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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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go"
   ]
  },
{
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id":"fig1",
    "label":"Figure 1.",
    "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."
},
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\"black\", \"range\": [-0.8, 0.8], \"showgrid\": false, \"showline\": true, \"tickvals\": [-0.5, 0, 0.5], \"title\": {\"text\": \"<b>IRR\"}, \"visible\": true}, \"yaxis\": {\"linecolor\": \"black\", \"linewidth\": 1, \"range\": [-5, 140], \"showgrid\": false, \"showline\": true, \"ticklen\": 5, \"ticks\": \"outside\", \"ticktext\": [\"FDR-P 0.05\", 50, 100], \"tickvals\": [1.3010299956639813, 50, 100], \"tickwidth\": 1, \"title\": {\"text\": \"<b>-log10(FDR-adjusted p-value)\"}, \"visible\": true}},                        {\"responsive\": true}                    ).then(function(){\n",
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     "metadata": {},
     "output_type": "display_data"
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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=1200,\n",
    "                  height=900\n",
    "                  )\n",
    "\n",
    "fig.show()"
   ]
  }  ,
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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     },
     "metadata": {
    "id":"fig2",
    "label":"Figure 2.",
    "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."
},
     "output_type": "display_data"
    }
   ],
   "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",
    "              color=\"black\",\n",
    "             width=0.5,\n",
    "            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",
    "                  \n",
    "                  font=dict(family=\"helvetica\"),\n",
    "                  \n",
    "                  xaxis=dict(\n",
    "                      anchor='y',\n",
    "                      title='<b>',\n",
    "                      visible=True,\n",
    "                      showline=True,\n",
    "                      gridcolor='white',\n",
    "                      tickangle = 300,\n",
    "                      range=[0,df.x.max()],\n",
    "                      showgrid=False,\n",
    "                      ticks=('inside'),\n",
    "                      ticklen=0,\n",
    "                     # tickangle=0,\n",
    "                      tickvals=mchap['median'],\n",
    "                      ticktext=mchap['names'].tolist(),\n",
    "                      linecolor='black',\n",
    "                      linewidth=1),\n",
    "\n",
    "                  xaxis2=dict(\n",
    "                      anchor='y',\n",
    "                      overlaying='x',\n",
    "                      title='<b>',\n",
    "                      visible=True,\n",
    "                      showline=True,\n",
    "                      gridcolor='white',\n",
    "                      tickangle = 0,\n",
    "                      range=[0,df.x.max()],\n",
    "                      showgrid=False,\n",
    "                      ticklen=5,\n",
    "                      tickvals=mchap['mins'],\n",
    "                      showticklabels=False,\n",
    "                   #   ticktext=[\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",],\n",
    "                      linecolor='black',\n",
    "                      tickcolor='black',\n",
    "                      ticks=('outside'),\n",
    "                      linewidth=1),\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=[-5,190],\n",
    "                  #yaxis_tickangle = 90,\n",
    "                  yaxis_tickvals=[-(np.log10(0.05)),50, 100],\n",
    "                  yaxis_ticktext=['P 0.05',50,100],\n",
    "                               #yaxis_ticksuffix='%',\n",
    "                  yaxis_range=[-0.5,130],\n",
    "                  \n",
    "                  width=1500,\n",
    "                  height=1200\n",
    "                  )\n",
    "\n",
    "    \n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "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)).\n",
    "\n",
    "figure: Figure 3.\n",
    ":::\n",
    "![](elife-65658.ipynb.media/fig3.jpg)\n",
    "\n",
    "## All significant un-adjusted findings from the validation cohort.\n",
    "\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.\n",
    ":::\n",
    "{#fig3}\n",
    "\n",
    "A number of previously well-established associations were seen among the Bonferroni-adjusted results. For thrombosis, blood group A had a higher risk as compared to blood group O (e.g., pulmonary embolism, IRR 1.57 \\[95% CI, 1.51–1.64] and portal vein thrombosis, IRR 1.51 \\[95% CI, 1.25–1.83]). Bleeding disorders were more frequent in blood group O as compared to blood group A (e.g., gastric ulcer, IRR 0.92 \\[95% CI, 0.88–0.95] and duodenal ulcer, IRR 0.86 \\[95% CI, 0.82–0.9]). Thyrotoxicosis was also less common in blood groups A and AB as compared to blood group O (with IRRs of 0.90 \\[95% CI, 0.86–0.93] and 0.84 \\[95% CI, 0.77–0.92], for A and AB, respectively). Pregnancy-induced hypertension was less common in blood groups A and AB, as compared to blood group O (with IRRs of 0.95 \\[95% CI, 0.92–0.97] and 0.87 \\[95% CI, 0.83–0.92] for A and AB, respectively). Pancreatic cancer was the only malignancy that remained associated with a blood group, specifically blood group A as compared to blood group O (IRR, 1.29; 95% CI, 1.19–1.40). A new finding was that of calculus of the kidney and ureter, which were found to be less common in blood group B as compared to blood group O (IRR 0.93 \\[95% CI, 0.89–0.96]). Cholelithiasis, which has been disputed, was more common in blood groups A and AB as compared to blood group O (with IRRs of 1.07 \\[95% CI, 1.05–1.09] and 1.09 \\[95% CI, 1.05–1.13] for A and AB, respectively).\n",
    "\n",
    "In disease categories not significant after Bonferroni adjustment, some findings exhibited particularly strong effects, such as for viral and other specified intestinal infections, where blood group AB had a significantly lower risk, as compared to blood group O (IRR 0.74; 95% CI, 0.62–0.87). There was a lower risk for ankylosing spondylitis in blood group AB as compared to blood group O (IRR 0.79; 95% CI, 0.67–0.94), and for acute pancreatitis, again with a lower risk in blood group AB as compared to blood group O (IRR 1.14; 95% CI, 1.04–1.24). For the RhD positive as compared to RhD negative, only one disease category remained statistically significant after Bonferroni adjustment, namely pregnancy-induced hypertension (IRR 1.12; 95% CI, 1.09–1.16). Strong effects identified in the main cohort, but not in the validation cohort were hereditary factor VIII deficiency in blood group B (IRR 0.57; 95% CI, 0.42–0.77), well-differentiated thyroid cancer in blood groups AB (IRR 0.74; 95% CI, 0.62–0.88) and B (IRR 0.79; 95% CI, 0.70–0.90), measles in blood group A (IRR 1.72; 95% CI, 1.23–2.39), as well as both erythema nodosum (IRR 1.32; 95% CI, 1.15–1.53) and sarcoidosis in blood group B (IRR 1.15; 95% CI, 1.08–1.23), as compared to blood group O.\n",
    "\n",
    "# Discussion\n",
    "\n",
    "In this large cohort study of 5.1 million unique persons followed over 70 million person-years, we performed an agnostic analysis of associations between the ABO and RhD blood groups and the risk of 1217 distinct disease categories. After multiple testing adjustment and comparison with a validation cohort, 49 and 1 associations between disease categories and blood group for ABO and RhD remained significant, respectively. Overall, we were able to confirm a number of previously known associations such as risk of thrombosis and hemorrhagic events. In addition, we also identified novel associations, some with firm evidence and valid even after conservative Bonferroni adjustment, in the validation cohort, including calculus of the kidney and ureter. Furthermore, being the largest study so far, we also found that blood groups A and AB had a lower risk of gestational hypertension, compared to blood group O, which has previously been disputed [@bib5; @bib10; @bib15]. Of the identified associations, we speculate that some associations may be driven by increased screening due to other concomitant diseases that are associated with blood groups, which might be the case for hyperlipidemias that are screened for in heart disease. Most of the investigated disease or disease groups, however, do not seem to be strongly influenced by the ABO blood group of the individual. As to possible mechanisms explaining the associations identified, we may only generate hypothesis to be further tested. It is however peculiar to identify a condition, such as renal caliculi, with highly variable distribution, in regard to geographic region, to have an association to blood group. A possible mechanism could be, when considering the full spectrum of disease categories investigated, that lower urine pH in diabetic patients, a disease category associated with an increased incidence in blood group B, may result in altered stone formation [@bib21].\n",
    "\n",
    "In previous studies, intending to dissect the association between blood groups and disease, there is diversity in findings and non-findings, something that may be a consequence of ethnic or geographic group, sample size, complexity of statistical modeling, or disease classifications used. This, however, imposes difficulties when comparing our results to the vast number of studies available concentrating the mentioned discoveries to a select few disease categories.\n",
    "\n",
    "This is hitherto the largest study investigating blood group antigens and disease occurrence in an effort to find novel and confirm previously known associations. There are some particular strengths to our approach and data. Most notably, the study was based on a very large study population, representing one third of the Swedish population, with long-term and unbiased follow-up. This ensures both the reliability and the generalizability of the results. The agnostic approach also has the advantage of not being based on specific pre-set conceptions of specific disease categories and possible associations of blood group. All disease categories are treated equally and investigated using the same principles effectively removing researcher bias. Moreover, the data has been collected prospectively in various high-quality healthcare registries during a long time period with almost complete coverage. In addition, the fact that all blood group data in the SCANDAT3-S database were collected from clinical transfusion registers – the quality of which is essential for the safe administration of blood transfusions – ensures that there should be little or no errors in the blood group coding. Similarly, while the validity of the outcomes registration certainly varies between the different disease categories, the degree of such misclassification is unlikely to vary between blood groups, and so it should not affect the magnitude of point estimates.\n",
    "\n",
    "The current study is limited by several factors. One such factor is the disease classification scheme used, based primarily on ICD revision 10 categories that in some instances may lack precision. Smaller disease entities were not accounted for and thus there may be true associations that were missed. It is thus possible that some of the associations that we reported were driven by multiple unknown associations within a specific disease category that may have unequal, or even detrimental, effects on the outcome. However, we believe that this limitation is an opportunity for further sub-categorized investigations in the future when even more events and follow-up time are available.\n",
    "\n",
    "Another limitation that prevents strong casual inference is the possibility some of the observed associations between ABO blood group and disease categories were driven by other disease associations with ABO blood group. This might, for example, be the case for the associations between blood group A and diabetes as well as hyperlipidemia, which are potentially driven not by a causal association but possibly instead by an association between blood group A and ischemic heart disease, at the occurrence of which diabetes and lipid disorders are screened for and thus frequently diagnosed. We cannot exclude the possibility that some of the other associations were driven by similar non-causal mechanisms.\n",
    "\n",
    "To limit the possibility of false-positive findings, we handled over-dispersed Poisson models using Quasi-Poisson and also in the main cohort applied the FDR approach, described by Benjamini and Hochberg, and then utilized a Bonferroni adjustment on the sub-grouped outcomes in the validation cohort. The aim of this approach was to reduce type one errors without being overly conservative by first conducting an explorative analysis in the main cohort. We also employed a confirmatory analysis with a validation cohort to further limit the possibility of false-positive findings. However, because the validation cohort was both smaller and consisted only of blood donors, who were selected for their good health, the ensuing smaller number of events may result in failure to detect potentially interesting associations. Furthermore, selecting explicitly healthy donors with no known serious health issue at time of start of donation may result in failure to detect less common or specifically non-acquired or early acquired disease. However, in the validation cohort, only approximately 1% of the categories had fewer events than 50 and all findings from the main cohort can be seen in the live [Supplementary files 1](#supp1) and [!number(2)](#supp2) and [Supplementary files 5](#supp5)–[!number(8)](#supp8). It may still be informative to consider also some of the associations from the main cohort that were not corroborated in the validation cohort. This is exemplified by pancreatic cancer where we saw an increased risk in blood groups AB and B in the main cohort (IRR 1.37 and 1.129, p&lt;0.00001 and p&lt;0.00001 for AB and B, respectively) and a similar, yet non-significant effect in the validation cohort (IRR 1.14 and 1.26, p-value 0.8 and 0.6 for B and AB, respectively). This also expands to the non-findings in terms of cancerous disease were multiple relationships that have previously been demonstrated, but not in the validation cohort after Bonferroni adjustment [@bib28]. This strengthens our decision to not limit the presentation of findings to only disease categories identified in the conservative Bonferroni-adjusted analysis.\n",
    "\n",
    "Still, after these limitations we believe that our findings support and generate strong further evidence for previously known associations and indicate new and interesting relationships for disease such as calculus of the kidney and ureter, pregnancy-induced hypertension, well-differentiated thyroid cancer, and sarcoidosis. The new set of associations should be validated in other cohorts but also investigated using a mechanistic approach for a possible causal and biological interaction."
   ]
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      "name": "Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet",
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      "name": "Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet",
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     {
      "address": {
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       "addressLocality": "Stockholm",
       "type": "PostalAddress"
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      "name": "Cardiology Department, Södersjukhuset",
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  "dateAccepted": {
   "type": "Date",
   "value": "2021-04-15"
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  "datePublished": {
   "type": "Date",
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  "dateReceived": {
   "type": "Date",
   "value": "2020-12-11"
  },
  "description": [
   {
    "content": [
     "Background: There are multiple known associations between the ABO and RhD blood groups and disease. No systematic population-based studies elucidating associations between a large number of disease categories and blood group have been conducted."
    ],
    "type": "Paragraph"
   },
   {
    "content": [
     "Methods: Using SCANDAT3-S, a comprehensive nationwide blood donation-transfusion database, we modeled outcomes for 1217 disease categories including 70 million person-years of follow-up, accruing from 5.1 million individuals."
    ],
    "type": "Paragraph"
   },
   {
    "content": [
     "Results: We discovered 49 and 1 associations between a disease and ABO and RhD blood groups, respectively, after adjustment for multiple testing. We identified new associations such as a decreased risk of kidney stones and blood group B as compared to blood group O. We also expanded previous knowledge on other associations such as pregnancy-induced hypertension and blood groups A and AB as compared to blood group O and RhD positive as compared to negative."
    ],
    "type": "Paragraph"
   },
   {
    "content": [
     "Conclusions: Our findings generate strong further support for previously known associations, but also indicate new interesting relations."
    ],
    "type": "Paragraph"
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   {
    "content": [
     "Funding: Swedish Research Council."
    ],
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