200 km, and the HexPoints2SpatialPolygons function. We limited the scope of our study area to Western and Central Europe (Austria, Belgium, Czech Republic, Denmark, France, Germany, Hungary, Luxembourg, Netherlands, Poland, Switzerland, and Slovakia) based upon the broad ancestry results from PCA and ADMIXTURE.²² Results of the GGT analysis were plotted using the ggplot2 package.

Six control cases were run through the same GGT pipeline to validate the method (Methods S1M–S1O). Each control case was chosen based upon four or five generations (16 or 32 ancestors) of known ancestry from within 80 km of the same location. Given the expectation of Beethoven's origins in western Germany, we drew two control cases from each of three locations surrounding western Germany: northern Netherlands, southwestern Germany, and eastern Germany into the Czech Republic. We used the same number of matches (ca. 10,000) as for the main subject to maintain comparable sample sizes between each analysis.

## Y chromosome analyses

Thirty-fold coverage whole-genome sequencing of living descendants of Aert van Beethoven via 100-cycle paired-end sequencing was performed by BGI in Hong Kong using the DNBseq sequencing platform. An average of 1.11 billion reads were generated per sample following BGI's data filtering steps, with over 97% passing BGI's in-house Q20 filtering (Data S1C). GC content was estimated by BGI at an average of 41% for each sample. De-multiplexing was performed by BGI, and raw FASTQ files were delivered via physical hard-drive, whereupon they were uploaded onto the secure MPI-SHH-DAG computing cluster. FASTQ files were renamed for compatibility with the EAGER (version 1.92.56)⁹⁷ pipeline for downstream processing, which was used for adapter removal, mapping, and duplicate removal. Mapping was performed using BWA version 0.7.12⁸² only against the hg19 Y-chromosome, with MAPQ ≥ 30 and seeding of I = 32 enabled. Files were subsequently converted from SAM to BAM, sorted and indexed using samtools.⁸³ Duplicate reads were removed using DeDup version 0.12.1. Following duplicate removal, reads were again sorted and indexed using samtools.

For Y chromosome comparisons of both historical hair samples and living relatives against the FamilyTreeDNA database, we real-
igned reads to hg38 using BWA-ALN, and used mapDamage2.0 to downscale the base quality of C>T and G>A transitions for his-
torical samples.²⁸,⁸² We required a base quality and read mapping quality (MAPQ) ≥ 30 during variant calling. Variants that were
shared with modern NGS results on the same branch were not taken into consideration as private variants, as their placement
was already known. Variants that were found to be highly recurrent in the FamilyTreeDNA customer database were given less weight.
We checked for each remaining private variant in a custom-built database compiled from thousands of published ancient DNA re-
sults. Variants observed within this ancient genome dataset from different haplogroups and not seen in any modern results were
given less weight. We further gave less weight to variants in regions known to be problematic in ancient DNA, including the centro-
mere, Yq12 heterochromatic region and the DYZ19 repeat.

## Time to most recent common ancestor estimates

FamilyTreeDNA built the Y chromosome phylogeny based on all available Big Y results from customers using a combination of auto-
mated shared variant detection and manual curation. All available SNVs from the non-recombining Y (NRY) that passed
FamilyTreeDNA's variant filters were considered.

Analyses were restricted to SNV mutations within FTBED which covers approximately 11.25 Mbp of NRY. Private and shared SNVs within these regions were automatically determined and validated from the Big Y test results from present-day individuals (private variants in the LvB result were not considered). Reoccurring SNVs that were found to have occurred more than five times across the entire haplotype tree were automatically excluded from the analysis. Adjacent SNVs with the same phylogenetic placement located within 150 bp of each other were classified as multi-nucleotide polymorphisms (MNPs) or incorrect alignments and were also excluded from the analysis. FTBED SNV coverage was recursively calculated for each branch using the intersection of coverage for any two immediate child branches, which themselves were calculated as the union of all their downstream child branches.¹⁵⁸ The resulting intersect coverage was used to adjust the number of SNVs associated with each branch, to account for varying coverage in the NGS data.

We used a modified version of the PATHd8 algorithm¹⁰¹ to convert the mean path length of each clade into a divergence time. For calibration points of the major backbone clades in the Y-DNA haplotype tree, we first used BEAST 2.5.2.¹⁰²,¹⁵⁹ We created an alignment with 91 Big Y sequences spanning the major clades of the tree, using the GTR+Γ model, strict clock, and non-parametric Coalescent Bayesian Skyline model for the tree prior. We ran the model in two MCMC chains of 5×10⁷ steps sampled every 10³ steps, checked for convergence, and discarded 20% as burn-in. These calibration points were used to adjust downstream age estimates based on SNV counts in the PATHd8 algorithm. We converted SNV counts into time using the equation T = S / (μ × C), where S is the SNV count, μ is the Y-DNA mutation rate estimated by Poznik et al.,¹⁶⁰ and C is the intersect coverage of the downstream samples. For all branches less than 2,000 years old, we also averaged the SNV-based time estimates with Short Tandem Repeat (STR) based estimates to reduce stochastic variation caused by either marker set. STR pairwise genetic distances were ordinated against SNV-based time estimates, and modeled as a general additive model (GAM) with log link function in the mgcv package¹⁶¹ of R 3.5.1.⁹² The mean path lengths were modeled as gamma distributions to incorporate uncertainty both in interval between mutations, and mutation rate.

## Y-STR imputation

The Y chromosomal SNVs of Beethoven were analyzed to determine his placement on the Y-DNA tree. His two closest Big Y-tested paternal relatives, FT5 and FT6, in haplogroup I-FT244582 were identified. Their 111 Y-STR marker results were compared to the
modal values of the outgroup (I-FT126507) in order to reconstruct the most-likely STR haplotype of Beethoven. Markers were clas-
sified in different categories.

A) Match between FT5 and FT6 but different from the modal value of the outgroup

B) Match between FT5, FT6 and the outgroup modal value

C) Difference between FT5 and FT6, and FT5 matched the outgroup modal value

D) Difference between FT5 and FT6, and FT6 matched the outgroup modal value

E) Other (no clear pattern)

For category A the FT5/FT6 allele was used and given extra weight. For category B markers the shared allele was used. For category C and D the common allele was used and the uncommon allele was assumed to be the result of a private mutation in FT5 or FT6. Category E markers were not used.

100 Y-STR marker alleles were estimated in this way and compared to FamilyTreeDNA's STR matching database with over 500,000 Y-DNA haplotypes ranging from 12 to 111 marker resolution. The closest matches to the reconstructed haplotype were in turn correlated with their closest Y-STR matches in the general database and their Y-SNV test results were used to narrow down the list to the best candidates for belonging to the I-FT244582 haplogroup.

The final candidates were contacted and invited to participate in the study and their consent was sought to upgrade their Y-DNA
results to Big Y-700. Eight customers, FT1-FT4 and FT7-FT9 were upgraded and four of them, FT1-FT4, were found to belong to
I-FT244582 and formed a new subclade together with FT5; I-FT396000. FT7-FT9 were found to belong to outgroups of the target
clade.

Analyses of living descendants of Karl van Beethoven

Beethoven's genome was initially filtered according to the accessibility mask, after which it was filtered for depth ≥ 10-fold and ge-
notype quality ≥ 30 and converted to PLINK.⁸⁶ Genotype data for the living descendants generated using the HumanOmniExpress-
24 chip were downloaded from FamilyTreeDNA, converted into PLINK, and merged with Beethoven. We filtered the merged PLINK
file by -geno 0.01 resulting in 373,023 shared SNVs with zero missingness, after which the plink file was annotated with a recombi-
nation map.

We ran the phase-unaware IBD-detection algorithm IBIS<sup>31</sup> on the merged PLINK file using the default detection thresholds of ≥ 7cM, ≥ 436 SNVs, and tolerating a homozygous SNV mismatch rate of 0.004.

In order to simulate the expected IBD-sharing and the probabilities of detecting zero IBD-sharing with Beethoven both per individ-
ual and in combination, we used pedSIM<sup>32</sup> to generate 100,000 pedigree structure simulations on a reconstructed pedigree,
including individual sex information and using a sex-specific recombination map.<sup>162</sup> We converted pedSIM seg output files to
 coef files with the seg2coef tool from IBIS<sup>31</sup> using a genome length of 3400 cM. We then calculated total expected IBD-sharing
and probabilities for zero IBD-segment sharing ≥ 7 cM per individual and among all three individuals collectively.

Polygenic risk scoring

We generated polygenic scores for several complex diseases hypothesized by Beethoven's medical biographers (Data S1H; Methods S1R–S1X). We used publicly available GWAS summary statistics to assess polygenic risk for Crohn's disease (CD; Methods S1R), ulcerative colitis (UC; Methods S1S),³⁷ irritable bowel syndrome (IBS; Methods S1T),⁴⁹ systemic lupus erythematosus (SLE; Methods S1U),³⁸ primary biliary cirrhosis (PBC; Methods S1V),⁴⁶ primary sclerosing cholangitis (PSC; Methods S1W),⁴⁷ and cirrhosis (Methods S1X).⁴³ For parameter optimization during polygenic risk scoring for complex diseases, we considered case-control data from the UK Biobank using respective ICD-10 codes for each condition.¹⁶³

Beethoven's genome was filtered within accessible regions, with only genotype calls with a genotype quality ≥ 30 being included, after which it was merged with the full UK Biobank genotyped dataset. Individuals and positions within the merged UK Biobank-Beethoven dataset then underwent quality filtering recommended for polygenic risk scoring. ¹⁶⁴ Genotype calls were filtered by minor allele frequency (≥ 0.05), genotyping rate (≥ 99%), and Hardy-Weinberg Equilibrium (p ≥ 1E-06). Individuals were filtered for heterozygosity and relatedness using quality control criteria provided by the UK Biobank, as well as ancestry, with non-Europeans being excluded based on a list provided by the UK BioBank. Individuals were further filtered by imposing an individual missingness rate ≤ 0.01. The tool FlashPCA²¹ was used to calculate principle components from an LD-pruned version (window-size 200bp, step-size 50 variants, LD R² ≤ 0.25) of the merged and quality filtered UK Biobank-Beethoven file, with the first 50 principal components being incorporated as ancestry covariates during polygenic scoring, in addition to sex. In total, the merged and quality filtered UK Biobank-Beethoven dataset converged on 195,504 positions in 385,136 individuals. LD pruning was subsequently performed for each polygenic score generated using default settings in PRSice 2.0.⁸⁹ All filtering steps were performed in PLINK version 1.9.⁸⁶

GWAS summary statistics files underwent similar stringent quality filtering, including the removal of duplicate and ambiguous (C-G, G-C, A-T, T-A) variants, where strand-flipping may not be detectable. Where such information was reported, sites were filtered by MAF ≥ 0.05 and imputation score INFO ≥ 0.9.

15,000 population matched controls were drawn from the above mentioned quality filtered UK BioBank dataset for polygenic
scoring for all diseases, from which cases were subsequently excluded on a per-disease basis. Individuals who reported their country
of birth as “Germany” (Data Field 20115) and an ethnic background of “white” or “any other white background” (Data Field 21000)
were selected as an independent German cohort (n = 1,153), not included in regression analyses, to assess potential bias in polygenic scoring arising from population stratification. Polygenic risk scoring was performed using the clumping-and-thresholding tool PRSice-2, version 2.3.3.⁸⁹ Results were reported for the p-value threshold with the highest Nagelkerke R² value. For each PRS, we report the partial R² attributable to PRS with respect to the full model including sex and the first 50 principal component values, and after adjustment for disease prevalence.

For cirrhosis, we were able to retrieve only 12 genome-wide significant SNVs from publicly available association summary statistics.⁴³ We therefore performed polygenic risk scoring using imputed genotypes from the UK BioBank, and imputed genotypes from the Beethoven genome, after disabling both clumping-and-thresholding and the removal of ambiguous variants. Imputed genotype data from the UK BioBank was filtered for INFO ≥ 0.9 and converted to PLINK using QCTOOL v2.

## Variant Effect Predictor

To potentially identify rare and high-effect variants we considered variants with a read depth above two reads and with more than one sequence read of the alternative allele. Additionally, insertions or deletions (indels) were called in genes that could cause phenotypes relevant to Beethoven (Data S1F) by the UnifiedGenotyper in GATK, version 3.5.0.⁸⁴ We then annotated and filtered the variants with VEP version 96 in combination with external databases (gnomAD version 2.1.1, dbNSFP version 4.1, spliceAI and ClinVar 2021-10-02).⁹⁰ After annotation we excluded common variants (allele frequency of > 2% in subcohorts of the population-based databases gnomAD, Exome Variant Server (EVS), and 1000Genomes; or > 4 reported homozygous occurrences in gnomAD). Variants that were present in ClinVar but were not rated as being benign or likely benign were kept, even if the frequency in the general population exceeded the thresholds noted above. We then conducted two analyses. First, we analyzed variants in genes that could cause relevant phenotypes (for a list of the phenotypes/genes, see Data S1F). Second, we extended the analysis to variants in genes that are linked to phenotypes according to OMIM (2021-07-11). For the first analysis we filtered for variants with 1) a high impact according to VEP, with 2) a moderate impact, or with 3) a SpliceAI-score above 0.2 in any category. For the second analysis we used stricter filter criteria: We required variants with 2) a moderate effect to have a Combined Annotation-Dependent Depletion (CADD) score above 25 or 3) to have a spliceAI score above 0.5 in any category. Additionally, we required the variants of the second analysis to have a QUAL score above 30 and to have at least 3 sequence reads of the alternative allele. The sequencing reads in the region containing the potentially relevant variants were first manually inspected with the Integrative Genomics Viewer (IGV).⁸⁸ Variants without convincing evidence for validity, such as probable false calls likely to have arisen due to DNA damage, mapping and deduplication failures, were discarded. Remaining variants were further assessed according to the variant interpretation criteria of the American College of Medical Genetics and Genomics (ACMG) guidelines.

## Analysis of coverage

The 1.64Gb of Beethoven's genome retained within our accessibility regions contained 78.2% of the coding-sequence of protein coding genes, in which we identified a total of 16,692 variants after quality filtering of QUAL ≥ 30.

Coverage in regions that might contain causative variants for monogenic diseases was analyzed. Using Ensembl Biomart (GRCh37, version 104), transcripts with the longest protein-coding sequence in protein-coding genes were first selected. The coding sequences of these genes were then used for analysis. For non-protein coding genes, the range from gene start to gene end was chosen according to Ensembl Biomart. First, the overlap of the genomic regions obtained with the accessibility filters was determined. Then, for the overlapping regions, coverage in the aligned reads was determined using the Mosdepth tool.¹⁶⁵

For groups of genes that were analyzed in a prioritized manner, results of the analysis can be found in Table S7 and in Figure S6. The values of individual genes that were prioritized can be found in Data S1F.

## Retrospective cohort studies

We extracted encoded anonymized participant ID's (EIDs) for UK Biobank¹⁶³ males matching Beethoven's genotypes at rs1799945 and rs1800562 in HFE, and rs738409 and rs2294918 in PNPLA3 from genotyped and imputed UK Biobank participant genetic data using qctool v2 and PLINK version 1.9.⁸⁶ We then queried the UK Biobank ICD-10 Main and Secondary databases for ICD-10 codes matching Beethoven's genetic, infectious and lifestyle risk factors both singly and, where sufficient sample sizes permitted, in combinations of risk factors. Odds-Ratios, 95% confidence intervals and p-values were calculated for each retrospective cohort study using Fisher's Exact Test for Count Data in R⁹² (Data S1K-S1M). Heavy drinking (HD) males were included using ICD-10 Main and Secondary codes of F10.1 'Harmful use' and F10.2 'Alcohol dependence.' We must caution, however, that in a recent analysis, Beethoven was not found to meet the DSM-IV criteria for 'Alcohol abuse,' analogous to ICD-10 F10.1 'Harmful use', and only tentatively was argued to meet the minimum necessary criteria for 'Alcohol dependence'.⁸ Thus our heavy drinking cohort is chosen to illustrate disease prevalences in a hypothetical scenario in which Beethoven may have met criteria for one or both of these diagnoses.

## Screening, capture, sequencing and analysis of hepatitis B virus DNA

Shotgun sequencing data generated from the Stumpff (HEB001), Bermann (HEB002), Halm-Thayer (HEB003) and Müller (HEB004) Locks were screened for traces of hepatitis B virus (HBV) DNA using MALT v. 0.3.8,⁵⁷ as previously described.⁷⁶,¹⁶⁶ Reads assigned to HBV were inspected using MEGAN v. 6.13.1¹⁰³ and blasted against the NCBI-NT database to further assess the specificity of the match.
Some of the libraries showed putative traces of HBV DNA based on the screening of shotgun sequencing data (Data S1). In order to confirm this initial result, we then performed HBV-DNA enrichment of all libraries using in-solution capture, as previously described.⁷⁶,¹⁶⁶ Enriched libraries were pooled equimolarly (10 mM final concentration), and prepared for shotgun sequencing on an Illumina Miseq platform (Illumina, San Diego, CA) with 2x75 paired-end cycles. Additionally, all corresponding DNA extraction and library preparation blanks were sequenced on a separate Miseq run after HBV-DNA enrichment to provide negative controls.

Sequencing reads were demultiplexed based on sequenced P7 and P5 indices (allowing for one mismatch per index). Adapter trimming and paired-read merging were then performed using AdapterRemoval 2.3.0.¹⁶⁷ Only merged reads were retained, and fragments smaller than 30 bp were excluded. The following steps were then conducted for each library separately as well as for the whole data combined. Reads were mapped against the HBV reference genome (GenBank: NC_003977; genotype D) using the EAGER pipeline⁹⁷ with the CircularMapper option and a mismatch parameter (-n) of 0.01, a quality filtering (-q) of 30 and an elongation factor of 500. Mapped reads were deduplicated with Markduplicates (part of the Picard tools; http://broadinstitute.github.io/picard/) and realigned around indels using the GATK toolkit.¹⁶⁸ Damage assessment was performed using DamageProfiler.¹⁰⁴ We then filtered HBV-mapping reads based on their copy number in order to limit potential inter-sample contamination due to index-jumping.¹⁶⁹ As previously described,⁷⁶,¹⁷⁰ reads were kept only if their copy number was significantly higher than those carrying non-expected index combinations (assumed to have arisen from index-jumping), as assessed by the modified Thompson-test for outlier detection. BAM files were then used to produce consensus HBV sequences using a 1-fold coverage threshold and a 50% majority rule.

Most of these reads were highly duplicated (cluster factor: 60), suggesting that sequenced libraries were largely exhausted. After deduplication and filtering of low-copy-number reads, 92 unique reads remained, resulting in a mean HBV genome coverage of 1.26-fold. All positive libraries were prepared from the Stumpff lock, with the exception of one library prepared from the Bermann lock (HEB002.B0102). However, no reads remained from this library after low-copy-number filtering, suggesting that they had arisen from inter-sample contamination due to index-jumping (Data S1). Reads appeared well distributed along the HBV genome sequence, with higher coverage in the double-stranded region¹⁷¹ of the genome (Figure 6A).

The reconstructed sequence was aligned with a set of modern HBV genomes representative of the currently described diversity of the virus using MAFFT v7.475¹⁰⁵ with the iterative refinement method for global alignments. The alignment was inspected and corrected around large indels when necessary. We then used Gblocks¹⁰⁶ to remove highly divergent and potentially misaligned regions, allowing for a maximum of 50% of gaps and using default parameters otherwise. The resulting alignment was used to construct a phylogenetic tree with RAxML v. 8.2.12,⁵⁸ using the GTRCAT substitution model and the rapid bootstrap algorithm with the autoMRE bootstopping criterion (Figure 7). In order to assess the robustness of the phylogenetic placement with respect to reference bias, we repeated the analysis after mapping the reads on a HBV reference genome belonging to genotype F (i.e. the most dissimilar to genotype D; GenBank: FJ657525).

If the levels of HBV viremia observed in the Stumpff Lock hairs are assumed to have been constant over the entire interval during which hairs from the Müller, Bermann, Halm-Thayer and Stumpff Locks were forming, we would have expected to have sequenced small numbers of HBV DNA from the Müller, Bermann and Halm-Thayer Locks, respectively. However, the assumption of stable viremia is violated by known random fluctuations in HBV viremia,⁷⁷ which can span between two to three orders of magnitude. Owing to the limited sensitivity of our analyses, differences in extraction and sequencing efforts and numbers of libraries prepared from our samples, differences in preservation between samples, differences in library preparation methods (a majority of libraries from the Stumpff Lock were singled-stranded, whereas a larger proportion of libraries from the earlier locks were double-stranded) as well as unpredictable HBV viremia, the absence of HBV reads recovered from the Müller, Bermann and Halm-Thayer Locks from earlier in Beethoven's life cannot be taken to conclude that Beethoven was definitely HBV-negative during the intervals in which hairs from those locks were forming. Although an acute infection occurring shortly before Beethoven's death cannot be ruled out, a chronic infection appears statistically much more likely (~3.5% of the world population lives with an HBV chronic infection today, and this proportion can reach up to ~20% in non-vaccinated areas).⁷¹,¹⁷²

Although the provenance histories of several of the locks of hair document their acquisition on specific dates, the hair shafts tested correspond to periods of growth that would have preceded their acquisition by a period of months. Precise estimates of the length of Beethoven's hair at the time of cutting, and the corresponding interval of growth which tested hair shafts represent, cannot be determined. However, a tentative estimate of the latest probable period of Beethoven's life during which he was definitely HBV-positive can be deduced by comparing the minimum probable length of his hair as depicted in sketches contemporaneous to the cutting of the Stumpff Lock against the recorded average length of tested HBV-positive hairs and known scalp hair growth rates in European males.

The Stumpff Lock hairs lack any bulbs and represent the distal ends of Beethoven's hair. Judging from the length of Beethoven's hair as depicted in two of Josef Eduard Teltscher's three deathbed sketches of Beethoven on or before March 26th, 1827 (which, unlike the sketches by Josef Danhauser on March 28th, were made prior to Beethoven's autopsy and craniotomy), Beethoven's hair appears to have been at least 4" (101.6 mm) in length at the time of his death. Using an average European male scalp hair growth rate of 0.368 ± 0.058mm per day,¹⁷³ and judging from the average length of the sampled Stumpff Lock hairs of approximately 49mm, the latest plausible interval of growth that the sampled HBV-positive hairs represent would have been between approximately June to November of 1826 at the latest, though likely earlier. Taken in view of the incubation period for an acute infection that likely would have been necessary for HBV DNA levels to have risen to detectable levels, Beethoven's three paracenteses, performed on December 20th, 1826, January 8th, and February 3rd, 1827,⁷ are thus unlikely routes for HBV infection.