Efficient computation of the joint probability of multiple inherited risk alleles from pedigree data

The Elston–Stewart peeling algorithm enables estimation of an individual's probability of harboring germline risk alleles based on pedigree data, and serves as the computational backbone of important genetic counseling tools. However, it remains limited to the analysis of risk alleles at a small number of genetic loci because its computing time grows exponentially with the number of loci considered. We propose a novel, approximate version of this algorithm, dubbed the peeling and paring algorithm, which scales polynomially in the number of loci. This allows extending peeling‐based models to include many genetic loci. The algorithm creates a trade‐off between accuracy and speed, and allows the user to control this trade‐off. We provide exact bounds on the approximation error and evaluate it in realistic simulations. Results show that the loss of accuracy due to the approximation is negligible in important applications. This algorithm will improve genetic counseling tools by increasing the number of pathogenic risk alleles that can be addressed. To illustrate we create an extended five genes version of BRCAPRO, a widely used model for estimating the carrier probabilities of BRCA1 and BRCA2 risk alleles and assess its computational properties.     

Personalizing medicine: a review of adaptive treatment strategies

Much of current pharmacological practice focuses on identifying the single ‘best’ treatment (or course of treatments) for a particular disease. Recently, however, focus has begun to shift towards a more patient‐centric rather than disease‐centric approach, where personal characteristics are used to identify the optimal treatment for an individual. Adaptive treatment strategies (also known as dynamic treatment regimes) are part of a rapidly expanding area of research whereby such personalized treatments can be identified. These methods can lead to improved results over standard ‘one size fits all’ approaches, as well as provide a route to formalizing a common practice of using ad hoc approaches when deciding or updating management plans. Here, we provide an introduction to adaptive treatment strategies, explaining their background, their purpose, and how they can be employed in practice. Copyright © 2014 John Wiley & Sons, Ltd.     

Assessing the Causal Effects of Adipokines on Uric Acid and Gout: A Two-Sample Mendelian Randomization Study

Previous observational studies have highlighted associations between adipokines and hyperuricemia, as well as gout, but the causality and direction of these associations are not clear. Therefore, we attempted to assess whether there are causal effects of specific adipokines (such as adiponectin (ADP) and soluble leptin receptors (sOB-R)) on uric acid (UA) or gout in a two-sample Mendelian randomization (MR) analysis, based on summary statistics from large genome-wide association studies. The inverse-variance weighted (IVW) method was performed as the primary analysis. Sensitivity analyses (including MR-Egger regression, weighted median, penalized weighted median, and MR pleiotropy residual sum and outlier methods) were also performed, to ensure reliable results. In the IVW models, no causal effect was found for sOB-R (odds ratios (OR), 1.002; 95% confidence intervals (CI), 0.999–1.004; p = 0.274) on UA, or ADP (OR, 1.198; 95% CI, 0.865–1.659; p = 0.277) or sOB-R (OR, 0.988; 95% CI, 0.940–1.037; p = 0.616) on gout. The results were confirmed in sensitivity analyses. There was no notable directional pleiotropy or heterogeneity. This study suggests that these specific adipokines may not play causal roles in UA or gout development.     

Dietary-Derived Essential Nutrients and Amyotrophic Lateral Sclerosis: A Two-Sample Mendelian Randomization Study

Previous studies have suggested a close but inconsistent relationship between essential nutrients and the risk of amyotrophic lateral sclerosis (ALS), and whether this association is causal remains unknown. We aimed to investigate the potential causal relation between essential nutrients (essential amino acids, essential fatty acids, essential minerals, and essential vitamins) and the risk of ALS using Mendelian randomization (MR) analysis. Large-scale European-based genome-wide association studies’ (GWASs) summary data related to ALS (assembling 27,205 ALS patients and 110,881 controls) and essential nutrient concentrations were separately obtained. MR analysis was performed using the inverse variance–weighted (IVW) method, and sensitivity analysis was conducted by the weighted median method, simple median method, MR–Egger method and MR–PRESSO method. We found a causal association between genetically predicted linoleic acid (LA) and the risk of ALS (OR: 1.066; 95% CI: 1.011–1.125; p = 0.019). An inverse association with ALS risk was noted for vitamin D (OR: 0.899; 95% CI: 0.819–0.987; p = 0.025) and for vitamin E (OR: 0.461; 95% CI: 0.340–0.626; p = 6.25 × 10⁻⁷). The sensitivity analyses illustrated similar trends. No causal effect was observed between essential amino acids and minerals on ALS. Our study profiled the effects of diet-derived circulating nutrients on the risk of ALS and demonstrated that vitamin D and vitamin E are protective against the risk of ALS, and LA is a suggested risk factor for ALS.     

Alcohol,Coffee, and Milk Intake in Relation to Epilepsy Risk

Alcohol, coffee and milk intakes have been explored in relation to epilepsy risk in observational studies; however, the results were not consistent. We performed a Mendelian randomisation (MR) study to evaluate the causality of these relationships. Genetic variants associated with alcohol, coffee and milk intake were adopted as instrumental variables. We obtained the summary data of epilepsy from the International League Against Epilepsy (ILAE) Consortium (15,212 cases and 29,677 controls) and FinnGen consortium (4588 cases and 144,780 controls). Genetically predicted alcohol intake was associated with a higher risk of epilepsy in the ILAE Consortium (odds ratio (OR): 1.22, 95% confidence intervals (CI): 1.02–1.45). The association in the FinnGen consortium remained consistent in direction. Combined analysis of ILAE and FinnGen databases further indicated that genetically predicted alcohol intake was associated with a higher risk of epilepsy (OR = 1.24; 95% CI, 1.06–1.47, p = 0.009). Genetically predicted coffee intake was not related to epilepsy risk, while higher genetically predicted milk intake was related to a lower risk of epilepsy (OR = 0.957; 95% CI, 0.917–0.999, p = 0.044). Our results suggest a detrimental effect of alcohol intake on the risk of epilepsy, while milk intake might be associated with a decreased risk of epilepsy.     

Genetics of coronary heart disease: towards causal mechanisms,novel drug targets and more personalized prevention

Coronary heart disease (CHD) is an archetypical multifactorial disorder that is influenced by genetic susceptibility as well as both modifiable and nonmodifiable risk factors, and their interactions. Advances during recent years in the field of multifactorial genetics, in particular genomewide association studies (GWASs) and their meta‐analyses, have provided the statistical power to identify and replicate genetic variants in more than 50 risk loci for CHD and in several hundreds of loci for cardiometabolic risk factors for CHD such as blood lipids and lipoproteins. Although for a great majority of these loci both the causal variants and mechanisms remain unknown, progress in identifying the causal variants and underlying mechanisms has already been made for several genetic loci. Furthermore, identification of rare loss‐of‐function variants in genes such as PCSK9, NPC1L1, APOC3 and APOA5, which cause a markedly decreased risk of CHD and no adverse side effects, illustrates the power of translating genetic findings into novel mechanistic information and provides some optimism for the future of developing novel drugs, given the many genes associated with CHD in GWASs. Finally, Mendelian randomization can be used to reveal or exclude causal relationships between heritable biomarkers and CHD, and such approaches have already provided evidence of causal relationships between CHD and LDL cholesterol, triglycerides/remnant particles and lipoprotein(a), and indicated a lack of causality for HDL cholesterol, C‐reactive protein and lipoprotein‐associated phospholipase A2. Together, these genetic findings are beginning to lead to promising new drug targets and novel interventional strategies and thus have great potential to improve prevention, prediction and therapy of CHD.     

IFN‐γR1 defects: Mutation update and description of the IFNGR1 variation database

IFN‐γ signaling is essential for the innate immune defense against mycobacterial infections. IFN‐γ signals through the IFN‐γ receptor, which consists of a tetramer of two IFN‐γR1 chains in complex with two IFN‐γR2 chains, where IFN‐γR1 is the ligand‐binding chain of the interferon‐γ receptor and IFN‐γR2 is the signal‐transducing chain of the IFN‐γ receptor. Germline mutations in the gene IFNGR1 encoding the IFN‐γR1 cause a primary immunodeficiency that mainly leads to mycobacterial infections. Here, we review the molecular basis of this immunodeficiency in the 130 individuals described to date, and report mutations in five new individuals, bringing the total number to 135 individuals from 98 kindreds. Forty unique IFNGR1 mutations have been reported and they exert either an autosomal dominant or an autosomal recessive effect. Mutations resulting in premature stopcodons represent the majority of IFNGR1 mutations (60%; 24 out of 40), followed by amino acid substitutions (28%, 11 out of 40). All known mutations, as well as 287 other variations, have been deposited in the online IFNGR1 variation database ( www.LOVD.nl/IFNGR1 ). In this article, we review the function of IFN‐γR1 and molecular genetics of human IFNGR1.     

Functional genomic screen of human stem cell differentiation reveals pathways involved in neurodevelopment and neurodegeneration

Human embryonic stem cells (hESCs) can be induced and differentiated to form a relatively homogeneous population of neuronal precursors in vitro. We have used this system to screen for genes necessary for neural lineage development by using a pooled human short hairpin RNA (shRNA) library screen and massively parallel sequencing. We confirmed known genes and identified several unpredicted genes with interrelated functions that were specifically required for the formation or survival of neuronal progenitor cells without interfering with the self-renewal capacity of undifferentiated hESCs. Among these are several genes that have been implicated in various neurodevelopmental disorders (i.e., brain malformations, mental retardation, and autism). Unexpectedly, a set of genes mutated in late-onset neurodegenerative disorders and with roles in the formation of RNA granules were also found to interfere with neuronal progenitor cell formation, suggesting their functional relevance in early neurogenesis. This study advances the feasibility and utility of using pooled shRNA libraries in combination with next-generation sequencing for a high-throughput, unbiased functional genomic screen. Our approach can also be used with patient-specific human-induced pluripotent stem cell-derived neural models to obtain unparalleled insights into developmental and degenerative processes in neurological or neuropsychiatric disorders with monogenic or complex inheritance.