Please forward to relevant applicants in you network!

The article, Different historical generation intervals in human populations inferred from Neanderthal fragment lengths and mutation signatures, was published in Nature Communications, September 2021.

Find the article here DOI 10.1038/s41467-021-25524-4 

Doug and colleagues have observed that most existing prediction tools assume that each genetic variant is equally important. This assumption is sub-optimal, because recent work has shown that the importance of a variant depends on factors such as its frequency, local levels of linkage disequilibrium and functional annotations. Therefore, this new paper presents eight new prediction tools that allow for alternative assumptions, and shows that this enables substantially improved prediction across a wide range of traits.

Four of the new tools use individual-level data. The paper shows that the best of these, LDAK-Bolt-Predict, outperforms the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes considered. The remaining four new tools use summary statistics. The paper shows that the best of these, LDAK-BayesR-SS, outperforms the existing tools lassosum, sBLUP, LDpred and SBayesR for 223 of the 225 phenotypes considered. On average, the new tools outperform the existing tools by 14% (sd 1), which is equivalent to increasing the sample size by about a quarter.

You can read more about the new tools in the paper "Improved genetic prediction of complex traits from individual-level data or summary statistics" (https://www.nature.com/articles/s41467-021-24485-y), and try out the new tools in the software packages LDAK (www.ldak.org) and bigstatsr (https://github.com/privefl/bigstatsr).

See the press release here:

The human genome contains millions of genetic variants, the most simple of which are single nucleotide polymorphisms (SNPs). These SNPs influence a wide range of complex traits. For example, they determine how likely we are to develop psychiatric traits such as schizophrenia, autoimmune traits such as Crohn's Disease, or cardiovascular traits such as heart disease. When we analyze SNP data, it is often necessary to specify a "heritability model", which describes in advance how much we think each SNP will contribute. It turns out that for many analyses, the choice of heritability model is very important (i.e., if we change the heritability model, it substantially changes the results of the analysis). In recent years, many different heritability models have been proposed, but until now, there has been no way to decide which of these is the best one to use.

An international team has completed the most comprehensive study of whole cancer genomes to date, significantly improving our fundamental understanding of cancer and signposting new directions for its diagnosis and treatment.

The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Project (PCAWG), known as the Pan-Cancer Project), a collaboration involving more than 1,300 scientists and clinicians from 37 countries, analyzed more than 2,600 genomes of 38 different tumour types, creating a huge resource of primary cancer genomes. This was then the launch-point for 16 working groups studying multiple aspects of cancers development, causation, progression and classification. 

Read the press release 'Unprecedented exploration generates most comprehensive map of cancer genomes charted to date' here.

Read the Danish Press release 'Globalt studie kortlægger kræftmutationer i stort katalog' here.

Read the special edition of Nature, in which most of the PCAWG consortium's results are collected and published, here.

Read the article 'Analyses of non-coding somatic drivers in 2,658 cancer whole genomes' here.

Read more about the PCAWG Consortium here.

The article includes an interactive dataset of human testis at  https://cells.ucsc.edu/?ds=testis

Link to the scientific article in Nature Communications.