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

Author

Stevelink, R, Campbell, C, Chen, S, Abou-Khalil, B, Adesoji, OM, Afawi, Z, Amadori, E, Anderson, A, Anderson, J, Andrade, DM, Annesi, G, Auce, P, Avbersek, A, Bahlo, M, Baker, MD, Balagura, G, Balestrini, S, Barba, C, Barboza, K, Bartolomei, F, Bast, T, Baum, L, Baumgartner, T, Baykan, B, Bebek, N, Becker, AJ, Becker, F, Bennett, CA, Berghuis, B, Berkovic, SF, Beydoun, A, Bianchini, C, Bisulli, F, Blatt, I, Bobbili, DR, Borggraefe, I, Bosselmann, C, Braatz, V, Bradfield, JP, Brockmann, K, Brody, LC, Buono, RJ, Busch, RM, Caglayan, H, Campbell, E, Canafoglia, L, Canavati, C, Cascino, GD, Castellotti, B, Catarino, CB, Cavalleri, GL, Cerrato, F, Chassoux, F, Cherny, SS, Cheung, C-L, Chinthapalli, K, Chou, I-J, Chung, S-K, Churchhouse, C, Clark, PO, Cole, AJ, Compston, A, Coppola, A, Cosico, M, Cossette, P, Craig, JJ, Cusick, C, Daly, MJ, Davis, LK, de Haan, G-J, Delanty, N, Depondt, C, Derambure, P, Devinsky, O, Di Vito, L, Dlugos, DJ, Doccini, V, Doherty, CP, El-Naggar, H, Elger, CE, Ellis, CA, Eriksson, JG, Faucon, A, Feng, Y-CA, Ferguson, L, Ferraro, TN, Ferri, L, Feucht, M, Fitzgerald, M, Fonferko-Shadrach, B, Fortunato, F, Franceschetti, S, Franke, A, French, JA, Freri, E, Gagliardi, M, Gambardella, A, Geller, EB, Giangregorio, T, Gjerstad, L, Glauser, T, Goldberg, E, Goldman, A, Granata, T, Greenberg, DA, Guerrini, R, Gupta, N, Haas, KF, Hakonarson, H, Hallmann, K, Hassanin, E, Hegde, M, Heinzen, EL, Helbig, I, Hengsbach, C, Heyne, HO, Hirose, S, Hirsch, E, Hjalgrim, H, Howrigan, DP, Hucks, D, Hung, P-C, Iacomino, M, Imbach, LL, Inoue, Y, Ishii, A, Jamnadas-Khoda, J, Jehi, L, Johnson, MR, Kalviainen, R, Kamatani, Y, Kanaan, M, Kanai, M, Kantanen, A-M, Kara, B, Kariuki, SM, Kasperaviciute, D, Trenite, DK-N, Kato, M, Kegele, J, Kesim, Y, Khoueiry-Zgheib, N, King, C, Kirsch, HE, Klein, KM, Kluger, G, Knake, S, Knowlton, RC, Koeleman, BPC, Korczyn, AD, Koupparis, A, Kousiappa, I, Krause, R, Krenn, M, Krestel, H, Krey, I, Kunz, WS, Kurki, MI, Kurlemann, G, Kuzniecky, R, Kwan, P, Labate, A, Lacey, A, Lal, D, Landoulsi, Z, Lau, Y-L, Lauxmann, S, Leech, SL, Lehesjoki, A-E, Lemke, JR, Lerche, H, Lesca, G, Leu, C, Lewin, N, Lewis-Smith, D, Li, GH-Y, Li, QS, Licchetta, L, Lin, K-L, Lindhout, D, Linnankivi, T, Lopes-Cendes, I, Lowenstein, DH, Lui, CHT, Madia, F, Magnusson, S, Marson, AG, May, P, McGraw, CM, Mei, D, Mills, JL, Minardi, R, Mirza, N, Moller, RS, Molloy, AM, Montomoli, M, Mostacci, B, Muccioli, L, Muhle, H, Mueller-Schlueter, K, Najm, IM, Nasreddine, W, Neale, BM, Neubauer, B, Newton, CRJC, Noethen, MM, Nothnagel, M, Nuernberg, P, O'Brien, TJ, Okada, Y, Olafsson, E, Oliver, KL, Ozkara, C, Palotie, A, Pangilinan, F, Papacostas, SS, Parrini, E, Pato, CN, Pato, MT, Pendziwiat, M, Petrovski, S, Pickrell, WO, Pinsky, R, Pippucci, T, Poduri, A, Pondrelli, F, Powell, RHW, Privitera, M, Rademacher, A, Radtke, R, Ragona, F, Rau, S, Rees, MI, Regan, BM, Reif, PS, Rhelms, S, Riva, A, Rosenow, F, Ryvlin, P, Saarela, A, Sadleir, LG, Sander, JW, Sander, T, Scala, M, Scattergood, T, Schachter, SC, Schankin, CJ, Scheffer, IE, Schmitz, B, Schoch, S, Schubert-Bast, S, Schulze-Bonhage, A, Scudieri, P, Sham, P, Sheidley, BR, Shih, JJ, Sills, GJ, Sisodiya, SM, Smith, MC, Smith, PE, Sonsma, ACM, Speed, D, Sperling, MR, Stefansson, H, Stefansson, K, Steinhoff, BJ, Stephani, U, Stewart, WC, Stipa, C, Striano, P, Stroink, H, Strzelczyk, A, Surges, R, Suzuki, T, Tan, KM, Taneja, RS, Tanteles, GA, Tauboll, E, Thio, LL, Thomas, GN, Thomas, RH, Timonen, O, Tinuper, P, Todaro, M, Topaloglu, P, Tozzi, R, Tsai, M-H, Tumiene, B, Turkdogan, D, Unnsteinsdottir, U, Utkus, A, Vaidiswaran, P, Valton, L, van Baalen, A, Vetro, A, Vining, EPG, Visscher, F, von Brauchitsch, S, von Wrede, R, Wagner, RG, Weber, YG, Weckhuysen, S, Weisenberg, J, Weller, M, Widdess-Walsh, P, Wolff, M, Wolking, S, Wu, D, Yamakawa, K, Yang, W, Yapici, Z, Yucesan, E, Zagaglia, S, Zahnert, F, Zara, F, Zhou, W, Zimprich, F, Zsurka, G, Ali, QZ, Stevelink, R, Campbell, C, Chen, S, Abou-Khalil, B, Adesoji, OM, Afawi, Z, Amadori, E, Anderson, A, Anderson, J, Andrade, DM, Annesi, G, Auce, P, Avbersek, A, Bahlo, M, Baker, MD, Balagura, G, Balestrini, S, Barba, C, Barboza, K, Bartolomei, F, Bast, T, Baum, L, Baumgartner, T, Baykan, B, Bebek, N, Becker, AJ, Becker, F, Bennett, CA, Berghuis, B, Berkovic, SF, Beydoun, A, Bianchini, C, Bisulli, F, Blatt, I, Bobbili, DR, Borggraefe, I, Bosselmann, C, Braatz, V, Bradfield, JP, Brockmann, K, Brody, LC, Buono, RJ, Busch, RM, Caglayan, H, Campbell, E, Canafoglia, L, Canavati, C, Cascino, GD, Castellotti, B, Catarino, CB, Cavalleri, GL, Cerrato, F, Chassoux, F, Cherny, SS, Cheung, C-L, Chinthapalli, K, Chou, I-J, Chung, S-K, Churchhouse, C, Clark, PO, Cole, AJ, Compston, A, Coppola, A, Cosico, M, Cossette, P, Craig, JJ, Cusick, C, Daly, MJ, Davis, LK, de Haan, G-J, Delanty, N, Depondt, C, Derambure, P, Devinsky, O, Di Vito, L, Dlugos, DJ, Doccini, V, Doherty, CP, El-Naggar, H, Elger, CE, Ellis, CA, Eriksson, JG, Faucon, A, Feng, Y-CA, Ferguson, L, Ferraro, TN, Ferri, L, Feucht, M, Fitzgerald, M, Fonferko-Shadrach, B, Fortunato, F, Franceschetti, S, Franke, A, French, JA, Freri, E, Gagliardi, M, Gambardella, A, Geller, EB, Giangregorio, T, Gjerstad, L, Glauser, T, Goldberg, E, Goldman, A, Granata, T, Greenberg, DA, Guerrini, R, Gupta, N, Haas, KF, Hakonarson, H, Hallmann, K, Hassanin, E, Hegde, M, Heinzen, EL, Helbig, I, Hengsbach, C, Heyne, HO, Hirose, S, Hirsch, E, Hjalgrim, H, Howrigan, DP, Hucks, D, Hung, P-C, Iacomino, M, Imbach, LL, Inoue, Y, Ishii, A, Jamnadas-Khoda, J, Jehi, L, Johnson, MR, Kalviainen, R, Kamatani, Y, Kanaan, M, Kanai, M, Kantanen, A-M, Kara, B, Kariuki, SM, Kasperaviciute, D, Trenite, DK-N, Kato, M, Kegele, J, Kesim, Y, Khoueiry-Zgheib, N, King, C, Kirsch, HE, Klein, KM, Kluger, G, Knake, S, Knowlton, RC, Koeleman, BPC, Korczyn, AD, Koupparis, A, Kousiappa, I, Krause, R, Krenn, M, Krestel, H, Krey, I, Kunz, WS, Kurki, MI, Kurlemann, G, Kuzniecky, R, Kwan, P, Labate, A, Lacey, A, Lal, D, Landoulsi, Z, Lau, Y-L, Lauxmann, S, Leech, SL, Lehesjoki, A-E, Lemke, JR, Lerche, H, Lesca, G, Leu, C, Lewin, N, Lewis-Smith, D, Li, GH-Y, Li, QS, Licchetta, L, Lin, K-L, Lindhout, D, Linnankivi, T, Lopes-Cendes, I, Lowenstein, DH, Lui, CHT, Madia, F, Magnusson, S, Marson, AG, May, P, McGraw, CM, Mei, D, Mills, JL, Minardi, R, Mirza, N, Moller, RS, Molloy, AM, Montomoli, M, Mostacci, B, Muccioli, L, Muhle, H, Mueller-Schlueter, K, Najm, IM, Nasreddine, W, Neale, BM, Neubauer, B, Newton, CRJC, Noethen, MM, Nothnagel, M, Nuernberg, P, O'Brien, TJ, Okada, Y, Olafsson, E, Oliver, KL, Ozkara, C, Palotie, A, Pangilinan, F, Papacostas, SS, Parrini, E, Pato, CN, Pato, MT, Pendziwiat, M, Petrovski, S, Pickrell, WO, Pinsky, R, Pippucci, T, Poduri, A, Pondrelli, F, Powell, RHW, Privitera, M, Rademacher, A, Radtke, R, Ragona, F, Rau, S, Rees, MI, Regan, BM, Reif, PS, Rhelms, S, Riva, A, Rosenow, F, Ryvlin, P, Saarela, A, Sadleir, LG, Sander, JW, Sander, T, Scala, M, Scattergood, T, Schachter, SC, Schankin, CJ, Scheffer, IE, Schmitz, B, Schoch, S, Schubert-Bast, S, Schulze-Bonhage, A, Scudieri, P, Sham, P, Sheidley, BR, Shih, JJ, Sills, GJ, Sisodiya, SM, Smith, MC, Smith, PE, Sonsma, ACM, Speed, D, Sperling, MR, Stefansson, H, Stefansson, K, Steinhoff, BJ, Stephani, U, Stewart, WC, Stipa, C, Striano, P, Stroink, H, Strzelczyk, A, Surges, R, Suzuki, T, Tan, KM, Taneja, RS, Tanteles, GA, Tauboll, E, Thio, LL, Thomas, GN, Thomas, RH, Timonen, O, Tinuper, P, Todaro, M, Topaloglu, P, Tozzi, R, Tsai, M-H, Tumiene, B, Turkdogan, D, Unnsteinsdottir, U, Utkus, A, Vaidiswaran, P, Valton, L, van Baalen, A, Vetro, A, Vining, EPG, Visscher, F, von Brauchitsch, S, von Wrede, R, Wagner, RG, Weber, YG, Weckhuysen, S, Weisenberg, J, Weller, M, Widdess-Walsh, P, Wolff, M, Wolking, S, Wu, D, Yamakawa, K, Yang, W, Yapici, Z, Yucesan, E, Zagaglia, S, Zahnert, F, Zara, F, Zhou, W, Zimprich, F, Zsurka, G, and Ali, QZ

Abstract

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.

Published
2023

3. LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics

Author

Berrandou, T-E, Balding, D, Speed, D, Berrandou, T-E, Balding, D, and Speed, D

Abstract

We present LDAK-GBAT, a tool for gene-based association testing using summary statistics from genome-wide association studies that is computationally efficient, produces well-calibrated p values, and is significantly more powerful than existing tools. LDAK-GBAT takes approximately 30 min to analyze imputed data (2.9M common, genic SNPs), requiring less than 10 Gb memory. It shows good control of type 1 error given an appropriate reference panel. Across 109 phenotypes (82 from the UK Biobank, 18 from the Million Veteran Program, and nine from the Psychiatric Genetics Consortium), LDAK-GBAT finds on average 19% (SE: 1%) more significant genes than the existing tool sumFREGAT-ACAT, with even greater gains in comparison with MAGMA, GCTA-fastBAT, sumFREGAT-SKAT-O, and sumFREGAT-PCA.

Published
2023

4. SNP-based heritability and selection analyses: Improved models and new results

Author

Speed, D, Kaphle, A, Balding, DJ, Speed, D, Kaphle, A, and Balding, DJ

Abstract

Complex-trait genetics has advanced dramatically through methods to estimate the heritability tagged by SNPs, both genome-wide and in genomic regions of interest such as those defined by functional annotations. The models underlying many of these analyses are inadequate, and consequently many SNP-heritability results published to date are inaccurate. Here, we review the modelling issues, both for analyses based on individual genotype data and association test statistics, highlighting the role of a low-dimensional model for the heritability of each SNP. We use state-of-art models to present updated results about how heritability is distributed with respect to functional annotations in the human genome, and how it varies with allele frequency, which can reflect purifying selection. Our results give finer detail to the picture that has emerged in recent years of complex trait heritability widely dispersed across the genome. Confounding due to population structure remains a problem that summary statistic analyses cannot reliably overcome. Also see the video abstract here: https://youtu.be/WC2u03V65MQ.

Published
2022

5. Genome-wide association, prediction and heritability in bacteria with application to Streptococcus pneumoniae

Author

Mallawaarachchi, S, Tonkin-Hill, G, Croucher, NJ, Turner, P, Speed, D, Corander, J, Balding, D, Mallawaarachchi, S, Tonkin-Hill, G, Croucher, NJ, Turner, P, Speed, D, Corander, J, and Balding, D

Abstract

Whole-genome sequencing has facilitated genome-wide analyses of association, prediction and heritability in many organisms. However, such analyses in bacteria are still in their infancy, being limited by difficulties including genome plasticity and strong population structure. Here we propose a suite of methods including linear mixed models, elastic net and LD-score regression, adapted to bacterial traits using innovations such as frequency-based allele coding, both insertion/deletion and nucleotide testing and heritability partitioning. We compare and validate our methods against the current state-of-art using simulations, and analyse three phenotypes of the major human pathogen Streptococcus pneumoniae, including the first analyses of minimum inhibitory concentrations (MIC) for penicillin and ceftriaxone. We show that the MIC traits are highly heritable with high prediction accuracy, explained by many genetic associations under good population structure control. In ceftriaxone MIC, this is surprising because none of the isolates are resistant as per the inhibition zone criteria. We estimate that half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes a quarter of the ceftriaxone MIC heritability. For the within-host carriage duration phenotype, no associations were observed, but the moderate heritability and prediction accuracy indicate a moderately polygenic trait.

Published
2022

6. The Effects of Common Genetic Variation in 96 Genes Involved in Thyroid Hormone Regulation on TSH and FT4 Concentrations

Author

Sterenborg, R.B.T.M., Galesloot, T.E., Teumer, A., Netea-Maier, R.T., Speed, D., Meima, M.E., Visser, Wesley J., Smit, J.W.A., Peeters, R.P., Medici, M., Sterenborg, R.B.T.M., Galesloot, T.E., Teumer, A., Netea-Maier, R.T., Speed, D., Meima, M.E., Visser, Wesley J., Smit, J.W.A., Peeters, R.P., and Medici, M.

Abstract

Contains fulltext : 251756.pdf (Publisher’s version ) (Open Access), OBJECTIVE: While most of the variation in thyroid function is determined by genetic factors, single nucleotide polymorphisms (SNPs) identified via genome-wide association analyses have only explained ~5% to 9% of this variance so far. Most SNPs were in or nearby genes with no known role in thyroid hormone (TH) regulation. Therefore, we performed a large-scale candidate gene study investigating the effect of common genetic variation in established TH regulating genes on serum thyrotropin [thyroid-stimulating hormone (TSH)] and thyroxine (FT4) concentrations. METHODS: SNPs in or within 10 kb of 96 TH regulating genes were included (30 031 TSH SNPs, and 29 962 FT4 SNPs). Associations were studied in 54 288 individuals from the ThyroidOmics Consortium. Linkage disequilibrium-based clumping was used to identify independently associated SNPs. SNP-based explained variances were calculated using SumHer software. RESULTS: We identified 23 novel TSH-associated SNPs in predominantly hypothalamic-pituitary-thyroid axis genes and 25 novel FT4-associated SNPs in mainly peripheral metabolism and transport genes. Genome-wide SNP variation explained ~21% (SD 1.7) of the total variation in both TSH and FT4 concentrations, whereas SNPs in the 96 TH regulating genes explained 1.9% to 2.6% (SD 0.4). CONCLUSION: Here we report the largest candidate gene analysis on thyroid function, resulting in a substantial increase in the number of genetic variants determining TSH and FT4 concentrations. Interestingly, these candidate gene SNPs explain only a minor part of the variation in TSH and FT4 concentrations, which substantiates the need for large genetic studies including common and rare variants to unravel novel, yet unknown, pathways in TH regulation.

Published
2022

7. Assessing the role of rare genetic variants in drug-resistant, non-lesional focal epilepsy

Author

Wolking, S., Moreau, C., Mccormack, M., Krause, R., Krenn, M., Berkovic, S., Cavalleri, G. L., Delanty, N., Depondt, C., Johnson, M. R., Koeleman, B. P. C., Kunz, W. S., Lerche, H., Marson, A. G., O'Brien, T. J., Petrovski, S., Sander, J. W., Sills, G. J., Striano, P., Zara, F., Zimprich, F., Sisodiya, S. M., Girard, S. L., Cossette, P., Avbersek, A., Leu, C., Heggeli, K., Demurtas, R., Willis, J., Speed, D., Sargsyan, N., Chinthapalli, K., Borghei, M., Coppola, A., Gambardella, A., Becker, F., Rau, S., Hengsbach, C., Weber, Y. G., Berghuis, B., Campbell, E., Gudmundsson, L. J., Ingason, A., Stefansson, K., Schneider, R., Balling, R., Auce, P., Francis, B., Jorgensen, A., Morris, A., Langley, S., Srivastava, P., Brodie, M., Todaro, M., Hutton, J., Muhle, H., Klein, K. M., Moller, R. S., Nikanorova, M., Weckhuysen, S., Rener-Primec, Z., Craig, J., and Stefansson, H.

Subjects
0301 basic medicine, Male, Candidate gene, Drug Resistant Epilepsy, Neurology [D14] [Human health sciences], Neurosciences. Biological psychiatry. Neuropsychiatry, Drug resistance, Bioinformatics, Polymorphism, Single Nucleotide, Whole Exome Sequencing, Cohort Studies, 03 medical and health sciences, Epilepsy, 0302 clinical medicine, Exome Sequencing, medicine, Humans, Polymorphism, RC346-429, Gene, Exome sequencing, Research Articles, Genetic Association Studies, Neurologie [D14] [Sciences de la santé humaine], business.industry, General Neuroscience, Genetic variants, Genetic Variation, Single Nucleotide, medicine.disease, DEPDC5, Female, 030104 developmental biology, Cohort, Neurology (clinical), Neurology. Diseases of the nervous system, business, 030217 neurology & neurosurgery, RC321-571, Research Article
Abstract

Annals of Clinical and Translational Neurology 8(7), 1376-1387 (2021). doi:10.1002/acn3.51374, Published by Wiley, Chichester [u.a.]

Published
2021
Full Text
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8. Signatures of TSPAN8 variants associated with human metabolic regulation and diseases

Author

De, T. (Tisham), Goncalves, A. (Angela), Speed, D. (Doug), Froguel, P. (Philippe), NFBC consortium, . (), Gaffney, D. J. (Daniel J.), Johnson, M. R. (Michael R.), Jarvelin, M.-R. (Marjo-Riitta), and Coin, L. J. (Lachlan J. M.)

Subjects
endocrine system
Abstract

Summary Here, with the example of common copy number variation (CNV) in the TSPAN8 gene, we present an important piece of work in the field of CNV detection, that is, CNV association with complex human traits such as ¹H NMR metabolomic phenotypes and an example of functional characterization of CNVs among human induced pluripotent stem cells (HipSci). We report TSPAN8 exon 11 (ENSE00003720745) as a pleiotropic locus associated with metabolomic regulation and show that its biology is associated with several metabolic diseases such as type 2 diabetes (T2D) and cancer. Our results further demonstrate the power of multivariate association models over univariate methods and define metabolomic signatures for variants in TSPAN8.

Published
2021

9. Signatures of TSPAN8 variants associated with human metabolic regulation and diseases

Author

De, T, Goncalves, A, Speed, D, Froguel, P, Gaffney, DJ, Johnson, MR, Jarvelin, M-R, Coin, LJM, De, T, Goncalves, A, Speed, D, Froguel, P, Gaffney, DJ, Johnson, MR, Jarvelin, M-R, and Coin, LJM

Abstract

Here, with the example of common copy number variation (CNV) in the TSPAN8 gene, we present an important piece of work in the field of CNV detection, that is, CNV association with complex human traits such as 1H NMR metabolomic phenotypes and an example of functional characterization of CNVs among human induced pluripotent stem cells (HipSci). We report TSPAN8 exon 11 (ENSE00003720745) as a pleiotropic locus associated with metabolomic regulation and show that its biology is associated with several metabolic diseases such as type 2 diabetes (T2D) and cancer. Our results further demonstrate the power of multivariate association models over univariate methods and define metabolomic signatures for variants in TSPAN8.

Published
2021

12. The biosynthesis of betaine and related compounds in higher plants and fungi

Author

Speed, D. J.

Subjects
572.2
Abstract

A satisfactory synthesis of crystalline betaine aldehyde was achieved and methods were developed for the isolation and characterization of choline, betaine aldehyde, betaine and related compounds. The suitability of these methods (ion-exchange column chromatography and thin-layer chromatography in particular) were demonstrated by their application to the choline oxidase system known to exist in the mitochondria of rat liver. Attempts were made to demonstrate a similar choline oxidase system in plants. Experiments designed to demonstrate the existence of these enzymes by spectrophotometric and polarographic methods in mitochondria extracted from plant sources were largely unsuccessful. The mitochondria were structurally intact and were of high bio chemical integrity. On the isolated occasions when stimulation of respiration occurred when choline was supplied as substrate, the formation of betaine aldehyde or betaine was never confirmed by the characterization techniques which had been developed. The existence of a permeability barrier to the uptake of choline was discounted as an explanation of the 'latency' of the enzymes investigated. The methods employed to overcome such a barrier in mitochondria extracted from rat liver were without effect on the plant systems. Radioactive choline was taken up by mitochondria, from plants and a fraction of the labelled compound became closely associated with the mitochondrial membranes. However, the level of radioactivity did not increase with time indicating an upper limit of choline uptake or a continual utilization of the choline taken up. The existence of an alternative pathway for the oxidation of choline was superficially examined but no definite conclusions could be reached.

Published
1973

13. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI

Author

Couto Alves, A, De Silva, NMG, Karhunen, V, Sovio, U, Das, S, Taal, HR, Warrington, NM, Lewin, AM, Kaakinen, M, Cousminer, DL, Thiering, E, Timpson, NJ, Bond, TA, Lowry, E, Brown, CD, Estivill, X, Lindi, V, Bradfield, JP, Geller, F, Speed, D, Coin, LJM, Loh, M, Barton, SJ, Beilin, LJ, Bisgaard, H, Bønnelykke, K, Alili, R, Hatoum, IJ, Schramm, K, Cartwright, R, Charles, M, Salerno, V, Clément, K, Claringbould, AAJ, BIOS Consortium, van Duijn, CM, Moltchanova, E, Eriksson, JG, Elks, C, Feenstra, B, Flexeder, C, Franks, S, Frayling, TM, Freathy, RM, Elliott, P, Widén, E, Hakonarson, H, Hattersley, AT, Rodriguez, A, Banterle, M, Heinrich, J, Heude, B, Holloway, JW, Hofman, A, Hyppönen, E, Inskip, H, Kaplan, LM, Hedman, AK, Läärä, E, Prokisch, H, Grallert, H, Lakka, TA, Lawlor, DA, Melbye, M, Ahluwalia, TS, Marinelli, M, Millwood, IY, Palmer, LJ, Pennell, CE, Perry, JR, Ring, SM, Savolainen, MJ, Rivadeneira, F, Standl, M, Sunyer, J, Tiesler, CMT, Uitterlinden, AG, Schierding, W, O’Sullivan, JM, Prokopenko, I, Herzig, K, Smith, GD, O'Reilly, P, Felix, JF, Buxton, JL, Blakemore, AIF, Ong, KK, Jaddoe, VWV, Grant, SFA, Sebert, S, McCarthy, MI, Järvelin, M., and Early Growth Genetics (EGG) Consortium

Abstract

Copyright © 2019 The Authors. Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

Published
2019

16. A genome-wide association study of sodium levels and drug metabolism in an epilepsy cohort treated with carbamazepine and oxcarbazepine

Author

Berghuis, B., Stapleton, C., Sonsma, A. C. M., Hulst, J., de Haan, G. -J., Lindhout, D., Demurtas, R., Krause, R., Depondt, C., Kunz, W. S., Zara, F., Striano, P., Craig, J., Auce, P., Marson, A. G., Stefansson, H., O'Brien, T. J., Johnson, M. R., Sills, G. J., Wolking, S., Lerche, H., Sisodiya, S. M., Sander, J. W., Cavalleri, G. L., Koeleman, B. P. C., Mccormack, M., Avbersek, A., Leu, C., Heggeli, K., Willis, J., Speed, D., Sargsyan, N., Chinthapalli, K., Borghei, M., Coppola, A., Gambardella, A., Becker, F., Rau, S., Hengsbach, C., Weber, Y. G., Delanty, N., Campbell, E., Gudmundsson, L. J., Ingason, A., Stefansson, K., Schneider, R., Balling, R., Francis, B., Jorgensen, A., Morris, A., Langley, S., Srivastava, P., Brodie, M., Todaro, M., Petrovski, S., Hutton, J., Zimprich, F., Krenn, M., Muhle, H., Martin Klein, K., Moller, R., Nikanorova, M., Weckhuysen, S., Rener-Primec, Z., Berghuis, Bianca, Stapleton, Caragh, Sonsma, Anja C. M., Hulst, Janic, de Haan, Gerrit-Jan, Lindhout, Dick, Demurtas, Rita, Krause, Roland, Depondt, Chantal, Kunz, Wolfram S., Zara, Federico, Striano, Pasquale, Craig, John, Auce, Paul, Marson, Anthony G., Stefansson, Hreinn, O'Brien, Terence J., Johnson, Michael R., Sills, Graeme J., Wolking, Stefan, Lerche, Holger, Sisodiya, Sanjay M., Sander, Josemir W., Cavalleri, Gianpiero L., Koeleman, Bobby P. C., Mccormack, Mark, Weckhuysen, Sarah, EpiPGX Consortium, Imperial College Healthcare NHS Trust- BRC Funding, and Commission of the European Communities

Subjects
medicine.medical_specialty, hyponatremia, Clinical Neurology, Gastroenterology, 03 medical and health sciences, Epilepsy, 0302 clinical medicine, adverse effects, antiepileptic drugs, EpiPGX Consortium, GWAS, antiepileptic drug, Internal medicine, adverse effect, medicine, Oxcarbazepine, Adverse effect, 030304 developmental biology, 0303 health sciences, business.industry, Généralités, Carbamazepine, medicine.disease, 3. Good health, Neurology, Cohort, Full‐length Original Research, Phenobarbital, Human medicine, Neurology (clinical), Hyponatremia, business, 030217 neurology & neurosurgery, Drug metabolism, medicine.drug
Abstract

Objective: To ascertain the clinical and genetic factors contributing to carbamazepine- and oxcarbazepine-induced hyponatremia (COIH), and to carbamazepine (CBZ) metabolism, in a retrospectively collected, cross-sectional cohort of people with epilepsy. Methods: We collected data on serum sodium levels and antiepileptic drug levels in people with epilepsy attending a tertiary epilepsy center while on treatment with CBZ or OXC. We defined hyponatremia as Na+ ≤134 mEq/L. We estimated the CBZ metabolic ratio defined as the log transformation of the ratio of metabolite CBZ-diol to unchanged drug precursor substrate as measured in serum. Results: Clinical and genetic data relating to carbamazepine and oxcarbazepine trials were collected in 1141 patients. We did not observe any genome-wide significant associations with sodium level in a linear trend or hyponatremia as a dichotomous trait. Age, sex, number of comedications, phenytoin use, phenobarbital use, and sodium valproate use were significant predictors of CBZ metabolic ratio. No genome-wide significant associations with CBZ metabolic ratio were found. Significance: Although we did not detect a genetic predictor of hyponatremia or CBZ metabolism in our cohort, our findings suggest that the determinants of CBZ metabolism are multifactorial., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2019

17. SumHer better estimates the SNP heritability of complex traits from summary statistics

Author

Speed, D, Balding, DJ, Speed, D, and Balding, DJ

Abstract

We present SumHer, software for estimating confounding bias, SNP heritability, enrichments of heritability and genetic correlations using summary statistics from genome-wide association studies. The key difference between SumHer and the existing software LD Score Regression (LDSC) is that SumHer allows the user to specify the heritability model. We apply SumHer to results from 24 large-scale association studies (average sample size 121,000) using our recommended heritability model. We show that these studies tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci was under-reported by about a quarter. We also estimate enrichments for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further six categories with above threefold enrichment. By contrast, our analysis using SumHer finds that none of the categories have enrichment above twofold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

Published
2019

18. Summary statistic analyses can mistake confounding bias for heritability

Author

Holmes, JB, Speed, D, Balding, DJ, Holmes, JB, Speed, D, and Balding, DJ

Abstract

Linkage disequilibrium SCore regression (LDSC) has become a popular approach to estimate confounding bias, heritability, and genetic correlation using only genome-wide association study (GWAS) test statistics. SumHer is a newly introduced alternative with similar aims. We show using theory and simulations that both approaches fail to adequately account for confounding bias, even when the assumed heritability model is correct. Consequently, these methods may estimate heritability poorly if there was an inadequate adjustment for confounding in the original GWAS analysis. We also show that the choice of a summary statistic for use in LDSC or SumHer can have a large impact on resulting inferences. Further, covariate adjustments in the original GWAS can alter the target of heritability estimation, which can be problematic for test statistics from a meta-analysis of GWAS with different covariate adjustments.

Published
2019

19. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI

Author

Alves, AC, De Silva, NMG, Karhunen, V, Sovio, U, Das, S, Taal, HR, Warrington, NM, Lewin, AM, Kaakinen, M, Cousminer, DL, Thiering, E, Timpson, NJ, Bond, TA, Lowry, E, Brown, CD, Estivill, X, Lindi, V, Bradfield, JP, Geller, F, Speed, D, Coin, LJM, Loh, M, Barton, SJ, Beilin, LJ, Bisgaard, H, Bonnelykke, K, Alili, R, Hatoum, IJ, Schramm, K, Cartwright, R, Charles, M-A, Salerno, V, Clement, K, Claringbould, AAJ, van Duijn, CM, Moltchanova, E, Eriksson, JG, Elks, C, Feenstra, B, Flexeder, C, Franks, S, Frayling, TM, Freathy, RM, Elliott, P, Widen, E, Hakonarson, H, Hattersley, AT, Rodriguez, A, Banterle, M, Heinrich, J, Heude, B, Holloway, JW, Hofman, A, Hypponen, E, Inskip, H, Kaplan, LM, Hedman, AK, Laara, E, Prokisch, H, Grallert, H, Lakka, TA, Lawlor, DA, Melbye, M, Ahluwalia, TS, Marinelli, M, Millwood, IY, Palmer, LJ, Pennell, CE, Perry, JR, Ring, SM, Savolainen, MJ, Rivadeneira, F, Standl, M, Sunyer, J, Tiesler, CMT, Uitterlinden, AG, Schierding, W, O'Sullivan, JM, Prokopenko, I, Herzig, K-H, Smith, GD, O'Reilly, P, Felix, JF, Buxton, JL, Blakemore, AIF, Ong, KK, Jaddoe, VWV, Grant, SFA, Sebert, S, McCarthy, MI, Jarvelin, M-R, Alves, AC, De Silva, NMG, Karhunen, V, Sovio, U, Das, S, Taal, HR, Warrington, NM, Lewin, AM, Kaakinen, M, Cousminer, DL, Thiering, E, Timpson, NJ, Bond, TA, Lowry, E, Brown, CD, Estivill, X, Lindi, V, Bradfield, JP, Geller, F, Speed, D, Coin, LJM, Loh, M, Barton, SJ, Beilin, LJ, Bisgaard, H, Bonnelykke, K, Alili, R, Hatoum, IJ, Schramm, K, Cartwright, R, Charles, M-A, Salerno, V, Clement, K, Claringbould, AAJ, van Duijn, CM, Moltchanova, E, Eriksson, JG, Elks, C, Feenstra, B, Flexeder, C, Franks, S, Frayling, TM, Freathy, RM, Elliott, P, Widen, E, Hakonarson, H, Hattersley, AT, Rodriguez, A, Banterle, M, Heinrich, J, Heude, B, Holloway, JW, Hofman, A, Hypponen, E, Inskip, H, Kaplan, LM, Hedman, AK, Laara, E, Prokisch, H, Grallert, H, Lakka, TA, Lawlor, DA, Melbye, M, Ahluwalia, TS, Marinelli, M, Millwood, IY, Palmer, LJ, Pennell, CE, Perry, JR, Ring, SM, Savolainen, MJ, Rivadeneira, F, Standl, M, Sunyer, J, Tiesler, CMT, Uitterlinden, AG, Schierding, W, O'Sullivan, JM, Prokopenko, I, Herzig, K-H, Smith, GD, O'Reilly, P, Felix, JF, Buxton, JL, Blakemore, AIF, Ong, KK, Jaddoe, VWV, Grant, SFA, Sebert, S, McCarthy, MI, and Jarvelin, M-R

Abstract

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

Published
2019

20. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI

Author

Alves, A. C. (Alexessander Couto), De Silva, N. M. (N. Maneka G.), Karhunen, V. (Ville), Sovio, U. (Ulla), Das, S. (Shikta), Taal, H. R. (H. Rob), Warrington, N. M. (Nicole M.), Lewin, A. M. (Alexandra M.), Kaakinen, M. (Marika), Cousminer, D. L. (Diana L.), Thiering, E. (Elisabeth), Timpson, N. J. (Nicholas J.), Bond, T. A. (Tom A.), Lowry, E. (Estelle), Brown, C. D. (Christopher D.), Estivill, X. (Xavier), Lindi, V. (Virpi), Bradfield, J. P. (Jonathan P.), Geller, F. (Frank), Speed, D. (Doug), Coin, L. J. (Lachlan J. M.), Loh, M. (Marie), Barton, S. J. (Sheila J.), Beilin, L. J. (Lawrence J.), Bisgaard, H. (Hans), Bonnelykke, K. (Klaus), Alili, R. (Rohia), Hatoum, I. J. (Ida J.), Schramm, K. (Katharina), Cartwright, R. (Rufus), Charles, M.-A. (Marie-Aline), Salerno, V. (Vincenzo), Clement, K. (Karine), Claringbould, A. A. (Annique A. J.), van Duijn, C. M. (Cornelia M.), Moltchanova, E. (Elena), Eriksson, J. G. (Johan G.), Elks, C. (Cathy), Feenstra, B. (Bjarke), Flexeder, C. (Claudia), Franks, S. (Stephen), Frayling, T. M. (Timothy M.), Freathy, R. M. (Rachel M.), Elliott, P. (Paul), Widen, E. (Elisabeth), Hakonarson, H. (Hakon), Hattersley, A. T. (Andrew T.), Rodriguez, A. (Alina), Banterle, M. (Marco), Heinrich, J. (Joachim), Heude, B. (Barbara), Holloway, J. W. (John W.), Hofman, A. (Albert), Hypponen, E. (Elina), Inskip, H. (Hazel), Kaplan, L. M. (Lee M.), Hedman, A. K. (Asa K.), Läärä, E. (Esa), Prokisch, H. (Holger), Grallert, H. (Harald), Lakka, T. A. (Timo A.), Lawlor, D. A. (Debbie A.), Melbye, M. (Mads), Ahluwalia, T. S. (Tarunveer S.), Marinelli, M. (Marcella), Millwood, I. Y. (Iona Y.), Palmer, L. J. (Lyle J.), Pennell, C. E. (Craig E.), Perry, J. R. (John R.), Ring, S. M. (Susan M.), Savolainen, M. J. (Markku J.), Rivadeneira, F. (Fernando), Standl, M. (Marie), Sunyer, J. (Jordi), Tiesler, C. M. (Carla M. T.), Uitterlinden, A. G. (Andre G.), Schierding, W. (William), O'Sullivan, J. M. (Justin M.), Prokopenko, I. (Inga), Herzig, K.-H. (Karl-Heinz), Smith, G. D. (George Davey), O'Reilly, P. (Paul), Felix, J. F. (Janine F.), Buxton, J. L. (Jessica L.), Blakemore, A. I. (Alexandra I. F.), Ong, K. K. (Ken K.), Jaddoe, V. W. (Vincent W. V.), Grant, S. F. (Struan F. A.), Sebert, S. (Sylvain), McCarthy, M. I. (Mark I.), Jarvelin, M.-R. (Marjo-Riitta), Alves, A. C. (Alexessander Couto), De Silva, N. M. (N. Maneka G.), Karhunen, V. (Ville), Sovio, U. (Ulla), Das, S. (Shikta), Taal, H. R. (H. Rob), Warrington, N. M. (Nicole M.), Lewin, A. M. (Alexandra M.), Kaakinen, M. (Marika), Cousminer, D. L. (Diana L.), Thiering, E. (Elisabeth), Timpson, N. J. (Nicholas J.), Bond, T. A. (Tom A.), Lowry, E. (Estelle), Brown, C. D. (Christopher D.), Estivill, X. (Xavier), Lindi, V. (Virpi), Bradfield, J. P. (Jonathan P.), Geller, F. (Frank), Speed, D. (Doug), Coin, L. J. (Lachlan J. M.), Loh, M. (Marie), Barton, S. J. (Sheila J.), Beilin, L. J. (Lawrence J.), Bisgaard, H. (Hans), Bonnelykke, K. (Klaus), Alili, R. (Rohia), Hatoum, I. J. (Ida J.), Schramm, K. (Katharina), Cartwright, R. (Rufus), Charles, M.-A. (Marie-Aline), Salerno, V. (Vincenzo), Clement, K. (Karine), Claringbould, A. A. (Annique A. J.), van Duijn, C. M. (Cornelia M.), Moltchanova, E. (Elena), Eriksson, J. G. (Johan G.), Elks, C. (Cathy), Feenstra, B. (Bjarke), Flexeder, C. (Claudia), Franks, S. (Stephen), Frayling, T. M. (Timothy M.), Freathy, R. M. (Rachel M.), Elliott, P. (Paul), Widen, E. (Elisabeth), Hakonarson, H. (Hakon), Hattersley, A. T. (Andrew T.), Rodriguez, A. (Alina), Banterle, M. (Marco), Heinrich, J. (Joachim), Heude, B. (Barbara), Holloway, J. W. (John W.), Hofman, A. (Albert), Hypponen, E. (Elina), Inskip, H. (Hazel), Kaplan, L. M. (Lee M.), Hedman, A. K. (Asa K.), Läärä, E. (Esa), Prokisch, H. (Holger), Grallert, H. (Harald), Lakka, T. A. (Timo A.), Lawlor, D. A. (Debbie A.), Melbye, M. (Mads), Ahluwalia, T. S. (Tarunveer S.), Marinelli, M. (Marcella), Millwood, I. Y. (Iona Y.), Palmer, L. J. (Lyle J.), Pennell, C. E. (Craig E.), Perry, J. R. (John R.), Ring, S. M. (Susan M.), Savolainen, M. J. (Markku J.), Rivadeneira, F. (Fernando), Standl, M. (Marie), Sunyer, J. (Jordi), Tiesler, C. M. (Carla M. T.), Uitterlinden, A. G. (Andre G.), Schierding, W. (William), O'Sullivan, J. M. (Justin M.), Prokopenko, I. (Inga), Herzig, K.-H. (Karl-Heinz), Smith, G. D. (George Davey), O'Reilly, P. (Paul), Felix, J. F. (Janine F.), Buxton, J. L. (Jessica L.), Blakemore, A. I. (Alexandra I. F.), Ong, K. K. (Ken K.), Jaddoe, V. W. (Vincent W. V.), Grant, S. F. (Struan F. A.), Sebert, S. (Sylvain), McCarthy, M. I. (Mark I.), and Jarvelin, M.-R. (Marjo-Riitta)

Abstract

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

Published
2019

21. Describing the genetic architecture of epilepsy through heritability analysis

Author

Speed, D, O'Brien, TJ, Palotie, A, Shkura, K, Marson, AG, Balding, DJ, and Johnson, MR

Subjects
Asian Continental Ancestry Group, Genotype, Genotyping Techniques, European Continental Ancestry Group, Population, Clinical Neurology, SUSCEPTIBILITY, 16P13.11 PREDISPOSE, Polymorphism, Single Nucleotide, CLASSIFICATION, White People, 17 Psychology And Cognitive Sciences, Quantitative Trait, Heritable, Asian People, Seizures, Humans, Genetic Predisposition to Disease, GENOME-WIDE ASSOCIATION, Age of Onset, RISK, Science & Technology, Neurology & Neurosurgery, Epilepsy, Models, Statistical, complex trait prediction, ILAE, Neurosciences, 11 Medical And Health Sciences, Original Articles, MICRODELETIONS, heritability analysis, association studies, ROC Curve, Genetic Loci, DISEASES, Area Under Curve, Neurosciences & Neurology, IDIOPATHIC GENERALIZED EPILEPSY, Life Sciences & Biomedicine, Algorithms, Genome-Wide Association Study
Abstract

Epilepsy is highly heritable, but its genetic architecture is poorly understood. Speed et al. estimate the number of susceptibility loci, show that common variants account for the majority of heritability, and demonstrate that epilepsy consists of genetically distinct subtypes. They conclude that gene-based prediction models may have clinical utility in first-seizure settings., Epilepsy is a disease with substantial missing heritability; despite its high genetic component, genetic association studies have had limited success detecting common variants which influence susceptibility. In this paper, we reassess the role of common variants on epilepsy using extensions of heritability analysis. Our data set consists of 1258 UK patients with epilepsy, of which 958 have focal epilepsy, and 5129 population control subjects, with genotypes recorded for over 4 million common single nucleotide polymorphisms. Firstly, we show that on the liability scale, common variants collectively explain at least 26% (standard deviation 5%) of phenotypic variation for all epilepsy and 27% (standard deviation 5%) for focal epilepsy. Secondly we provide a new method for estimating the number of causal variants for complex traits; when applied to epilepsy, our most optimistic estimate suggests that at least 400 variants influence disease susceptibility, with potentially many thousands. Thirdly, we use bivariate analysis to assess how similar the genetic architecture of focal epilepsy is to that of non-focal epilepsy; we demonstrate both significant differences (P = 0.004) and significant similarities (P = 0.01) between the two subtypes, indicating that although the clinical definition of focal epilepsy does identify a genetically distinct epilepsy subtype, there is also scope to improve the classification of epilepsy by incorporating genotypic information. Lastly, we investigate the potential value in using genetic data to diagnose epilepsy following a single epileptic seizure; we find that a prediction model explaining 10% of phenotypic variation could have clinical utility for deciding which single-seizure individuals are likely to benefit from immediate anti-epileptic drug therapy.

Published
2014

22. S02. Pre-Implantation Genetic Diagnosis (PGD) in Ireland - from validation to introduction of a clinical service

Author

Morrison, PJ, Campbell, E, Kennedy, F, Russell, A, Smithson, WH, Parsons, L, Liggan, B, Irwin, B, Delanty, N, Hunt, SJ, Craig, J, Morrow, J, Dineen, T, Zhang, X, Flanagan, J, Kovacs, A, Mihart, R, O'Callaghan, J, Culligan, J, Daly, N, Waterstone, J, Magee, AC, Stewart, FJ, Dabir, TA, McConachie, M, McCoubrey, A, McConnell, VPM, Stack, D, O'Meara, E, Phelan, S, McDonagh, N, Kelly, L, Sciot, R, Debiec-Rychter, M, Morris, T, Cochrane, D, Sorensen, P, O'Sullivan, MJ, O'Byrne, JJ, Sweeney, M, Donnelly, D, Lambert, D, Beattie, D, Gervin, C, Graham, CA, Barton, DE, Lynch, SA, Whelan, CD, Hibar, DP, Stein, JL, Speed, D, Sisodiya, S, Ohnson, M, Goldstein, D, Medland, SE, Ranke, B, Thompson, PM, Cavalleri, G, Coleman, C, Quinn, EM, Ryan, AW, Anney, RJL, Trimble, V, Morris, DW, Donohoe, G, Conroy, J, Trynka, G, Wijmenga, C, Ennis, S, McManus, R, O'Halloran, ET, Magalhaes, TR, Cole, A, Cox, S, Jeong, C, Witonsky, D, Robbins, P, Montgomery, H, Ota, M, Hanaoka, M, Droma, Y, Beall, CM, Rienzo, A Di, Casey, J, McGettigan, P, Crushell, E, Hughes, J, Smyth, LJ, Kilner, JK, Benson, KA, Maxwell, AP, McKnight, AJ, Donnelly, DE, Jeffers, L, Hampton, S, Baillie, N, Cooke, S, O'Connell, SM, McDonald, A, O'Toole, N, Bradfield, A, Bradley, M, Hattersley, A, Ellard, S, Proks, P, Mattis, KK, Ashcroft, F, O'Riordan, SMP, Coyle, D, McDermott, M, O'Sullivan, M, Roche, E, Quinn, F, Cody, D, MacMahon, JM, Morrissey, R, Green, A, Thompson, AR, Kulkarni, A, Marks, KJ, Snape, K, Taylor, R, Bradley, L, Ramachandrappa, S, Pinto, CF, Dabir, T, Logan, P, Liew, S., Znaczko, A, Ryan, H., McDevitt, T, Higgins, M, Crowley, A, Rogers, M, Geoghegan, S, Shorto, J, Ramsden, S, O'Riordan, MP, Moore, M, Murphy, M, Irvine, A, Znaczko, Anna, Wilson, A, Stewart, F, Cather, MH, Young, IS, Nicholls, DP, O'Kane, M, Sharpe, P, Hanna, E, Hart, PJ, Savage, N, Humphreys, MW, Shaw-Smith, C, Osio, D, Collinson, MN, McKee, S, McNerlan, S, McGorrian, C, Galvin, J, O'Byrne, J, Stewart, S, Heggarty, SV, Hegarty, SP, McConnell, V, Turner, J, Ward, A, Kelly, R, Joyce, C, ó hIcí, B, Meaney, K, Gibson, L, Kelly, PM, Costigan, C, Gul, R, Byrne, S, Hughes, L, Ozaki, M, O'Sullivan, F, Parle-McDermott, A, Heavin, SB, McCormack, M, Slattery, L, Walley, N, Avbersek, A, Novy, J, Sinha, S, S, Alarts, N, Legros, B, Radtke, R., Sisodiya, Depondt, C, Cavalleri, GL, Connolly, S, Heron, EA, Irvine, MAG, Hughes, AE, Darlow, JM, Darlay, R, Hunziker, M, Kutasy, B, Green, AJ, Cordell, H, Puri, P, Chand, S, McCaughan, JA, Shabir, S, Chan, W, Kilner, J, Borrows, R, Douglas, AP, O'Neill, T, Shepherd, C, Hardy, R, Kenny, Molloy, B, Freeley, M, Quinn, E, McGinn, R, Long, A, Gahan, JM, Connolly, E, Byrne, MM, Gray, SG, Murphy, RT, Gui, H, Heinzen, E, Goldstein, D B, Petrovski, S, O'Brien, TJ, Cherny, S, Sham, PC, Baum, L, Duffy, S, Catherwood, N, McVeigh, TP, Sweeney, KJ, Miller, N, Kerin, MJ, and Weidhaas, JB

Subjects
Poster Presentations, Abstracts, Spoken Papers
Published
2014

23. Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies

Author

Abou-Khalil, B, Auce, P, Avbersek, A, Bahlo, M, Balding, DJ, Bast, T, Baum, L, Becker, AJ, Becker, F, Berghuis, B, Berkovic, SF, Boysen, KE, Bradfield, JP, Brody, LC, Buono, RJ, Campbell, E, Cascino, GD, Catarino, CB, Cavalleri, GL, Cherny, SS, Chinthapalli, K, Coffey, AJ, Compston, A, Coppola, A, Cossette, P, Craig, JJ, de Haan, G-J, De Jonghe, P, de Kovel, CGF, Delanty, N, Depondt, C, Devinsky, O, Dlugos, DJ, Doherty, CP, Elger, CE, Eriksson, JG, Ferraro, TN, Feucht, M, Francis, B, Franke, A, French, JA, Freytag, S, Gaus, V, Geller, EB, Gieger, C, Glauser, T, Glynn, S, Goldstein, DB, Gui, H, Guo, Y, Haas, KF, Hakonarson, H, Hallmann, K, Haut, S, Heinzen, EL, Helbig, I, Hengsbach, C, Hjalgrim, H, Iacomino, M, Ingason, A, Jamnadas-Khoda, J, Johnson, MR, Kalviainen, R, Kantanen, A-M, Kasperaviciute, D, Trenite, DK-N, Kirsch, HE, Knowlton, RC, Koeleman, BPC, Krause, R, Krenn, M, Kunz, WS, Kuzniecky, R, Kwan, P, Lal, D, Lau, Y-L, Lehesjoki, A-E, Lerche, H, Leu, C, Lieb, W, Lindhout, D, Lo, WD, Lopes-Cendes, I, Lowenstein, DH, Malovini, A, Marson, AG, Mayer, T, McCormack, M, Mills, JL, Mirza, N, Moerzinger, M, Moller, RS, Molloy, AM, Muhle, H, Newton, M, Ng, P-W, Noethen, MM, Nuernberg, P, O'Brien, TJ, Oliver, KL, Palotie, A, Pangilinan, F, Peter, S, Petrovski, S, Poduri, A, Privitera, M, Radtke, R, Rau, S, Reif, PS, Reinthaler, EM, Rosenow, F, Sander, JW, Sander, T, Scattergood, T, Schachter, SC, Schankin, CJ, Scheffer, IE, Schmitz, B, Schoch, S, Sham, PC, Shih, JJ, Sills, GJ, Sisodiya, SM, Slattery, L, Smith, A, Smith, DF, Smith, MC, Smith, PE, Sonsma, ACM, Speed, D, Sperling, MR, Steinhoff, BJ, Stephani, U, Stevelink, R, Strauch, K, Striano, P, Stroink, H, Surges, R, Tan, KM, Thio, LL, Thomas, GN, Todaro, M, Tozzi, R, Vari, MS, Vining, EPG, Visscher, F, von Spiczak, S, Walley, NM, Weber, YG, Wei, Z, Weisenberg, J, Whelan, CD, Widdess-Walsh, P, Wolff, M, Wolking, S, Yang, W, Zara, F, Zimprich, F, Abou-Khalil, B, Auce, P, Avbersek, A, Bahlo, M, Balding, DJ, Bast, T, Baum, L, Becker, AJ, Becker, F, Berghuis, B, Berkovic, SF, Boysen, KE, Bradfield, JP, Brody, LC, Buono, RJ, Campbell, E, Cascino, GD, Catarino, CB, Cavalleri, GL, Cherny, SS, Chinthapalli, K, Coffey, AJ, Compston, A, Coppola, A, Cossette, P, Craig, JJ, de Haan, G-J, De Jonghe, P, de Kovel, CGF, Delanty, N, Depondt, C, Devinsky, O, Dlugos, DJ, Doherty, CP, Elger, CE, Eriksson, JG, Ferraro, TN, Feucht, M, Francis, B, Franke, A, French, JA, Freytag, S, Gaus, V, Geller, EB, Gieger, C, Glauser, T, Glynn, S, Goldstein, DB, Gui, H, Guo, Y, Haas, KF, Hakonarson, H, Hallmann, K, Haut, S, Heinzen, EL, Helbig, I, Hengsbach, C, Hjalgrim, H, Iacomino, M, Ingason, A, Jamnadas-Khoda, J, Johnson, MR, Kalviainen, R, Kantanen, A-M, Kasperaviciute, D, Trenite, DK-N, Kirsch, HE, Knowlton, RC, Koeleman, BPC, Krause, R, Krenn, M, Kunz, WS, Kuzniecky, R, Kwan, P, Lal, D, Lau, Y-L, Lehesjoki, A-E, Lerche, H, Leu, C, Lieb, W, Lindhout, D, Lo, WD, Lopes-Cendes, I, Lowenstein, DH, Malovini, A, Marson, AG, Mayer, T, McCormack, M, Mills, JL, Mirza, N, Moerzinger, M, Moller, RS, Molloy, AM, Muhle, H, Newton, M, Ng, P-W, Noethen, MM, Nuernberg, P, O'Brien, TJ, Oliver, KL, Palotie, A, Pangilinan, F, Peter, S, Petrovski, S, Poduri, A, Privitera, M, Radtke, R, Rau, S, Reif, PS, Reinthaler, EM, Rosenow, F, Sander, JW, Sander, T, Scattergood, T, Schachter, SC, Schankin, CJ, Scheffer, IE, Schmitz, B, Schoch, S, Sham, PC, Shih, JJ, Sills, GJ, Sisodiya, SM, Slattery, L, Smith, A, Smith, DF, Smith, MC, Smith, PE, Sonsma, ACM, Speed, D, Sperling, MR, Steinhoff, BJ, Stephani, U, Stevelink, R, Strauch, K, Striano, P, Stroink, H, Surges, R, Tan, KM, Thio, LL, Thomas, GN, Todaro, M, Tozzi, R, Vari, MS, Vining, EPG, Visscher, F, von Spiczak, S, Walley, NM, Weber, YG, Wei, Z, Weisenberg, J, Whelan, CD, Widdess-Walsh, P, Wolff, M, Wolking, S, Yang, W, Zara, F, and Zimprich, F

Abstract

The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology.

Published
2018

24. Genetic variation in CFH predicts phenytoin-induced maculopapular exanthema in European-descent patients

Author

McCormack, M, Gui, H, Ingason, A, Speed, D, Wright, GEB, Zhang, EJ, Secolin, R, Yasuda, C, Kwok, M, Wolking, S, Becker, F, Rau, S, Avbersek, A, Heggeli, K, Leu, C, Depondt, C, Sills, GJ, Marson, AG, Auce, P, Brodie, MJ, Francis, B, Johnson, MR, Koeleman, BPC, Striano, P, Coppola, A, Zara, F, Kunz, WS, Sander, JW, Lerche, H, Klein, KM, Weckhuysen, S, Krenn, M, Gudmundsson, LJ, Stefansson, K, Krause, R, Shear, N, Ross, CJD, Delanty, N, Pirmohamed, M, Carleton, BC, Cendes, F, Lopes-Cendes, I, Liao, W-P, O'Brien, TJ, Sisodiya, SM, Cherny, S, Kwan, P, Baum, L, Cavalleri, GL, McCormack, M, Gui, H, Ingason, A, Speed, D, Wright, GEB, Zhang, EJ, Secolin, R, Yasuda, C, Kwok, M, Wolking, S, Becker, F, Rau, S, Avbersek, A, Heggeli, K, Leu, C, Depondt, C, Sills, GJ, Marson, AG, Auce, P, Brodie, MJ, Francis, B, Johnson, MR, Koeleman, BPC, Striano, P, Coppola, A, Zara, F, Kunz, WS, Sander, JW, Lerche, H, Klein, KM, Weckhuysen, S, Krenn, M, Gudmundsson, LJ, Stefansson, K, Krause, R, Shear, N, Ross, CJD, Delanty, N, Pirmohamed, M, Carleton, BC, Cendes, F, Lopes-Cendes, I, Liao, W-P, O'Brien, TJ, Sisodiya, SM, Cherny, S, Kwan, P, Baum, L, and Cavalleri, GL

Abstract

OBJECTIVE: To characterize, among European and Han Chinese populations, the genetic predictors of maculopapular exanthema (MPE), a cutaneous adverse drug reaction common to antiepileptic drugs. METHODS: We conducted a case-control genome-wide association study of autosomal genotypes, including Class I and II human leukocyte antigen (HLA) alleles, in 323 cases and 1,321 drug-tolerant controls from epilepsy cohorts of northern European and Han Chinese descent. Results from each cohort were meta-analyzed. RESULTS: We report an association between a rare variant in the complement factor H-related 4 (CFHR4) gene and phenytoin-induced MPE in Europeans (p = 4.5 × 10-11; odds ratio [95% confidence interval] 7 [3.2-16]). This variant is in complete linkage disequilibrium with a missense variant (N1050Y) in the complement factor H (CFH) gene. In addition, our results reinforce the association between HLA-A*31:01 and carbamazepine hypersensitivity. We did not identify significant genetic associations with MPE among Han Chinese patients. CONCLUSIONS: The identification of genetic predictors of MPE in CFHR4 and CFH, members of the complement factor H-related protein family, suggest a new link between regulation of the complement system alternative pathway and phenytoin-induced hypersensitivity in European-ancestral patients.

Published
2018

26. Reevaluation of SNP heritability in complex human traits

Author

Speed, D, Cai, N, Johnson, MR, Nejentsev, S, Balding, DJ, Speed, D, Cai, N, Johnson, MR, Nejentsev, S, and Balding, DJ

Abstract

SNP heritability, the proportion of phenotypic variance explained by SNPs, has been reported for many hundreds of traits. Its estimation requires strong prior assumptions about the distribution of heritability across the genome, but current assumptions have not been thoroughly tested. By analyzing imputed data for a large number of human traits, we empirically derive a model that more accurately describes how heritability varies with minor allele frequency (MAF), linkage disequilibrium (LD) and genotype certainty. Across 19 traits, our improved model leads to estimates of common SNP heritability on average 43% (s.d. 3%) higher than those obtained from the widely used software GCTA and 25% (s.d. 2%) higher than those from the recently proposed extension GCTA-LDMS. Previously, DNase I hypersensitivity sites were reported to explain 79% of SNP heritability; using our improved heritability model, their estimated contribution is only 24%.

Published
2017

27. Genetic Interactions with Sex Make a Relatively Small Contribution to the Heritability of Complex Traits in Mice

Author

Krohn, J, Speed, D, Palme, R, Touma, C, Mott, R, and Flint, J

Subjects
Male, Heredity, Quantitative Trait Loci, lcsh:Medicine, Molecular Genetics, Mice, Quantitative Trait, Heritable, Sex Factors, Genetics, Animals, lcsh:Science, Trait Locus Analysis, Quantitative Traits, Trait Loci, Complex Traits, lcsh:R, Biology and Life Sciences, Computational Biology, Chromosome Mapping, Genetic Variation, Bayes Theorem, Genomics, Genome Analysis, Phenotypes, Phenotype, Genetics of Disease, Epistasis, lcsh:Q, Female, Animal Genetics, Research Article
Abstract

The extent to which sex-specific genetic effects contribute to phenotypic variation is largely unknown. We applied a novel Bayesian method, sparse partitioning, to detect gene by sex (GxS) and gene by gene (GxG) quantitative loci (QTLs) in 1,900 outbred heterogeneous stock mice. In an analysis of 55 phenotypes, we detected 16 GxS and 6 GxG QTLs. The increase in the amount of phenotypic variance explained by models including GxS was small, ranging from 0.14% to 4.30%. We conclude that GxS rarely make a large overall contribution to the heritability of phenotypes, however there are cases where these will be individually important.

Published
2014

28. Abstracts of the 24th international isotope society (UK group) symposium: synthesis and applications of labelled compounds 2015

Author

Aigbirhio, F. I., primary, Allwein, S., additional, Anwar, A., additional, Atzrodt, J., additional, Audisio, D., additional, Badman, G, additional, Bakale, R., additional, Berthon, F., additional, Bragg, R., additional, Brindle, K. M., additional, Bushby, N., additional, Campos, S., additional, Cant, A. A., additional, Chan, M. Y. T., additional, Colbon, P., additional, Cornelissen, B., additional, Czarny, B., additional, Derdau, V., additional, Dive, V., additional, Dunscombe, M., additional, Eggleston, I., additional, Ellis-Sawyer, K., additional, Elmore, C. S., additional, Engstrom, P., additional, Ericsson, C., additional, Fairlamb, I. J. S., additional, Georgin, D., additional, Godfrey, S. P., additional, He, L., additional, Hickey, M. J., additional, Huscroft, I. T., additional, Kerr, W. J., additional, Lashford, A., additional, Lenz, E., additional, Lewinton, S., additional, L'Hermite, M. M., additional, Lindelöf, Å., additional, Little, G., additional, Lockley, W. J. S., additional, Loreau, O., additional, Maddocks, S., additional, Marguerit, M., additional, Mirabello, V., additional, Mudd, R. J., additional, Nilsson, G. N., additional, Owens, P. K., additional, Pascu, S.I., additional, Patriarche, G., additional, Pimlott, S. L., additional, Pinault, M., additional, Plastow, G., additional, Racys, D. T., additional, Reif, J., additional, Rossi, J., additional, Ruan, J., additional, Sarpaki, S., additional, Sephton, S. M., additional, Simonsson, R., additional, Speed, D. J., additional, Sumal, K., additional, Sutherland, A., additional, Taran, F., additional, Thuleau, A., additional, Wang, Y., additional, Waring, M., additional, Watters, W. H., additional, Wu, J, additional, and Xiao, J., additional

Published
2016
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29. Correction: Genome-wide analysis of blood pressure variability and ischemic stroke

Author

Yadav, S, Cotlarciuc, I, Munroe, PB, Khan, MS, Nalls, MA, Bevan, S, Cheng, Y-C, Chen, W-M, Malik, R, McCarthy, NS, Holliday, EG, Speed, D, Hasan, N, Pucek, M, Rinne, PE, Sever, P, Stanton, A, Shields, DC, Maguire, JM, McEvoy, M, Scott, RJ, Ferrucci, L, Macleod, MJ, Attia, J, Markus, HS, Sale, MM, Worrall, BB, Mitchell, BD, Dichgans, M, Sudlow, C, Meschia, JF, Rothwell, PM, Caulfield, M, and Sharma, P

Subjects
Science & Technology, Neurology & Neurosurgery, Peripheral Vascular Disease, Clinical Neurology, Cardiovascular System & Cardiology, 1103 Clinical Sciences, Neurosciences & Neurology, 1109 Neurosciences, Life Sciences & Biomedicine, 1102 Cardiovascular Medicine And Haematology
Published
2013

31. Abstracts of the 23rd International Isotope Society (UK group) Symposium: synthesis and applications of labelled compounds 2014

Author

Anwar, A., primary, Archibald, S., additional, Audisio, D., additional, Badman, G., additional, Bergin, J., additional, Bew, S. P., additional, Bloom, J., additional, Bushby, N., additional, Busigin, A., additional, Chan, M. Y. T., additional, Davies, J., additional, Dilworth, J., additional, Dunscombe, M., additional, Elmore, C. S., additional, Engstrom, P., additional, Fuchter, M. J., additional, Geach, N. J., additional, Georgin, D., additional, Griffiths, A., additional, Hansen, P., additional, Hardcastle, G., additional, Hiatt-Gipson, G. D., additional, Hickey, M. J., additional, Kitson, S. L., additional, Lashford, A., additional, Lenz, E., additional, Lewinton, S., additional, Lockley, W. J. S., additional, Loreau, O., additional, Maddocks, S., additional, Marlière, P., additional, McEwen, A., additional, Moody, T. S., additional, Morgan, P., additional, Roe, S. J., additional, Schenk, D. J., additional, Speed, D. J., additional, Stockman, R. A., additional, Sumal, K., additional, Taran, F., additional, Thurston, S., additional, Waring, M., additional, and Watters, W. H., additional

Published
2015
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32. Production of a comparative physical genome map of the turkey (Meleagris gallopavo)

Author

Robertson, L. B., Tempest, H. G., Patel, A. P., Alain Vignal, Valerie Fillon, Crooijmans, R. P. M. A., Groenen, M. A. M., Hillier, L. W., Morrice, D. R., Speed, D., Bentley, J., Masabanda, J. S., Burt, D. W., Griffin, D. K., Laboratoire de Génétique Cellulaire (LGC), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Inconnu, and ProdInra, Migration

Subjects
[SDV] Life Sciences [q-bio], HYSICAL MAPS, COMPARATIVE MAPPING, [SDV]Life Sciences [q-bio], TURKEYS, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2005

33. Investigating the causal relationship between neuroticism and depression via Mendelian randomization.

Author

Speed, D., Hemani, G., Speed, M. S., Børglum, A. D., and Østergaard, S. D.

Subjects
*NEUROSES, *NEUROTICISM
Abstract

The article reports about a research on the causal risk of depression from neuroticism using Mendelian randomization (MR). Data from genome-wide association study (GWAS) of neuroticism and major depression were analyzed. Some independent single nucleotide polymorphisms (SNPs) for neuroticism showed a causal effect on depression. The evidence suggested neuroticism to be a causal risk factor for depression.

Published
2019
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34. EPENDYMOMA

Author

Hoffman, L. M., primary, Donson, A. M., additional, Nakachi, I., additional, Griesinger, A. M., additional, Birks, D. K., additional, Amani, V., additional, Hemenway, M. S., additional, Liu, A. K., additional, Wang, M., additional, Hankinson, T. C., additional, Handler, M. H., additional, Foreman, N. K., additional, Zakrzewska, M., additional, Zakrzewski, K., additional, Fendler, W., additional, Stefanczyk, L., additional, Liberski, P. P., additional, Massimino, M., additional, Gandola, L., additional, Ferroli, P., additional, Valentini, L., additional, Biassoni, V., additional, Garre, M. L., additional, Sardi, I., additional, Genitori, L., additional, Giussani, C., additional, Massimi, L., additional, Bertin, D., additional, Mussano, A., additional, Viscardi, E., additional, Modena, P., additional, Mastronuzzi, A., additional, Barra, S., additional, Scarzello, G., additional, Cinalli, G., additional, Peretta, P., additional, Giangaspero, F., additional, Boschetti, L., additional, Schiavello, E., additional, Calareso, G., additional, Antonelli, M., additional, Pecori, E., additional, Di Meco, F., additional, Migliorati, R., additional, Taborelli, A., additional, Witt, H., additional, Sill, M., additional, Wani, K., additional, Mack, S. C., additional, Capper, D., additional, Pajtler, K., additional, Lambert, S., additional, Tzaridis, T., additional, Milde, T., additional, Northcott, P. A., additional, Kulozik, A. E., additional, Witt, O., additional, Collins, V. P., additional, Ellison, D. W., additional, Taylor, M. D., additional, Kool, M., additional, Jones, D. T. W., additional, Korshunov, A., additional, Ken, A., additional, Pfister, S. M., additional, Makino, K., additional, Nakamura, H., additional, Kuroda, J.-i., additional, Kuratsu, J.-i., additional, Toledano, H., additional, Margolin, Y., additional, Ohali, A., additional, Michowiz, S., additional, Johann, P., additional, Tabori, U., additional, Walker, E., additional, Hawkins, C., additional, Taylor, M., additional, Yaniv, I., additional, Avigad, S., additional, Hoffman, L., additional, Plimpton, S. R., additional, Stence, N. V., additional, Vibhakar, R., additional, Lourdusamy, A., additional, Rahman, R., additional, Ward, J., additional, Rogers, H., additional, Grundy, R., additional, Punchihewa, C., additional, Lee, R., additional, Lin, T., additional, Orisme, W., additional, Dalton, J., additional, Aronica, E., additional, Smith, A., additional, Gajjar, A., additional, Onar, A., additional, Pounds, S., additional, Tatevossian, R., additional, Merchant, T., additional, Ellison, D., additional, Parker, M., additional, Mohankumar, K., additional, Weinlich, R., additional, Phoenix, T., additional, Thiruvenkatam, R., additional, White, E., additional, Gupta, K., additional, Boop, F., additional, Ding, L., additional, Mardis, E., additional, Wilson, R., additional, Downing, J., additional, Gilbertson, R., additional, Speed, D., additional, Gould, T., additional, Consortium, t. I. E., additional, Hoffman, L. M., additional, Griesinger, A., additional, Donson, A., additional, Birks, D., additional, Ohe, N., additional, Yano, H., additional, Nakayama, N., additional, Iwama, T., additional, Wright, K., additional, Hassall, T., additional, Bowers, D. C., additional, Crawford, J., additional, Bendel, A., additional, Fisher, P. G., additional, Klimo, P., additional, Armstrong, G., additional, Qaddoumi, I., additional, Robinson, G., additional, Wetmore, C., additional, Broniscer, A., additional, Chapman, R., additional, Mayne, C., additional, Duane, H., additional, Kilday, J.-P., additional, Coyle, B., additional, Graul-Conroy, A., additional, Hartsell, W., additional, Bragg, T., additional, Goldman, S., additional, Rebsamen, S., additional, Puccetti, D., additional, Salamat, S., additional, Patel, N. J., additional, Gomi, A., additional, Oguma, H., additional, Hayase, T., additional, Kawahara, Y., additional, Yagi, M., additional, Morimoto, A., additional, Wilbur, C., additional, Dunham, C., additional, Mabbott, D., additional, Carret, A.-S., additional, Lafay-Cousin, L., additional, McNeely, P. D., additional, Eisenstat, D., additional, Wilson, B., additional, Johnston, D., additional, Hukin, J., additional, Mynarek, M., additional, Kortmann, R. D., additional, Kaatsch, P., additional, Pietsch, T., additional, Timmermann, B., additional, Fleischhack, G., additional, Benesch, M., additional, Friedrich, C., additional, von Bueren, A. O., additional, Gerber, N. U., additional, Muller, K., additional, Tippelt, S., additional, Warmuth-Metz, M., additional, Rutkowski, S., additional, von Hoff, K., additional, Murugesan, M. K., additional, Poppleton, H., additional, Currle, S., additional, Kranenburg, T., additional, Eden, C., additional, Boulos, N., additional, Dapper, J., additional, Patel, Y., additional, Freeman, B., additional, Shelat, A., additional, Stewart, C., additional, Guy, R., additional, Adamski, J., additional, Huang, A., additional, Bartels, U., additional, Ramaswamy, V., additional, Krishnatry, R., additional, Laperriere, N., additional, Bouffet, E., additional, Araki, A., additional, Chocholous, M., additional, Gojo, J., additional, Dorfer, C., additional, Czech, T., additional, Dieckmann, K., additional, Slavc, I., additional, Haberler, C., additional, Doerner, E., additional, Muehlen, A. z., additional, Kortmann, R., additional, von Buehren, A., additional, Ottensmeier, H., additional, Resch, A., additional, Kwiecien, R., additional, Faldum, A., additional, Kuehl, J., additional, Sabnis, D., additional, Storer, L., additional, Simmonds, L., additional, Blackburn, S., additional, Lowe, J., additional, Kerr, I., additional, Wohlers, I., additional, Goschzik, T., additional, Dreschmann, V., additional, Denkhaus, D., additional, Rahmann, S., additional, Klein-Hitpass, L., additional, Iglesias, M. J. L., additional, Riet, F. G., additional, Dhermain, F. D., additional, Canale, S., additional, Dufour, C., additional, Rose, C. S., additional, Puget, S., additional, Grill, J., additional, Bolle, S., additional, Parkes, J., additional, Davidson, A., additional, Figaji, A., additional, Pillay, K., additional, Kilborn, T., additional, Padayachy, L., additional, Hendricks, M., additional, Van Eyssen, A., additional, Piccinin, E., additional, Lorenzetto, E., additional, Brenca, M., additional, Aldape, K., additional, Cho, Y.-J., additional, Weiss, W., additional, Phillips, J., additional, Jabado, N., additional, Mora, J., additional, Fan, X., additional, Jung, S., additional, Lee, J. Y., additional, Zitterbart, K., additional, French, P., additional, Kros, J. M., additional, Hauser, P., additional, Faria, C., additional, and Pfister, S., additional

Published
2014
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35. Genome-wide analysis of blood pressure variability and ischemic stroke

Author

Yadav, S, Cotlarciuc, I, Munroe, PB, Khan, MS, Nalls, MA, Bevan, S, Cheng, YC, Chen, WM, Malik, R, Mccarthy, NS, Holliday, EG, Speed, D, Hasan, N, Pucek, M, Rinne, PE, Sever, P, Stanton, A, Shields, DC, Maguire, JM, Mcevoy, M, Scott, RJ, Ferrucci, L, Macleod, MJ, Attia, J, Markus, HS, Sale, MM, Worrall, BB, Mitchell, BD, Dichgans, M, Sudlow, C, Meschia, JF, Rothwell, PM, Caulfield, M, Sharma, P, Yadav, S, Cotlarciuc, I, Munroe, PB, Khan, MS, Nalls, MA, Bevan, S, Cheng, YC, Chen, WM, Malik, R, Mccarthy, NS, Holliday, EG, Speed, D, Hasan, N, Pucek, M, Rinne, PE, Sever, P, Stanton, A, Shields, DC, Maguire, JM, Mcevoy, M, Scott, RJ, Ferrucci, L, Macleod, MJ, Attia, J, Markus, HS, Sale, MM, Worrall, BB, Mitchell, BD, Dichgans, M, Sudlow, C, Meschia, JF, Rothwell, PM, Caulfield, M, and Sharma, P

Abstract

Background and Purpose-Visit-to-visit variability in blood pressure (vBP) is associated with ischemic stroke. We sought to determine whether such variability has genetic causes and whether genetic variants associated with BP variability are also associated with ischemic stroke. Methods-A Genome Wide Association Study (GWAS) for loci influencing BP variability was undertaken in 3802 individuals from the Anglo-Scandinavian Cardiac Outcome Trial (ASCOT) study, in which long-term visit-to-visit and within-visit BP measures were available. Because BP variability is strongly associated with ischemic stroke, we genotyped the sentinel single nucleotide polymorphism in an independent ischemic stroke population comprising 8624 cases and 12 722 controls and in 3900 additional (Scandinavian) participants from the ASCOT study to replicate our findings. Results-The ASCOT discovery GWAS identified a cluster of 17 correlated single nucleotide polymorphisms within the NLGN1 gene (3q26.31) associated with BP variability. The strongest association was with rs976683 (P=1.4×10-8). Conditional analysis of rs976683 provided no evidence of additional independent associations at the locus. Analysis of rs976683 in patients with ischemic stroke found no association for overall stroke (odds ratio, 1.02; 95% CI, 0.97-1.07; P=0.52) or its subtypes: cardioembolic (odds ratio, 1.07; 95% CI, 0.97-1.16; P=0.17), large vessel disease (odds ratio, 0.98; 95% CI, 0.89-1.07; P=0.60), and small vessel disease (odds ratio, 1.07; 95% CI, 0.97-1.17; P=0.19). No evidence for association was found between rs976683 and BP variability in the additional (Scandinavian) ASCOT participants (P=0.18). Conclusions-We identified a cluster of single nucleotide polymorphisms at the NLGN1 locus showing significant association with BP variability. Follow-up analyses did not support an association with risk of ischemic stroke and its subtypes. © 2013 American Heart Association, Inc.

Published
2013

37. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups

Author

Curtis, C, Shah, SP, Chin, S-F, Turashvili, G, Rueda, OM, Dunning, MJ, Speed, D, Lynch, AG, Samarajiwa, S, Yuan, Y, Graef, S, Ha, G, Haffari, G, Bashashati, A, Russell, R, McKinney, S, Langerod, A, Green, A, Provenzano, E, Wishart, G, Pinder, S, Watson, P, Markowetz, F, Murphy, L, Ellis, I, Purushotham, A, Borresen-Dale, A-L, Brenton, JD, Tavare, S, Caldas, C, Aparicio, S, Curtis, C, Shah, SP, Chin, S-F, Turashvili, G, Rueda, OM, Dunning, MJ, Speed, D, Lynch, AG, Samarajiwa, S, Yuan, Y, Graef, S, Ha, G, Haffari, G, Bashashati, A, Russell, R, McKinney, S, Langerod, A, Green, A, Provenzano, E, Wishart, G, Pinder, S, Watson, P, Markowetz, F, Murphy, L, Ellis, I, Purushotham, A, Borresen-Dale, A-L, Brenton, JD, Tavare, S, Caldas, C, and Aparicio, S

Abstract

The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA–RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the ‘CNA-devoid’ subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.

Published
2012

38. A genome-wide association study and biological pathway analysis of epilepsy prognosis in a prospective cohort of newly treated epilepsy

Author

Speed, D., primary, Hoggart, C., additional, Petrovski, S., additional, Tachmazidou, I., additional, Coffey, A., additional, Jorgensen, A., additional, Eleftherohorinou, H., additional, De Iorio, M., additional, Todaro, M., additional, De, T., additional, Smith, D., additional, Smith, P. E., additional, Jackson, M., additional, Cooper, P., additional, Kellett, M., additional, Howell, S., additional, Newton, M., additional, Yerra, R., additional, Tan, M., additional, French, C., additional, Reuber, M., additional, Sills, G. E., additional, Chadwick, D., additional, Pirmohamed, M., additional, Bentley, D., additional, Scheffer, I., additional, Berkovic, S., additional, Balding, D., additional, Palotie, A., additional, Marson, A., additional, O'Brien, T. J., additional, and Johnson, M. R., additional

Published
2013
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40. Dark current measurement in photoconductors

Author

Young, E. T and Speed, D

Subjects
Instrumentation And Photography
Abstract

The Space Infrared Telescope Facility (SIRTF) is envisioned as a next generation space observatory for observations between 2 and 700 microns. In order to address many of the important scientific questions in areas such as cosmology, star formation, and galaxy evolution, infrared detectors of unparalleled sensitivity will be required. Dark current measurements are described for a number of different discrete photoconductive detectors that may be of importance at the very low backgrounds expected with SIRTF.

Published
1986

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