A cautionary note on the impact of protocol changes for Genome-Wide Association SNP x SNP Interaction studies: an example on ankylosing spondylitis

Genome-wide association interaction (GWAI) studies have increased in popularity. Yet to date, no standard protocol exists. In practice, any GWAI workflow involves making choices about quality control strategy, SNP filtering, linkage disequilibrium (LD) pruning, analytic tool to model or to test for genetic interactions. Each of these can have an impact on the final epistasis findings and may affect their reproducibility in follow-up analyses. Choosing an analytic tool is not straightforward, as different such tools exist and current understanding about their performance is based on often very particular simulation settings. In the present study, we wish to create awareness for the impact of (minor) changes in a GWAI analysis protocol can have on final epistasis findings. In particular, we investigate the influence of marker selection and marker prioritization strategies, LD pruning and the choice of epistasis detection analytics on study results, giving rise to 8 GWAI protocols. Discussions are made in the context of the ankylosing spondylitis (AS) data obtained via the Wellcome Trust Case Control Consortium (WTCCC2). As expected, the largest impact on AS epistasis findings is caused by the choice of marker selection criterion, followed by marker coding and LD pruning. In MB-MDR, co-dominant coding of main effects is more robust to the effects of LD pruning than additive coding. We were able to reproduce previously reported epistasis involvement of HLA-B and ERAP1 in AS pathology. In addition, our results suggest involvement of MAGI3 and PARK2, responsible for cell adhesion and cellular trafficking. Gene Ontology (GO) biological function enrichment analysis across the 8 considered GWAI protocols also suggested that AS could be associated to the Central Nervous System (CNS) malfunctions, specifically, in nerve impulse propagation and in neurotransmitters metabolic processes

Incorporation of Biological Knowledge into the Study of Gene-Environment Interactions

A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine

Improving CNV detection from short-read MPS data in neuromuscular disorders

Neuromuscular disorders (NMD) are highly heterogenic with around 1000 reported different subtypes. Most are genetic in origin, and some 500 genes are currently identified to cause NMDs. Massively parallel sequencing (MPS) approaches have been widely used to increase the cost-effectiveness and diagnostic yield in the work-up of the genetic molecular diagnosis and to speed up the process. Copy number variants (CNVs), deletions and duplications larger than 50 base pairs, explain approximately 10% of the Mendelian disorders. No best practices pipelines have been developed yet for CNV analysis from MPS data. Therefore, the detection and verification of CNV findings has often involved complementary methods, such as array comparative genomic hybridization (array CGH), multiplex ligation-dependent probe amplification (MLPA) and quantitative PCR approaches. Recently, various CNV detection programs have been developed, but for widely different types of designated research settings, which complicates choosing the correct approach for NMDs. These individual programs have generally exhibited less than ideal sensitivity and specificity for CNV detection. Our aim was to develop a comprehensive pipeline for the detection and annotation of CNVs with high accuracy from targeted gene panel sequencing and whole exome sequencing (WES) data of patients with NMDs. Four different CNV analysis programs were chosen for this study: CoNIFER, XHMM, ExomeDepth and CODEX. The targeted gene panel MYOcap includes 349 genes for myopathic disorders and MNDcap 302 genes for neurogenic disorders in their current panel versions. 2359 samples were sequenced with MYOcap, 942 samples with MNDcap and 262 samples with WES. This included for the targeted gene panels 24 positive control samples with previously characterized CNVs and 31 negative control samples with certain genes verified to not have CNVs. A detection sensitivity of 100% and specificity of 100% were reached for these control samples. Previously undetected CNVs from MYOcap or MNDcap sequenced samples were verified as true positive detections in 36 cases with MLPA, PCR or array CGH, and eight CNVs were verified as false positive detections. These and the positive control samples were utilized in validation of a predictive logistic regression model. In silico CNV generation into MYOcap sequenced samples provided 18,677 specific and 3892 unspecific CNV detections to initially train the model. The model was trained to differentiate true positive detections from false positive detections in order to increase the specificity of the CNV detection pipeline. The advantage of using four different CNV detection programs compared to using them individually, or with any other combination, was demonstrated by CNV detection sensitivity from the set of in silico CNVs. The predictive model with variables from all four programs provided the highest sensitivity (96.6%) and specificity (87.5%) for predicting CNV detections correctly, indicating an accuracy of 95.5% (95% CI 87.3–99.1%). The CNV detection pipeline together with the predictive model was validated for WES samples with control samples with 235 previously characterized CNVs. For CNVs spanning at least three exons, the detection sensitivity was 97.3% and the sensitivity of the predicative model was 99.3% after adjusting the model threshold for WES data. The CNV annotation platform cnvScan was expanded to contain the most recent CNV population databases as well as in-house CNV databases for all the sequenced sample sets. CNV detection results were filtered by < 1% frequency with reciprocal overlap of 90% in the common CNV population databases, with both it and < 5% frequency with 50% reciprocal overlap in the in-house CNV database, and by the true positive prediction with the model. These procedures significantly decreased the workload (with 3–13% of the original CNV detections preserved) in evaluating the CNVs further regarding clinical significance. The added value, i.e. the additional diagnostic yield from CNVs for both the targeted gene panel sequenced samples and WES samples was estimated to be 1.9%. Altogether 39 final genetic diagnoses were solved with these CNV findings. In addition, 18 patient cases had a likely pathogenic finding, and five had a heterozygous CNV likely pathogenic for a recessive disease without association to the patient’s phenotype. The clarified cases included six different DMD deletions or duplications causing dystrophinopathies. In three sequenced familial cases, the detected CNVs in CACNA1A, SGCD and TTN genes co-segregated with the disease. One case had two separate genetic diseases, tibial muscular dystrophy (TMD) and BMD, caused by the founder mutation FINmaj in the gene TTN and a deletion in DMD. Some of the solved cases had novel findings: the second ever reported large intragenic deletion in NEB causing dominant disease, and the first CNV, an intragenic deletion, in TIA1 in a patient diagnosed with Welander distal myopathy (WDM). Some of the genes associated with NMDs are challenging to analyze from short-read sequencing data due to homology or repetitive regions. An additional script was thus written to differentiate copy numbers of the highly homologous genes, SMN1 and SMN2. Two SMN1/SMN2 copy number 0/3 control cases were successfully recognized, and five cases were identified with a possible exon 7 conversion in SMN1 and a compatible spinal muscular atrophy phenotype. The latter findings were considered likely pathogenic and are awaiting further validation on the genomic level. Comparison of CNV detections within the in-house CNV database revealed divergences in the CNV detections within the triplicate repetitive region of NEB with potentially clinically significant changes. One array CGH validated change correlated well with the nemaline rod pathology observed in the patient. CNV analysis utilizing MPS data from targeted gene panels and WES samples provided increased diagnostic yield as reported also in other studies on NMDs. Our multi-algorithm and -platform approach decreased the workload in variant analysis and provided more insight into the many difficult to analyze genomic regions involved in NMDs. In the future, whole genome sequencing and long-read sequencing will likely provide higher resolution for CNV detections and reveal an even wider spectrum of structural genomic variants, together with other emerging comprehensive methods, such as optical mapping.Lihastaudit ovat hyvin heterogeenisiä, ja niistä on kuvattu noin tuhat alatyyppiä. Suurin osa on perinnöllisiä tauteja, ja tähän mennessä on tunnistettu noin 500 eri lihastauteja aiheuttavaa geeniä. Massiivista rinnakkaissekvensointia (MPS) on käytetty laajalti perinnöllisten tautien diagnostisen prosessin nopeuttamiseksi, kustannustehokkuuden parantamiseksi ja lopullisen geeniperäisen diagnoosin saavuttamiseksi. Kopiolukumuutokset, yli 50 emäsparin deleetiot tai duplikaatiot, aiheuttavat arviolta 10 % Mendelin mukaisesti periytyvistä taudeista. Kopiolukumuutosten havaitsemiseen sekvensointidatasta ei ole vielä kehitetty yleisesti hyväksyttyjä ja suositeltuja käytänteitä. Kopiolukumuutosten havaitsemiseksi ja varmistamiseksi käytetäänkin usein täydentäviä menetelmiä, kuten vertaileva genominen hybridisaatio sirulla (aCGH), rinnastettu ligaatio-riippuvainen alukemonistus (MLPA) ja kvantitatiivinen PCR. Kopiolukumuutosten havaitsemiseen sekvensointidatasta on kehitetty useita työkaluja vaihtelevissa tutkimusasetelmissa, mikä hankaloittaa oikean lähestymistavan valitsemista lihastaudeille. Yksittäisten ohjelmien on todettu tuottavan usein epätäsmällisiä ja herkkyydeltään vaihtelevia tai riittämättömiä havaintoja. Tämän tutkimuksen tavoitteena oli kehittää kattava menetelmä kopiolukumuutosten havaitsemiseen ja annotointiin suurella tarkkuudella kohdennetun geenipaneelin ja koko eksomin (WES) sekvensointidatasta lihastautipotilailta. Tutkimukseen valittiin neljä kopiolukumuutosanalyysin työkalua: CoNIFER, XHMM, ExomeDepth ja CODEX. Kohdennetuista geenipaneeleista MYOcap kattaa 349 geeniä lihaspainotteisille taudeille ja MNDcap 302 hermopainotteisille taudeille nykyisissä paneeliversioissa. MYOcap:lla sekvensointiin 2359 näytettä, MNDcap:lla 942 ja WES:llä 262. Kohdennetuilla geenipaneeleilla sekvensointiin 24 positiivista kontrollinäytettä, joissa on aiemmin tunnistettu kopiolukumuutos, ja 31 negatiivista kontrollinäytettä, joissa tietyt geenit oli varmistettu kopiolukumuutoksia sisältämättömiksi. Kontrollinäytteille saavutettiin kehittämällämme menetelmällä 100 % havaitsemisherkkyys ja 100 % tarkkuus. MYOcap:lla tai MNDcap:lla sekvensoiduista näytteistä havaituista kopiolukumuutoksista 36 varmistettiin todellisiksi havainnoiksi MLPA:lla, PCR:lla tai aCGH:llä ja kahdeksan varmistettiin vääriksi positiivisiksi. Nämä ja positiiviset kontrollinäytteet sisällytettiin logistiseen regressioon perustuvan tilastollisen mallin validointiin. Erottelumallin kehitysvaiheessa MYOcap-sekvensoituihin näytteisiin tehtiin in silico kopiolukumuutoksia, mikä tuotti 18677 spesifiä ja 3892 ei-spesifiä kopiolukumuutoshavaintoa mallinnukseen. Malli kehitettiin erottelemaan todelliset kopiolukumuutoshavainnot vääristä positiivista havainnoista havaintomenetelmän tarkkuuden lisäämiseksi. Neljän ohjelman havaintojen käyttämisen paremmuus verrattuna ohjelmien käyttämiseen yksittäin tai muilla yhdistelmillä todennettiin in silico kopiolukumuutosten havaitsemisen herkkyyden tuloksilla. Erottelumalli, jossa oli muuttujia kaikilta neljältä ohjelmalta, saavutti korkeimman herkkyyden (96,6 %), täsmällisyyden (87,5 %) ja tarkkuuden 95,5 % (95 % CI 87,3–99,1 %) kopiolukumuutosten erottelulle. Kopiolukumuutoshavaitsemismenetelmä ja erottelumalli validoitiin WES-kontrollinäytteillä, joissa oli 235 aiemmin tunnistettua kopiolukumuutosta. Havaitsemisherkkyys kopiolukumuutoksille, jotka sisältävät vähintään kolme eksonia oli 97,3 %, ja erottelumallin herkkyys oli 99,3 % kunhan mallin arviointiraja oli uudelleensäädetty WES-datalle. Kopiolukumuutosten annotaatiotyökalu cnvScan laajennettiin sisältämään uusimmat kopiolukumuutospopulaatiotietokannat ja talonsisäinen kopiolukumuutostietokanta kaikista sekvensointinäytejoukoista. Alkuperäiset kopiolukumuutoshavainnot neljältä ohjelmalta suodatettiin 1 % enimmäisyleisyyden ja vastavuoroisen 90 % muutoksen kattamisen vaatimuksella yleisissä kopiolukumuutospopulaatiotietokannoissa, tällä sekä 5 % enimmäisyleisyyden ja vastavuoroisen 50 % muutoksen kattamisen vaatimuksella talonsisäisessä tietokannassa, ja lisäksi erottelumallilla todellisiin havaintoihin. Nämä toimenpiteet vähensivät merkittävästi työmäärää kliinisen merkityksen arvioinnille kopiolukumuutoksille säästäen 3–13 % alkuperäisistä havainnoista. Lisääntyneiden diagnoosien määrä kopiolukumuutoshavaintojen myötä sekä kohdennetuilla geenipaneeleilla että WES-sekvensoiduilla näytteillä oli noin 1,9 %. Kopiolukumuutoshavainnoilla saavutettiin 39 lopullista geneettistä diagnoosia potilaille. Lisäksi 18:lla tutkitulla oli todennäköisesti patogeeninen löydös, ja viidellä tutkitulla havaittiin heterotsygoottinen kopiolukumuutos, jonka arvioitiin olevan patogeeninen peittyvästi periytyvän taudin variantti ilman yhteyttä potilaan taudinkuvaan. Selvitettyihin tapauksiin sisältyi kuusi eri DMD-geenissä olevaa deleetiota tai duplikaatiota, jotka aiheuttivat dystrofinopatioita. Kolme potilasta, joilla oli oireisia perheenjäseniä, sekvensointiin perhetapauksina, ja havaitut kopiolukumuutokset geeneissä CACNA1A, SGCD ja TTN segregoituivat yhdessä taudin kanssa. Yhdellä tutkitulla havaittiin kaksi perinnöllistä tautia, tibiaalinen lihasdystrofia (TMD) ja BMD, joiden aiheuttajina olivat perustajamutaatio FINmaj TTN-geenissä ja deleetio DMD-geenissä. Osalla selvitetyistä tapauksista oli ennen havaitsemattomia löydöksiä: NEB-geenissä toinen koskaan raportoitu iso geeninsisäinen deleetio, joka aiheuttaa vallitsevasti periytyvän taudin, sekä TIA1-geenin geeninsisäinen deleetio, joka on ensimmäinen havaittu kopiolukumuutos TIA1:ssä Welanderin distaalimyopatiaa (WDM) sairastavalla potilaalla. Jotkin geeneistä, jotka on liitetty lihastauteihin, ovat haastavia analysoitavia lyhytlukuisesta sekvensointidatasta homologian ja toistojaksojen takia. Hyvin homologisille geeneille SMN1 ja SMN2 kehitettiin erillinen ohjelma erottelemaan geenien kopiolukumäärät. Kaksi kontrollitapausta tunnistettiin onnistuneesti SMN1 ja SMN2 kopiolukumäärillä 0 ja 3, ja lisäksi tunnistettiin viisi tapausta, joilla on mahdollisesti eksonin 7 konversio SMN1:ssä ja yhteensopiva spinaalinen lihasatrofia. Jälkimmäiset löydökset luokiteltiin todennäköisesti patogeeniseksi, ja ne odottavat genomista lisävarmistusta. Kopiolukumuutoshavaintojen vertailu NEB-geenin triplikaattitoistoalueella talonsisäisessä tietokannassa paljasti eroavaisuuksia, joilla on potentiaalisesti kliinisesti merkitystä. Yksi aCGH:llä varmistettu muutos korreloi selkeästi nemaliinisauvakappalepatologian kanssa, joka potilaalla oli havaittu. Kopiolukumuutoshavainnointi käyttäen sekvensointidataa kohdennetusta geenipaneelista tai WES-näytteistä lisäsi diagnoosien määrää kuten aiemmissa vastaavissa tutkimuksissa lihastaudeille. Käyttämämme usean algoritmin ja alustan lähestymistapa vähensi varianttianalyysin työmäärää ja tarjosi lisää tietoa useista hankalasti analysoitavista genomisista alueista, jotka on liitetty lihastauteihin. Tulevaisuudessa koko genomin sekvensointi ja pitkälukuinen sekvensointi tarjonnevat paremman resoluution kopiolukumuutoksille ja paljastavat enemmän rakenteellisia genomin muutoksia yhdessä muiden kehitteillä olevien kattavien menetelmien kanssa, kuten optinen kartoitus

High Dimensional Analysis of Genetic Data for the Classification of Type 2 Diabetes Using Advanced Machine Learning Algorithms

The prevalence of type 2 diabetes (T2D) has increased steadily over the last thirty years and has now reached epidemic proportions. The secondary complications associated with T2D have significant health and economic impacts worldwide and it is now regarded as the seventh leading cause of mortality. Therefore, understanding the underlying causes of T2D is high on government agendas. The condition is a multifactorial disorder with a complex aetiology. This means that T2D emerges from the convergence between genetics, the environment and diet, and lifestyle choices. The genetic determinants remain largely elusive, with only a handful of identified candidate genes. Genome-wide association studies (GWAS) have enhanced our understanding of genetic-based determinants in common complex human diseases. To date, 120 single nucleotide polymorphisms (SNPs) for T2D have been identified using GWAS. Standard statistical tests for single and multi-locus analysis, such as logistic regression, have demonstrated little effect in understanding the genetic architecture of complex human diseases. Logistic regression can capture linear interactions between SNPs and traits however it neglects the non-linear epistatic interactions that are often present within genetic data. Complex human diseases are caused by the contributions made by many interacting genetic variants. However, detecting epistatic interactions and understanding the underlying pathogenesis architecture of complex human disorders remains a significant challenge. This thesis presents a novel framework based on deep learning to reduce the high-dimensional space in GWAS and learn non-linear epistatic interactions in T2D genetic data for binary classification tasks. This framework includes traditional GWAS quality control, association analysis, deep learning stacked autoencoders, and a multilayer perceptron for classification. Quality control procedures are conducted to exclude genetic variants and individuals that do not meet a pre-specified criterion. Logistic association analysis under an additive genetic model adjusted for genomic control inflation factor is also conducted. SNPs generated with a p-value threshold of 10−2 are considered, resulting in 6609 SNPs (features), to remove statistically improbable SNPs and help minimise the computational requirements needed to process all SNPs. The 6609 SNPs are used for epistatic analysis through progressively smaller hidden layer units. Latent representations are extracted using stacked autoencoders to initialise a multilayer feedforward network for binary classification. The classifier is fine-tuned to discriminate between cases and controls using T2D genetic data. The performance of a deep learning stacked autoencoder model is evaluated and benchmarked against a multilayer perceptron and a random forest learning algorithm. The findings show that the best results were obtained using 2500 compressed hidden units (AUC=94.25%). However, the classification accuracy when using 300 compressed neurons remains reasonable with (AUC=80.78%). The results are promising. Using deep learning stacked autoencoders, it is possible to reduce high-dimensional features in T2D GWAS data and learn non-linear epistatic interactions between SNPs while enhancing overall model performance for binary classification purposes

Sequence based analysis of microbial communities in anaerobic digestion reveal key organisms and rate limiting hydrolysis pathways in the methanisation of whisky distillation waste

Anaerobic digestion is the breakdown of an organic material into a methane rich biogas through the action of a complex microbial community. This research primarily uses sequence based technologies such as gene amplicon sequencing, qPCR, metagenomics and metatranscriptomics to investigate population composition, dynamics and functionality in these communities when fed a liquid waste product from malt whisky distillation termed pot ale. Initial batch tests complemented with gene amplicon sequencing explored the potential of used wastewater dewatering polymer, a cationic polyacrylamide, and a community inoculum. Communities produced from this material showed production of biogas and methane and increased abundance in carbohydrate degrading organisms such as Bacterodia. Poor methane yields could be attributed to the inhibitory effects of the acrylamide containing inoculum so subsequent digestion of pot ale used an alternative inoculum. To quantitatively determine how the microbial community was changing, a microbial spike-in method was developed. This spike-in method involved characterisation of an organism not typically found in anaerobic digestion, Sulfolobus solfataricus, and showed to normalise DNA extraction procedures in addition to population quantification. Further pot ale anaerobic digestion was performed in a custom-built lab scale system over a period of six months. This revealed that methane production from pot ale occurs in two distinct stages, the first fast and the second slow. DNA samples were collected over this time and metagenomic sequencing was used to reconstruct key metabolic pathways which revealed a functionally diverse, and functionally robust community. Genes specifically related to the two stage degradation of pot ale digestion were investigated using metatranscriptomics which revealed initial methane production was caused by hydrolysis of malto-oligosaccharides by Clostridia followed by the second, rate limiting, hydrolysis of beta-glucans by Bacterodia. By identifying this rate limiting enzymatic hydrolysis step, this opens the door to feedstock specific enzyme supplementation to increase digestion efficiency