A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

Soluble endoglin versus sFlt-1/PlGF ratio: detection of preeclampsia, HELLP syndrome, and FGR in a high-risk cohort

The angiogenic factors sFlt-1 and PlGF play an established role in the detection of preeclampsia (PE). Recent data suggest that sEng might contribute to the pathogenesis of PE. However, only a few studies so far have addressed its role. This monocentric cross-sectional study of high-risk pregnancies aims to compare the levels of sFlt-1/PlGF ratio and sEng depending on different placental-related adverse pregnancy outcomes. The statistical analysis takes into account Pearson’s correlation coefficient between angiogenic factors, the area under the curve estimates (AUCs) for detection, and adjusted odds ratios (aOR) with 95% confidence intervals (95%-CIs). The analysis included 206 patients: 60 controls, 90 PE (59 EOPE, 35 LOPE), 94 FGR, and 35 HELLP cases. Some outcomes overlapped because FGR commonly complicated PE and HELLP syndrome. Serum levels of sFlt-1/PlGF and sEng correlated with each other. Higher levels were observed in HELLP syndrome and EOPE cases. AUCs for sFlt-1/PlGF ratio and sEng were, respectively, 0.915 (95%-Cl 0.87-0.96) and 0.872 (95%-Cl 0.81-0.93) in PE, 0.895 (95%-Cl 0.83-0.96) and 0.878 (95%-Cl 0.81-0.95) in HELLP syndrome, 0.891 (95%-Cl 0.84-0.94), and 0.856 (95%-Cl 0.79-0.92) in FGR.aORsfor sFlt-1/PlGF ratio and sEng were, respectively: 2.69 (95%-Cl 1.86-3.9) and 2.33 (95%-Cl 1.59-3.48) in PE, 2.38 (95%-Cl 1.64-3.44) and 2.28 (95%-Cl 1.55-3.4) in FGR, and 2.10 (95%-Cl 1.45-3.05) and 1.88 (95%-Cl 1.31-2.69) in HELLP syndrome. In addition, the aORs between sFlt-1/PlGF and sEng were very similar but higher for PE and FGR than HELLP syndrome.In conclusion,sEng performed similarly to sFlt-1/PlGF to detect placental dysfunctions

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.</p

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.</p