Detecting and Correcting Contamination in Genetic Data.

While technological innovation has dramatically increased the amount and variety of genomic data available to geneticists, no assay is perfect and both human error and technical artifacts can lead to erroneous data. A proper analysis pipeline must both detect errors, and, if possible, correct them. One common source of errors in genetic data is sample-to-sample contamination. This dissertation will identify methods to address contamination in the most common types of genetic studies. Chapter 2 focuses on methods for detecting and quantifying contamination in both array-based and next-generation sequencing (NGS) genotype data. For the array-based data, we use the observed intensities from the genotyping instruments to quantify contamination with two distinct methods: 1) a regression-based model using intensities and population allele frequencies and 2) a multivariate normal mixture model that looks at the clustering of intensities. For NGS data, we model the reads using a mixture model to determine the proportion of reads from the true sample and the contaminating sample. Chapter 3 outlines a method to make accurate genotype calls with contaminated NGS data. Given an estimated level of contamination, we propose a likelihood that can be maximized to call genotypes and estimate allele frequencies for samples with no previous genotype data. We investigate the method from data from two common sequencing strategies: 1) low-pass (2-4x depth) genome-wide sequencing and 2) high-depth (50-100x depth) exome sequencing. Chapter 4 looks at contamination in the context of RNA sequencing (RNA-Seq) data. While the technology to generate RNA-Seq data is similar to exome sequencing, the difference in expression between the contaminating and true sample makes it more difficult to accurately estimate the contamination proportion. We propose methods to improve the quality of these estimates.PhDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120783/1/mflick_1.pd

Distinguishing grade I meningioma from higher grade meningiomas without biopsy

BACKGROUND: Many meningiomas are identified by imaging and followed, with an assumption that they are WHO Grade I tumors. The purpose of our investigation is to find clinical or imaging predictors of WHO Grade II/III tumors to distinguish them from Grade I meningiomas. METHODS: Patients with a pathologic diagnosis of meningioma from 2002-2009 were included if they had pre-operative MRI studies and pathology for review. A Neuro-Pathologist reviewed and classified all tumors by WHO 2007. All Brain MRI imaging was reviewed by a Neuro-radiologist. Pathology and Radiology reviews were blinded from each other and clinical course. Recursive partitioning was used to create predictive models for identifying meningioma grades. RESULTS: Factors significantly correlating with a diagnosis of WHO Grade II-III tumors in univariate analysis: prior CVA (p = 0.005), CABG (p = 0.010), paresis (p = 0.008), vascularity index = 4/4: (p = 0.009), convexity vs other (p = 0.014), metabolic syndrome (p = 0.025), non-skull base (p = 0.041) and non-postmenopausal female (p = 0.045). Recursive partitioning analysis identified four categories: 1. prior CVA, 2. vascular index (vi) = 4 (no CVA), 3. premenopausal or male, vi \u3c 4, no CVA. 4. Postmenopausal, vi \u3c 4, no CVA with corresponding rates of 73, 54, 35 and 10% of being Grade II-III meningiomas. CONCLUSIONS: Meningioma patients with prior CVA and those grade 4/4 vascularity are the most likely to have WHO Grade II-III tumors while post-menopausal women without these features are the most likely to have Grade I meningiomas. Further study of the associations of clinical and imaging factors with grade and clinical behavior are needed to better predict behavior of these tumors without biopsy

Genome-wide association study identifies 30 loci associated with bipolar disorder.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder

PPS, a Large Multidomain Protein, Functions with Sex-Lethal to Regulate Alternative Splicing in Drosophila

Alternative splicing controls the expression of many genes, including the Drosophila sex determination gene Sex-lethal (Sxl). Sxl expression is controlled via a negative regulatory mechanism where inclusion of the translation-terminating male exon is blocked in females. Previous studies have shown that the mechanism leading to exon skipping is autoregulatory and requires the SXL protein to antagonize exon inclusion by interacting with core spliceosomal proteins, including the U1 snRNP protein Sans-fille (SNF). In studies begun by screening for proteins that interact with SNF, we identified PPS, a previously uncharacterized protein, as a novel component of the machinery required for Sxl male exon skipping. PPS encodes a large protein with four signature motifs, PHD, BRK, TFS2M, and SPOC, typically found in proteins involved in transcription. We demonstrate that PPS has a direct role in Sxl male exon skipping by showing first that loss of function mutations have phenotypes indicative of Sxl misregulation and second that the PPS protein forms a complex with SXL and the unspliced Sxl RNA. In addition, we mapped the recruitment of PPS, SXL, and SNF along the Sxl gene using chromatin immunoprecipitation (ChIP), which revealed that, like many other splicing factors, these proteins bind their RNA targets while in close proximity to the DNA. Interestingly, while SNF and SXL are specifically recruited to their predicted binding sites, PPS has a distinct pattern of accumulation along the Sxl gene, associating with a region that includes, but is not limited to, the SxlPm promoter. Together, these data indicate that PPS is different from other splicing factors involved in male-exon skipping and suggest, for the first time, a functional link between transcription and SXL–mediated alternative splicing. Loss of zygotic PPS function, however, is lethal to both sexes, indicating that its role may be of broad significance

Endocrinologic, neurologic, and visual morbidity after treatment for craniopharyngioma

Craniopharyngiomas are locally aggressive tumors which typically are focused in the sellar and suprasellar region near a number of critical neural and vascular structures mediating endocrinologic, behavioral, and visual functions. The present study aims to summarize and compare the published literature regarding morbidity resulting from treatment of craniopharyngioma. We performed a comprehensive search of the published English language literature to identify studies publishing outcome data of patients undergoing surgery for craniopharyngioma. Comparisons of the rates of endocrine, vascular, neurological, and visual complications were performed using Pearson’s chi-squared test, and covariates of interest were fitted into a multivariate logistic regression model. In our data set, 540 patients underwent surgical resection of their tumor. 138 patients received biopsy alone followed by some form of radiotherapy. Mean overall follow-up for all patients in these studies was 54 ± 1.8 months. The overall rate of new endocrinopathy for all patients undergoing surgical resection of their mass was 37% (95% CI = 33–41). Patients receiving GTR had over 2.5 times the rate of developing at least one endocrinopathy compared to patients receiving STR alone or STR + XRT (52 vs. 19 vs. 20%, χ2P < 0.00001). On multivariate analysis, GTR conferred a significant increase in the risk of endocrinopathy compared to STR + XRT (OR = 3.45, 95% CI = 2.05–5.81, P < 0.00001), after controlling for study size and the presence of significant hypothalamic involvement. There was a statistical trend towards worse visual outcomes in patients receiving XRT after STR compared to GTR or STR alone (GTR = 3.5% vs. STR 2.1% vs. STR + XRT 6.4%, P = 0.11). Given the difficulty in obtaining class 1 data regarding the treatment of this tumor, this study can serve as an estimate of expected outcomes for these patients, and guide decision making until these data are available

Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

publisher: Elsevier articletitle: Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes journaltitle: Cell articlelink: https://doi.org/10.1016/j.cell.2018.05.046 content_type: article copyright: © 2018 Elsevier Inc

The genetics of the mood disorder spectrum:genome-wide association analyses of over 185,000 cases and 439,000 controls

Background Mood disorders (including major depressive disorder and bipolar disorder) affect 10-20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Despite their diagnostic distinction, multiple approaches have shown considerable sharing of risk factors across the mood disorders. Methods To clarify their shared molecular genetic basis, and to highlight disorder-specific associations, we meta-analysed data from the latest Psychiatric Genomics Consortium (PGC) genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; non-overlapping N = 609,424). Results Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More genome-wide significant loci from the PGC analysis of major depression than bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell-types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment – positive in bipolar disorder but negative in major depressive disorder. Conclusions The mood disorders share several genetic associations, and can be combined effectively to increase variant discovery. However, we demonstrate several differences between these disorders. Analysing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum

Bipolar multiplex families have an increased burden of common risk variants for psychiatric disorders.

Multiplex families with a high prevalence of a psychiatric disorder are often examined to identify rare genetic variants with large effect sizes. In the present study, we analysed whether the risk for bipolar disorder (BD) in BD multiplex families is influenced by common genetic variants. Furthermore, we investigated whether this risk is conferred mainly by BD-specific risk variants or by variants also associated with the susceptibility to schizophrenia or major depression. In total, 395 individuals from 33 Andalusian BD multiplex families (166 BD, 78 major depressive disorder, 151 unaffected) as well as 438 subjects from an independent, BD case/control cohort (161 unrelated BD, 277 unrelated controls) were analysed. Polygenic risk scores (PRS) for BD, schizophrenia (SCZ), and major depression were calculated and compared between the cohorts. Both the familial BD cases and unaffected family members had higher PRS for all three psychiatric disorders than the independent controls, with BD and SCZ being significant after correction for multiple testing, suggesting a high baseline risk for several psychiatric disorders in the families. Moreover, familial BD cases showed significantly higher BD PRS than unaffected family members and unrelated BD cases. A plausible hypothesis is that, in multiplex families with a general increase in risk for psychiatric disease, BD development is attributable to a high burden of common variants that confer a specific risk for BD. The present analyses demonstrated that common genetic risk variants for psychiatric disorders are likely to contribute to the high incidence of affective psychiatric disorders in the multiplex families. However, the PRS explained only part of the observed phenotypic variance, and rare variants might have also contributed to disease development

Genetic Overlap Between Alzheimer’s Disease and Bipolar Disorder Implicates the MARK2 and VAC14 Genes

Background: Alzheimer's disease (AD) and bipolar disorder (BIP) are complex traits influenced by numerous common genetic variants, most of which remain to be detected. Clinical and epidemiological evidence suggest that AD and BIP are related. However, it is not established if this relation is of genetic origin. Here, we applied statistical methods based on the conditional false discovery rate (FDR) framework to detect genetic overlap between AD and BIP and utilized this overlap to increase the power to identify common genetic variants associated with either or both traits. Methods: We obtained genome wide association studies data from the International Genomics of Alzheimer's Project part 1 (17,008 AD cases and 37,154 controls) and the Psychiatric Genetic Consortium Bipolar Disorder Working Group (20,352 BIP cases and 31,358 controls). We used conditional QQ-plots to assess overlap in common genetic variants between AD and BIP. We exploited the genetic overlap to re-rank test-statistics for AD and BIP and improve detection of genetic variants using the conditional FDR framework. Results: Conditional QQ-plots demonstrated a polygenic overlap between AD and BIP. Using conditional FDR, we identified one novel genomic locus associated with AD, and nine novel loci associated with BIP. Further, we identified two novel loci jointly associated with AD and BIP implicating the MARK2 gene (lead SNP rs10792421, conjunctional FDR=0.030, same direction of effect) and the VAC14 gene (lead SNP rs11649476, conjunctional FDR=0.022, opposite direction of effect). Conclusions: We found polygenic overlap between AD and BIP and identified novel loci for each trait and two jointly associated loci. Further studies should examine if the shared loci implicating the MARK2 and VAC14 genes could explain parts of the shared and distinct features of AD and BIP