Expressive

rs10493544 - TNNI3K, FPGT-TNNI3K

Magnitude 2.2 · 3 studies on file

Reported associations

  • Characterization of the genetic architecture of infant and early childhood body mass index. - Nature metabolism (2022) · Helgeland Ø, Vaudel M, Sole-Navais P, Flatley C, Juodakis J, Bacelis J, Koløen IL, Knudsen GP, Johansson BB, Magnus P, Kjennerud TR, Juliusson PB, Stoltenberg C, Holmen OL, Andreassen OA, Jacobsson B, Njølstad PR, Johansson S · PubMed 35315439

    Early childhood obesity is a growing global concern; however, the role of common genetic variation on infant and child weight development is unclear. Here, we identify 46 loci associated with early childhood body mass index at specific ages, matching different child growth phases, and representing four major trajectory patterns. We perform genome-wide association studies across 12 time points from birth to 8 years in 28,681 children and their parents (27,088 mothers and 26,239 fathers) in the Norwegian Mother, Father and Child Cohort Study. Monogenic obesity genes are overrepresented near identified loci, and several complex association signals near LEPR, GLP1R, PCSK1 and KLF14 point towards a major influence for common variation affecting the leptin-melanocortin system in early life, prov

  • A trans-ancestral meta-analysis of genome-wide association studies reveals loci associated with childhood obesity. - Human molecular genetics (2020) · Bradfield JP, Vogelezang S, Felix JF, Chesi A, Helgeland Ø, Horikoshi M, Karhunen V, Lowry E, Cousminer DL, Ahluwalia TS, Thiering E, Boh ET, Zafarmand MH, Vilor-Tejedor N, Wang CA, Joro R, Chen Z, Gauderman WJ, Pitkänen N, Parra EJ, Fernandez-Rhodes L, Alyass A, Monnereau C, Curtin JA, Have CT, McCormack SE, Hollensted M, Frithioff-Bøjsøe C, Valladares-Salgado A, Peralta-Romero J, Teo YY, Standl M, Leinonen JT, Holm JC, Peters T, Vioque J, Vrijheid M, Simpson A, Custovic A, Vaudel M, Canouil M, Lindi V, Atalay M, Kähönen M, Raitakari OT, van Schaik BDC, Berkowitz RI, Cole SA, Voruganti VS, Wang Y, Highland HM, Comuzzie AG, Butte NF, Justice AE, Gahagan S, Blanco E, Lehtimäki T, Lakka TA, Hebebrand J, Bonnefond A, Grarup N, Froguel P, Lyytikäinen LP, Cruz M, Kobes S, Hanson RL, Zemel BS, Hinney A, Teo KK, Meyre D, North KE, Gilliland FD, Bisgaard H, Bustamante M, Bonnelykke K, Pennell CE, Rivadeneira F, Uitterlinden AG, Baier LJ, Vrijkotte TGM, Heinrich J, Sørensen TIA, Saw SM, Pedersen O, Hansen T, Eriksson J, Widén E, McCarthy MI, Njølstad PR, Power C, Hyppönen E, Sebert S, Brown CD, Järvelin MR, Timpson NJ, Johansson S, Hakonarson H, Jaddoe VWV, Grant SFA · PubMed 31504550

    Although hundreds of genome-wide association studies-implicated loci have been reported for adult obesity-related traits, less is known about the genetics specific for early-onset obesity and with only a few studies conducted in non-European populations to date. Searching for additional genetic variants associated with childhood obesity, we performed a trans-ancestral meta-analysis of 30 studies consisting of up to 13 005 cases (≥95th percentile of body mass index (BMI) achieved 2-18 years old) and 15 599 controls (consistently <50th percentile of BMI) of European, African, North/South American and East Asian ancestry. Suggestive loci were taken forward for replication in a sample of 1888 cases and 4689 controls from seven cohorts of European and North/South American ancestry. In ad

  • The Genetic and Neural Substrates of Externalizing Behavior - Unknown journal (n.d.) · Unknown authors · PubMed 36324656

    ABSTRACT: Background To gain more insight into the biological factors that mediate vulnerability to display externalizing behaviors, we leveraged genome-wide association study summary statistics on 13 externalizing phenotypes. Methods After data classification based on genetic resemblance, we performed multivariate genome-wide association meta-analyses and conducted extensive bioinformatic analyses, including genetic correlation assessment with other traits, Mendelian randomization, and gene set and gene expression analyses. Results The genetic data could be categorized into disruptive behavior (DB) and risk-taking behavior (RTB) factors, and subsequent genome-wide association meta-analyses provided association statistics for DB and RTB (Neff = 523,150 and 1,506,537, respectively), yieldi

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