Notes

Lorrie der Waals epitaxy of practically single-crystalline nitride videos upon amorphous graphene-glass wafer.
Assessing extinction risk from climate drivers is a major goal of conservation science. Few studies, however, include a long-term perspective of climate change. Without explicit integration, such long-term temperature trends and their interactions with short-term climate change may be so dominant that they blur or even reverse the apparent direct relationship between climate change and extinction. Here we evaluate how observed genus-level extinctions of arthropods, bivalves, cnidarians, echinoderms, foraminifera, gastropods, mammals and reptiles in the geological past can be predicted from the interaction of long-term temperature trends with short-term climate change. We compare synergistic palaeoclimate interaction (a short-term change on top of a long-term trend in the same direction) to antagonistic palaeoclimate interaction such as long-term cooling followed by short-term warming. Synergistic palaeoclimate interaction increases extinction risk by up to 40%. The memory of palaeoclimate interaction including the climate history experienced by ancestral lineages can be up to 60 Myr long. The effect size of palaeoclimate interaction is similar to other key factors such as geographic range, abundance or clade membership. Insights arising from this previously unknown driver of extinction risk might attenuate recent predictions of climate-change-induced biodiversity loss.Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants.To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P  less then  5 × 10-8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal P = 1.28 × 10-20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10-10  less then  P  less then  5 × 10-8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.Despite the strong genetic basis of psychiatric disorders, the underlying molecular mechanisms are largely unmapped. RNA-binding proteins (RBPs) are responsible for most post-transcriptional regulation, from splicing to translation to localization. RBPs thus act as key gatekeepers of cellular homeostasis, especially in the brain. However, quantifying the pathogenic contribution of noncoding variants impacting RBP target sites is challenging. Here, we leverage a deep learning approach that can accurately predict the RBP target site dysregulation effects of mutations and discover that RBP dysregulation is a principal contributor to psychiatric disorder risk. RBP dysregulation explains a substantial amount of heritability not captured by large-scale molecular quantitative trait loci studies and has a stronger impact than common coding region variants. We share the genome-wide profiles of RBP dysregulation, which we use to identify DDHD2 as a candidate schizophrenia risk gene. find more This resource provides a new analytical framework to connect the full range of RNA regulation to complex disease.The intestinal microbiome is implicated as an important modulating factor in multiple inflammatory1,2, neurologic3 and neoplastic diseases4. Recent genome-wide association studies yielded inconsistent, underpowered and rarely replicated results such that the role of human host genetics as a contributing factor to microbiome assembly and structure remains uncertain5-11. Nevertheless, twin studies clearly suggest host genetics as a driver of microbiome composition11. In a genome-wide association analysis of 8,956 German individuals, we identified 38 genetic loci to be associated with single bacteria and overall microbiome composition. Further analyses confirm the identified associations of ABO histo-blood groups and FUT2 secretor status with Bacteroides and Faecalibacterium spp. Mendelian randomization analysis suggests causative and protective effects of gut microbes, with clade-specific effects on inflammatory bowel disease. This holistic investigative approach of the host, its genetics and its associated microbial communities as a 'metaorganism' broaden our understanding of disease etiology, and emphasize the potential for implementing microbiota in disease treatment and management.
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