Person:

Kanai, Masahiro

Population studies elucidating the genetic architecture of reproductive ageing have been largely limited to European ancestries, restricting the generalizability of the findings and overlooking possible key genes poorly captured by common European genetic variation. Here, we report 26 loci (all P < 5 × 10–8) for reproductive ageing, i.e. puberty timing or age at menopause, in a non-European population (up to 67,029 women of Japanese ancestry). Highlighted genes for menopause include GNRH1, which supports a primary, rather than passive, role for hypothalamic-pituitary GnRH signalling in the timing of menopause. For puberty timing, we demonstrate an aetiological role for receptor-like protein tyrosine phosphatases by combining evidence across population genetics and pre- and peri-pubertal changes in hypothalamic gene expression in rodent and primate models. Furthermore, our findings demonstrate widespread differences in allele frequencies and effect estimates between Japanese and European associated variants, highlighting the benefits and challenges of large-scale trans-ethnic approaches.

Understanding natural selection is crucial to unveiling evolution of modern humans. Here, we report natural selection signatures in the Japanese population using 2234 high-depth whole-genome sequence (WGS) data (25.9×). Using rare singletons, we identify signals of very recent selection for the past 2000–3000 years in multiple loci (ADH cluster, MHC region, BRAP-ALDH2, SERHL2). In large-scale genome-wide association study (GWAS) dataset (n = 171,176), variants with selection signatures show enrichment in heterogeneity of derived allele frequency spectra among the geographic regions of Japan, highlighted by two major regional clusters (Hondo and Ryukyu). While the selection signatures do not show enrichment in archaic hominin-derived genome sequences, they overlap with the SNPs associated with the modern human traits. The strongest overlaps are observed for the alcohol or nutrition metabolism-related traits. Our study illustrates the value of high-depth WGS to understand evolution and their relationship with disease risk.

Elevated serum urate levels cause gout and correlate with cardio-metabolic diseases via poorly understood mechanisms. We performed a trans-ethnic genome-wide association study of serum urate among 457,690 individuals, identifying 183 loci (147 novel) that improve prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardio-metabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci, and co-localization with gene expression in 47 tissues implicated kidney and liver as main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A trans-activated the promoter of the major urate transporter ABCG2 in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional co-regulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardio-metabolic traits.

Abstract: Effective interpretation of genome function and genetic variation requires a shift from epigenetic mapping of cis-regulatory elements (CREs) to characterization of endogenous function. We developed HCR-FlowFISH, a broadly applicable approach to characterize CRISPR-perturbed CREs via accurate quantification of native transcripts, alongside CASA (CRISPR Activity Screen Analysis), a hierarchical Bayesian model to quantify CRE activity. Across >325,000 perturbations, we provide evidence that CREs can regulate multiple genes, skip over the nearest gene, and can display activating and/or silencing effects. At the cholesterol-level associated FADS locus, we combine endogenous screens with reporter assays to exhaustively characterize multiple genome-wide association signals, functionally nominating causal variants and identifying their target genes.