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Transmission ratio distortion (TRD) is a phenomenon where parental transmission of disease allele to the child does not follow the Mendelian inheritance ratio. TRD occurs in a sex-of-parent-specific or non-sex-of-parent-specific manner. An offset computed from the transmission probability of the minor allele in control-trios can be added to the loglinear model to adjust for TRD. Adjusting the model removes the inflation in the genotype relative risk (RR) estimate and Type 1 error introduced by non-sex-of-parent-specific TRD. We now propose to further extend this model to estimate an imprinting parameter. Some evidence suggests that more than 1% of all mammalian genes are imprinted. In the presence of imprinting, for example, the offspring inheriting an over-transmitted disease allele from the parent with a higher expression level in a neighboring gene is over-represented in the sample. TRD mechanisms such as meiotic drive and gametic competition occur in a sex-of-parent-specific manner. Therefore, sex-of-parent-specific TRD (ST) leads to over-representation of maternal or paternal alleles in the affected child. As a result, ST may bias the imprinting effect when present in the sample. We propose a sex-of-parent-specific transmission offset in adjusting the loglinear model to account for ST. This extended model restores the correct RR estimates for child and imprinting effects, adjusts for inflation in Type 1 error, and improves performance on sensitivity and specificity compared to the original model without ST offset. We conclude that to correctly interpret the association signal of an imprinting effect, adjustment for ST is necessary to ensure valid conclusions.

Metabotropic glutamate receptor type 5 (mGluR5) is a G protein-coupled receptor that has been implicated in several psychiatric and neurological diseases. The radiopharmaceutical [(11)C]ABP688 allows for in vivo quantification of mGluR5 availability using positron emission tomography (PET). In this study, we aimed to detail the regional distribution of [(11)C]ABP688 binding potential (BPND) and the existence of age/sex effects in healthy individuals.Thirty-one healthy individuals aged 20 to 77 years (men, n = 18, 45.3 ± 18.2 years; females, n = 13, 41.5 ± 19.6 years) underwent imaging with [(11)C]ABP688 using the high-resolution research tomograph (HRRT). We developed an advanced partial volume correction (PVC) method using surface-based analysis in order to accurately estimate the regional variation of radioactivity. BPND was calculated using the simplified reference tissue model, with the cerebellum as the reference region. Surface-based and volume-based analyses were performed for 39 cortical and subcortical regions of interest per hemisphere.We found the highest [(11)C]ABP688 BPND in the lateral prefrontal and anterior cingulate cortices. The lowest [(11)C]ABP688 BPND was observed in the pre- and post-central gyri as well as the occipital lobes and the thalami. No sex effect was observed. Associations between age and [(11)C]ABP688 BPND without PVC were observed in the right amygdala and left putamen, but were not significant after multiple comparisons correction.The present results highlight complexities underlying brain adaptations during the aging process, and support the notion that certain aspects of neurotransmission remain stable during the adult life span.

Transmission ratio distortion (TRD) occurs when one of the two alleles from either parent is preferentially transmitted to the offspring. This leads to a statistical departure from the Mendelian law of inheritance, which states that each of the two parental alleles is transmitted to offspring with a probability of 0.5. A number of mechanisms are thought to induce TRD such as meiotic drive, gametic competition, and embryo lethality. TRD has been extensively studied in animals, but the prevalence of TRD in humans remains largely unknown. Nevertheless, understanding the TRD phenomenon and taking it into consideration in many aspects of human genetics has potential benefits that have not been sufficiently emphasized in the current literature. In this review, we discuss the importance of TRD in three distinct but related fields of genetics: developmental genetics which studies the genetic abnormalities in zygotic and embryonic development, statistical genetics/genetic epidemiology which utilizes population study designs and statistical models to interpret the role of genes in human health, and population genetics which is concerned with genetic diversity in populations in an evolutionary context. From the perspective of developmental genetics, studying TRD leads to the identification of the processes and mechanisms for differential survival observed in embryos. As a result, it is a genetic force which affects allele frequency at the population, as well as, at the organismal level. Therefore, it has implications on genetic diversity of the population over time. From the perspective of genetic epidemiology, the TRD influence on a marker locus is a confounding factor which has to be adequately dealt with to correctly interpret linkage or association study results. These aspects are developed in this review. In addition to these theoretical notions, a brief summary of the empirical evidence of the TRD phenomenon in human and mouse studies is provided. The objective of our paper is to show the potentially important role of TRD in many areas of genetics, and to create an incentive for future research.

Background Many different methods exist to adjust for variability in cell-type mixture proportions when analyzing DNA methylation studies. Here we present the result of an extensive simulation study, built on cell-separated DNA methylation profiles from Illumina Infinium 450K methylation data, to compare the performance of eight methods including the most commonly used approaches. Results We designed a rich multi-layered simulation containing a set of probes with true associations with either binary or continuous phenotypes, confounding by cell type, variability in means and standard deviations for population parameters, additional variability at the level of an individual cell-type-specific sample, and variability in the mixture proportions across samples. Performance varied quite substantially across methods and simulations. In particular, the number of false positives was sometimes unrealistically high, indicating limited ability to discriminate the true signals from those appearing significant through confounding. Methods that filtered probes had consequently poor power. QQ plots of p values across all tested probes showed that adjustments did not always improve the distribution. The same methods were used to examine associations between smoking and methylation data from a case–control study of colorectal cancer, and we also explored the effect of cell-type adjustments on associations between rheumatoid arthritis cases and controls. Conclusions We recommend surrogate variable analysis for cell-type mixture adjustment since performance was stable under all our simulated scenarios. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0935-y) contains supplementary material, which is available to authorized users.

This study introduces a novel way to use the lifetime ratings of symptoms of psychosis, mania and depression in genetic linkage analysis of schizophrenia (SZ) and bipolar disorder (BP). It suggests using a latent class model developed for family data to define more homogeneous symptom subtypes that are influenced by a smaller number of genes that will thus be more easily detectable. In a two-step approach, we proposed: (i) to form homogeneous clusters of subjects based on the symptom dimensions and (ii) to use the information from these homogeneous clusters in linkage analysis. This framework was applied to a unique SZ and BP sample composed of 1278 subjects from 48 large kindreds from the Eastern Quebec population. The results suggest that our strategy has the power to increase linkage signals previously obtained using the diagnosis as phenotype and allows for a better characterization of the linkage signals. This is the case for a linkage signal, which we formerly obtained in chromosome 13q and enhanced using the dimension mania. The analysis also suggests that the methods may detect new linkage signals not previously uncovered by using diagnosis alone, as in chromosomes 2q (delusion), 15q (bizarre behavior), 7p (anhedonia) and 9q (delusion). In the case of the 15q and 2q region, the results coincide with linkage signals detected in other studies. Our results support the view that dissecting phenotypic heterogeneity by modeling symptom dimensions may provide new insights into the genetics of SZ and BP.

The study of genetic properties of a disease requires the collection of information concerning the subjects in a set of pedigrees. The main focus of this study was the detection of susceptible genes. However, even with large pedigrees, the heterogeneity of phenotypes in complex diseases such as Schizophrenia, Bipolar and Autism, makes the detection of susceptible genes difficult to accomplish. This is mainly due to a genetic heterogeneity: many genes phenomena are involved in the disease. In order to reduce this heterogeneity, our idea consists in sub-typing the disease and in partitioning the population into more alike sub-groups. We developed a probabilistic model based on a Latent Class Analysis (LCA) that takes into account the familial dependence inside a pedigree, even for large pedigrees. It also takes into account individuals with missing and partially missing measurements. Estimation of model parameters is performed by an EM algorithm, and computations for the E step inside a pedigree are achieved using a pedigree peeling algorithm. When more than one model are fitted, we use model selection strategies such as cross-validation or/and BIC approaches to choose the suitable model among a set of candidates. Moreover, we present a simulation based on a genetic disease class model and we show that our model leads to better individual classification than the model that assumes independence among subjects. An application of our model to a Schizophrenia-Bipolar pedigree data set from Eastern Quebec is also performed.

We evaluate the impact of three pre-processing methods for Affymetrix microarray data on expression quantitative trait locus (eQTL) mapping, using 14 CEPH Utah families (GAW Problem 1 data). Different sets of expression traits were chosen according to different selection criteria: expression level, variance, and heritability. For each gene, three expression phenotypes were obtained by different pre-processing methods. Each quantitative phenotype was then submitted to a whole-genome scan, using multipoint variance component LODs. Pre-processing methods were compared with respect to their linkage outcomes (number of linkage signals with LODs greater than 3, consistencies in the location of the trait-specific linkage signals, and type of cis/trans-regulating loci). Overall, we found little agreement between linkage results from the different pre-processing methods: most of the linkage signals were specific to one pre-processing method. However, agreement rates varied according to the criteria used to select the traits. For instance, these rates were higher in the set of the most heritable traits. On the other hand, the pre-processing method had little impact on the relative proportion of detected cis and trans-regulating loci. Interestingly, although the number of detected cis-regulating loci was relatively small, pre-processing methods agreed much better in this set of linkage signals than in the trans-regulating loci. Several potential factors explaining the discordance observed between the methods are discussed.

Background This paper deals with the detection of significant linkage for quantitative traits using a variance components approach. Microsatellite markers were obtained for the Genetic Analysis Workshop 14 Collaborative Study on the Genetics of Alcoholism data. Ethnic heterogeneity, highly skewed quantitative measures, and a high rate of missing values are all present in this dataset and well known to impact upon linkage analysis. This makes it a good candidate for investigation. Results As expected, we observed a number of changes in LOD scores, especially for chromosomes 1, 7, and 18, along with the three factors studied. A dramatic example of such changes can be found in chromosome 7. Highly significant linkage to one of the quantitative traits became insignificant when a proper normalizing transformation of the trait was used and when analysis was carried out on an ethnically homogeneous subset of the original pedigrees. Conclusion In agreement with existing literature, transforming a trait to ensure normality using a Box-Cox transformation is highly recommended in order to avoid false-positive linkages. Furthermore, pedigrees should be sorted by ethnic groups and analyses should be carried out separately. Finally, one should be aware that the inclusion of covariates with a high rate of missing values reduces considerably the number of subjects included in the model. In such a case, the loss in power may be large. Imputation methods are then recommended.

There is significant variability in antidepressant treatment outcome, with ∼30-40% of patients with major depressive disorder (MDD) not presenting with adequate response even following several trials. To identify potential biomarkers of response, we investigated peripheral gene expression patterns of response to antidepressant treatment in MDD. We did this using Affymetrix HG-U133 Plus2 microarrays in blood samples, from untreated individuals with MDD (N=63) ascertained at a community outpatient clinic, pre and post 8-week treatment with citalopram, and used a regression model to assess the impact of gene expression differences on antidepressant response. We carried out technical validation of significant probesets by quantitative reverse transcriptase PCR and conducted central nervous system follow-up of the most significant result in post-mortem brain samples from 15 subjects who died during a current MDD episode and 11 sudden-death controls. A total of 32 probesets were differentially expressed according to response to citalopram treatment following false discovery rate correction. Interferon regulatory factor 7 (IRF7) was the most significant differentially expressed gene and its expression was upregulated by citalopram treatment in individuals who responded to treatment. We found these results to be concordant with our observation of decreased expression of IRF7 in the prefrontal cortex of MDDs with negative toxicological evidence for antidepressant treatment at the time of death. These findings point to IRF7 as a gene of interest in studies investigating genomic factors associated with antidepressant response.