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.

Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Heritability and polygenic predictionIn the EUR sample, the SNP-based heritability (h 2 SNP ) (that is, the proportion of variance in liability attributable to all measured SNPs)

Although there have been numerous reports from around the world of mutations in the gene of chromosome 7 known as CFTR (cystic fibrosis transmembrane conductance regulator), little attention has been given to integrating these mutant alleles into a global understanding of the population molecular genetics associated with cystic fibrosis (CF). We determined the distribution of CFTR mutations in as many regions throughout the world as possible in an effort designed to: 1) increase our understanding of ancestrygenotype relationships, 2) compare mutational arrays with disease incidence, and 3) gain insight for decisions regarding screening program enhancement through CFTR multi-mutational analyses. Information on all mutations that have been published since the identification and cloning of the CFTR genes most common allele, DF508 (or F508del), was reviewed and integrated into a centralized database. The data were then sorted and regional CFTR arrays were determined using mutations that appeared in a given region with a frequency of 0.5% or greater. Final analyses were based on 72,431 CF chromosomes, using data compiled from over 100 original papers, and over 80 regions from around the world, including all nations where CF has been studied using analytical molecular genetics. Initial results confirmed wide mutational heterogeneity throughout the world; however, characterization of the most common mutations across most populations was possible. We also examined CF incidence, DF508 frequency, and regional mutational heterogeneity in a subset of populations. Data for these analyses were filtered for reliability and methodological strength before being incorporated into the final analysis. Statistical assessment of these variables revealed that there is a significant positive correlation between DF508 frequency and the CF incidence levels of regional populations. Regional analyses were also performed to search for trends in the distribution of CFTR mutations across migrant and related populations; this led to clarification of ancestrygenotype patterns that can be used to design CFTR multi-mutation panels for CF screening programs. From comprehensive assessment of these data, we offer recommendations that multiple CFTR alleles should eventually be included to increase the sensitivity of newborn screening programs employing two-tier testing with trypsinogen and DNA analysis. Hum Mutat 19:575606, 2002.

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It is often challenging for the clinician interested in cystic fibrosis (CF) to interpret molecular genetic results, and to integrate them in the diagnostic process. The limitations of genotyping technology, the choice of mutations to be tested, and the clinical context in which the test is administered can all influence how genetic information is interpreted. This paper describes the conclusions of a consensus conference to address the use and interpretation of CF mutation analysis in clinical settings. Although the diagnosis of CF is usually straightforward, care needs to be exercised in the use and interpretation of genetic tests: genotype information is not the final arbiter of a clinical diagnosis of CF or CF transmembrane conductance regulator (CFTR) protein related disorders. The diagnosis of these conditions is primarily based on the clinical presentation, and is supported by evaluation of CFTR function (sweat testing, nasal potential difference) and genetic analysis. None of these features are sufficient on their own to make a diagnosis of CF or CFTR-related disorders. Broad genotype/phenotype associations are useful in epidemiological studies, but CFTR genotype does not accurately predict individual outcome. The use of CFTR genotype for prediction of prognosis in people with CF at the time of their diagnosis is not recommended. The importance of communication between clinicians and medical genetic laboratories is emphasized. The results of testing and their implications should be reported in a manner understandable to the clinicians caring for CF patients.