A Low-Frequency Inactivating AKT2 Variant Enriched in the Finnish Population Is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk

Alisa Manning 1 2 3, Heather M Highland 4 5, Jessica Gasser 1, Xueling Sim 6 7, Taru Tukiainen 1 8 9, Pierre Fontanillas 1 10, Niels Grarup 11, Manuel A Rivas 12, Anubha Mahajan 12, Adam E Locke 6, Pablo Cingolani 13 14, Tune H Pers 1 11 15 16, Ana Viñuela 17 18 19, Andrew A Brown 20 21, Ying Wu 22, Jason Flannick 1 23, Christian Fuchsberger 6, Eric R Gamazon 24 25, Kyle J Gaulton 12 26, Hae Kyung Im 24, Tanya M Teslovich 6, Thomas W Blackwell 6, Jette Bork-Jensen 11, Noël P Burtt 1, Yuhui Chen 12, Todd Green 1, Christopher Hartl 1, Hyun Min Kang 6, Ashish Kumar 12 27, Claes Ladenvall 28, Clement Ma 6, Loukas Moutsianas 12, Richard D Pearson 12, John R B Perry 12 29 30, N William Rayner 12 31 32, Neil R Robertson 12 31, Laura J Scott 6, Martijn van de Bunt 12 31, Johan G Eriksson 33 34 35 36 37, Antti Jula 37, Seppo Koskinen 37, Terho Lehtimäki 38, Aarno Palotie 1 2 39, Olli T Raitakari 40 41, Suzanne B R Jacobs 1, Jennifer Wessel 42 43, Audrey Y Chu 44, Robert A Scott 30, Mark O Goodarzi 45 46, Christine Blancher 47, Gemma Buck 47, David Buck 47, Peter S Chines 48, Stacey Gabriel 1, Anette P Gjesing 11, Christopher J Groves 31, Mette Hollensted 11, Jeroen R Huyghe 6, Anne U Jackson 6, Goo Jun 6, Johanne Marie Justesen 11, Massimo Mangino 17, Jacquelyn Murphy 1, Matt Neville 31, Robert Onofrio 1, Kerrin S Small 17, Heather M Stringham 6, Joseph Trakalo 47, Eric Banks 1, Jason Carey 1, Mauricio O Carneiro 1, Mark DePristo 1, Yossi Farjoun 1, Timothy Fennell 1, Jacqueline I Goldstein 1 8, George Grant 1, Martin Hrabé de Angelis 49 50 51, Jared Maguire 1, Benjamin M Neale 1 8, Ryan Poplin 1, Shaun Purcell 1 2 52, Thomas Schwarzmayr 53, Khalid Shakir 1, Joshua D Smith 54, Tim M Strom 53 55, Thomas Wieland 53, Jaana Lindstrom 56, Ivan Brandslund 57 58, Cramer Christensen 59, Gabriela L Surdulescu 17, Timo A Lakka 60 61 62, Alex S F Doney 63, Peter Nilsson 64, Nicholas J Wareham 30, Claudia Langenberg 30, Tibor V Varga 65, Paul W Franks 65 66 67, Olov Rolandsson 67, Anders H Rosengren 28, Vidya S Farook 68, Farook Thameem 69, Sobha Puppala 68, Satish Kumar 68, Donna M Lehman 69, Christopher P Jenkinson 69 70, Joanne E Curran 68, Daniel Esten Hale 71, Sharon P Fowler 69, Rector Arya 71, Ralph A DeFronzo 69, Hanna E Abboud 69, Ann-Christine Syvänen 72, Pamela J Hicks 73 74 75, Nicholette D Palmer 73 74 75, Maggie C Y Ng 73 74, Donald W Bowden 73 74 75, Barry I Freedman 76, Tõnu Esko 1 9 77 78, Reedik Mägi 78, Lili Milani 78, Evelin Mihailov 78, Andres Metspalu 78, Narisu Narisu 48, Leena Kinnunen 37, Lori L Bonnycastle 48, Amy Swift 48, Dorota Pasko 29, Andrew R Wood 29, João Fadista 28, Toni I Pollin 79, Nir Barzilai 80, Gil Atzmon 80 81, Benjamin Glaser 82, Barbara Thorand 50 83, Konstantin Strauch 84 85, Annette Peters 50 83 86, Michael Roden 87 88, Martina Müller-Nurasyid 84 85 86 89, Liming Liang 90 91, Jennifer Kriebel 50 83 92, Thomas Illig 92 93 94, Harald Grallert 50 83 92, Christian Gieger 84, Christa Meisinger 83, Lars Lannfelt 95, Solomon K Musani 96, Michael Griswold 97, Herman A Taylor Jr 98, Gregory Wilson Sr 99, Adolfo Correa 98, Heikki Oksa 100, William R Scott 101, Uzma Afzal 101, Sian-Tsung Tan 102 103, Marie Loh 101 104 105, John C Chambers 101 103 106, Jobanpreet Sehmi 102 103, Jaspal Singh Kooner 102, Benjamin Lehne 101, Yoon Shin Cho 107, Jong-Young Lee 108, Bok-Ghee Han 109, Annemari Käräjämäki 110 111, Qibin Qi 66 112, Lu Qi 66 113, Jinyan Huang 90, Frank B Hu 66 90, Olle Melander 114, Marju Orho-Melander 115, Jennifer E Below 116, David Aguilar 117, Tien Yin Wong 118 119, Jianjun Liu 7 120, Chiea-Chuen Khor 7 118 119 120 121, Kee Seng Chia 7, Wei Yen Lim 7, Ching-Yu Cheng 7 118 119 122, Edmund Chan 123, E Shyong Tai 7 123 124, Tin Aung 118 119, Allan Linneberg 125 126 127, Bo Isomaa 35 128, Thomas Meitinger 53 55 86, Tiinamaija Tuomi 35 129, Liisa Hakaste 35, Jasmina Kravic 28, Marit E Jørgensen 130, Torsten Lauritzen 131, Panos Deloukas 32, Kathleen E Stirrups 132 133, Katharine R Owen 31 134, Andrew J Farmer 135, Timothy M Frayling 29, Stephen P O'Rahilly 136, Mark Walker 137, Jonathan C Levy 31, Dylan Hodgkiss 17, Andrew T Hattersley 138, Teemu Kuulasmaa 139, Alena Stančáková 139, Inês Barroso 32 136, Dwaipayan Bharadwaj 140, Juliana Chan 141 142 143, Giriraj R Chandak 144, Mark J Daly 8, Peter J Donnelly 12 145, Shah B Ebrahim 146, Paul Elliott 101 147, Tasha Fingerlin 148, Philippe Froguel 149, Cheng Hu 150, Weiping Jia 150, Ronald C W Ma 141 142 143, Gilean McVean 12, Taesung Park 151 152, Dorairaj Prabhakaran 146, Manjinder Sandhu 32 153, James Scott 102, Rob Sladek 14 154 155, Nikhil Tandon 156, Yik Ying Teo 7 157 158, Eleftheria Zeggini 32, Richard M Watanabe 159 160 161, Heikki A Koistinen 37 162 163, Y Antero Kesaniemi 164, Matti Uusitupa 165, Timothy D Spector 17, Veikko Salomaa 37, Rainer Rauramaa 166, Colin N A Palmer 167, Inga Prokopenko 12 31 168, Andrew D Morris 169, Richard N Bergman 170, Francis S Collins 48, Lars Lind 171, Erik Ingelsson 72 172, Jaakko Tuomilehto 56 173 174 175, Fredrik Karpe 31 134, Leif Groop 28, Torben Jørgensen 125 176, Torben Hansen 11 177, Oluf Pedersen 11, Johanna Kuusisto 139 178, Gonçalo Abecasis 6, Graeme I Bell 179, John Blangero 68, Nancy J Cox 24, Ravindranath Duggirala 68, Mark Seielstad 180 181, James G Wilson 182, Josee Dupuis 183 184, Samuli Ripatti 20 39 185, Craig L Hanis 116, Jose C Florez 1 2 3 186, Karen L Mohlke 22, James B Meigs 1 3 187, Markku Laakso 139 178, Andrew P Morris 12 78 188, Michael Boehnke 6, David Altshuler 1 3 9 23 186 189, Mark I McCarthy 12 31 134, Anna L Gloyn 12 31 134, Cecilia M Lindgren 1 12 190

Affiliations

01 July 2017

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doi: 10.2337/db16-1329

Abstract

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.

Figures

Figure 1 AKT2 Pro50Thr association with FI levels. A: For each study, the square represents the estimate of the additive genetic effect for the association of the AKT2 Pro50Thr allele with log-transformed FI levels and the horizontal line gives the corresponding 95% CI of the estimate. Inverse-variance meta-analyses were performed for all discovery studies, all replication studies, and all studies combined. The vertical dashed lines indicate the 95% CI for the estimate obtained in the meta-analysis of all studies combined. DPS, The Finnish Diabetes Prevention Study; DR’s EXTRA, Dose-Responses to Exercise Training study; FIN-D2D, National Diabetes Prevention Programme in Finland; PPP, Prevalence, Prediction and Prevention of Diabetes (PPP)-Botnia study. B: MAF for each available region and ancestry. Across countries of the world, the MAF ranges from 0 to 1.1%. The relative sample sizes (N) for each region/ancestry are displayed with the blue circles and the relative MAFs of AKT2 Pro50Thr are displayed with the purple circles, with the size of the circles showing comparative differences. Within Finland (inset), where the MAF ranges from 0.9 to 1.7%, birthplace and study center data were used to show the allele distribution across the country. aFINRISK 2007, bFIN-D2D 2007, cFINRISK 1997 and 2002.

Figure 1 AKT2 Pro50Thr association with FI levels. A: For each study, the square represents the estimate of the additive genetic effect for the association of the AKT2 Pro50Thr allele with log-transformed FI levels and the horizontal line gives the corresponding 95% CI of the estimate. Inverse-variance meta-analyses were performed for all discovery studies, all replication studies, and all studies combined. The vertical dashed lines indicate the 95% CI for the estimate obtained in the meta-analysis of all studies combined. DPS, The Finnish Diabetes Prevention Study; DR’s EXTRA, Dose-Responses to Exercise Training study; FIN-D2D, National Diabetes Prevention Programme in Finland; PPP, Prevalence, Prediction and Prevention of Diabetes (PPP)-Botnia study. B: MAF for each available region and ancestry. Across countries of the world, the MAF ranges from 0 to 1.1%. The relative sample sizes (N) for each region/ancestry are displayed with the blue circles and the relative MAFs of AKT2 Pro50Thr are displayed with the purple circles, with the size of the circles showing comparative differences. Within Finland (inset), where the MAF ranges from 0.9 to 1.7%, birthplace and study center data were used to show the allele distribution across the country. aFINRISK 2007, bFIN-D2D 2007, cFINRISK 1997 and 2002.

Figure 2 Expression and conservation properties. A: Amino acid alignment and conservation of the three AKT proteins in vertebrates. The x-axis gives the amino acid position and the height of the lines shows the conservation score across 100 vertebrate genome alignments. The functional domains are the PH domain (blue) and the kinase domain (green). The position of AKT2 Pro50Thr is shown in red and the locations of the other AKT2 disease-causing mutations (–40) are shown in orange: Glu17Lys, Arg208Lys, Arg274His, and Arg467Trp. B: WebLogo plots of amino acids 35–60 are shown for AKT2, AKT1, and AKT3, contrasting the homology of the three isoforms. The height of letters gives the relative frequency of different amino acids across the 100 vertebrate species, with the colors showing amino acids with similar charge. C: Expression of AKT1, AKT2, and AKT3 in eight insulin-sensitive tissues using RNA sequencing data from the GTEx Consortium. subcut., subcutaneous.

Figure 2 Expression and conservation properties. A: Amino acid alignment and conservation of the three AKT proteins in vertebrates. The x-axis gives the amino acid position and the height of the lines shows the conservation score across 100 vertebrate genome alignments. The functional domains are the PH domain (blue) and the kinase domain (green). The position of AKT2 Pro50Thr is shown in red and the locations of the other AKT2 disease-causing mutations (–40) are shown in orange: Glu17Lys, Arg208Lys, Arg274His, and Arg467Trp. B: WebLogo plots of amino acids 35–60 are shown for AKT2, AKT1, and AKT3, contrasting the homology of the three isoforms. The height of letters gives the relative frequency of different amino acids across the 100 vertebrate species, with the colors showing amino acids with similar charge. C: Expression of AKT1, AKT2, and AKT3 in eight insulin-sensitive tissues using RNA sequencing data from the GTEx Consortium. subcut., subcutaneous.

Figure 3 Functional properties of AKT2-Thr50. A: Predicted protein structure of AKT2. Domain and variants are highlighted as in Fig. 2A. The relative spatial positioning of the AKT2-Pro50 residue is magnified within the inset. B: HeLa cells were infected with lentiviral V5-AKT2, V5-AKT2-Lys17, V5-AKT2-Thr50, V5-AKT2-Lys208, V5-AKT2-His274, or V5-AKT2-Trp467; starved for 18 h (white bar); and stimulated for 20 min with 100 nmol/L insulin (gray bar). V5-tagged AKT2 was isolated from cell lysates with anti-V5 agarose beads and incubated with GSK3β-GST peptide in an in vitro kinase assay. Quantification of phosphorylated substrate peptide (pGSK3β) relative to total peptide (GST-GSK3β) is shown at the inset. Immunoblots and quantification shown are representative of three independent replicates. Linear model statistical analyses across all three independent replicates are available in Supplementary Fig. 9. The in vitro kinase was immunoblotted (IB) with the indicated antibodies. C: HuH7 cells were infected with lentiviral V5-AKT2, V5-AKT2-Thr50, or control pLX304. At 72 h, relative cellular proliferation was determined with WST-1 assay of HuH7 cells. Error bars represent SD. ***P = 4.5 × 10−5.

Figure 3 Functional properties of AKT2-Thr50. A: Predicted protein structure of AKT2. Domain and variants are highlighted as in Fig. 2A. The relative spatial positioning of the AKT2-Pro50 residue is magnified within the inset. B: HeLa cells were infected with lentiviral V5-AKT2, V5-AKT2-Lys17, V5-AKT2-Thr50, V5-AKT2-Lys208, V5-AKT2-His274, or V5-AKT2-Trp467; starved for 18 h (white bar); and stimulated for 20 min with 100 nmol/L insulin (gray bar). V5-tagged AKT2 was isolated from cell lysates with anti-V5 agarose beads and incubated with GSK3β-GST peptide in an in vitro kinase assay. Quantification of phosphorylated substrate peptide (pGSK3β) relative to total peptide (GST-GSK3β) is shown at the inset. Immunoblots and quantification shown are representative of three independent replicates. Linear model statistical analyses across all three independent replicates are available in Supplementary Fig. 9. The in vitro kinase was immunoblotted (IB) with the indicated antibodies. C: HuH7 cells were infected with lentiviral V5-AKT2, V5-AKT2-Thr50, or control pLX304. At 72 h, relative cellular proliferation was determined with WST-1 assay of HuH7 cells. Error bars represent SD. ***P = 4.5 × 10−5.

Figure 4 Genetic architecture of rare, low-frequency, and common variants associated with FI levels. In this plot, the absolute values of the percent change in FI level due to rare monogenic mutations (diamonds) and common genetic variants (circles) are plotted against the MAF of the variant. The extremely rare monogenic mutations (above the dashed line to the left of the x-axis) were observed in 2–18 individuals (,–40,48,53,54), with the height of the point indicating the percent change in FI levels of mutation carriers from 40 pmol/L, an estimate of population mean FI level. Mutations in INSR and AKT2 p.Arg274His cause compensatory hyperinsulinemia, individuals with TBC1D4 p.Arg363Ter show normal FI levels but postprandial hyperinsulinemia, and mutations in PTEN cause enhanced insulin sensitivity providing protection against type 2 diabetes. For common variants, the percent change in FI levels per insulin-increasing allele is plotted above the solid horizontal axis. These observations are from sequencing (6) and array-based genome-wide association studies (3). For several genes, the effects from rare mutations can be compared with the effects of common variants in or near the gene: PPARG (blue), TBC1D4 (green), PTEN (orange), and AKT2 (red). aDonohue syndrome: biallelic LoF mutations in INSR (54). bRabson-Mendenhall syndrome: biallelic LoF mutations in INSR (54). cPostpubertal severe insulin resistance: heterozygous or homozygous LoF mutations in INSR (54). dLoF PTEN mutations cause Cowden syndrome in which carriers exhibit a lowered FI level (mean 29 pmol/L) compared with matched control subjects (3). eCarriers with the AKT2 p.Glu17Lys mutation were described with hypoinsulinemic hypoketotic hypoglycemia and hemihypertrophy with undetectable serum insulin (37,38).

Figure 4 Genetic architecture of rare, low-frequency, and common variants associated with FI levels. In this plot, the absolute values of the percent change in FI level due to rare monogenic mutations (diamonds) and common genetic variants (circles) are plotted against the MAF of the variant. The extremely rare monogenic mutations (above the dashed line to the left of the x-axis) were observed in 2–18 individuals (,–40,48,53,54), with the height of the point indicating the percent change in FI levels of mutation carriers from 40 pmol/L, an estimate of population mean FI level. Mutations in INSR and AKT2 p.Arg274His cause compensatory hyperinsulinemia, individuals with TBC1D4 p.Arg363Ter show normal FI levels but postprandial hyperinsulinemia, and mutations in PTEN cause enhanced insulin sensitivity providing protection against type 2 diabetes. For common variants, the percent change in FI levels per insulin-increasing allele is plotted above the solid horizontal axis. These observations are from sequencing (6) and array-based genome-wide association studies (3). For several genes, the effects from rare mutations can be compared with the effects of common variants in or near the gene: PPARG (blue), TBC1D4 (green), PTEN (orange), and AKT2 (red). aDonohue syndrome: biallelic LoF mutations in INSR (54). bRabson-Mendenhall syndrome: biallelic LoF mutations in INSR (54). cPostpubertal severe insulin resistance: heterozygous or homozygous LoF mutations in INSR (54). dLoF PTEN mutations cause Cowden syndrome in which carriers exhibit a lowered FI level (mean 29 pmol/L) compared with matched control subjects (3). eCarriers with the AKT2 p.Glu17Lys mutation were described with hypoinsulinemic hypoketotic hypoglycemia and hemihypertrophy with undetectable serum insulin (37,38).

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