gene expression through spatial interactions that involve distal loci, in a tissue- and development-specific manner. Here, we used three-dimensional (3D) genome organization data to identify genes that physically co-localized with DNA regions that contained T1D-associated SNPs in the nucleus. Analysis of these SNP-gene pairs using the Genotype-Tissue Expression database identified a subset of SNPs that significantly affected gene expression. We identified 246 spatially regulated genes including HLA-DRB1, LAT, MICA, BTN3A2, CTLA4, CD226, NOTCH1, TRIM26, PTEN, TYK2, CTSH, and FLRT3, which exhibit tissue-specific effects in multiple tissues. We observed that the T1D-associated variants interconnect through networks that form part of the immune regulatory pathways, including immune-cell activation, cytokine signaling, and programmed cell death protein-1 (PD-1). Our results implicate T1D-associated variants in tissue and cell-type specific regulatory networks that contribute to pancreatic beta cell inflammation and destruction, adaptive immune signaling, and immune-cell proliferation and activation. A number of other regulatory changes we identified are not typically considered to be central to the pathology of T1D. Collectively, our data represent a novel resource for the hypothesis-driven development of diagnostic, prognostic, and therapeutic interventions in T1D.

Physical Interactions and Expression Quantitative Traits Loci Identify Regulatory Connections for Obesity and Type 2 Diabetes Associated SNPs

The mechanisms that underlie the association between obesity and type 2 diabetes are not fully understood. Here, we investigated the role of the 3D genome organization in the pathogeneses of obesity and type-2 diabetes. We interpreted the combined and differential impacts of 196 diabetes and 390 obesity associated single nucleotide polymorphisms (SNPs) by integrating data on the genes with which they physically interact (as captured by Hi-C) and the functional [i.e., expression quantitative trait loci (eQTL)] outcomes associated with these interactions. We identified 861 spatially regulated genes (e.g., AP3S2, ELP5, SVIP, IRS1, FADS2, WFS1, RBM6, HORMAD1, PYROXD2), which are enriched in tissues (e.g., adipose, skeletal muscle, pancreas) and biological processes and canonical pathways (e.g., lipid metabolism, leptin, and glucose-insulin signaling pathways) that are important for the pathogenesis of type 2 diabetes and obesity. Our discovery-based approach also identifies enrichment for eQTL SNP-gene interactions in tissues that are not classically associated with diabetes or obesity. We propose that the combinatorial action of active obesity and diabetes spatial eQTL SNPs on their gene pairs within different tissues reduces the ability of these tissues to contribute to the maintenance of a healthy energy metabolism.

GWAS on prolonged gestation (post-term birth): analysis of successive Finnish birth cohorts

Background

Gestation is a crucial timepoint in human development. Deviation from a term gestational age correlates with both acute and long-term adverse health effects for the child. Both being born preterm and post-term, that is, having short and long gestational ages, are heritable and influenced by the prenatal and perinatal environment. Despite the obvious heritable component, specific genetic influences underlying differences in gestational age are poorly understood.

Methods

We investigated the genetic architecture of gestational age in 9141 individuals, including 1167 born post-term, across two Northern Finland cohorts born in 1966 or 1986.

Results

Here we identify one globally significant intronic genetic variant within the ADAMTS13 gene that is associated with prolonged gestation (p=4.85×10−8). Additional variants that reached suggestive levels of significance were identified within introns at the ARGHAP42 and TKT genes, and in the upstream (5’) intergenic regions of the B3GALT5 and SSBP2 genes. The variants near the ADAMTS13, B3GALT5, SSBP2 and TKT loci are linked to alterations in gene expression levels (cis-eQTLs). Luciferase assays confirmed the allele specific enhancer activity for the BGALT5 and TKT loci.

Conclusions

Our findings provide the first evidence of a specific genetic influence associated with prolonged gestation. This study forms a foundation for a better understanding of the genetic and long-term health risks faced by induced and post-term individuals. The long-term risks for induced individuals who have a previously overlooked post-term potential may be a major issue for current health providers.

Genome organization: connecting the developmental origins of disease and genetic variation

An adverse early life environment can increase the risk of metabolic and other disorders later in life. Genetic variation can modify an individual's susceptibility to these environmental challenges. These gene by environment interactions are important, but difficult, to dissect. The nucleus is the primary organelle where environmental responses impact directly on the genetic variants within the genome, resulting in changes to the biology of the genome and ultimately the phenotype. Understanding genome biology requires the integration of the linear DNA sequence, epigenetic modifications and nuclear proteins that are present within the nucleus. The interactions between these layers of information may be captured in the emergent spatial genome organization. As such genome organization represents a key research area for decoding the role of genetic variation in the Developmental Origins of Health and Disease.

Genetics of Combined Pituitary Hormone Deficiency: Roadmap into the Genome Era

The genetic basis for combined pituitary hormone deficiency (CPHD) is complex, involving 30 genes in a variety of syndromic and nonsyndromic presentations. Molecular diagnosis of this disorder is valuable for predicting disease progression, avoiding unnecessary surgery, and family planning. We expect that the application of high throughput sequencing will uncover additional contributing genes and eventually become a valuable tool for molecular diagnosis. For example, in the last 3 years, six new genes have been implicated in CPHD using whole-exome sequencing. In this review, we present a historical perspective on gene discovery for CPHD and predict approaches that may facilitate future gene identification projects conducted by clinicians and basic scientists. Guidelines for systematic reporting of genetic variants and assigning causality are emerging. We apply these guidelines retrospectively to reports of the genetic basis of CPHD and summarize modes of inheritance and penetrance for each of the known genes. In recent years, there have been great improvements in databases of genetic information for diverse populations. Some issues remain that make molecular diagnosis challenging in some cases. These include the inherent genetic complexity of this disorder, technical challenges like uneven coverage, differing results from variant calling and interpretation pipelines, the number of tolerated genetic alterations, and imperfect methods for predicting pathogenicity. We discuss approaches for future research in the genetics of CPHD.

A potential role for genome structure in the translation of mechanical force during immune cell development

Immune cells react to a wide range of environments, both chemical and physical. While the former has been extensively studied, there is growing evidence that physical and in particular mechanical forces also affect immune cell behavior and development. In order to elicit a response that affects immune cell behavior or development, environmental signals must often reach the nucleus. Chemical and mechanical signals can initiate signal transduction pathways, but mechanical forces may also have a more direct route to the nucleus, altering nuclear shape via mechanotransduction. The three-dimensional organization of DNA allows for the possibility that altering nuclear shape directly remodels chromatin, redistributing critical regulatory elements and proteins, and resulting in wide-scale gene expression changes. As such, integrating mechanotransduction and genome architecture into the immunology toolkit will improve our understanding of immune development and disease.