The Illumina NovaSeq 6000 system is the largest of the Illumina sequencing instruments, able to run two flow cells independently of each other and generate massive sequencing depth at competitive prices.
D Karasik, MC Zillikens, YH Hsu, A Aghdassi, K Akesson, N Amin, I Barroso, DA Bennett, L Bertram, M Bochud, IB Borecki, L Broer, AS Buchman, L Byberg, H Campbell, N Campos-Obando, JA Cauley, PM Cawthon, JC Chambers, Z Chen, NH Cho, HJ Choi, WC Chou, SR Cummings, LCPGM de Groot, PL De Jager, I Demuth, L Diatchenko, MJ Econs, G Eiriksdottir, AW Enneman, J Eriksson, JG Eriksson, K Estrada, DS Evans, MF Feitosa, M Fu, C Gieger, H Grallert, V Gudnason, LJ Lenore, C Hayward, A Hofman, G Homuth, KM Huffman, LB Husted, T Illig, E Ingelsson, T Ittermann, JO Jansson, T Johnson, R Biffar, JM Jordan, A Jula, M Karlsson, KT Khaw, TO Kilpeläinen, N Klopp, JSL Kloth, DL Koller, JS Kooner, WE Kraus, S Kritchevsky, Z Kutalik, T Kuulasmaa, J Kuusisto, M Laakso, J Lahti, T Lang, BL Langdahl, MM Lerch, JR Lewis, C Lill, L Lind, C Lindgren, Y Liu, G Livshits, Ö Ljunggren, RJF Loos, M Lorentzon, J Luan, RN Luben, I Malkin, FE McGuigan, C Medina-Gomez, T Meitinger, H Melhus, D Mellström, K Michaëlsson, BD Mitchell, AP Morris, L Mosekilde, M Nethander, AB Newman, JR O'Connell, BA Oostra, ES Orwoll, A Palotie, M Peacock, M Perola, A Peters, RL Prince, BM Psaty, K Räikkönen, SH Ralston, S Ripatti, F Rivadeneira, JA Robbins, JI Rotter, I Rudan, V Salomaa, S Satterfield, S Schipf, CS Shin, AV Smith, SB Smith, N Soranzo, TD Spector, A Stancáková, K Stefansson, E Steinhagen-Thiessen, L Stolk, EA Streeten, U Styrkarsdottir, KMA Swart, P Thompson, CA Thomson, G Thorleifsson, U Thorsteinsdottir, E Tikkanen, GJ Tranah, AG Uitterlinden, CM van Duijn, NM van Schoor, L Vandenput, P Vollenweider, H Völzke, J Wactawski-Wende, M Walker, N J Wareham, D Waterworth, MN Weedon, HE Wichmann, E Widen, FMK Williams, JF Wilson, NC Wright, LM Yerges-Armstrong, L Yu, W Zhang, JH Zhao, Y Zhou, CM Nielson, TB Harris, S Demissie, DP Kiel, C Ohlsson
Am. J. Clin. Nutr., 109 (2) 1938-3207 (2019)
Lean body mass (LM) plays an important role in mobility and metabolic function. We previously identified five loci associated with LM adjusted for fat mass in kilograms. Such an adjustment may reduce the power to identify genetic signals having an association with both lean mass and fat mass.
To determine the impact of different fat mass adjustments on genetic architecture of LM and identify additional LM loci.
We performed genome-wide association analyses for whole-body LM (20 cohorts of European ancestry with n = 38,292) measured using dual-energy X-ray absorptiometry) or bioelectrical impedance analysis, adjusted for sex, age, age2, and height with or without fat mass adjustments (Model 1 no fat adjustment; Model 2 adjustment for fat mass as a percentage of body mass; Model 3 adjustment for fat mass in kilograms).
Seven single-nucleotide polymorphisms (SNPs) in separate loci, including one novel LM locus (TNRC6B), were successfully replicated in an additional 47,227 individuals from 29 cohorts. Based on the strengths of the associations in Model 1 vs Model 3, we divided the LM loci into those with an effect on both lean mass and fat mass in the same direction and refer to those as "sumo wrestler" loci (FTO and MC4R). In contrast, loci with an impact specifically on LM were termed "body builder" loci (VCAN and ADAMTSL3). Using existing available genome-wide association study databases, LM increasing alleles of SNPs in sumo wrestler loci were associated with an adverse metabolic profile, whereas LM increasing alleles of SNPs in "body builder" loci were associated with metabolic protection.
In conclusion, we identified one novel LM locus (TNRC6B). Our results suggest that a genetically determined increase in lean mass might exert either harmful or protective effects on metabolic traits, depending on its relation to fat mass.
Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine.
A Calleja-Rodriguez, J Pan, T Funda, Z Chen, J Baison, F Isik, S Abrahamsson, HX Wu
BMC Genomics, 21 (1) 1471-2164 (2020)
Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof-of-concept for Scots pine (Pinus sylvestris L.), a genomic prediction study was conducted with 694 individuals representing 183 full-sib families that were genotyped with genotyping-by-sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers.
Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP) and Bayesian LASSO, with some exceptions. A subset of approximately 6000 SNP markers, was enough to provide similar prediction efficiencies as the full set of 8719 markers. Additionally, prediction efficiencies of genomic models were enough to achieve a higher selection response, that varied between 50-143% higher than the traditional pedigree-based selection.
Although prediction efficiencies were similar for genomic and pedigree models, the relative selection response was doubled for genomic models by assuming that earlier selections can be done at the seedling stage, reducing the progeny testing time, thus shortening the breeding cycle length roughly by 50%.
Association of Chromosome 9p21 with Subsequent Coronary Heart Disease Events: A GENIUS-CHD Study of Individual Participant Data.
RS Patel, AF Schmidt, V Tragante, RO McCubrey, MV Holmes, LJ Howe, K Direk, A Åkerblom, K Leander, SS Virani, KA Kaminski, JD Muehlschlegel, MP Dubé, H Allayee, P Almgren, M Alver, EV Baranova, H Behlouli, B Boeckx, PS Braund, LP Breitling, G Delgado, NE Duarte, L Dufresne, N Eriksson, L Foco, CM Gijsberts, Y Gong, J Hartiala, M Heydarpour, JA Hubacek, M Kleber, D Kofink, P Kuukasjärvi, VV Lee, A Leiherer, PA Lenzini, D Levin, LP Lyytikäinen, N Martinelli, U Mons, CP Nelson, K Nikus, AP Pilbrow, R Ploski, YV Sun, MWT Tanck, WHW Tang, S Trompet, SW van der Laan, J Van Setten, RO Vilmundarson, C Viviani Anselmi, E Vlachopoulou, E Boerwinkle, C Briguori, JF Carlquist, KF Carruthers, G Casu, J Deanfield, P Deloukas, F Dudbridge, N Fitzpatrick, B Gigante, S James, ML Lokki, PA Lotufo, N Marziliano, IR Mordi, JB Muhlestein, C Newton-Cheh, J Pitha, CH Saely, A Samman-Tahhan, PB Sandesara, A Teren, A Timmis, F Van de Werf, E Wauters, AAM Wilde, I Ford, DJ Stott, A Algra, MG Andreassi, D Ardissino, BJ Arsenault, CM Ballantyne, TO Bergmeijer, CR Bezzina, SC Body, P Bogaty, GJ de Borst, H Brenner, R Burkhardt, C Carpeggiani, G Condorelli, RM Cooper-DeHoff, S Cresci, U de Faire, RN Doughty, H Drexel, JC Engert, KAA Fox, D Girelli, E Hagström, SL Hazen, C Held, H Hemingway, IE Hoefer, GK Hovingh, JA Johnson, PA de Jong, JW Jukema, MP Kaczor, M Kähönen, J Kettner, M Kiliszek, OH Klungel, B Lagerqvist, D Lambrechts, JO Laurikka, T Lehtimäki, D Lindholm, BK Mahmoodi, AH Maitland-van der Zee, R McPherson, O Melander, A Metspalu, W Pepinski, O Olivieri, G Opolski, CN Palmer, G Pasterkamp, CJ Pepine, AC Pereira, L Pilote, AA Quyyumi, AM Richards, M Sanak, M Scholz, A Siegbahn, J Sinisalo, JG Smith, JA Spertus, AFR Stewart, W Szczeklik, A Szpakowicz, JM Ten Berg, G Thanassoulis, J Thiery, Y van der Graaf, FLJ Visseren, J Waltenberger, P Van der Harst, JC Tardif, N Sattar, CC Lang, G Paré, JM Brophy, JL Anderson, W März, L Wallentin, VA Cameron, BD Horne, NJ Samani, AD Hingorani, FW Asselbergs
Circ Genom Precis Med, 2574-8300 (2019)
Genetic variation at chromosome 9p21 is a recognized risk factor for coronary heart disease (CHD). However, its effect on disease progression and subsequent events is unclear, raising questions about its value for stratification of residual risk.
A variant at chromosome 9p21 (rs1333049) was tested for association with subsequent events during follow-up in 103,357 Europeans with established CHD at baseline from the GENIUS-CHD Consortium (73.1% male, mean age 62.9 years). The primary outcome, subsequent CHD death or myocardial infarction (CHD death/MI), occurred in 13,040 of the 93,115 participants with available outcome data. Effect estimates were compared to case/control risk obtained from CARDIoGRAMPlusC4D including 47,222 CHD cases and 122,264 controls free of CHD.
Meta-analyses revealed no significant association between chromosome 9p21 and the primary outcome of CHD death/MI among those with established CHD at baseline (GENIUS-CHD OR 1.02; 95% CI 0.99-1.05). This contrasted with a strong association in CARDIoGRAMPlusC4D OR 1.20; 95% CI 1.18-1.22; p for interaction Conclusions: In contrast to studies comparing individuals with CHD to disease free controls, we found no clear association between genetic variation at chromosome 9p21 and risk of subsequent acute CHD events when all individuals had CHD at baseline. However, the association with subsequent revascularization may support the postulated mechanism of chromosome 9p21 for promoting atheroma development.
Digital gene expression profiling of primary acute lymphoblastic leukemia cells.
J Nordlund, A Kiialainen, O Karlberg, EC Berglund, H Göransson-Kultima, M Sønderkær, KL Nielsen, MG Gustafsson, M Behrendtz, E Forestier, M Perkkiö, S Söderhäll, G Lönnerholm, AC Syvänen
Leukemia, 26 (6) 1476-5551 (2012)
We determined the genome-wide digital gene expression (DGE) profiles of primary acute lymphoblastic leukemia (ALL) cells from 21 patients taking advantage of 'second-generation' sequencing technology. Patients included in this study represent four cytogenetically distinct subtypes of B-cell precursor (BCP) ALL and T-cell lineage ALL (T-ALL). The robustness of DGE combined with supervised classification by nearest shrunken centroids (NSC) was validated experimentally and by comparison with published expression data for large sets of ALL samples. Genes that were differentially expressed between BCP ALL subtypes were enriched to distinct signaling pathways with dic(9;20) enriched to TP53 signaling, t(9;22) to interferon signaling, as well as high hyperdiploidy and t(12;21) to apoptosis signaling. We also observed antisense tags expressed from the non-coding strand of ~50% of annotated genes, many of which were expressed in a subtype-specific pattern. Antisense tags from 17 gene regions unambiguously discriminated between the BCP ALL and T-ALL subtypes, and antisense tags from 76 gene regions discriminated between the 4 BCP subtypes. We observed a significant overlap of gene regions with alternative polyadenylation and antisense transcription (P<1 × 10(-15)). Our study using DGE profiling provided new insights into the RNA expression patterns in ALL cells.
Deep targeted sequencing in pediatric acute lymphoblastic leukemia unveils distinct mutational patterns between genetic subtypes and novel relapse-associated genes.
CM Lindqvist, A Lundmark, J Nordlund, E Freyhult, D Ekman, J Carlsson Almlöf, A Raine, E Övernäs, J Abrahamsson, BM Frost, D Grandér, M Heyman, J Palle, E Forestier, G Lönnerholm, EC Berglund, AC Syvänen
To characterize the mutational patterns of acute lymphoblastic leukemia (ALL) we performed deep next generation sequencing of 872 cancer genes in 172 diagnostic and 24 relapse samples from 172 pediatric ALL patients. We found an overall greater mutational burden and more driver mutations in T-cell ALL (T-ALL) patients compared to B-cell precursor ALL (BCP-ALL) patients. In addition, the majority of the mutations in T-ALL had occurred in the original leukemic clone, while most of the mutations in BCP-ALL were subclonal. BCP-ALL patients carrying any of the recurrent translocations ETV6-RUNX1, BCR-ABL or TCF3-PBX1 harbored few mutations in driver genes compared to other BCP-ALL patients. Specifically in BCP-ALL, we identified ATRX as a novel putative driver gene and uncovered an association between somatic mutations in the Notch signaling pathway at ALL diagnosis and increased risk of relapse. Furthermore, we identified EP300, ARID1A and SH2B3 as relapse-associated genes. The genes highlighted in our study were frequently involved in epigenetic regulation, associated with germline susceptibility to ALL, and present in minor subclones at diagnosis that became dominant at relapse. We observed a high degree of clonal heterogeneity and evolution between diagnosis and relapse in both BCP-ALL and T-ALL, which could have implications for the treatment efficiency.
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