Biology subjects

Mesut Erzurumluoglu

Publications and source records attributed to Mesut Erzurumluoglu.

3 recordsLinked to original sources

bioRxiv2016

Y chromosome and mitochondrial DNA haplogroups across behavioural traits in children from the general population

ObjectiveTo evaluate the association between Y chromosome and mitochondrial DNA haplogroups and a number of sexually-dimorphic behavioural and psychiatric traits.\n\nMethodsThe study sample included 4,211 males and 4,009 females with mitochondrial DNA haplogroups and 4,788 males with Y chromosome haplogroups who are part of the Avon Longitudinal Study of Parents and Children (ALSPAC). Different subsets of these populations were assessed using the Developmental and Well-being Assessment (DAWBA), Strengths and Difficulties Questionnaire (SDQ), SCDC (Social and Communication Disorder Checklist) and Psychotic Like Symptom Interview (PLIKSi) as measures of behavioural and psychiatric traits. Logistic regression was used to measure the association between haplogroups and the traits above.\n\nResultsWe found that the majority of behavioural traits in our cohort differed between males and females. However, Y chromosome and mitochondrial DNA major haplogroups were not associated with any of the variables. In addition, secondary analyses of Y chromosome and mitochondrial DNA subgroups also showed no association.\n\nConclusionY chromosome and mitochondrial DNA haplogroups are not associated with behavioural and psychiatric traits in a sample representative of the UK population.

Genetics↗

bioRxiv2016

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis

MotivationLD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.\n\nResultsIn this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 177 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies.\n\nAvailability and implementationThe web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/

Bioinformatics↗

bioRxiv2014

Identifying highly-penetrant disease causal mutations using next generation sequencing: Guide to whole process

Recent technological advances have created challenges for geneticists and a need to adapt to a wide range of new bioinformatics tools and an expanding wealth of publicly available data (e.g. mutation databases, software). This wide range of methods and a diversity of file formats used in sequence analysis is a significant issue, with a considerable amount of time spent before anyone can even attempt to analyse the genetic basis of human disorders. Another point to consider is although many possess just enough knowledge to analyse their data, they do not make full use of the tools and databases that are available and also do not know how their data was created. The primary aim of this review is to document some of the key approaches and provide an analysis schema to make the analysis process more efficient and reliable in the context of discovering highly penetrant causal mutations/genes. This review will also compare the methods used to identify highly penetrant variants when data is obtained from consanguineous individuals as opposed to non-consanguineous; and when Mendelian disorders are analysed as opposed to common-complex disorders.

Bioinformatics↗