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.

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44–66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.

Common 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 sizes. Here we show, using GWAS data from 5.4 million individuals of diverse ancestries, 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 median size of ~90 kb, covering ~21% of the genome. The density of independent associations varies across the genome and the regions of elevated density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs account for 40% of phenotypic variance in European ancestry populations but only ~10%-20% in other ancestries. Effect sizes, associated regions, and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely explained by linkage disequilibrium and allele frequency differences within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than needed to implicate causal genes and variants. Overall, this study, the largest GWAS to date, provides an unprecedented saturated map of specific genomic regions containing the vast majority of common height-associated variants.

Repeat pregnancies with different perinatal outcomes minimize underlying maternal genetic diversity and provide unique opportunities to investigate nongenetic risk factors and epigenetic mechanisms of pregnancy complications. We investigated gestational diabetes mellitus (GDM)-related differential DNA methylation in early pregnancy peripheral blood samples collected from women who had a change in GDM status in repeat pregnancies. Six study participants were randomly selected from among women who had 2 consecutive pregnancies, only 1 of which was complicated by GDM (case pregnancy) and the other was not (control pregnancy). Epigenome-wide DNA methylation was profiled using Illumina HumanMethylation 27 BeadChips. Differential Identification using Mixture Ensemble and false discovery rate (<10%) cutoffs were used to identify differentially methylated targets between the 2 pregnancies of each participant. Overall, 27 target sites, 17 hypomethylated (fold change [FC] range: 0.77-0.99) and 10 hypermethylated (FC range: 1.01-1.09), were differentially methylated between GDM and control pregnancies among 5 or more study participants. Novel genes were related to identified hypomethylated (such as NDUFC1, HAPLN3, HHLA3, and RHOG) or hypermethylated sites (such as SEP11, ZAR1, and DDR). Genes related to identified sites participated in cell morphology, cellular assembly, cellular organization, cellular compromise, and cell cycle. Our findings support early pregnancy peripheral blood DNA methylation differences in repeat pregnancies with change in GDM status. Similar, larger, and repeat pregnancy studies can enhance biomarker discovery and mechanistic studies of GDM.