Rediscovering the value of families for psychiatric genetics research

As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the “Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders” consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals.This research was supported by National Institute of Mental Health grants U01 MH105630 (DCG), U01 MH105634 (REG), U01 MH105632 (JB), R01 MH078143 (DCG), R01 MH083824 (DCG & JB), R01 MH078111 (JB), R01 MH061622 (LA), R01 MH042191 (REG), and R01 MH063480 (VLN).UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Biología Celular y Molecular (CIBCM)UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de Biologí

Quantification of Bacteria Adherent to Gastrointestinal Mucosa by Real-Time PCR

The use of real-time quantitative PCR (5′ nuclease PCR assay) as a tool to study the gastrointestinal microflora that adheres to the colonic mucosa was evaluated. We developed primers and probes based on the 16S ribosomal DNA gene sequences for the detection of Escherichia coli and Bacteroides vulgatus. DNA was isolated from pure cultures and from gut biopsy specimens and quantified by the 5′ nuclease PCR assay. The assay showed a very high sensitivity: as little as 1 CFU of E. coli and 9 CFU of B. vulgatus could be detected. The specificities of the primer-probe combinations were evaluated with samples that were spiked with the species most closely related to E. coli and B. vulgatus and with eight other gut microflora species. Mucosal samples spiked with known amounts of E. coli or B. vulgatus DNA showed no PCR inhibition. We conclude that the 5′ nuclease PCR assay may be a useful alternative to conventional culture techniques to study the actual in vivo composition of a complex microbial community like the gut microflora

Performances of Adaptive MultiBLUP, Bayesian regressions, and weighted-GBLUP approaches for genomic predictions in Belgian Blue beef cattle

BACKGROUND: Genomic selection has been successfully implemented in many livestock and crop species. The genomic best linear unbiased predictor (GBLUP) approach, assigning equal variance to all SNP effects, is one of the reference methods. When large-effect variants contribute to complex traits, it has been shown that genomic prediction methods that assign a higher variance to subsets of SNP effects can achieve higher prediction accuracy. We herein compared the efficiency of several such approaches, including the Adaptive MultiBLUP (AM-BLUP) that uses local genomic relationship matrices (GRM) to automatically identify and weight genomic regions with large effects, to predict genetic merit in Belgian Blue beef cattle. RESULTS: We used a population of approximately 10,000 genotyped cows and their phenotypes for 14 traits, mostly related to muscular development and body dimensions. According to the trait, we found that 4 to 25% of the genetic variance could be associated with 2 to 12 genomic regions harbouring large-effect variants. Noteworthy, three previously identified recessive deleterious variants presented heterozygote advantage and were among the most significant SNPs for several traits. The AM-BLUP resulted in increased reliability of genomic predictions compared to GBLUP (+ 2%), but Bayesian methods proved more efficient (+ 3%). Overall, the reliability gains remained thus limited although higher gains were observed for skin thickness, a trait affected by two genomic regions having particularly large effects. Higher accuracies than those from the original AM-BLUP were achieved when applying the Bayesian Sparse Linear Mixed Model to pre-select groups of SNPs with large effects and subsequently use their estimated variance to build a weighted GRM. Finally, the single-step GBLUP performed best and could be further improved (+ 3% prediction accuracy) by using these weighted GRM. CONCLUSIONS: The AM-BLUP is an attractive method to automatically identify and weight genomic regions with large effects on complex traits. However, the method was less accurate than Bayesian methods. Overall, weighted methods achieved modest accuracy gains compared to GBLUP. Nevertheless, the computational efficiency of the AM-BLUP might be valuable at higher marker density, including with whole-genome sequencing data. Furthermore, weighted GRM are particularly useful to account for large variance loci in the single-step GBLUP

Heterozygous STAT1 gain-of-function mutations underlie an unexpectedly broad clinical phenotype

Since their discovery in patients with autosomal dominant (AD) chronic mucocutaneous candidiasis (CMC) in 2011, heterozygous STAT1 gain-of-function (GOF) mutations have increasingly been identified worldwide. The clinical spectrum associated with them needed to be delineated. We enrolled 274 patients from 167 kindreds originating from 40 countries from 5 continents. Demographic data, clinical features, immunological parameters, treatment, and outcome were recorded. The median age of the 274 patients was 22 years (range, 1-71 years); 98% of them had CMC, with a median age at onset of 1 year (range, 0-24 years). Patients often displayed bacterial (74%) infections, mostly because of Staphylococcus aureus (36%), including the respiratory tract and the skin in 47% and 28% of patients, respectively, and viral (38%) infections, mostly because of Herpesviridae (83%) and affecting the skin in 32% of patients. Invasive fungal infections (10%), mostly caused by Candida spp. (29%), and mycobacterial disease (6%) caused by Mycobacterium tuberculosis, environmental mycobacteria, or Bacille Calmette-Guérin vaccines were less common. Many patients had autoimmune manifestations (37%), including hypothyroidism (22%), type 1 diabetes (4%), blood cytopenia (4%), and systemic lupus erythematosus (2%). Invasive infections (25%), cerebral aneurysms (6%), and cancers (6%) were the strongest predictors of poor outcome. CMC persisted in 39% of the 202 patients receiving prolonged antifungal treatment. Circulating interleukin-17A-producing T-cell count was low for most (82%) but not all of the patients tested. STAT1 GOF mutations underlie AD CMC, as well as an unexpectedly wide range of other clinical features, including not only a variety of infectious and autoimmune diseases, but also cerebral aneurysms and carcinomas that confer a poor prognosis