Genomic insights into the formation of human populations in East Asia.

The deep population history of East Asia remains poorly understood owing to a lack of ancient DNA data and sparse sampling of present-day people1,2. Here we report genome-wide data from 166 East Asian individuals dating to between 6000 BC and AD 1000 and 46 present-day groups. Hunter-gatherers from Japan, the Amur River Basin, and people of Neolithic and Iron Age Taiwan and the Tibetan Plateau are linked by a deeply splitting lineage that probably reflects a coastal migration during the Late Pleistocene epoch. We also follow expansions during the subsequent Holocene epoch from four regions. First, hunter-gatherers from Mongolia and the Amur River Basin have ancestry shared by individuals who speak Mongolic and Tungusic languages, but do not carry ancestry characteristic of farmers from the West Liao River region (around 3000 BC), which contradicts theories that the expansion of these farmers spread the Mongolic and Tungusic proto-languages. Second, farmers from the Yellow River Basin (around 3000 BC) probably spread Sino-Tibetan languages, as their ancestry dispersed both to Tibet-where it forms approximately 84% of the gene pool in some groups-and to the Central Plain, where it has contributed around 59-84% to modern Han Chinese groups. Third, people from Taiwan from around 1300 BC to AD 800 derived approximately 75% of their ancestry from a lineage that is widespread in modern individuals who speak Austronesian, Tai-Kadai and Austroasiatic languages, and that we hypothesize derives from farmers of the Yangtze River Valley. Ancient people from Taiwan also derived about 25% of their ancestry from a northern lineage that is related to, but different from, farmers of the Yellow River Basin, which suggests an additional north-to-south expansion. Fourth, ancestry from Yamnaya Steppe pastoralists arrived in western Mongolia after around 3000 BC but was displaced by previously established lineages even while it persisted in western China, as would be expected if this ancestry was associated with the spread of proto-Tocharian Indo-European languages. Two later gene flows affected western Mongolia: migrants after around 2000 BC with Yamnaya and European farmer ancestry, and episodic influences of later groups with ancestry from Turan.

Enhancer variants on chromosome 2p14 regulating SPRED2 and ACTR2 act as a signal amplifier to protect against rheumatoid arthritis.

Genome-wide association studies (GWASs) have repeatedly reported multiple non-coding single-nucleotide polymorphisms (SNPs) at 2p14 associated with rheumatoid arthritis (RA), but their functional roles in the pathological mechanisms of RA remain to be explored. In this study, we integrated a series of bioinformatics and functional experiments and identified three intronic RA SNPs (rs1876518, rs268131, and rs2576923) within active enhancers that can regulate the expression of SPRED2 directly. At the same time, SPRED2 and ACTR2 influence each other as a positive feedback signal amplifier to strengthen the protective role in RA by inhibiting the migration and invasion of rheumatoid fibroblast-like synoviocytes (FLSs). In particular, the transcription factor CEBPB preferentially binds to the rs1876518-T allele to increase the expression of SPRED2 in FLSs. Our findings decipher the molecular mechanisms behind the GWAS signals at 2p14 for RA and emphasize SPRED2 as a potential candidate gene for RA, providing a potential target and direction for precise treatment of RA.

High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes.

Most of the single-nucleotide polymorphisms (SNPs) associated with insulin resistance (IR)-relevant phenotypes by genome-wide association studies (GWASs) are located in noncoding regions, complicating their functional interpretation. Here, we utilized an adapted STARR-seq to evaluate the regulatory activities of 5,987 noncoding SNPs associated with IR-relevant phenotypes. We identified 876 SNPs with biased allelic enhancer activity effects (baaSNPs) across 133 loci in three IR-relevant cell lines (HepG2, preadipocyte, and A673), which showed pervasive cell specificity and significant enrichment for cell-specific open chromatin regions or enhancer-indicative markers (H3K4me1, H3K27ac). Further functional characterization suggested several transcription factors (TFs) with preferential allelic binding to baaSNPs. We also incorporated multi-omics data to prioritize 102 candidate regulatory target genes for baaSNPs and revealed prevalent long-range regulatory effects and cell-specific IR-relevant biological functional enrichment on them. Specifically, we experimentally verified the distal regulatory mechanism at IRS1 locus, in which rs952227-A reinforces IRS1 expression by long-range chromatin interaction and preferential binding to the transcription factor HOXC6 to augment the enhancer activity. Finally, based on our STARR-seq screening data, we predicted the enhancer activity of 227,343 noncoding SNPs associated with IR-relevant phenotypes (fasting insulin adjusted for BMI, HDL cholesterol, and triglycerides) from the largest available GWAS summary statistics. We further provided an open resource (http://www.bigc.online/fnSNP-IR) for better understanding genetic regulatory mechanisms of IR-relevant phenotypes.

Transcriptome-wide association study identifies multiple genes and pathways associated with thyroid function.

Thyroid dysfunction is a common endocrine disease measured by thyroid-stimulating hormone (TSH) level. Although >70 genetic loci associated with TSH have been reported through genome-wide association studies (GWASs), the variants can only explain a small fraction of the thyroid function heritability. To identify novel candidate genes for thyroid function, we conducted the first large-scale transcriptome-wide association study (TWAS) for thyroid function using GWAS-summary data for TSH levels in up to 119 715 individuals combined with precomputed gene expression weights of six panels from four tissue types. The candidate genes identified by TWAS were further validated by TWAS replication and gene expression profiles. We identified 74 conditionally independent genes significantly associated with thyroid function, such as PDE8B (P = 1.67 × 10-282), PDE10A (P = 7.61 × 10-119), NR3C2 (P = 1.50 × 10-92) and CAPZB (P = 3.13 × 10-79). After TWAS replication using UKBB datasets, 26 genes were replicated for significant associations with thyroid-relevant diseases/traits. Among them, 16 genes were causal for their associations to thyroid-relevant diseases/traits and further validated in differential expression analyses, including two novel genes (MFSD6 and RBM47) that did not implicate in previous GWASs. Enrichment analyses detected several pathways associated with thyroid function, such as the cAMP signaling pathway (P = 7.27 × 10-4), hemostasis (P = 3.74 × 10-4), and platelet activation, signaling and aggregation (P = 9.98 × 10-4). Our study identified multiple candidate genes and pathways associated with thyroid function, providing novel clues for revealing the genetic mechanisms of thyroid function and disease.

ExsgRNA: reduce off-target efficiency by on-target mismatched sgRNA.

Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 gene editing technology has been widely used to facilitate efficient genome editing. Current popular sgRNA design tools only consider the sgRNA perfectly matched to the target site and provide the results without any on-target mismatch. We suppose taking on-target gRNA-DNA mismatches into consideration might provide better sgRNA with similar binding activity and reduced off-target sites. Here, we trained a seq2seq-attention model with feedback-loop architecture, to automatically generate sgRNAs with on-target mismatches. Dual-luciferase reporter experiment showed that multiple sgRNAs with three mismatches could achieve the 80% of the relative activity of the perfect matched sgRNA. Meanwhile, it could reduce the number of off-target sites using sgRNAs with on-target mismatches. Finally, we provided a freely accessible web server sgRNA design tool named ExsgRNA. Users could submit their target sequence to this server and get optimal sgRNAs with less off-targets and similar on-target activity compared with the perfect-matched sgRNA.

Two-sample Mendelian randomization analysis reveals causal relationships between blood lipids and venous thromboembolism.

Venous Thromboembolism (VTE) is a complex disease that can be classified into two subtypes: Deep Vein Thrombosis (DVT) and Pulmonary Embolism (PE). Previous observational studies have shown associations between lipids and VTE, but causality remains unclear. Hence, by utilizing 241 lipid-related traits as exposures and data from the FinnGen consortium on VTE, DVT, and PE as outcomes, we conducted two-sample Mendelian randomization (MR) analysis to investigate causal relationships between lipids and VTE, DVT and PE. The MR results identified that fatty acid (FA) unsaturation traits (Ratio of bis-allylic bonds to double bonds in lipids, and Ratio of bis-allylic bonds to total fatty acids in lipids) were associated with VTE (OR [95% CI]: 1.21 [1.15-1.27]; 1.21 [1.13-1.30]), DVT (OR [95%CI]: 1.24 [1.16-1.33]; 1.26 [1.16-1.36]) and PE (OR [95%CI]: 1.18 [1.08-1.29]; 1.18 [1.09-1.27]). Phosphatidylcholines exhibit potential causal effects on VTE and PE. Phosphatidylcholine acyl-alkyl C40:4 (PC ae C40:4) was negatively associated with VTE (OR [95% CI]: 0.79 [0.73-0.86]), while phosphatidylcholine diacyl C42:6 (PC aa C42:6) and phosphatidylcholine acyl-alkyl C36:4 (PC ae C36:4) were positively associated with PE (OR [95%CI]: 1.44 [1.20-1.72]; 1.22 [1.10-1.35]). Additionally, we found that medium LDL had a protective effect on VTE. Our study indicates that higher FA unsaturation may increase the risk of VTE, DVT, and PE. Different types of phosphatidylcholine have either promotive or inhibitory effects on VTE and PE, contributing to a better understanding of the risk factors for VTE.

Multi-tissue transcriptome-wide association study reveals susceptibility genes and drug targets for insulin resistance-relevant phenotypes.

AIM: Genome-wide association studies (GWAS) have identified multiple susceptibility loci associated with insulin resistance (IR)-relevant phenotypes. However, the genes responsible for these associations remain largely unknown. We aim to identify susceptibility genes for IR-relevant phenotypes via a transcriptome-wide association study. MATERIALS AND METHODS: We conducted a large-scale multi-tissue transcriptome-wide association study for IR (Insulin Sensitivity Index, homeostasis model assessment-IR, fasting insulin) and lipid-relevant traits (high-density lipoprotein cholesterol, triglycerides, low-density lipoprotein cholesterol and total cholesterol) using the largest GWAS summary statistics and precomputed gene expression weights of 49 human tissues. Conditional and joint analyses were implemented to identify significantly independent genes. Furthermore, we estimated the causal effects of independent genes by Mendelian randomization causal inference analysis. RESULTS: We identified 1190 susceptibility genes causally associated with IR-relevant phenotypes, including 58 genes that were not implicated in the original GWAS. Among them, 11 genes were further supported in differential expression analyses or a gene knockout mice database, such as KRIT1 showed both significantly differential expression and IR-related phenotypic effects in knockout mice. Meanwhile, seven proteins encoded by susceptibility genes were targeted by clinically approved drugs, and three of these genes (H6PD, CACNB2 and DRD2) have been served as drug targets for IR-related diseases/traits. Moreover, drug repurposing analysis identified four compounds with profiles opposing the expression of genes associated with IR risk. CONCLUSIONS: Our study provided new insights into IR aetiology and avenues for therapeutic development.

Assessment of bidirectional relationships between brain imaging-derived phenotypes and stroke: a Mendelian randomization study.

BACKGROUND: Stroke is a major cause of mortality and long-term disability worldwide. Whether the associations between brain imaging-derived phenotypes (IDPs) and stroke are causal is uncertain. METHODS: We performed two-sample bidirectional Mendelian randomization (MR) analyses to explore the causal associations between IDPs and stroke. Summary data of 587 brain IDPs (up to 33,224 individuals) from the UK Biobank and five stroke types (sample size range from 301,663 to 446,696, case number range from 5,386 to 40,585) from the MEGASTROKE consortium were used. RESULTS: Forward MR indicated 14 IDPs belong to projection fibers or association fibers were associated with stroke. For example, higher genetically determined mean diffusivity (MD) in the right external capsule was causally associated with an increased risk of small vessel stroke (IVW OR = 2.76, 95% CI 2.07 to 3.68, P = 5.87 × 10-12). Reverse MR indicated that genetically determined higher risk of any ischemic stroke was associated with increased isotropic or free water volume fraction (ISOVF) in body of corpus callosum (IVW ß = 0.23, 95% CI 0.14 to 0.33, P = 3.22 × 10-7). This IDP is a commissural fiber and it is not included in the IDPs identified by forward MR. CONCLUSIONS: We identified 14 IDPs with statistically significant evidence of causal effects on stroke or stroke subtypes. We also identified potential causal effects of stroke on one IDP of commissural fiber. These findings might guide further work toward identifying preventative strategies at the brain imaging levels.

LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files.

The triangular correlation heatmap aiming to visualize the linkage disequilibrium (LD) pattern and haplotype block structure of SNPs is ubiquitous component of population-based genetic studies. However, current tools suffered from the problem of time and memory consuming. Here, we developed LDBlockShow, an open source software, for visualizing LD and haplotype blocks from variant call format files. It is time and memory saving. In a test dataset with 100 SNPs from 60 000 subjects, it was at least 10.60 times faster and used only 0.03-13.33% of physical memory as compared with other tools. In addition, it could generate figures that simultaneously display additional statistical context (e.g. association P-values) and genomic region annotations. It can also compress the SVG files with a large number of SNPs and support subgroup analysis. This fast and convenient tool will facilitate the visualization of LD and haplotype blocks for geneticists.

Application of Hi-C technology in three-dimensional genomics research and disease pathogenesis analysis.

3D genomics aims to investigate the spatial structure of chromatin in the nucleus on the basis of genomic sequences, gene structures and relevant regulatory elements. The spatial organization of chromosomes is fundamental for gene expression regulation. Recent advances of high-throughput chromosome conformation capture (Hi-C) technology and its derivatives, has enabled capture of chromatin architecture with high resolution. In this review, we summarize the development and applications of various technologies of 3D genomes in disease research, particularly in the elucidation of pathogenic mechanisms in cancers and other systemic disorders.

Causal Associations Between Blood Lipids and COVID-19 Risk: A Two-Sample Mendelian Randomization Study.

[Figure: see text].

An Osteoporosis Risk SNP at 1p36.12 Acts as an Allele-Specific Enhancer to Modulate LINC00339 Expression via Long-Range Loop Formation.

Genome-wide association studies (GWASs) have reproducibly associated variants within intergenic regions of 1p36.12 locus with osteoporosis, but the functional roles underlying these noncoding variants are unknown. Through an integrative functional genomic and epigenomic analyses, we prioritized rs6426749 as a potential causal SNP for osteoporosis at 1p36.12. Dual-luciferase assay and CRISPR/Cas9 experiments demonstrate that rs6426749 acts as a distal allele-specific enhancer regulating expression of a lncRNA (LINC00339) (∼360 kb) via long-range chromatin loop formation and that this loop is mediated by CTCF occupied near rs6426749 and LINC00339 promoter region. Specifically, rs6426749-G allele can bind transcription factor TFAP2A, which efficiently elevates the enhancer activity and increases LINC00339 expression. Downregulation of LINC00339 significantly increases the expression of CDC42 in osteoblast cells, which is a pivotal regulator involved in bone metabolism. Our study provides mechanistic insight into how a noncoding SNP affects osteoporosis by long-range interaction, a finding that could indicate promising therapeutic targets for osteoporosis.

Integrating regulatory features data for prediction of functional disease-associated SNPs.

Genome-wide association studies (GWASs) are an effective strategy to identify susceptibility loci for human complex diseases. However, missing heritability is still a big problem. Most GWASs single-nucleotide polymorphisms (SNPs) are located in noncoding regions, which has been considered to be the unexplored territory of the genome. Recently, data from the Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomics projects have shown that many GWASs SNPs in the noncoding regions fall within regulatory elements. In this study, we developed a pipeline named functional disease-associated SNPs prediction (FDSP), to identify novel susceptibility loci for complex diseases based on the interpretation of the functional features for known disease-associated variants with machine learning. We applied our pipeline to predict novel susceptibility SNPs for type 2 diabetes (T2D) and hypertension. The predicted SNPs could explain heritability beyond that explained by GWAS-associated SNPs. Functional annotation by expression quantitative trait loci analyses showed that the target genes of the predicted SNPs were significantly enriched in T2D or hypertension-related pathways in multiple tissues. Our results suggest that combining GWASs and regulatory features data could identify additional functional susceptibility SNPs for complex diseases. We hope FDSP could help to identify novel susceptibility loci for complex diseases and solve the missing heritability problem.

Modeling circRNA expression pattern with integrated sequence and epigenetic features demonstrates the potential involvement of H3K79me2 in circRNA expression.

MOTIVATION: CircRNAs are an abundant class of non-coding RNAs with widespread, cell-/tissue-specific patterns. Previous work suggested that epigenetic features might be related to circRNA expression. However, the contribution of epigenetic changes to circRNA expression has not been investigated systematically. Here, we built a machine learning framework named CIRCScan, to predict circRNA expression in various cell lines based on the sequence and epigenetic features. RESULTS: The predicted accuracy of the expression status models was high with area under the curve of receiver operating characteristic (ROC) values of 0.89-0.92 and the false-positive rates of 0.17-0.25. Predicted expressed circRNAs were further validated by RNA-seq data. The performance of expression-level prediction models was also good with normalized root-mean-square errors of 0.28-0.30 and Pearson's correlation coefficient r over 0.4 in all cell lines, along with Spearman's correlation coefficient ρ of 0.33-0.46. Noteworthy, H3K79me2 was highly ranked in modeling both circRNA expression status and levels across different cells. Further analysis in additional nine cell lines demonstrated a significant enrichment of H3K79me2 in circRNA flanking intron regions, supporting the potential involvement of H3K79me2 in circRNA expression regulation. AVAILABILITY AND IMPLEMENTATION: The CIRCScan assembler is freely available online for academic use at https://github.com/johnlcd/CIRCScan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files.

MOTIVATION: Linkage disequilibrium (LD) decay is of great interest in population genetic studies. However, no tool is available now to do LD decay analysis from variant call format (VCF) files directly. In addition, generation of pair-wise LD measurements for whole genome SNPs usually resulting in large storage wasting files. RESULTS: We developed PopLDdecay, an open source software, for LD decay analysis from VCF files. It is fast and is able to handle large number of variants from sequencing data. It is also storage saving by avoiding exporting pair-wise results of LD measurements. Subgroup analyses are also supported. AVAILABILITY AND IMPLEMENTATION: PopLDdecay is freely available at https://github.com/BGI-shenzhen/PopLDdecay.

Genetic substructure and admixture of Mongolians and Kazakhs inferred from genome-wide array genotyping.

BACKGROUND: Mongolian populations are widely distributed geographically, showing abundant ethnic diversity with geographic and tribal differences. AIM: To infer the genetic substructure, admixture and ancient genetic sources of Mongolians together with Kazakhs. SUBJECTS AND METHODS: We genotyped more than 690,000 genome-wide SNPs from 33 Mongolian and Chinese Kazakh individuals and compared these with both ancient and present-day Eurasian populations using Principal Component Analysis (PCA), ADMIXTURE, Refine-IBD, f statistics, qpWave and qpAdm. RESULTS: We found genetic substructures within Mongolians corresponding to Ölöd, Chahar, and Inner Mongolian clusters, which was consistent with tribe classifications. Mongolian and Kazakh groups derived about 6-40% of West Eurasian related ancestry, most likely from Bronze Age Steppe populations. The East Asian related ancestry in Mongolian and Kazakh groups was well represented by the Neolithic DevilsCave related nomadic lineage, comprising 42-64% of studied groups. We also detected 10-51% of Han Chinese related ancestry in Mongolian and Kazakh groups, especially in Inner Mongolians. The average admixture times for Inner Mongolian, Mongolian_Chahar, Mongolian_Ölöd and Chinese Kazakh were about 1381, 626, 635 and 632 years ago, respectively, with Han and French as the sources. CONCLUSION: The DevilsCave related ancestry was once widespread westwards covering a wide geographical range from Far East Russia to the Mongolia Plateau. The formation of present-day Mongolic and Turkic-speaking populations has also received genetic influence from agricultural expansion.

The osteoporosis susceptible SNP rs4325274 remotely regulates the SOX6 gene through enhancers.

Osteoporosis is a typical polygenic disease, and its heritability is as high as 85%. The incidence of osteoporosis has jumped to the fifth among the common diseases. Although a large number of osteoporosis-susceptible SNPs have been identified, most of them are in the non-coding regions of the genome and the functional mechanisms are unknown. The purpose of this study was to explore the function of non-coding osteoporosis-susceptible SNP rs4325274 and dissect the molecular regulatory mechanisms through integrating bioinformatics analysis and functional experiments. Firstly, we found the SNP rs4325274 resided in a putative enhancer element through functional annotation. eQTL and Hi-C analysis found that the SOX6 gene might be a potential distal target of rs4325274. We conducted the motif prediction using multiple databases and verified the result using ChIP-seq data from GEO database. The result showed that the transcription factor HNF1A could preferentially bind to SNP rs4325274-G allele. We further demonstrated that SNP rs4325274 acted as an enhancer regulating SOX6 gene expression by using dual-luciferase reporter assays. Knockdown of HNF1A decreased the SOX6 gene expression. Taken together, our results uncovered a new mechanism of a non-coding functional SNP rs4325274 as a distal enhancer to modulate SOX6 expression, which provides new insights into deciphering molecular regulatory mechanisms underlying non-coding susceptibility SNPs on complex diseases.

Detecting epistasis within chromatin regulatory circuitry reveals CAND2 as a novel susceptibility gene for obesity.

BACKGROUND: Genome-wide association studies have identified many susceptibility loci for obesity. However, missing heritability problem is still challenging and ignorance of genetic interactions is believed to be an important cause. Current methods for detecting interactions usually do not consider regulatory elements in non-coding regions. Interaction analyses within chromatin regulatory circuitry may identify new susceptibility loci. METHODS: We developed a pipeline named interaction analyses within chromatin regulatory circuitry (IACRC), to identify genetic interactions impacting body mass index (BMI). Potential interacting SNP pairs were obtained based on Hi-C datasets, PreSTIGE (Predicting Specific Tissue Interactions of Genes and Enhancers) algorithm, and super enhancer regions. SNP × SNP analyses were next performed in three GWAS datasets, including 2286 unrelated Caucasians from Kansas City, 3062 healthy Caucasians from the Gene Environment Association Studies initiative, and 3164 Hispanic subjects from the Women's Health Initiative. RESULTS: A total of 16,643,227 SNP × SNP analyses were performed. Meta-analyses showed that two SNP pairs, rs6808450-rs9813534 (combined P = 2.39 × 10-9) and rs6808450-rs3773306 (combined P = 2.89 × 10-9) were associated with BMI after multiple testing corrections. Single-SNP analyses did not detect significant association signals for these three SNPs. In obesity relevant cells, rs6808450 is located in intergenic enhancers, while rs9813534 and rs3773306 are located in the region of strong transcription regions of CAND2 and RPL32, respectively. The expression of CAND2 was significantly downregulated after the differentiation of human Simpson-Golabi-Behmel syndrome (SGBS) preadipocyte cells (P = 0.0241). Functional validation in the International Mouse Phenotyping Consortium database showed that CAND2 was associated with increased lean body mass and decreased total body fat amount. CONCLUSIONS: Detecting epistasis within chromatin regulatory circuitry identified CAND2 as a novel obesity susceptibility gene. We hope IACRC could facilitate the interaction analyses for complex diseases and offer new insights into solving the missing heritability problem.

A functional SNP regulated by miR-196a-3p in the 3'UTR of FGF2 is associated with bone mineral density in the Chinese population.

Previous studies have identified FGF2 as a susceptibility gene for osteoporosis in Caucasians. Evaluating the genetic associations in different ethnicities is necessary. Moreover, elucidating the functional mechanism for the susceptibility loci is important to offer new targets for therapeutic studies. Here, we genotyped 10 SNPs in FGF2 and tested for associations with bone mineral density (BMD) in a discovery sample of 1,300 Chinese subjects. Nominally significant results were subjected to replication in another sample of 1,039 Chinese subjects. We identified one SNP rs1048201:C>T in FGF2 3'untranslated region significantly associated with spine BMD (combined cohorts, P = 1.53×10-3 ). Expression quantitative trait locus analyses revealed that rs1048201 also affected FGF2 gene expression (P = 7.03×10-4 ). Bioinformatics prediction suggested that rs1048201 T allele could disrupt miRNA binding. Luciferase assay validated that the C allele had a repressive effect on FGF2 gene expression. We found that hsa-miR-196a-3p affected expression on both mRNA and protein levels of FGF2. In conclusion, our study provided evidence that a functional SNP rs1048201 was associated with BMD, and SNP rs1048201:C>T variant may act by affecting binding of hsa-miR-196a-3p. The SNP-modified posttranscriptional gene regulation by miRNA could be a potentially pathogenetic mechanism of osteoporosis.

Identification of a novel FGFRL1 MicroRNA target site polymorphism for bone mineral density in meta-analyses of genome-wide association studies.

MicroRNAs (miRNAs) are critical post-transcriptional regulators. Based on a previous genome-wide association (GWA) scan, we conducted a polymorphism in microRNA target sites (poly-miRTS)-centric multistage meta-analysis for lumbar spine (LS)-, total hip (HIP)- and femoral neck (FN)-bone mineral density (BMD). In stage I, 41 102 poly-miRTSs were meta-analyzed in seven cohorts with a genome-wide significance (GWS) α = 0.05/41 102 = 1.22 × 10(-6). By applying α = 5 × 10(-5) (suggestive significance), 11 poly-miRTSs were selected, with FGFRL1 rs4647940 and PRR5 rs3213550 as top signals for FN-BMD (P = 7.67 × 10(-6) and 1.58 × 10(-5)) in gender-combined sample. In stage II in silico replication (two cohorts), FGFRL1 rs4647940 was the only signal marginally replicated for FN-BMD (P = 5.08 × 10(-3)) at α = 0.10/11 = 9.09 × 10(-3). PRR5 rs3213550 was also selected based on biological significance. In stage III de novo genotyping replication (two cohorts), FGFRL1 rs4647940 was the only signal significantly replicated for FN-BMD (P = 7.55 × 10(-6)) at α = 0.05/2 = 0.025 in gender-combined sample. Aggregating three stages, FGFRL1 rs4647940 was the single stage I-discovered and stages II- and III-replicated signal attaining GWS for FN-BMD (P = 8.87 × 10(-12)). Dual-luciferase reporter assays demonstrated that FGFRL1 3' untranslated region harboring rs4647940 appears to be hsa-miR-140-5p's target site. In a zebrafish microinjection experiment, dre-miR-140-5p is shown to exert a dramatic impact on craniofacial skeleton formation. Taken together, we provided functional evidence for a novel FGFRL1 poly-miRTS rs4647940 in a previously known 4p16.3 locus, and experimental and clinical genetics studies have shown both FGFRL1 and hsa-miR-140-5p are important for bone formation.