Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment

Study of EEG rhythms as a contribution in sleep physiology and the pathophysiology of epileptic syndromes

The study of sleep EEG rhythms can provide information on the underlying neurophysiological mechanisms that control and organize normal sleep. The thalamocortical circuit and the reticular activating system are two neural circuits with central role in this function. These systems are involved in the pathophysiology of epilepsy, for example in the genesis of spike-wave complexes from the mechanisms producing sleep spindles, as well as the temporal relationship between epileptic phenomena and periods of arousal fluctuations.In the first part of the thesis a tool for the clustering of similar EEG periods based on the spectral contents as provided by time-frequency analysis was developed. This tool facilitates the analysis of sleep architecture. As an application example, hypnograms of 10 normal sleep recordings based on standard scoring and based on scoring derived from matching sleep stages with groups identified by the clustering algorithm were compared. The algorithm is able to identify the different sleep stage and the two methods are in very good agreement based on kappa coefficient (0,61).In the second part of the thesis a methodology of analysis of interactions between EEG elements is presented. This is based on time-frequency analysis and allows the estimation of statistical significance of the changes of brain rhythms that emerge around an EEG element. This method was used on the analysis of long-term interaction between K complexes and sleep spindles, which was based on older observations on studies of micro-fluctuations on the level of arousal following evoked K complexes. Analysis on 2401 EEG elements was performed, where a short-term reduction of sleep spindle activity was shown, 2 – 3 s following K complexes, which was attributed on sleep spindle periodicity. No statistically significant long term reduction on the period 10 – 15 s following spontaneous K complexes was found. Based on these results, it seems that single spontaneous K complexes that do not lead to microarousals do not have long-term effects on the level of arousal.Finally, the variability of fast sleep spindle topography was examined, using amplitude analysis of the first, middle and last peak. In 722 sleep spindles from 6 subjects, groups of spindles with significant difference from the mean topography were found. Differences between peaks show movements that, although usually following a parietal to centrofrontal direction, include groups of spindles with movements in both left – right axis and frontal to posterior directions. This variability may be related to the concept of local spindles that are reported on intracranial and MEG recordings, as well as the attributes of “core” and “matrix” thalamocortical circuits.The three analysis techniques which were developed can be used for the study of sleep rhythms fluctuations both on a macro- and a micro- architectural level, providing insights on the function of arousal systems. The identification of topographical patterns can be used to study synchronization and function of underlying neural circuits, like the thalamocortical system during the creation spindles during sleep and spike-wave discharges during absence epilepsy.Η μελέτη των εγκεφαλογραφικών ρυθμών του ύπνου μπορεί να δώσει πληροφορίες για τους υποκείμενους νευροφυσιολογικούς μηχανισμούς που ελέγχουν και οργανώνουν τον φυσιολογικό ύπνο. Το θαλαμοφλοιικό κύκλωμα και τα συστήματα εγρήγορσης του δικτυωτού σχηματισμού είναι δυο νευρικά κυκλώματα με κεντρικό ρόλο σε αυτή τη λειτουργία. Τα συστήματα αυτά εμπλέκονται στην παθοφυσιολογία της επιληψίας, με χαρακτηριστικό παράδειγμα τη γένεση των συμπλεγμάτων αιχμής-κύματος από τους μηχανισμούς παραγωγής των ατράκτων του ύπνου, καθώς και τη χρονική σχέση των επιληπτικών φαινομένων με περιόδους μεταβολών της εγρήγορσης.Στο πρώτο τμήμα της διατριβής αναπτύχθηκε ένα εργαλείο για την ομαδοποίηση παρόμοιων περιόδων του εγκεφαλογραφήματος με βάση το φασματικό περιεχόμενο όπως προκύπτει από την ανάλυση χρόνου-συχνοτήτων. Με το εργαλείο αυτό γίνεται ευκολότερη η ανάλυση της αρχιτεκτονικής του ύπνου. Ως παράδειγμα εφαρμογής, έγινε σύγκριση των υπνογραμμάτων από 10 φυσιολογικούς ύπνους με βάση την κλασσική σταδιοποίηση και με βάση τη σταδιοποίηση που προκύπτει από την αντιστοίχιση των σταδίων με τις ομάδες που αναγνωρίζει ο αλγόριθμος συσταδοποίησης. Ο αλγόριθμος είναι σε θέση να αναγνωρίσει τα διαφορετικά στάδια του ύπνου και οι δυο μέθοδοι παρουσιάζουν πολύ καλή συμφωνία μεταξύ τους με βάση τον συντελεστή κάππα (0,61).Στο δεύτερο τμήμα της διατριβής παρουσιάζεται μια μεθοδολογία ανάλυσης της αλληλεπίδρασης στοιχείων του εγκεφαλογραφήματος με βάση την μελέτη χρόνου-συχνοτήτων που επιτρέπει την εκτίμηση της στατιστικής σημαντικότητας των μεταβολών των ρυθμών που προκύπτουν γύρω από ένα εγκεφαλογραφικό συμβάν. Η μέθοδος αυτή χρησιμοποιήθηκε για την ανάλυση της μακροπρόθεσμης αλληλεπίδρασης συμπλεγμάτων Κ και ατράκτων του ύπνου, που στηρίχθηκε πάνω σε παλαιότερες παρατηρήσεις από μελέτες μικρο-μεταβολών του επιπέδου εγρήγορσης μετά από προκλητά συμπλέγματα Κ. Πραγματοποιήθηκε ανάλυση σε 2401 εγκεφαλογραφικά συμβάντα, όπου αναδείχθηκε μια βραχυπρόθεσμη μείωση της δραστηριότητας των ατράκτων 2 – 3 s μετά από συμπλέγματα Κ η οποία αποδόθηκε στην περιοδικότητα των ατράκτων, ενώ δεν αναδείχθηκε στατιστικά σημαντική μακροπρόθεσμη μεταβολή στο διάστημα 10 – 15 s μετά τα αυθόρμητα συμπλέγματα Κ. Με βάση αυτά τα αποτελέσματα φαίνεται ότι τα μονήρη αυθόρμητα συμπλέγματα Κ που δεν οδηγούν σε μικροαφύπνιση δεν έχουν μακροπρόθεσμη επίδραση στο επίπεδο εγρήγορσης.Τέλος, μελετήθηκε η ποικιλομορφία της τοπογραφίας των γρήγορων ατράκτων με ανάλυση του δυναμικού της πρώτης, της μεσαίας και της τελευταίας κορυφής. Σε 722 ατράκτους από 6 υποκείμενα διαπιστώθηκε ότι υπάρχουν ομάδες ατράκτων που διαφέρουν σημαντικά από το μέσο όρο. Οι διαφορές μεταξύ των τριών κορυφών αναδεικνύουν μετακινήσεις που αν και συνήθως ακολουθούν μια κατεύθυνση από βρεγματικά προς κεντρικά-μετωπιαία, περιλαμβάνουν ομάδες ατράκτων με μετακινήσεις τόσο στον άξονα αριστερά – δεξιά, όσο και από μπροστά προς τα πίσω. Η ποικιλομορφία αυτή μπορεί να σχετίζεται με την έννοια των τοπικών ατράκτων που έχουν αναφερθεί σε ενδοκράνιες καταγραφές και μαγνητοεγκεφαλογραφήματα, καθώς και με τις ιδιότητες των “core” και “matrix” θαλαμοφλοιικών κυκλωμάτων.Οι τρεις τεχνικές ανάλυσης που αναπτύχθηκαν μπορούν να χρησιμοποιηθούν για τη μελέτη των μεταβολών στους ρυθμούς του ύπνου τόσο σε επίπεδο μάκρο- όσο και μίκρο-αρχιτεκτονικής, παρέχοντας πληροφορίες για τη λειτουργία των συστημάτων εγρήγορσης. Με την αναγνώριση τοπογραφικών προτύπων μπορεί να μελετηθεί ο συγχρονισμός και η λειτουργία των υποκείμενων νευρικών κυκλωμάτων, όπως το θαλαμοφλοιικό σύστημα κατά την δημιουργία ατράκτων στον ύπνο και συμπλεγμάτων αιχμής-κύματος στην αφαιρετική επιληψία

An intra-K-complex oscillation with independent and labile frequency and topography in NREM sleep

NREM sleep is characterized by K-complexes (KCs), over the negative phase of which we identified brief activity in the theta range. We recorded high resolution EEG of whole-night sleep from 7 healthy volunteers and visually identified 2nd and 3rd stage NREM spontaneous KCs. We identified 3 major categories: a) KCs without intra-KC-activity (iKCa), b) KCs with non-oscillatory iKCa, and c) KCs with oscillatory iKCa. The latter group of KCs with intra-KC-oscillation (iKCo), was clustered according to the duration of the iKCo. iKCa was observed in most KCs (1150/1522, 75%). iKCos with 2, 3 and 4 waves were observed in 52% (786/1522) of KCs in respective rates of 49% (386/786), 44% and 7%. Successive waves of iKCos showed on average a shift of their maximal amplitude in the anterio-posterior axis, while the average amplitude of the slow KC showed no spatial shift in time. The iKCo spatial shift was accompanied by transient increases in instantaneous frequency from the theta band towards the alpha band, followed by decreases to upper theta. The study shows that the KC is most often concurrently accompanied by an independent brief iKCo exhibiting topographical relocation of amplitude maxima with every consecutive peak and transient increases in frequency. The iKCo features are potentially reflecting arousing processes taking place during the KC

Spindle Power Is Not Affected after Spontaneous K-Complexes during Human NREM Sleep

<div><p>K-complexes and sleep spindles often grouped together characterize the second stage of NREM sleep and interest has been raised on a possible interaction of their underlying mechanisms. The reported inhibition of spindles power for about 15 seconds following evoked K-complexes has implications on their role in arousal. Our objective was to assess this inhibition following spontaneous K-complexes. We used time-frequency analysis of spontaneous K-complexes selected from whole-night EEG recordings of normal subjects. Our results show that spindles are most often observed at the positive phase following the peak of a spontaneous KC (70%). At latencies of 1–3 s following the peak of the K-complex, spindles almost disappear. Compared to long-term effects described for evoked KCs, sleep spindle power is not affected by spontaneous KCs for latencies of 5–15 s. Observation of the recurrence rate of sporadic spindles suggests that the reduction of power at 1–3 s most likely reflects a refractory period of spindles lasting for 1–2 s, rather than an effect of KCs. These results suggest that the mechanisms underlying spontaneous KCs do not affect spindle power as in the case of evoked KCs.</p> </div

All graphs show Spindle Band Power developing over time: Raster images composed of individual time-frequency plots of EEG power near the frequencies of each subject's individual spindle spectral frequency band, for 15 s before and after each event (sporadic spindles in A and KCs in B–D).

<p>Average power change is shown below each raster. <b>A1–2:</b> Spindles as reference events (at time zero). In the y-axis spindle event successive number; all averaged in A2. <b>B1–2:</b> KCs as reference events, spindle data sorted by KC group (from top to bottom: KC₀₀, KC₀₁, KC₁₀, KC₁₁); all averaged in B2. <b>C1–6:</b> KCs as reference events, spindle data sorted by KCs time of occurrence during the night and separated in successive sleep cycles; data from cycles 1–5 averaged in C2–C6 respectively. <b>D1–3:</b> KCs as reference events, spindles data sorted by the amplitude of KCs negative peak. D2 and D3 average data for the relatively larger and smaller KCs respectively. Relative absence of spindles is prominent 2–3 s after the negative peak (B1,C1,D1) and a relative long-term (10–15 s) reduction in their rate of appearance is shown for the about 80 top amplitude-sorted KCs (D1–3). All images, from subject 1.</p

Average spectrogram (left), event-related spectral perturbation (middle) and significant changes (right) as in Fig. 3 but for subject 2.

<p>Average spectrogram (left), event-related spectral perturbation (middle) and significant changes (right) as in Fig. 3 but for subject 2.</p

Descriptive Summary of Sleep Patterns.

<p>Sleep patterns for 7 subjects. TSP: Total Sleep Period, TST: Total Sleep Time, SE: Sleep Efficiency, WASO: Wakefulness after sleep onset, NREM1–4, REM, MA: Minutes in each sleep stage and percentage relative to TST, KCs and spindles included in the study and percentage relative to total number of events included.</p

Average spectrogram (left), event-related spectral perturbation (middle) and significant changes (right) for a time period 15 s before and 25 s after the negative peak of KCs sorted by group (KC₀₀, KC₀₁, KC₁₀, KC₁₁ in rows 1–4 respectively) and the negative middle peak for sporadic spindles (in 5th row) of subject 1.

<p>Average spectrogram (left), event-related spectral perturbation (middle) and significant changes (right) for a time period 15 s before and 25 s after the negative peak of KCs sorted by group (KC₀₀, KC₀₁, KC₁₀, KC₁₁ in rows 1–4 respectively) and the negative middle peak for sporadic spindles (in 5th row) of subject 1.</p