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Biology subjects

Sham, P.

Publications and source records attributed to Sham, P..

3 recordsLinked to original sources

The Maudsley Environmental Risk Score for Psychosis

Risk prediction algorithms have long been used in health research and practice (e.g., in prediction of cardiovascular disease, diabetes, etc.) However, similar tools have not been developed for mental health problems, despite extensive research on risk factors. For example, for psychotic disorders, attempts to sum environmental risk are rare, usually unsystematic and dictated by available data. In light of this, we sought to develop a valid, easy to use measure of the total environmental risk for psychotic disorders, which can be used in research and clinical practice.\n\nWe first reviewed the literature to identify well-replicated and validated environmental risk factors for psychosis and, then, used the largest available meta-analyses to derive current best estimates of risk. We devised a method of scoring individuals based on the level of exposure to each risk factor, using odds ratios from the meta-analyses, to produce an Environmental Risk Score (ERS).\n\nSix risk factors (ethnic minority status, urbanicity, high paternal age, obstetric complications, cannabis use, and childhood adversity) were used to generate the ERS. A distribution for different levels of risk based on permuted data showed that most of population would be at low/moderate risk with a small minority at increased environmental risk for psychosis.\n\nThis is the first systematic approach to develop an aggregate measure of environmental risk for psychoses. This can be used as a continuous measure of liability to disease or transformed to a relative risk. Its predictive ability will improve with the collection of additional, population specific data.

epidemiology

Genomic characterization of additional cancer-driver genes using a weighted iterative regression accurately modelling background mutation rate

Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modeling distribution of background mutations, existing statistical methods are often underpowered to discriminate driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER), to detect cancer-driver genes showing an excess of somatic mutations. By solving the problem of inaccurately modeling background mutations, this approach works even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in all tested cancers. Applying this approach, we estimated 178 driver genes in 26 different cancers types. In silico validation confirmed 90.5% of predicted genes as likely known drivers and 7 genes unique for individual cancers as likely new drivers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects, rescuing more genes missed by alternative tools.

bioinformatics

KS-Burden: Assessing distributional differences of rare variants in dichotomous traits

A number of rare variant tests have been developed to explore the effect of low frequency genetic variations on complex phenotypes. However, an often neglected aspect in these tests is the position of genetic variations. Here we are proposing a way to assess the differences in spatial organization of rare variants by assessing their distributional differences between affected and unaffected subjects. To do so, we have formulated an adaptation of the well know Kolmogorov-Smirnov (KS) test, combining both KS and a simple gene burden approach, called KS-Burden.\n\nThe performance of our test was evaluated under a comprehensive simulations framework using real data and various scenarios. Our results show that the KS-Burden test is able to outperform the commonly used SKAT-O test, as well as others, in the presents of clusters of causal variants within a genomic region. Furthermore, our test is able to maintain competitive statistical power in scenarios unfavorable to its original assumptions. Hence, the KS-Burden test is a valuable alternative to existing tests and provides better statistical power in the presents of causal clusters within a gene.

bioinformatics