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    "# Introduction\n",
    "\n",
    "The blood group antigens of the ABO and RhD systems play a pivotal role in transfusion medicine because of their role in the safe administration of blood transfusions. In addition, these cell surface antigens have been demonstrated to have direct effects on the susceptibility for several diseases [@bib9; @bib23; @bib24]. One of the first such studies was published in 1962, demonstrating a relationship between the ABO system and ischemic heart disease [@bib3]. Multiple subsequent studies have revealed associations with a range of diseases, with a prominent example being a decreased risk of thromboembolic events and increased risks of some hemorrhagic events in individuals with blood group O [@bib9; @bib8; @bib27]. The difference in thrombotic and hemorrhagic phenotypes has been attributed to variability in levels of _Factor VIII_ and _von Willebrand factor_, where ABO status may explain as much as 30% of this variability [@bib9; @bib19; @bib12; @bib16; @bib18] Other prominent examples include associations with risks of a number of infectious diseases, to the extent that the allele distribution of the blood group antigens has evolved to reflect some areas endemic to these infectious diseases [@bib11]. This is in part true for infectious disease such as _Plasmodium falciparum_ malaria, _Helicobacter pylori_, and _Vibrio cholera_, where ABO blood groups are involved in different aspects of pathogenesis, from microbe attachment and entry into cells to subsequent disease development and severity of disease [@bib23; @bib11; @bib6; @bib7]. Surface antigens of the ABO blood groups are defined by the immunodominant, terminal sugar residues on certain glycolipids and glycoproteins anchored to the membrane of red blood cells and exposed extracellularly. Even if expressed from a single-gene locus, the _ABO_ gene on chromosome 9, A and B antigens are present not only on erythroid cells but also in many other tissues, and due to the diverse tissue, expression may result in differences in disease occurrence [@bib13]. The _A_ and _B_ allelic variants of the _ABO_ locus encode the A and B glycosyltransferases, which differ only by a few amino acid residues, add the donor substrates UDP-_N_-acetylgalactosamine or UDP-galactose, respectively, to a common acceptor substrate, namely a carbohydrate chain terminating with the so-called H antigen, in turn dependent on fucosyltransferase activity expressed from the _FUT1_ and _FUT2_ genes on chromosome 19. In blood group O, the H antigen is left unaltered due to lack of ABO enzyme activity, most commonly by a single-nucleotide deletion in the _ABO_ coding region [@bib22].\n",
    "\n",
    "The RhD antigen, on the other hand, has a less clear link to health outcomes. RhD status has mainly been linked to alloimmunization of the pregnant women with hemolytic disease of the fetus and newborn as a consequence [@bib25]. Beyond these direct effects, little is known about its role in disease pathogenesis. The difference between RhD-positive and -negative blood group is the presence or absence of the RhD protein on the red blood cell surface. However, both individuals with and without RhD possess the homologous RhCE protein and Rh-associated glycoprotein (RhAG) on their red cells. Thus, functions carried out by RhD are likely performed RhCE and RhAG in RhD-negative individuals, and this redundancy may in part explain the scarcity of findings related to RhD status [@bib1].\n",
    "\n",
    "Using the Scandinavian Donation and Transfusion (SCANDAT) database, we have previously studied associations between ABO blood groups and cancer subtypes, cardiovascular and thromboembolic disease, the occurrence of dementia and degradation of bioprosthetic aortic valves in relation to ABO blood group [@bib27; @bib20; @bib28; @bib26]. However, these and most other prior studies into the association between ABO blood group and disease outcomes have been limited by potentially misdirected a priori hypotheses and phenome-wide disease associations have not been thoroughly explored in a systematic manner. Therefore, in the current study, we aimed to agnostically investigate the association between ABO and RhD blood group and disease occurrence for a large number of disease phenotypes using large-scale population-based Swedish healthcare registries.\n",
    "\n",
    "# Materials and methods\n",
    "\n",
    "## Study population and study design\n",
    "\n",
    "Individuals in the study were identified using an updated version of the Scandinavian donations and transfusion database (SCANDAT3-S). This database includes close to 8 million individuals who have donated blood, received a blood transfusion, or have had blood group testing done for other reasons. Other reasons for blood group testing would typically be pre-emptive testing for example, before surgery or in antenatal care. The database contains detailed information on blood donations, transfusions, as well as blood group antigen and antibody testing results and is thoroughly described elsewhere [@bib29]. It is nationally complete since 1995, but information dates back to 1968 with various levels of completeness, mainly depending on the geographical region. Using unique national registration numbers assigned to all inhabitants of Sweden, the SCANDAT3-S database has been linked to a range of national health outcomes registers, for hospital care, cancer, cause of death, and drug prescriptions . From SCANDAT3-S, we extracted information on ABO and RhD blood group and created a main cohort and a validation cohort. The main cohort consisted of all individuals who were born in Sweden where at least one parent was born in Sweden and who, for any reason, had undergone ABO and RhD blood group typing with a conclusive result, but who did not donate blood within 90 days of the test. Person-time for blood donors were excluded from the main cohort to maximize the representativeness of the study population. In the validation cohort, we included all individuals in the SCANDAT3-S database who had ever donated blood. As such, an individual could contribute person-time in both cohorts, such as in the case a person started to donate blood more than 90 days later from a blood grouping test that was initially performed for other reasons. The person-time before blood donation would contribute to the main cohort censoring at entry in the validation cohort starting at the time of blood grouping before the blood donation.\n",
    "\n",
    "## Outcomes\n",
    "\n",
    "We defined and studied a large number of disease categories. Non-cancer disease categories were based on discharge diagnoses from the national patient register, which covers all hospital inpatient care in Sweden since 1987 and all specialist outpatient care since 1997, and from the Cause of Death register, which records underlying causes of death for all persons in Sweden since 1964 [@bib4; @bib17]. Because the 10th revision of the International Classification of Disease (ICD) was implemented in 1997, we limited outcomes ascertainment to events from 1997 or later to avoid inconsistencies between ICD revisions. Cancer outcomes were based on the Cancer Register, which records all incident cancer cases in Sweden since 1958 [@bib2]. All of these registries are held and maintained by the Swedish National Board of Health and Welfare and have a high level of completeness and accuracy. Dates of death and emigration were obtained from population registers kept by Statistics Sweden.\n",
    "\n",
    "Details of non-cancerous disease categories are presented in [Supplementary file 3](#supp3). Non-cancer diseases were classified into disease categories based on the first three codes of the diagnosis, according to the ICD-10 codebook. We did not consider external causes of disease, traumatic injuries, or symptom-based codes as these were deemed unlikely to be related to blood group antigens. Cancer disease categories were based on anatomical coding using the 7th revision of the ICD for all non-hematological malignancies and the 8th revision of the ICD for hematological malignancies. For details of cancer categories, see [Supplementary file 4](#supp4) (SAS code for cancer disease grouping is available upon request).\n",
    "\n",
    "In total, we considered 1217 distinct disease categories. After database construction, we excluded disease categories with fewer than 50 events before analysis as we would be unlikely to detect sufficient events in the validation cohort in categories with fewer than 50 events in the main cohort.\n",
    "\n",
    "## Statistical methods\n",
    "\n",
    "All persons were followed from the date of the first blood grouping test, from their 18th birthday, or from January 1, 1997, whichever occurred last. Follow-up was extended until the first incident event in each disease category, emigration, death or December 31, 2017, whichever occurred first. A person could thus be included in follow-up for all disease categories investigated.\n",
    "\n",
    "Descriptive statistics were presented for cohort baseline data. For the main analysis, we used a Poisson regression model. In the model, we incorporated the following covariates: ABO blood group (A, AB, B or O), RhD status (weak or category expression variants were excluded), sex, calendar-period, and age. A restricted cubic spline functions with four or five knots placed according to Harrell’s method were applied to the age and calendar-period covariates [@bib14]. The regression model was fitted separately to each disease category resulting in incidence rate ratios (IRR) for each ABO blood group and RhD status using blood group O and RhD negative as reference, respectively. Wald’s method was used to construct 95% confidence intervals (CI). Equi-dispersion was tested using a Lagrange multiplier test. For disease categories where data demonstrated significant over- or under-dispersion after also performing the same analysis but reducing the number of knots from 5 to 4, analyses were instead run using quasi-Poisson regression.\n",
    "\n",
    "Multiple testing was handled using a two-stage approach. First, in the exploratory analysis using the main cohort, we applied a false discovery rate (FDR) adjustment of raw p-values assuming positive dependency of stochastic ordering between outcomes. Second, in the confirmatory analysis using the validation cohort, we used the disease categories with significant effects from explorative analysis, with results presented both without adjustment and using a Bonferroni adjustment. In effect, this allowed us to limit type one errors presenting confirmed associations with high certainty, but still not to compromise type two errors for future confirmatory analysis in other cohorts.\n",
    "\n",
    "# Results\n",
    "\n",
    "Characteristics of the main and validation cohorts are presented in [Table 1](#table1). When combining the main and validation cohort, there were a total of 5.1 million unique individuals. The main cohort consisted of 4.2 million individuals who at any point had undertaken an ABO and RhD blood antigen test. The distribution of A, AB, B, and O were 47%, 5%, 10%, and 38%, respectively, and 84% of individuals were RhD positive. Women constituted 60% of the cohort. The median age at cohort entry was 52 years (interquartile range \\[IQR], 30–71) and the median year of birth was 1949 (IQR, 1931–1971).\n",
    "\n",
    "table: Table 1.\n",
    ":::\n",
    "## Baseline characteristics of main and validation cohort.\n",
    "\n",
    "|                             | Main cohort |             | Validation cohort |             |\n",
    "|-----------------------------|-------------|-------------|-------------------|-------------|\n",
    "| Number                      | 4,204,234   |             | 1,197,522         |             |\n",
    "| Age, median (IQR)           |             |             |                   |             |\n",
    "| Age, median (IQR)           | 52          | (30-71)     | 30                | (23-41)     |\n",
    "| Year of birth, median (IQR) | 1949        | (1931–1971) | 1966              | (1953–1978) |\n",
    "| Sex, %                      | 60          |             | 49                |             |\n",
    "| Blood group, %              |             |             |                   |             |\n",
    "| A                           | 47          |             | 45                |             |\n",
    "| AB                          | 5           |             | 5                 |             |\n",
    "| B                           | 10          |             | 11                |             |\n",
    "| O                           | 38          |             | 39                |             |\n",
    "| RhD positive, %             | 84          |             | 82                |             |\n",
    ":::\n",
    "{#table1}\n",
    "\n",
    "Not accounting for censoring due to disease events, the main cohort accrued a total of 49.9 million person-years of follow-up, 23.7 million in blood group A, 2.3 million in blood group AB, 4.9 million in blood group B, and 18.9 million in blood group O.\n",
    "\n",
    "Of the original 1217 disease categories, 1090 remained available for analyses after excluding disease categories with fewer than 50 events. The median number of events per disease category in the main cohort was 4748 (IQR, 869–231,166). A meta-summary of results of regression analyses is presented in [Table 2](#table2), and graphically in the form of a volcano plot in [Figure 1](#fig1) (also, as an interactive, online variant as [Supplementary file 1](#supp1)). Alternatively, results are also presented as an ICD chapter-based, variant Manhattan plot in [Figure 2](#fig2) (also as an interactive, online variant as [Supplementary file 2](#supp2)). Overall, in the main cohort and before FDR adjustment for multiple testing, there were 343 and 98 statistically significant associations for the ABO and RhD blood group systems and unique disease categories, respectively. Of these, a total of 143 (41%) and 13 (13%) associations between blood group and unique outcome remained statistically significant for ABO and RhD blood group systems, respectively, after FDR adjustment. For the ABO system, IRRs for statistically significant associations after FDR adjustment ranged from 0.57 to 0.99 for negative associations and from 1.01 to 1.52 for positive associations. For RhD status, IRRs ranged from 0.90 to 0.97 for negative associations and from 1.02 to 1.08 for positive associations. Details of all associations identified after FDR are presented in [Supplementary file 5](#supp5) (for ABO blood groups) and [Supplementary file 6](#supp6) (for RhD)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "caption": "### Volcano plot of all findings from main and validation cohort.\n Volcano plot depicting spread of p-values of significant and non-significant ABO and RhD blood groups for main cohort and validated results. The labels represent the nine findings with the lowest p-value in the validation cohort.",
    "id": "fig1",
    "label": "Figure 1."
   },
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           "style": "light"
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           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
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          "scene": {
           "xaxis": {
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           "baxis": {
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
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           "x": 0.05
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          "xaxis": {
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        "title": {
         "font": {
          "color": "#333333",
          "size": 14
         },
         "text": "<b>Volcanoplot showing Incidence Rate Ratios/False Discovery Rate Adjusted P-Values Depending On ABO Blood Group and RhD Status<br>"
        },
        "width": 1000,
        "xaxis": {
         "gridcolor": "white",
         "linecolor": "black",
         "range": [
          -0.8,
          0.8
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         "showgrid": false,
         "showline": true,
         "tickvals": [
          -0.5,
          0,
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          "text": "<b>IRR"
         },
         "visible": true
        },
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         "linecolor": "black",
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          -5,
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         "ticktext": [
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          1.3010299956639813,
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         "tickwidth": 1,
         "title": {
          "text": "<b>-log10(FDR-adjusted p-value)"
         },
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     "metadata": {}
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   ],
   "source": [
    "df = pd.read_csv('df.csv')\n",
    "df_r = pd.read_csv('df_r.csv')\n",
    "concordant = pd.read_csv('concordant.csv')\n",
    "\n",
    "fig = go.Figure()\n",
    "fdr_p=df['fdr_p']\n",
    "\n",
    "\n",
    "grp=df['bg'].unique().tolist()\n",
    "for groups in grp:\n",
    "        data_filtered=df[(df.bg==groups) & (df.fdr_p < 0.05)]\n",
    "        fig.add_trace(go.Scatter(x=data_filtered['x'], \n",
    "                         y=data_filtered['y'], \n",
    "                         mode='markers',\n",
    "                             opacity=0.5,\n",
    "                         marker=dict(color=data_filtered['color'],line=dict(color='white',width=0)),\n",
    "                         text=data_filtered['bg_cat'],\n",
    "                         customdata=data_filtered['event_pyrs'],\n",
    "                         hovertext=data_filtered['fdr_p'],\n",
    "                        meta=data_filtered['combined'],\n",
    "                        hovertemplate = \"<b>%{text}</b>\" +\n",
    "                         \"<br>FDR-P: %{hovertext:,.2g}:\" +\n",
    "                         #\"<br>IRR: %{marker.size:,.2f}\"+\n",
    "                         \"<br>IRR (95% CI): %{meta}\" +\n",
    "                         \"<br>Events/Person-years: %{customdata}\" +\n",
    "                         \"<extra></extra>\",\n",
    "                         marker_size=(8*data_filtered['irr']),\n",
    "                         name=groups\n",
    "                        )\n",
    "              )\n",
    "\n",
    "\n",
    "\n",
    "df_r2=df_r[df_r.fdr_p<0.05]\n",
    "fig.add_trace(go.Scatter(x=df_r2['x'], \n",
    "                         y=df_r2['y'], \n",
    "                         mode='markers',\n",
    "                             opacity=0.5,\n",
    "                         marker=dict(color=df_r2['color'],opacity=df_r2['opacity'],line=dict(color='white',width=0)),\n",
    "                         text=df_r2['bg_cat'],\n",
    "                         customdata=df_r2['event_pyrs'],\n",
    "                         hovertext=df_r2['fdr_p'],\n",
    "                        meta=df_r2['combined'],\n",
    "                        hovertemplate = \"<b>%{text}</b>\" +\n",
    "                         \"<br>FDR-P: %{hovertext:,.2g}:\" +\n",
    "                         #\"<br>IRR: %{marker.size:,.2f}\"+\n",
    "                         \"<br>IRR (95% CI): %{meta}\" +\n",
    "                         \"<br>Events/Person-years: %{customdata}\" +\n",
    "                         \"<extra></extra>\",\n",
    "                         marker_size=(8*df_r2['irr']),\n",
    "                         name='RhD positive'\n",
    "                        )\n",
    "              )\n",
    "              \n",
    "fig.add_trace(go.Scatter(x=pd.concat([df_r.x[(df_r.fdr_p>=0.05)],df.x[(df.fdr_p>=0.05)]]), \n",
    "                         y=pd.concat([df_r.y[(df_r.fdr_p>=0.05)],df.y[(df.fdr_p>=0.05)]]), \n",
    "                         mode='markers',\n",
    "                             opacity=0.7,\n",
    "                         marker=dict(color=pd.concat([df_r.color[(df_r.fdr_p>=0.05)],df.color[(df.fdr_p>=0.05)]]),opacity=0.7,line=dict(color='white',width=0)),\n",
    "                         name='Non-significant ABO and RhD',\n",
    "                         showlegend=True,\n",
    "                        )\n",
    "              )\n",
    "    \n",
    "    \n",
    "\n",
    "fig.add_trace(go.Scatter(x=concordant.x, \n",
    "                         y=concordant.y, \n",
    "                         mode='markers',\n",
    "                             opacity=0.8,\n",
    "                         marker=dict(color='black',opacity=0.8,line=dict(color='white',width=0)),\n",
    "                         text=concordant['bg_cat'],\n",
    "                         customdata=concordant['event_pyrs'],\n",
    "                         hovertext=concordant['pvalue'],\n",
    "                        meta=concordant['combined'],\n",
    "                        hovertemplate = \"<b>%{text}</b>\" +\n",
    "                         \"<br>P-value: %{hovertext:,.2g}:\" +\n",
    "                         \"<br>IRR: %{marker.size:,.2f}\"+\n",
    "                         \"<br>IRR (95% CI): %{meta}\" +\n",
    "                         \"<br>Events/Person-years: %{customdata}\" +\n",
    "                         \"<extra></extra>\",\n",
    "                         marker_size=(5*concordant['irr']),\n",
    "                         name='Concordant between Main and Donor cohort, ABO and RhD'\n",
    "                        )\n",
    "              )\n",
    "\n",
    "\n",
    "fig.add_shape(\n",
    "       # Line Horizontal\n",
    "          type=\"line\",\n",
    "         x0=-0.8,\n",
    "        x1=0.8,\n",
    "        y0=-(np.log10(0.05)),\n",
    "        y1=-(np.log10(0.05)),\n",
    "           line=dict(\n",
    "              color=\"black\",\n",
    "             width=0.5,\n",
    "            dash=\"dot\"\n",
    "       ), opacity=1,\n",
    ")\n",
    "   \n",
    "    \n",
    "#Layout\n",
    "fig.update_layout(title='<b>Volcanoplot showing Incidence Rate Ratios/False Discovery Rate Adjusted P-Values Depending On ABO Blood Group and RhD Status'\n",
    "                  ''\n",
    "                  '<br>',\n",
    "                  title_font_color = '#333333',\n",
    "                  title_font_size = 14,\n",
    "                  \n",
    "                  font=dict(family=\"helvetica\"),\n",
    "                  \n",
    "                  xaxis_title='<b>IRR',\n",
    "                  xaxis_visible=True,\n",
    "                  xaxis_showline=True,\n",
    "\n",
    "                  #xaxis_type='category',\n",
    "                  xaxis_gridcolor='white',\n",
    "                  #xaxis_tickangle = 90,\n",
    "                  xaxis_range=[-0.8,0.8],\n",
    "                  xaxis_showgrid=False,\n",
    "                  xaxis_tickvals=[-0.5, 0, 0.5],\n",
    "                  xaxis_linecolor='black',\n",
    "                  \n",
    "                  plot_bgcolor='white',\n",
    "                  showlegend=True,\n",
    "                  legend= {'itemsizing': 'constant'},\n",
    "                  hoverlabel=dict(\n",
    "                        bgcolor=\"white\", \n",
    "                        font_size=18, \n",
    "                        font_family=\"helvetica\"),\n",
    "                  \n",
    "                  yaxis_ticks=('outside'),\n",
    "                  yaxis_tickwidth=1,\n",
    "                  yaxis_ticklen=5,\n",
    "                  yaxis_title='<b>-log10(FDR-adjusted p-value)',\n",
    "                  yaxis_showline=True,\n",
    "                  yaxis_showgrid=False,\n",
    "                  yaxis_visible=True,\n",
    "                  yaxis_linewidth=1,\n",
    "                  yaxis_linecolor='black',\n",
    "                  yaxis_range=[-5,140],\n",
    "                  #yaxis_tickangle = 90,\n",
    "                  yaxis_tickvals=[-(np.log10(0.05)),50, 100],\n",
    "                  yaxis_ticktext=['FDR-P 0.05',50,100],\n",
    "                  #yaxis_ticksuffix='%',\n",
    "                  \n",
    "                  width=1000,\n",
    "                  height=900\n",
    "                  )\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "caption": "### Manhattan plot of all findings from main and validation cohort mapped by ICD chapter.\n Manhattan plot depicting distribution of p-values for significant and non-significant associations between ABO and RhD blood groups and available outcomes for main cohort and validated results mapped by disease chapter in ICD.",
    "id": "fig2",
    "label": "Figure 2."
   },
   "outputs": [
    {
     "output_type": "display_data",
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          "Circulatory Disease",
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          "Gastro-intestinal Disease",
          "Dermatological Disease",
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   ],
   "source": [
    "df = pd.read_csv('df_man.csv')\n",
    "df_r = pd.read_csv('df_r_man.csv')\n",
    "concordant = pd.read_csv('concordant_man.csv')\n",
    "mchap = pd.read_csv('mchap.csv')\n",
    "\n",
    "fig = go.Figure()\n",
    "fdr_p=df['fdr_p']\n",
    "\n",
    "grp=df['bg'].unique().tolist()\n",
    "for groups in grp:\n",
    "        data_filtered=df[(df.bg==groups) & (df.fdr_p < 0.05)]\n",
    "        fig.add_trace(go.Scatter(x=data_filtered['row'], \n",
    "                         y=data_filtered['y'], \n",
    "                         mode='markers',\n",
    "                             opacity=0.5,\n",
    "                         marker=dict(color=data_filtered['color'],line=dict(color='white',width=0)),\n",
    "                         text=data_filtered['bg_cat'],\n",
    "                         customdata=data_filtered['event_pyrs'],\n",
    "                         hovertext=data_filtered['fdr_p'],\n",
    "                     \n",
    "                        meta=data_filtered['combined'],\n",
    "                        hovertemplate = \"<b>%{text}</b>\" +\n",
    "                         \"<br>FDR-P: %{hovertext:,.2g}:\" +\n",
    "                         \"<br>IRR (95% CI): %{meta}\" +\n",
    "                         \"<br>Events/Person-years: %{customdata}\" +\n",
    "                         \"<extra></extra>\",\n",
    "                         marker_size=(9*data_filtered['irr']),\n",
    "                         name=groups\n",
    "                        )\n",
    "              )\n",
    "\n",
    "\n",
    "\n",
    "df_r2=df_r[df_r.fdr_p<0.05]\n",
    "fig.add_trace(go.Scatter(x=df_r2['x'], \n",
    "                         y=df_r2['y'], \n",
    "                         mode='markers',\n",
    "                             opacity=0.5,\n",
    "                         marker=dict(color=df_r2['color'],opacity=df_r2['opacity'],line=dict(color='white',width=0)),\n",
    "                         text=df_r2['bg_cat'],\n",
    "                         customdata=df_r2['event_pyrs'],\n",
    "                         hovertext=df_r2['fdr_p'],\n",
    "                        meta=df_r2['combined'],\n",
    "                        hovertemplate = \"<b>%{text}</b>\" +\n",
    "                         \"<br>FDR-P: %{hovertext:,.2g}:\" +\n",
    "                         \"<br>IRR (95% CI): %{meta}\" +\n",
    "                         \"<br>Events/Person-years: %{customdata}\" +\n",
    "                         \"<extra></extra>\",\n",
    "                         name='RhD positive'\n",
    "                        )\n",
    "              )\n",
    "              \n",
    "fig.add_trace(go.Scatter(x=pd.concat([df_r.x[(df_r.fdr_p>=0.05)],df.x[(df.fdr_p>=0.05)]]), \n",
    "                         y=pd.concat([df_r.y[(df_r.fdr_p>=0.05)],df.y[(df.fdr_p>=0.05)]]), \n",
    "                         mode='markers',\n",
    "                             opacity=0.7,\n",
    "                         marker=dict(color=pd.concat([df_r.color[(df_r.fdr_p>=0.05)],df.color[(df.fdr_p>=0.05)]]),opacity=0.7,line=dict(color='white',width=0)),\n",
    "\n",
    "                         marker_size=2,\n",
    "                         name='Non-significant ABO and RhD',\n",
    "                         showlegend=True,\n",
    "                        )\n",
    "              )\n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "fig.add_trace(go.Scatter(x=concordant.x, \n",
    "                         y=concordant.y, \n",
    "                         mode='markers',\n",
    "                             opacity=0.8,\n",
    "                         marker=dict(color=concordant['color'],opacity=0.8,line=dict(color='white',width=0)),\n",
    "                         text=concordant['bg_cat'],\n",
    "                        customdata=concordant['event_pyrs'],\n",
    "                         hovertext=concordant['bon_p'],\n",
    "                        meta=concordant['combined'],\n",
    "                        hovertemplate = \"<b>%{text}</b>\" +\n",
    "                         \"<br>BON-P: %{hovertext:,.2g}:\" +\n",
    "                         \"<br>IRR (95% CI): %{meta}\" +\n",
    "                         \"<br>Events/Person-years: %{customdata}\" +\n",
    "                         \"<extra></extra>\",\n",
    "                         marker_size=(5*concordant['irr']),\n",
    "                         name='Concordant in validation cohort, after bonferroni-adjustment'\n",
    "                        )\n",
    "              )\n",
    "\n",
    "fig.add_trace(go.Scatter(x=[], \n",
    "                         y=[],\n",
    "                        mode='markers',\n",
    "                         xaxis='x2'\n",
    "                        )\n",
    "             )\n",
    "\n",
    "fig.add_shape(\n",
    "       # Line Horizontal\n",
    "          type=\"line\",\n",
    "         x0=0,\n",
    "        x1=df.x.max()+1,\n",
    "        y0=-(np.log10(0.05)),\n",
    "        y1=-(np.log10(0.05)),\n",
    "           line=dict(\n",
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    "            dash=\"dot\"\n",
    "       ), opacity=1,\n",
    ")\n",
    "\n",
    "    \n",
    "#Layout\n",
    "fig.update_layout(\n",
    "    title='<b>Manhattanplot - Disease catagories/False Discovery Rate Adjusted P-Values In Main Cohort Depending On ABO Blood Group. </b> <br>'\n",
    "                  ''\n",
    "                  '',\n",
    "                  title_font_color = '#333333',\n",
    "                  title_font_size = 14,\n",
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    "                  xaxis=dict(\n",
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    "                      showgrid=False,\n",
    "                      ticklen=5,\n",
    "                      tickvals=mchap['mins'],\n",
    "                      showticklabels=False,\n",
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    "                      linecolor='black',\n",
    "                      tickcolor='black',\n",
    "                      ticks=('outside'),\n",
    "                      linewidth=1),\n",
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    "                  plot_bgcolor='white',\n",
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    "                  legend= {'itemsizing': 'constant'},\n",
    "                  hoverlabel=dict(\n",
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    "                  yaxis_ticks=('outside'),\n",
    "                  yaxis_tickwidth=1,\n",
    "                  yaxis_ticklen=5,\n",
    "                  yaxis_title='<b>-log10(FDR-adjusted p-value)',\n",
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    "                  yaxis_showgrid=False,\n",
    "                  yaxis_visible=True,\n",
    "                  yaxis_linewidth=1,\n",
    "                  yaxis_linecolor='black',\n",
    "                  #yaxis_range=[-5,190],\n",
    "                  #yaxis_tickangle = 90,\n",
    "                  yaxis_tickvals=[-(np.log10(0.05)),50, 100],\n",
    "                  yaxis_ticktext=['P 0.05',50,100],\n",
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    "                  yaxis_range=[-0.5,130],\n",
    "                  \n",
    "                  width=1200,\n",
    "                  height=1200\n",
    "                  )\n",
    "\n",
    "    \n",
    "\n",
    "fig.show()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "table: Table 2.\n",
    ":::\n",
    "## Meta-summary of results.\n",
    "\n",
    "|                              |    Main cohort    |                  |                  |           |                  |   |  Validation cohort |                  |                  |           |                  |\n",
    "|:----------------------------:|:-----------------:|:----------------:|:----------------:|:---------:|:----------------:|:-:|:------------------:|:----------------:|:----------------:|:---------:|------------------|\n",
    "| Individuals                  | 4,204,234         |                  |                  |           |                  |   | 1,197,522          |                  |                  |           |                  |\n",
    "| Person-years, sum            | 50 M              |                  |                  |           |                  |   | 22 M               |                  |                  |           |                  |\n",
    "| Events, median (IQR)         | 4748 (869–23,166) |                  |                  |           |                  |   | 7129 (2464–19,973) |                  |                  |           |                  |\n",
    "|                              | A                 | AB               | B                | ABO total | RhD positive     |   | A                  | AB               | B                | ABO total | RhD positive     |\n",
    "| Before adjustment            | 229               | 129              | 179              | 537       | 106              |   | 64                 | 37               | 42               | 143       | 5                |\n",
    "| Positive effects, N          | 150               | 79               | 107              | 336       | 61               |   | 47                 | 23               | 22               | 92        | 3                |\n",
    "| Negative effects, N          | 79                | 50               | 72               | 201       | 45               |   | 17                 | 14               | 20               | 51        | 2                |\n",
    "| After adjustment*            | 108               | 56               | 70               | 234       | 13               |   | 26                 | 13               | 10               | 49        | 1                |\n",
    "| Positive effects, N          | 66                | 38               | 36               | 140       | 10               |   | 19                 | 10               | 6                | 35        | 1                |\n",
    "| Negative effects, N          | 42                | 18               | 34               | 94        | 3                |   | 7                  | 3                | 4                | 14        | 0                |\n",
    "| Positive IRR, median (range) | 1.05 (1.01–1.72)  | 1.09 (1.03–1.52) | 1.09 (1.02–1.39) |           | 1.05 (1.02–1.08) |   | 1.1 (1.03–1.57)    | 1.32 (1.07–1.89) | 1.5 (1.07–1.64)  |           | 1.12 (1.12–1.12) |\n",
    "| Negative IRR, median (range) | 0.95 (0.77–0.99)  | 0.92 (0.74–0.97) | 0.92 (0.57–0.98) |           | 0.97 (0.9–0.97)  |   | 0.92 (0.86–0.95)   | 0.84 (0.81–0.87) | 0.88 (0.83–0.93) |           | -                |\n",
    ":::\n",
    "{#table2}\n",
    "\n",
    "In our validation cohort, consisting of almost 1.2 million blood donors accruing 22 million person-years of follow-up, we validated the findings from the significant disease categories from the first analysis. Among the 143 and 5 significant disease categories for ABO and RhD, respectively, the median number of events was 7129 (IQR 2464–19,973). Before multiple testing adjustment, we identified 160 associations between a blood group in 143 and 5 disease categories, for the ABO and RhD blood group, respectively. After Bonferroni adjustment, there were 49 and 1 associations remaining between ABO and RhD blood group, respectively ([Table 2](#table2) and [Figure 3](#fig3))."
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   "execution_count": 4,
   "metadata": {
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    "label": "Figure 3.",
    "caption": "### All significant un-adjusted findings from the validation cohort.\n Significant disease categories in the validation cohort. Blood group as compared to blood group O and log10(IRR) displayed with 95% confidence bands. All p-values are raw, highlighted p-value indicates associations that remained statistically significant also after Bonferroni adjustment."
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