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.

Epigenome-wide association studies of Alzheimer's disease have highlighted neuropathologyassociated DNA methylation differences, although existing studies have been limited in sample size and utilized different brain regions. Here, we combine data from six methylomic studies of Alzheimer's disease (N=1,453 unique individuals) to identify differential methylation associated with Braak stage in different brain regions and across cortex. At an experiment-wide significance threshold (P<1.238 x10 -7 ) we identified 236 CpGs in the prefrontal cortex, 95CpGs in the temporal gyrus and ten CpGs in the entorhinal cortex, with none in the cerebellum.Our cross-cortex meta-analysis (N=1,408 donors) identified 220 CpGs associated with neuropathology, annotated to 121 genes, of which 96 genes had not been previously reported at experiment-wide significance. Polyepigenic scores derived from these 220 CpGs explain 24.7% of neuropathological variance, whilst polygenic scores accounted for 20.2% of variance in these samples. The meta-analysis summary statistics are available in our online data resource (www.epigenomicslab.com/ad-meta-analysis/).

Alteration of protein abundance and conformation are widely believed to be the hallmark of neurodegenerative diseases. Yet relatively little is known about the genetic variation that controls protein abundance in the healthy human brain. The genetic control of protein abundance is generally thought to parallel that of RNA expression, but there is little direct evidence to support this view. Here, we performed a large-scale protein quantitative trait locus (pQTL) analysis using single nucleotide variants (SNVs) from whole-genome sequencing and tandem mass spectrometry-based proteomic quantification of 12,691 unique proteins (7,901 after quality control) from the dorsolateral prefrontal cortex (dPFC) in 144 cognitively normal individuals. We identified 28,211 pQTLs that were significantly associated with the abundance of 864 proteins. These pQTLs were compared to dPFC expression quantitative trait loci (eQTL) in cognitive normal individuals (n=169; 81 had protein data) and a meta-analysis of dPFC eQTLs (n=1,433). We found that strong pQTLs are generally only weak eQTLs, and that the majority of strong eQTLs are not detectable pQTLs. These results suggest that the genetic control of mRNA and protein abundance may be substantially distinct and suggests inference concerning protein abundance made from mRNA in human brain should be treated with caution.

Empirical accounting research frequently makes use of data sets with a time-series and a cross-sectional dimension -a panel of data. The literature review indicates that South African researchers infrequently allow for heterogeneity between firms when using panel data and the empirical example shows that regression results that allow for firm heterogeneity are materially different from regression results that assume homogeneity among firms. The econometric analysis of panel data has advanced significantly in recent years and accounting researchers should benefit from those improvements.