Merging microsatellite data: enhanced methodology and software to combine genotype data for linkage and association analysis

<p>Abstract</p> <p>Background</p> <p>Correctly merged data sets that have been independently genotyped can increase statistical power in linkage and association studies. However, alleles from microsatellite data sets genotyped with different experimental protocols or platforms cannot be accurately matched using base-pair size information alone. In a previous publication we introduced a statistical model for merging microsatellite data by matching allele frequencies between data sets. These methods are implemented in our software MicroMerge version 1 (v1). While MicroMerge v1 output can be analyzed by some genetic analysis programs, many programs can not analyze alignments that do not match alleles one-to-one between data sets. A consequence of such alignments is that codominant genotypes must often be analyzed as phenotypes. In this paper we describe several extensions that are implemented in MicroMerge version 2 (v2).</p> <p>Results</p> <p>Notably, MicroMerge v2 includes a new one-to-one alignment option that creates merged pedigree and locus files that can be handled by most genetic analysis software. Other features in MicroMerge v2 enhance the following aspects of control: 1) optimizing the algorithm for different merging scenarios, such as data sets with very different sample sizes or multiple data sets, 2) merging small data sets when a reliable set of allele frequencies are available, and 3) improving the quantity and 4) quality of merged data. We present results from simulated and real microsatellite genotype data sets, and conclude with an association analysis of three familial dyslipidemia (FD) study samples genotyped at different laboratories. Independent analysis of each FD data set did not yield consistent results, but analysis of the merged data sets identified strong association at locus D11S2002.</p> <p>Conclusion</p> <p>The MicroMerge v2 features will enable merging for a variety of genotype data sets, which in turn will facilitate meta-analyses for powering association analysis.</p

Genome-wide association study of patient and clinician rated global impression severity during antipsychotic treatment

Examine the unique and congruent findings between multiple raters in a genome-wide association studies (GWAS) in the context of understanding individual differences in treatment response during antipsychotic therapy for schizophrenia

Genotype-Based Ancestral Background Consistently Predicts Efficacy and Side Effects across Treatments in CATIE and STAR*D

Only a subset of patients will typically respond to any given prescribed drug. The time it takes clinicians to declare a treatment ineffective leaves the patient in an impaired state and at unnecessary risk for adverse drug effects. Thus, diagnostic tests robustly predicting the most effective and safe medication for each patient prior to starting pharmacotherapy would have tremendous clinical value. In this article, we evaluated the use of genetic markers to estimate ancestry as a predictive component of such diagnostic tests. We first estimated each patient’s unique mosaic of ancestral backgrounds using genome-wide SNP data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) (n = 765) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (n = 1892). Next, we performed multiple regression analyses to estimate the predictive power of these ancestral dimensions. For 136/89 treatment-outcome combinations tested in CATIE/STAR*D, results indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis assuming no predictive power (p<0.01, both samples). Thus, ancestry showed robust and pervasive correlations with drug efficacy and side effects in both CATIE and STAR*D. Comparison of the marginal predictive power of MDS ancestral dimensions and self-reported race indicated significant improvements to model fit with the inclusion of MDS dimensions, but mixed evidence for self-reported race. Knowledge of each patient’s unique mosaic of ancestral backgrounds provides a potent immediate starting point for developing algorithms identifying the most effective and safe medication for a wide variety of drug-treatment response combinations. As relatively few new psychiatric drugs are currently under development, such personalized medicine offers a promising approach toward optimizing pharmacotherapy for psychiatric conditions

Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders

Personality is influenced by genetic and environmental factors1 and associated with mental health. However, the underlying genetic determinants are largely unknown. We identified six genetic loci, including five novel loci2,3, significantly associated with personality traits in a meta-analysis of genome-wide association studies (N = 123,132–260,861). Of these genomewide significant loci, extraversion was associated with variants in WSCD2 and near PCDH15, and neuroticism with variants on chromosome 8p23.1 and in L3MBTL2. We performed a principal component analysis to extract major dimensions underlying genetic variations among five personality traits and six psychiatric disorders (N = 5,422–18,759). The first genetic dimension separated personality traits and psychiatric disorders, except that neuroticism and openness to experience were clustered with the disorders. High genetic correlations were found between extraversion and attention-deficit– hyperactivity disorder (ADHD) and between openness and schizophrenia and bipolar disorder. The second genetic dimension was closely aligned with extraversion–introversion and grouped neuroticism with internalizing psychopathology (e.g., depression or anxiety)

Genomewide Association Study of Movement-Related Adverse Antipsychotic Effects

Understanding individual differences in the development of extra-pyramidal side effects (EPS) as a response to antipsychotic therapy is essential to individualize treatment

Finding Genes for Schizophrenia

Schizophrenia is one of our most common psychiatric diseases. It severely affects all aspects of psychological functions and results in loss of contact with reality. No cure exists and the treatments available today produce only partial relief for disease symptoms. The aim of this work is to better understand the etiology of schizophrenia by identification of candidate genes and gene pathways involved in the development of the disease. In a preliminarily study, the effects of medication and genetic factors were investigated in a candidate gene, serotonin 2C receptor. This study distinguished pharmacological effects, caused by neuroleptics, and/or genetic effects, caused by unique polymorphisms, from other effects responsible for mRNA expression changes on candidate genes. The core of the thesis describes a new candidate gene for schizophrenia, the quaking homolog, KH domain RNA binding (mouse) or QKI, located on chromosome 6q26-q27. The identification of QKI is supported by previous linkage studies, current association studies and mRNA expression studies using three different sample sets. The investigated samples included a 12-generation pedigree with 16 distantly related schizophrenic cases and their parents, 176 unrelated nuclear families with at least one affected child in each family and human brain autopsies from 55 schizophrenic cases and from 55 controls. Indirect evidence showing involvement of QKI in myelin regulation of central nervous system is presented. Myelin plays an important role in development of normal brains and disruption of QKI might lead to schizophrenia symptoms. In a forth sample set, including extended pedigrees originated from a geographically isolated area above the Arctic Circle, in northeast Sweden, two additional schizophrenia susceptibility loci were identified, 2q13 and 5q21. Both these regions have previously been highlighted as potential schizophrenia loci in several other investigations, including a large Finnish study. This suggests common schizophrenia susceptibility loci for Nordic populations. A pilot investigation including a genome wide haplotype analysis is presented. This statistical strategy could be further developed and applied to the artic Swedish families, including analysis of 900 microsatellites and 10,000 SNPs. These findings will facilitate the understanding of the schizophrenia etiology and may lead to development of more efficient treatments for patients that suffer from schizophrenia

Universitetsstudenters copingstrategier vid studierelaterad stress

Många universitetsstudenter upplever studierelaterad stress och copingstrategier är avgörande för hanteringen av denna stress. Studiens syfte var att undersöka studierelaterad stress och copingstrategier hos universitetsstudenter. Studien konkretiserades i följande frågeställningar: Vilka upplevda faktorer kan relateras till studierelaterad stress hos universitetsstudenter? och Vilka copingstrategier använder sig universitetsstudenter av för att hantera studierelaterad stress? Semistrukturerade intervjuer genomfördes med 11 kvinnor och 3 män i åldrarna 19-25 år. Teman som identifierades till den första frågeställningen var hög arbetsbelastning, ekonomisk stress samt press från föräldrar. Teman som identifierades till den andra frågeställningen var få kontroll genom att skapa struktur, skaffa socialt stöd, prokrastinering samt avslappning. Studiens resultat visar att hög arbetsbelastning är en stor stressor bland universitetsstudenter och att få kontroll genom att skapa struktur är den mest framträdande copingstrategin. Resultatet kan bidra till en ökad medvetenhet kring stress och coping bland universitetsstudenter och även vad som är bra att tänka på som förälder

Applying Novel Genome-Wide Linkage Strategies to Search for Loci Influencing Type 2 Diabetes and Adult Height in American Samoa

Type 2 diabetes mellitus (T2DM) is a common complex phenotype that by the year 2010 is predicted to affect 221 million people globally. In the present study we performed a genome-wide linkage scan using the allele-sharing statistic Sall implemented in Allegro and a novel two-dimensional genome-wide strategy implemented in Merloc that searches for pairwise interaction between genetic markers located on different chromosomes linked to T2DM. In addition, we used a robust score statistic from the newly developed QTL-ALL software to search for linkage to variation in adult height. The strategies were applied to a study sample consisting of 238 sib-pairs affected with T2DM from American Samoa. We did not detect any genome-wide significant susceptibility loci for T2DM. However, our two-dimensional linkage investigation detected several loci pairs of interest, including 11q22 and 21q21, 9q21 and 11q22, 1p22–p21 and 4p15, and 4p15 and 15q11–q14, with a two-loci maximum LOD score (MLS) greater than 2.00. Most detected individual loci have previously been identified as susceptibility loci for diabetes-related traits. Our two-dimensional linkage results may facilitate the selection of potential candidate genes and molecular pathways for further diabetes studies because these results, besides providing candidate loci, also demonstrate that polygenic effects may play an important role in T2DM. Linkage was detected (p value of 0.005) for variation in adult height on chromosome 9q31, which was reported previously in other populations. Our finding suggests that the 9q31 region may be a strong quantitative trait locus for adult height, which is likely to be of importance across populations

Methylome-wide comparison of human genomic DNA extracted from whole blood and from EBV-transformed lymphocyte cell lines

DNA from Epstein-Barr virus-transformed lymphocyte cell lines (LCLs) has proven useful for studies of genetic sequence polymorphisms. Whether LCL DNA is suitable for methylation studies is less clear. We conduct a genome-wide methylation investigation using an array set with 45 million probes to investigate the methylome of LCL DNA and technical duplicates of WB DNA from the same 10 individuals. We focus specifically on methylation sites that show variation between individuals and, therefore, are potentially useful as biomarkers. The sample correlations for the methylation variable probes ranged from 0.69 to 0.78 for the WB duplicates and from 0.27 to 0.72 for WB vs LCL. To compare the pattern of the methylation signals, we grouped adjacent probes based on their inter-correlations. These analyses showed ∼29 000 and ∼14 000 blocks in WB and LCL, respectively. Merely 31% of the methylated regions detected in WB were detectable in LCLs. Furthermore, we observed significant differences in mean difference between WB and LCL as compared with duplicates of WB (P-value = 2.2 × 10-16). Our study shows that there are substantial differences in the DNA methylation patterns between LCL and WB. Thus, LCL DNA should not be used as a proxy for WB DNA in methylome-wide studies