Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explain one-fifth of heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ~2,000, ~3,700 and ~9,500 SNPs explained ~21%, ~24% and ~29% of phenotypic variance. Furthermore, all common variants together captured the majority (60%) of heritability. The 697 variants clustered in 423 loci enriched for genes, pathways, and tissue-types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/beta-catenin, and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

Breast cancer risk is influenced by rare coding variants in susceptibility genes such as BRCA1 and many common, mainly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. We report results from a genome-wide association study (GWAS) of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry1. We identified 65 new loci associated with overall breast cancer at p<5x10-8. The majority of credible risk SNPs in the new loci fall in distal regulatory elements, and by integrating in-silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all SNPs in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the utility of genetic risk scores for individualized screening and prevention.

We conducted a genome-wide association study (GWAS) of breast cancer by genotyping 528,173 SNPs in 1,145 postmenopausal women of European ancestry with invasive breast cancer and 1,142 controls. We identified four SNPs in intron 2 of FGFR2 (which encodes a receptor tyrosine kinase and is amplified or overexpressed in some breast cancers) that were highly associated with breast cancer and confirmed this association in 1,776 affected individuals and 2,072 controls from three additional studies. Across the four studies, the association with all four SNPs was highly statistically significant (P(trend) for the most strongly associated SNP (rs1219648) = 1.1 x 10(-10); population attributable risk = 16%). Four SNPs at other loci most strongly associated with breast cancer in the initial GWAS were not associated in the replication studies. Our summary results from the GWAS are available online in a form that should speed the identification of additional risk loci.

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Genetic alterations activating NOTCH1 signaling and T cell transcription factors, coupled with inactivation of the INK4/ARF tumor suppressors are hallmarks of T-ALL, but detailed genome-wide sequencing of large T-ALL cohorts has not been performed. Using integrated genomic analysis of 264 T-ALL cases, we identify 106 putative driver genes, half of which were not previously described in childhood T-ALL (e.g. CCND3, CTCF, MYB, SMARCA4, ZFP36L2 and MYCN). We described new mechanisms of coding and non-coding alteration, and identify 10 recurrently altered pathways, with associations between mutated genes and pathways, and stage or subtype of T-ALL. For example, NRAS/FLT3 mutations were associated with immature T-ALL, JAK3/STAT5B mutations in HOX1 deregulated ALL, PTPN2 mutations in TLX1 T-ALL, and PIK3R1/PTEN mutations in TAL1 ALL, suggesting that different signaling pathways have distinct roles according to maturational stage. This genomic landscape provides a logical framework for the development of faithful genetic models and new therapeutic approaches.

Recently, common variants on human chromosome 8q24 were found to be associated with prostate cancer risk. While conducting a genome-wide association study in the Cancer Genetic Markers of Susceptibility project with 550,000 SNPs in a nested case-control study (1,172 cases and 1,157 controls of European origin), we identified a new association at 8q24 with an independent effect on prostate cancer susceptibility. The most significant signal is 70 kb centromeric to the previously reported SNP, rs1447295, but shows little evidence of linkage disequilibrium with it. A combined analysis with four additional studies (total: 4,296 cases and 4,299 controls) confirms association with prostate cancer for rs6983267 in the centromeric locus (P = 9.42 x 10(-13); heterozygote odds ratio (OR): 1.26, 95% confidence interval (c.i.): 1.13-1.41; homozygote OR: 1.58, 95% c.i.: 1.40-1.78). Each SNP remained significant in a joint analysis after adjusting for the other (rs1447295 P = 1.41 x 10(-11); rs6983267 P = 6.62 x 10(-10)). These observations, combined with compelling evidence for a recombination hotspot between the two markers, indicate the presence of at least two independent loci within 8q24 that contribute to prostate cancer in men of European ancestry. We estimate that the population attributable risk of the new locus, marked by rs6983267, is higher than the locus marked by rs1447295 (21% versus 9%).

We followed our initial genome-wide association study (GWAS) of 527,869 SNPs on 1,172 individuals with prostate cancer and 1,157 controls of European origin-nested in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial prospective study-by testing 26,958 SNPs in four independent studies (total of 3,941 cases and 3,964 controls). In the combined joint analysis, we confirmed three previously reported loci (two independent SNPs at 8q24 and one in HNF1B (formerly known as TCF2 on 17q); P < 10(-10)). In addition, loci on chromosomes 7, 10 (two loci) and 11 were highly significant (between P < 7.31 x 10(-13) and P < 2.14 x 10(-6)). Loci on chromosome 10 include MSMB, which encodes beta-microseminoprotein, a primary constituent of semen and a proposed prostate cancer biomarker, and CTBP2, a gene with antiapoptotic activity; the locus on chromosome 7 is at JAZF1, a transcriptional repressor that is fused by chromosome translocation to SUZ12 in endometrial cancer. Of the nine loci that showed highly suggestive associations (P < 2.5 x 10(-5)), four best fit a recessive model and included candidate susceptibility genes: CPNE3, IL16 and CDH13. Our findings point to multiple loci with moderate effects associated with susceptibility to prostate cancer that, taken together, in the future may predict high risk in select individuals.

SUMMARY Analysis of molecular aberrations across multiple cancer types, known as pan-cancer analysis, identifies commonalities and differences in key biological processes dysregulated in cancer cells from diverse lineages. Pan-cancer analyses have been performed for adult1–4 but not pediatric cancers, which commonly occur in developing mesodermic rather than adult epithelial tissues5. Here we present a pan-cancer study of somatic alterations, including single nucleotide variants (SNVs), small insertion/deletions (indels), structural variations (SVs), copy number alterations (CNAs), gene fusions and internal tandem duplications (ITDs), in 1,699 pediatric leukemia and solid tumours across six histotypes, with whole-genome (WGS), whole-exome (WES) and transcriptome (RNA-seq) sequencing data processed under a uniform analytical framework (Online Methods and Extended Data Fig. 1). We report 142 driver genes in pediatric cancers, of which only 45% matched those found in adult pan-cancer studies and CNAs and SVs constituted the majority (62%) of events. Eleven genome-wide mutational signatures were identified, including one attributed to ultraviolet-light exposure in eight aneuploid leukemias. Transcription of the mutant allele was detectable for 34% of protein-coding mutations, and 20% exhibited allele-specific expression. These data provide a comprehensive genomic architecture for pediatric cancers and emphasize the need for pediatric cancer-specific development of precision therapies.