Skip to content

Advertisement

  • Methodology article
  • Open Access

In-solution Y-chromosome capture-enrichment on ancient DNA libraries

  • 1, 2, 3, 4,
  • 5,
  • 6,
  • 7,
  • 8, 9,
  • 5, 10,
  • 6, 11,
  • 1, 3, 4Email author and
  • 2Email author
BMC Genomics201819:608

https://doi.org/10.1186/s12864-018-4945-x

  • Received: 22 November 2017
  • Accepted: 16 July 2018
  • Published:

Abstract

Background

As most ancient biological samples have low levels of endogenous DNA, it is advantageous to enrich for specific genomic regions prior to sequencing. One approach—in-solution capture-enrichment—retrieves sequences of interest and reduces the fraction of microbial DNA. In this work, we implement a capture-enrichment approach targeting informative regions of the Y chromosome in six human archaeological remains excavated in the Caribbean and dated between 200 and 3000 years BP. We compare the recovery rate of Y-chromosome capture (YCC) alone, whole-genome capture followed by YCC (WGC + YCC) versus non-enriched (pre-capture) libraries.

Results

The six samples show different levels of initial endogenous content, with very low (< 0.05%, 4 samples) or low (0.1–1.54%, 2 samples) percentages of sequenced reads mapping to the human genome. We recover 12–9549 times more targeted unique Y-chromosome sequences after capture, where 0.0–6.2% (WGC + YCC) and 0.0–23.5% (YCC) of the sequence reads were on-target, compared to 0.0–0.00003% pre-capture. In samples with endogenous DNA content greater than 0.1%, we found that WGC followed by YCC (WGC + YCC) yields lower enrichment due to the loss of complexity in consecutive capture experiments, whereas in samples with lower endogenous content, the libraries’ initial low complexity leads to minor proportions of Y-chromosome reads. Finally, increasing recovery of informative sites enabled us to assign Y-chromosome haplogroups to some of the archeological remains and gain insights about their paternal lineages and origins.

Conclusions

We present to our knowledge the first in-solution capture-enrichment method targeting the human Y-chromosome in aDNA sequencing libraries. YCC and WGC + YCC enrichments lead to an increase in the amount of Y-DNA sequences, as compared to libraries not enriched for the Y-chromosome. Our probe design effectively recovers regions of the Y-chromosome bearing phylogenetically informative sites, allowing us to identify paternal lineages with less sequencing than needed for pre-capture libraries. Finally, we recommend considering the endogenous content in the experimental design and avoiding consecutive rounds of capture, as clonality increases considerably with each round.

Keywords

  • Ancient DNA
  • Capture-enrichment
  • Y chromosome

Background

Uniparentally inherited markers such as those on the mitochondrial chromosome (mtDNA) and on Y-chromosome DNA (Y-DNA) are widely used to infer the demographic histories of specific human lineages [1]. Although much smaller than the nuclear genome, the inheritance mechanism and lack of recombination make them powerful tools for inferring ancestry and estimating the ages of pedigrees and the times to the most recent common ancestors (TMRCA) of the mtDNAs and Y-DNAs of present-day populations [2]. Analyses of modern and ancient mtDNA and Y lineages have broadened our knowledge of diversification and founder events from human population history [36].

Due to the large number of copies in each cell, mtDNA has been at the forefront of ancient DNA research [7]. In contrast, each cell possesses just one copy of the Y chromosome. Thus, when analyzing ancient samples, the probability of retrieving any given portion of Y-chromosome DNA is much lower than for mtDNA. Furthermore, as endogenous DNA is often found highly fragmented and in low quantity, recovering ancient DNA (aDNA) from a pool of endogenous and contaminating environmental DNA is extremely challenging and costly. To overcome these challenges, methods have been developed to increase the endogenous DNA proportion of sequencing libraries. These methods target select genomic regions, such as SNPs, whole chromosomes, or mitochondrial or nuclear genomes [3, 812] prior to sequencing. They consequently increase the proportion of genomic regions of interest while reducing sequencing costs.

There are two main types of enrichment methods: solid phase enrichment [13, 14] and in-solution enrichment [8, 11, 15]. Both approaches require DNA or RNA probes to hybridize to the targeted molecules of a DNA library. The resulting complex (probe and target) is retained either by being attached to the array (solid phase) or pulled down with streptavidin-coated beads (in-solution). Finally, the remaining fragments that did not hybridize with the probes, including those from microbial DNA contamination, are washed away. Capture-enrichment approaches have enabled DNA retrieval from samples that initially showed small amounts of endogenous DNA [10, 16, 17]. Consequently, these enrichment methods have positively impacted ancient genomics research by lowering endogenous content requirements, thereby increasing the number of samples that can be employed for genotyping.

Capture-enrichment strategies have been applied to target genome-wide SNP sets and to specific subsets of the genome to study the phylogenetic context of ancient populations. Recent implementations [5, 8, 12], include probes targeting thousands of autosomal and Y SNPs characterized by the Simons Genome Diversity Project [18] and the International Society of Genetic Genealogy (ISOGG, https://isogg.org/). However, due to the provenance of the samples of the ISOGG consortium, ISOGG SNPs are best suited to genotype present-day European haplogroups. Consequently, aDNA enrichment has been applied to study Y-chromosome variation in ancient European and Middle-Eastern individuals, while studies of Africans [19] and Native Americans have been restricted either to direct interrogation of known Y-DNA markers with targeted PCR-based sequencing [20] or to low and medium-depth whole-genome sequencing [19, 2123].

An important consideration of enrichment designs targeting pre-selected SNPs is the ascertainment bias introduced and the impediment of discovering new variants. An ideal strategy would involve capturing the whole Y chromosome, however its abundance of repetitive sequences makes it less amenable for capture experiments [15]. To overcome these constrains, we made use of a probe design targeting 8.9 megabases (Mb) out of the 10.3 Mb defined by Poznik and colleagues [2]. These 10.3 Mb were initially selected to fall within the non-recombining portion of the Y chromosome, be depleted of repeats, and well suited for genotype calling from short read sequence data [2]. Furthermore, we were interested in assessing whether Y-DNA could be enriched from libraries with very low endogenous content that had been subjected to WGC and for which we also had pre-capture libraries. We thus tested this approach and compared different enrichment strategies, on samples excavated from the Caribbean, a region that poses a particular challenge for DNA preservation. Previous studies on some of these samples failed to obtain enough Y-DNA data to reliably call a haplogroup even after WGC [24]. Consequently, we investigated the parameters affecting the quality and the quantity of the data and, at the same time, described the extent to which the enrichment improved the resolution of the Y-chromosome haplogroup assignment. Our results illustrate the benefits of Y-DNA enrichment experiments for studying the paternal genetic ancestry of ancient human populations.

Methods

Samples

We performed 18 capture-enrichment experiments on DNA libraries obtained from the archaeological remains of six individuals excavated from Caribbean contexts. Two samples (STM1 and STM2) belong to seventeenth-Century enslaved males of African origin from Saint Martin (Lesser Antilles) and were previously reported in [24]. The other four (PI174, PI383, PI435, and PI437) were obtained from archaeological remains from the Paso del Indio site (PI) in Puerto Rico. These four dated between 824 and 1039 CE, as described in [25, 26].

Ancient DNA extraction

DNA from the STM samples was extracted from tooth roots using a silica-based method [27], as described in [24]. Sampling and DNA extractions for PI samples were conducted at the Arizona State University Ancient DNA Laboratory, a Class 10,000 clean-room facility. Teeth were cleaned with a 1% sodium hypochlorite solution, and the outer surfaces of the tooth roots were mechanically removed with a Dremel tool. Teeth were sliced transversely at the cemento-enamel junction using the Dremel. The roots were then covered in aluminum foil and pulverized by blunt force with a hammer, as in [28]. To avoid contamination, additional precautions were taken, including single use of Dremel wheels, bleach decontamination and UV irradiation of tools and the work area before and between uses, as well as full body coverings for all researchers [29]. DNA was extracted following an improved silica-based extraction protocol [16], using 50 mg of pulverized tooth material. Extracts and extraction blanks were quantified with the Qubit 2.0 High Sensitivity assay [30].

Ancient DNA library preparation

DNA extracted from STM samples was built into 6-bp-indexed double-stranded Illumina libraries, as described in [24], following the protocol in [31]. For PI samples, double-stranded Illumina libraries were prepared following the protocol in [31]. Extraction blanks were also converted into libraries, and an additional negative library control containing only ddH20 was also included. 1:100 dilutions of each library were prepared for quality screening through Real-Time PCR (qPCR) using the Thermo Scientific Dynamo SYBR Green qPCR kit with ROX. Reactions were run in triplicate and prepared in final volumes of 20 μl with the following conditions: 10 μl of 2X Dynamo SYBR Green qPCR Master Mix with 0.3× ROX, 1 μl of primer IS7 (5’-ACACTCTTTCCCTACACGAC-3′) at 10 μM, 1 μl of primer IS8 (5’-GTGACTGGAGTTCAGACGTGT-3′) at 10 μM, 7 μl of ddH20, and 1 μl of library dilution. Reactions were heated to 95 °C for 10 min for initial denaturation, and further denaturations were performed at 95 °C for 15 s and for 40 1-min cycles at 60 °C. A final disassociation stage was added at the end of these cycles: 95 °C for 15 s, 60 °C for 15 s and 95 °C for 15 s. Quantification was performed using an ABI7900HT thermocycler and analyzed with SDS software. After qPCR, all libraries were double-indexed as in [32]. To retain library complexity, four 100 μl indexing reactions were performed per library with the following conditions: 10 μl of Pfu Turbo Buffer, 2.50 μl of 10 mM dNTPs, 1.50 μl of 10 mg/ml Bovine Serum Albumin, 2 μl of P5 indexing primer (5’-AATGATACGGCGACCACCGAGATCTACACxxxxxxACACTCTTTCCCTACACGACGCTCTT-3′) at 10,000 nM, 2 μl of P7 indexing primer (5’-CAAGCAGAAGACGGCATACGAGATxxxxxxGTGACTGGAGTTCAGACGTGT-3′) at 10,000 nM, 72 μl of ddH20, 1.00 μl of Pfu Turbo enzyme (Agilent), and 9 μl of DNA library. Reactions were heated to 95 °C for 15 min for initial denaturation. Further denaturation, annealing, and elongation were performed at 95 °C for 30 s, at 58 °C for 30 s, and for 10 45-s cycles at 72 °C. Final extension was performed at 72 °C for 10 min and reactions were then kept at 10 °C. All four aliquots of each amplified library were combined, and the library was purified with the Qiagen MinElute PCR purification kit following manufacturer’s instructions with the following modification: the EB buffer was preheated to 65 °C before use, and reactions were eluted in 30 μl. A 1-μl aliquot of each library was used for quantification with the Qubit 2.0 Broad Range assay. Purified libraries were further diluted to a factor of 1:1000 and quantified with the KAPA Library Quantification kit (Kapa Biosystems) following manufacturer’s instructions.

Indexed libraries were amplified a second time to increase the amount of DNA. To retain library complexity, four 100-μl amplification reactions were performed per library. PCR conditions were: 10 μl of 10X Accuprime Pfx reaction mix, 3 μl of IS5 primer at 10 μM, 3 μl of IS6 primer at 10 μM, 76 μl of ddH20, 1 μl of Accuprime™ Pfx enzyme, and 7 μl of DNA library. Reactions were heated to 95 °C for 2 min for initial denaturation, and further denaturation, annealing, and elongation were performed at 95 °C for 15 s, 60 °C for 30 s, and for 7–13 1-min cycles at 68 °C. Final extension was performed at 68 °C for 5 min and reactions were then kept at 4 °C. All four aliquots of each amplified library were combined, and the library was purified with Qiagen MinElute PCR purification kit as detailed above. 1 μl of each purified and amplified library were used for flourometric quantification. Purified libraries were further diluted to a factor of 1:10,000 and quantified with the KAPA Library Quantification kit (Kapa Biosystems) following the manufacturer’s instructions. 1 μl of each library was used for fragment analysis with the Agilent 2100 Bioanalyzer DNA 1000 chip.

Whole-genome capture

Whole-Genome Capture was performed on each of the libraries obtained from the six archaeological samples (STM1, STM2, PI174, PI383, PI435, PI437) following published protocols. We used the human whole-genome enrichment kit MYbaits (MYcroarray, Ann Arbor, online version 1.3.8) to capture STM libraries, as reported in [17]. For PI libraries, we implemented the WISC approach [10], starting with 500 ng per library and hybridizing for 66 h. The libraries were PCR amplified for 15–20 cycles.

Y-chromosome bait design

We used DNA biotinylated probes (baits) from Nimblegen’s SeqCap EZ Choice XL Enrichment Kit for Y capture. Baits were designed using Roche’s NimbleDesign proprietary probe-design algorithm (https://design.nimblegen.com/nimbledesign) to target 10.3 Mb well suited for reliable genotype calling and haplogroup inference [2]. Out of these 10.3 Mb, the software defined 8.9 Mb (7.4 Mb in 17,934 regions plus 100 bp offset), as suitable for probe design, and returned 2.1 million probes 105 bp in length and an average tiling of ~ 25 bp (~ 4.2 bp between adjacent probes). Probes were designed using the hg19/GRCh37 reference sequence for the Y chromosome, most of which is derived from a single European haplogroup. A file containing the regions defined as suitable for probe design is available in Additional file 1.

Y-chromosome capture (YCC)

We performed Y-chromosome capture-enrichment experiments on both pre-capture and WGC libraries. Libraries were pooled in equal masses. Capture reactions were performed according to NimbleGen SeqCap EZ XL protocol, with the following modifications: due to limited sample availability, the total mass of the pooled libraries was ~ 500 ng rather than the recommended 1.25 μg; hybridization was performed for a total of 65 h (48–72 h recommended); and the adapter-blocking oligonucleotides were IDT xGen blocking oligos. Following capture, libraries were amplified with 6 cycles of PCR, and quality was assessed using the Agilent Bioanalyzer High Sensitivity kit.

Illumina sequencing

Pre-capture and WGC libraries for the STM samples were sequenced at the National High Throughput DNA Sequencing Centre in Copenhagen, Denmark, on a HiSeq2000 platform using single-end 100-bp runs, as reported in [17, 24]. PI pre-capture and WGC libraries were paired-end sequenced on the NextSeq500 using a High Output 150-cycle kit with paired-end 76-bp reads. All libraries subjected to YCC (i.e., YCC and WGC + YCC libraries from all samples) were sequenced on the NextSeq500 using the High Output 150 cycle kit at Stanford University using paired-end 76-bp mode.

Sequence data processing and mapping

FASTQ-format reads from the pre-capture and WGC conditions are available for the STM samples through the European Nucleotide Archive, project PRJEB8269, experiment accession numbers ERX682089, ERX682243, ERX682248, and ERX682249 [24]. We processed these reads, as well as the reads generated for this study (YCC and WGC + YCC libraries for the STM and all PI libraries) with the following steps. To trim adapters and low quality bases, we used AdapterRemoval v2 with the default options in single-end mode for STM pre-capture and WGC libraries, and in paired-end mode for all PI and STM YCC libraries [33]. As the yield per experiment is variable and can bias the comparisons, we subsampled 10 times an equal number of reads for each experimental condition and for each individual using seqtk (https://github.com/lh3/seqtk). To determine the total number of sequences to subsample, we selected the lowest number of reads that passed the trimming filters for each individual across experiments (Table 1). The sequences were then aligned to the Homo sapiens reference genome build 37 (hg19) using the BWA aligner [34] implemented in PALEOMIX [35], with a mapping quality threshold set at 30. The quality of the aligned bases was rescaled with mapDamage2 [36] to lower the quality of mismatches to the reference sequence that likely derive from DNA damage.
Table 1

Samples, methods, total number (total data) and average (down-sampled data) number of reads, and fold-enrichment (down-sampled data)

Endogenous content (pre-capture)

STM1

STM2

PI174

PI383

PI435

PI437

1.54%

0.12%

0.01%

0.03%

0.04%

0.02%

Site

Saint Martin

Saint Martin

Paso del Indio

Paso del Indio

Paso del Indio

Paso del Indio

Methods

Extraction

Rohland et al., 2007

Rohland et al., 2007

Dabney et al., 2013

Dabney et al., 2013

Dabney et al., 2013

Dabney et al., 2013

Library building

Meyer and Kircher, 2010

Meyer and Kircher, 2010

Meyer and Kircher, 2010

Meyer and Kircher, 2010

Meyer and Kircher, 2010

Meyer and Kircher, 2010

WGC

MYbaits (MYcroarray, Ann Arbor)

MYbaits (MYcroarray, Ann Arbor)

WISC (Carpenter et al., 2013)

WISC (Carpenter et al., 2013)

WISC (Carpenter et al., 2013)

WISC (Carpenter et al., 2013)

YCC

YCC (this study)

YCC (this study)

YCC (this study)

YCC (this study)

YCC (this study)

YCC (this study)

WGC + YCC

MYbaits + YCC

MYbaits + YCC

WISC + YCC

WISC + YCC

WISC + YCC

WISC + YCC

Total data

Pre-capture

Total reads

34,025,874

14,973,474

9,173,100

2,795,632

4,617,394

9,986,135

Mapping to chrY

1384

301

4

3

6

6

On-target

924

197

1

2

3

2

% of sequenced on-target

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

% of chrY on-target

67%

65%

25%

67%

50%

33%

YCC

Total reads

68,795

41,884

159,728

107,796

41,810

56,157

Mapping to chrY

16,430

2562

17

27

26

12

On-target

16,191

2541

17

27

25

12

% of sequenced on-target

23.54%

6.07%

0.01%

0.03%

0.06%

0.02%

% of chrY on-target

99%

99%

100%

100%

96%

100%

WGC

Total reads

14,973,474

29,884,294

10,052,798

10,265,430

12,363,127

9,132,306

Mapping to chrY

6407

1925

5

8

57

7

On-target

2930

903

2

6

29

4

% of sequenced on-target

0.020%

0.003%

0.000%

0.000%

0.000%

0.000%

% of chrY on-target

46%

47%

40%

75%

51%

57%

WGC + Y

Total reads

98,540

30,629

17,212,495

14,557,575

10,181,433

11,356,277

Mapping to chrY

4854

236

37

152

242

47

On-target

4643

222

37

150

235

45

% of sequenced on-target

4.71%

0.72%

0.00%

0.00%

0.00%

0.00%

% of chrY on-target

96%

94%

100%

99%

97%

96%

Down-sampled data

Pre-capture

Total reads

68,795

30,629

159,728

107,796

41,810

56,157

Mapping to chrY

2.8

0.2

0.2

0.1

On-target

2.3

0.2

0.1

% of sequenced on-target

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

% of chrY on-target

82%

100%

100%

YCC

Total reads

68,795

30,629

159,728

107,796

41,810

56,157

Mapping to chrY

16,430

1925

17

27

26

12

On-target

16,191.0

1909.8

17.0

27.0

25.0

12.0

% of sequenced on-target

23.54%

6.24%

0.01%

0.03%

0.06%

0.02%

% of chrY on-target

99%

99%

100%

100%

96%

100%

WGC

Total reads

68,795

30,629

159,728

107,796

41,810

56,157

Mapping to chrY

38.8

2.7

0.2

0.1

0.2

On-target

16.8

1.5

0.1

0.1

% of sequenced on-target

0.02%

0.00%

0.00%

0.00%

0.00%

0.00%

% of chrY on-target

43%

56%

50%

100%

0%

WGC + Y

Total reads

68,795

30,629

159,728

107,796

41,810

56,157

Mapping to chrY

4414.0

236.0

17.1

82.5

111.9

25.5

On-target

4239.5

222.0

17.1

81.3

109.7

24.5

% of sequenced on-target

6.16%

0.72%

0.01%

0.08%

0.26%

0.04%

% of chrY on-target

96%

94%

100%

99%

98%

96%

 

Condition 1

Condition 2

      

Fold-enrichment (down-sampled data)

YCC

Pre-capture

7039.6

9549.0

17.0

27.0

250.0

12.0

YCC

WGC

963.8

1273.2

170.0

270.0

25.0

12.0

YCC

WGC + YCC

3.8

8.6

1.0

0.3

0.2

0.5

WGC

Pre-capture

7.3

7.5

0.1

0.1

0.0

0.0

WGC + YCC

Pre-capture

1843.3

1110.0

17.1

81.3

1097.0

24.5

WGC + YCC

WGC

252.4

148.0

171.0

813.0

109.7

24.5

Ten replicates per library were obtained by down-sampling to the minimum number of retained reads within each sample (underlined in “Total data” rows). The “Mapping to chrY” section indicates the number of unique reads mapping to the Y-chromosome. “On-target” refers to the unique on-target reads. “%of sequenced on-target” and “% of chrY on-target” refer to the percentage of on-target reads with respect to the total sequenced reads and to the total reads mapping to the Y-chromosome, respectively. The fold-enrichments were calculated with the down-sampled data, by dividing the number of on-target reads in Condition 1 by the number of on-target reads in Condition 2; when the denominator was 0, we assigned the number of on-target reads in Condition 1

To calculate the enrichment rate, we used the subsampled data and computed the average number of unique reads mapped to the on-target regions in each experiment. Then, we calculated fold-enrichment by dividing the on-target average of YCC or WGC + YCC experiments by that of the pre-capture libraries. Specifically, we calculated this fold-enrichment for YCC using the pre-capture condition as a baseline and compared the WGC + YCC experiments to both the pre-capture and WGC conditions. For the cases with replicates with no reads aligning to the Y chromosome or target regions, we used the maximum number of reads observed across the replicates of a given library as a baseline. We computed binomial proportion confidence intervals for the mean endogenous content, the proportion of on- and off-target reads, and clonality, and we conducted a t test for the length estimate. All statistical tests were computed in R software, version 3.3.1 [37].

To call Y-chromosome genotypes for each sample, we first merged data across experiments. We used the haploid genotype caller implemented in ANGSD, retaining only bases with quality scores of at least 13 and sampling one random base at each site [38]. Finally, we performed a binary tree search with a custom script to find the most derived SNP that determines the haplogroup of the individuals. We used as input the phylogenetic tree constructed from the Y-SNPs reported in Phase 3 of the 1000 Genomes Project [4].

Sex determination

To determine the biological sex of the six individuals, we used the script in [39] to calculate from the ratio (Ry) of reads mapping to the Y-chromosome to those mapping to both sex chromosomes [39]. Ry values above 0.075 are consistent with a male genotype [39].

Yield and enrichment curves

We estimated the yields and complexities of the libraries, with respect to the reads mapping to the targeted regions, with the PreSeq package implemented in R (preseqR, [40]) and corrected the amount of required sequencing by the fraction of on-target reads in the libraries. Since the method relies on having a fraction of duplicated reads to estimate the yield, for the cases where the pre-capture libraries did not have duplicated on-target reads to adjust a yield curve, we instead assumed a linear relationship with a slope equal to the proportion of unique on-target reads present in the library. We then modeled an “enrichment curve” to explore the level of enrichment predicted for different amounts of sequencing. To this end, we used the median unique on-target reads as returned by PreSeq to calculate an expected fold-enrichment. We divided the median value estimated by PreSeq of each captured library by the median of its pre-captured counterpart (i.e., YCC vs. pre-capture, WGC + YCC vs. pre-capture, and WGC + YCC vs. WGC).

Results

Enrichment rates

We tested the performance of Y-chromosome capture on Illumina sequencing libraries obtained from the archaeological remains of six individuals excavated in the Caribbean islands of Saint Martin (STM1 and STM2) and Puerto Rico (PI174, PI383, PI435, and PI437) (Table 1). For each sample, we performed a series of enrichment experiments, as depicted in Fig. 1. First, we shotgun-sequenced a DNA library without performing any enrichment. We then performed a capture reaction targeting a set of DNA probes covering ~ 8.9 Mb of the non-recombining portion of the Y chromosome. These regions were validated by Poznik and colleagues in [2] as being well suited for unambiguous read mapping and for yielding reliable genotype and haplogroup calls from short-read sequencing. Additionally, we performed another set of capture experiments, either enriching only the whole-genome (WGC) or the on-target regions after having enriched the whole genome (WGC + YCC). We confirmed the molecular sex of the samples and determined that the six individuals each had a karyotype consistent with XY [39]. We then assessed the performance of the capture experiments.
Fig. 1
Fig. 1

Experimental enrichment scheme. We have four different conditions: Pre-capture, YCC, WGC, and WGC + YCC. The pre-capture condition is our initial library preparation prior to any enrichment. The WGC is designed to target all autosomal and sex chromosomes. The Y-capture in the YCC and the WGC + YCC conditions targets ~ 10.3 Mb of Y-chromosome sequence

Since experiments yielded differing numbers of reads per sample, we down-sampled to equal numbers per individual as described in Methods (Table 1). The pre-capture libraries yielded 0.01 to 1.54% unique reads aligning to the human genome (Fig. 2), and less than 0.004% mapping to the Y-chromosome. After implementing the YCC and WGC + YCC enrichments, the endogenous DNA content increased, on average, by factors of 24.2 to 122.0 for the STM samples (Fig. 2a) and by factors between 3.2 and 38.9 for the PI samples (Fig. 2b). Moreover, in the YCC and WGC + YCC libraries, 5.3 to 68.3% of the human STM reads mapped to the Y chromosome, with 17.7 to 50.0% the corresponding figures for the PI samples.
Fig. 2
Fig. 2

Endogenous DNA content in enriched libraries. Percentage of the unique retained reads that aligned to the human genome in (a) Saint Martin and (b) Puerto Rico samples. “STM” stands for Saint Martin and “PI” for Paso del Indio, Puerto Rico. The percentages in parentheses below the x-axis indicate the number of down-sampled reads per library. The error bars represent 95% confidence intervals of endogenous DNA content found in the samples across the 10 down-sampled replicates. Darker colors correspond to the proportion of the unique reads that aligned to the Y chromosome. Whole-genome enriched libraries have < 0.04% reads aligning to the Y chromosome

To evaluate whether the enrichment experiments effectively recovered the targeted regions, we compared the total number of unique reads mapping to the targeted regions as well as the number of off-target reads (Fig. 3). YCC experiments on the STM samples yielded 7039 to 9549-fold increases of on-target sequences compared to the pre-capture condition (Table 1). The WGC + YCC experiments on the same samples resulted in 148- to 252.35-fold-enrichment compared to WGC alone. For the PI samples, YCC experiments resulted in 12- to 250-fold enrichment, and we observed 24.5- to 813-fold enrichment for the WGC + YCC approach. Although we observed an increase in the off-target content for the STM enrichments (Fig. 3), it is one order of magnitude smaller than their respective on-target enrichment. Above 94% of all reads mapping to the Y chromosome were on target in both YCC and WGC + YCC, in contrast to the pre-capture and WGC experiments where these figures range between 25 and 75% (Table 1, Total data section). Overall, the distribution of the on-target sequences in all Y-chromosome enrichment experiments is qualitatively even (Fig. 4, Additional file 2: Figure S1). In summary, all Y-chromosome capture enrichment experiments consistently increased the number of unique on-target reads.
Fig. 3
Fig. 3

On- and off-target reads. We show the mean and the 95% confidence interval for 10 replicates. a Unique reads mapping to the Y-chromosome target regions. b Unique reads mapping to the Y chromosome but not to the targeted regions. For some libraries, no reads mapped to the Y chromosome, across the 10 replicates

Fig. 4
Fig. 4

Depth of coverage across the Y-chromosome. From top to bottom, rows depict the coverage levels for the pre-capture, YCC, WGC and WGC + YCC conditions. Red boxes represent the targeted regions. Each blue point represents sequencing coverage within a 1000-bp window, averaged across 10 subsampled replicates per sample per condition explaining depths of coverage below 1. To help with readability, we increased the opacity of the points in the PI383 column

Length distribution and clonality

To explore which features of the pre-capture libraries may have influenced the differences in enrichment rates between the STM and PI groups, we contrasted the lengths and complexities of the individual libraries across experiments. The extraction protocols differ between the STM and the PI samples (see Table 1). This should impact the read length distribution and we therefore avoided to compare absolute values across samples and instead performed paired tests. We observed a significant trend toward longer fragments after enrichment for all experiments (Fig. 5, Additional file 3: Figure S2) (paired samples t-test, p-value = 0.002), consistent with previous findings [8, 17, 41]. Reads from the two STM samples were 91.7 to 92.1 base pairs (bp) long for the pre-capture condition, 87.4 to 94.5 bp after YCC, and 105.5 to 108.3 bp long after WGC + YCC. Likewise, whereas the average length of PI reads in the pre-capture libraries ranged from 63.6 to 69.1 bp, average lengths increased to 76.3–102.3 and 69.3–82.2 bp after YCC (paired samples t-test, p-value = 0.02) and WGC + YCC (paired samples t-test, p-value = 0.03), respectively. On-target clonality levels (percentage of PCR duplicates) were considerable for all samples in the YCC and WGC + YCC experiments. We observed 9.2 to 28.6% clonality for YCC of the STM samples at 68,795 and 30,629 down-sampled reads. The remaining WGC + YCC (STM), and all WGC + YCC and YCC (PI) libraries had greater clonality values, ranging from 65.4 to 94.1% (Fig. 6). We did not observe on-target duplicates with which to calculate the clonality in any of the down-sampled pre-capture libraries (Additional file 4: Table S2), so instead we used the whole data to estimate the clonality. For STM1, the pre-capture library had 10.0% on-target clonal reads (for 34,025,874 sequenced reads, Additional file 4: Table S1), while for all the other pre-capture libraries the on-target clonality is 0%, as they have 1 to 197 reads on-target (Additional file 4: Table S1).
Fig. 5
Fig. 5

Lengths of mapped reads. a Reads aligned to the nuclear genome. b On-target reads. c and (d) depict the length distributions of reads mapping to the whole genome for STM1 and STM2 samples, respectively. The length distribution was smoothed by fitting a polynomial curve to the observed frequencies; the ribbons correspond to 95% confidence intervals

Fig. 6
Fig. 6

Clonality. a Clonal reads mapping to the nuclear human genome. b Clonality of the reads mapping to the targeted regions. c Clonal reads mapping to the Y chromosome but not to the targeted regions. Error bars represent 95% confidence intervals across 10 subsampled replicates

On-target yield for aDNA libraries

The yield curves for both capture conditions (YCC and WGC + YCC) corroborate the high clonality of the PI libraries (Fig. 7a, b). We observed that the YCC libraries of these samples plateaued at very shallow sequencing, saturating at ~ 25,000 sequenced reads, compared to saturations at 50,000–100,000 sequenced reads for the WGC + YCC libraries. However, the complexity curves indicate that after sequencing 100,000 reads of the WGC + YCC libraries, we would not retrieve more than 150 different reads, regardless of the capture approach. On the other hand, although we sequenced fewer than 100,000 reads for each of the YCC experiments on the STM individuals, the complexity curves suggest that these libraries could be further sequenced to increase the coverage of the targeted regions.
Fig. 7
Fig. 7

Expected yield and on-target fold-enrichment. Dashed lines indicate the number of down-sampled reads. a and (b): Predicted median value and variance (across 100 bootstrap replicates) of the number of on-target reads, as a function of total sequenced reads. The points depict the observed numbers of on-target reads in the down-sampled libraries. c and (d): Expected enrichment of on-target reads versus number of sequenced reads for each condition and each sample

Enrichment curves based on yield estimates

We computed enrichment curves (Fig. 7c, d) to predict enrichment rates for deeper sequencing. Consistently, we observe that the YCC of STM2 (Additional file 5: Figure S3 H) recovers 1790 more on-target reads than the pre-capture experiment at the down-sample point. However, as it has not reached saturation, this enrichment value can increase to at least 5000-fold, similar to the maximum enrichment of STM1 (Fig. 7a). In contrast, the projected enrichment of the PI WGC + YCC libraries at 200,000 total reads is only 100- and 72-fold versus the pre-capture and WGC libraries, respectively. We also note that, for PI samples, the three enrichment rates calculated “decelerate” at 100,000 sequenced reads or fewer, once more reflecting the low complexity levels of the initial pool (Fig. 7, Additional file 5: Figure S3).

Y haplogroup calls

Finally, we combined all the sequence data generated for each sample to call Y-chromosome haplogroups. With the new data generated, we were able to call haplogroups for both STM individuals. The combined on-target depth of coverage was 0.04× to 0.28×, with a sequencing depth of at least one for 0.32–1.84 Mb (Table 2, Additional file 6: Figure S4 for Y-SNPs coverage). In the STM1 individual we observed derived alleles belonging to the R1b-M343 clade, consistent with its previously reported haplogroup (R1b1c-V88) [24]. In the same study, even after WGC, the haplogroup for STM2 could not be resolved [24]. Only after integrating the previously generated data with that produced in this study, were we able to assign the Y-haplogroup as E1b1a1a1-M80. Regarding the PI samples, we did not find any reads covering the SNPs in the database in the pre-capture or the WGC libraries. After YCC or WGC + YCC, we observe between 5 and 74 variant sites per library, leading to the identification of haplogroups defining broader regions. For instance, the haplogroup found for PI383, P-M45, is the immediate ancestor of haplogroups R and Q. In Table 2, we show the haplogroups for all the individuals according to the most derived SNP identified in each condition.
Table 2

Numbers of Y-chromosome bases, Y-SNPs and haplogroups retrieved

Sample

Condition

Positions recovered

Total SNPs

Ancestral SNPs

Derived SNPs

Haplogroup retrieved

STM1

All

2,205,331

12,061

11,625

436

R1b-M343

Pre-capture

93,725

595

577

18

R1b-M343

YCC

1,372,731

8103

7805

298

R1b-M343

WGC

354,255

2414

2321

93

R1b-M343

WGC + YCC

493,085

2971

2871

100

R1b-M343

STM2

All

428,846

2183

2091

92

E1b1a1a1-M80

Pre-capture

19,994

109

100

9

E1b1a1a1-M80

YCC

220,732

1262

1215

47

E1b1a1-M2

WGC

109,411

760

726

34

E1b1a1a1-M80

WGC + YCC

219,152

114

110

4

CT-M168

PI174

All

3224

19

18

1

A1-V168

Pre-capture

129

0

0

0

YCC

1993

12

11

1

A1-V168

WGC

147

2

2

0

A1-V168

WGC + YCC

1818

11

10

1

A1-V168

PI383

All

7738

46

45

1

P-M45

Pre-capture

146

0

0

0

YCC

1809

13

12

1

P-M45

WGC

473

5

5

0

P-M45

WGC + YCC

6890

42

41

1

P-M45

PI435

All

16,469

100

97

3

BT-M42

Pre-capture

164

0

0

0

YCC

1918

15

15

0

BT-M42

WGC

2632

18

18

0

WGC + YCC

12,399

86

83

3

BT-M42

PI437

All

3444

14

14

0

Pre-capture

103

0

0

0

YCC

938

6

6

0

WGC

296

0

0

0

WGC + YCC

2320

11

11

0

Discussion

In this paper, we have described the efficiency of Y-DNA recovery from in-solution Y-chromosome capture-enrichment experiments and from different WGC protocols followed by Y-chromosome enrichment on aDNA libraries obtained from the archaeological remains of six males excavated in the Caribbean and dating between 300 and 2000 years old. We performed silica-based extraction and built double stranded libraries for all samples (see Table 1). The experimental design involved the targeted enrichment of 8.9 Mb of the Y chromosome on both standard and WGC libraries. As the WGC enrichment protocols differed between sample groups, we evaluated the success of the enrichment using within-sample comparisons. Overall, both approaches succeed in increasing the proportion of on-target sequences as compared to pre-capture libraries. For both enrichment approaches and for all samples, we observed that most of the sequenced reads mapped to the targeted regions. Moreover, we succeeded in assigning refined haplogroups by sequencing between 40,000 and 70,000 reads of Y-chromosome captured libraries for the two samples with endogenous content above 0.1% (0.12 and 1.54%, respectively). Finally, despite having successfully increased the yield of Y-chromosome reads in every instance we tested, our results suggest that the amount of data retrieved with this capture strategy will only be meaningful (i.e. enough to call a haplogroup or to inform a Y-phylogeny) if the starting libraries have adequate complexity and endogenous content. Note that further work on samples with more variables endogenous contents and clonality is needed to make quantitative statements regarding the complexity levels and the endogenous amounts that are prohibitive for cost-effective capture experiments. Moreover, it would be interesting to compare the effect of the extraction methods on the efficiency of the capture experiments.

Factors influencing enrichment

We observed consistent enrichment of Y-DNA on aDNA libraries. However, we also observed marked differences of the performance between samples (STM and PI) and library types (standard vs. WGC libraries). We tentatively conclude that this is due to differences in starting endogenous content, read-length distributions, and the complexities of the libraries. Although it is now possible to increase the endogenous content of poorly preserved tissues, it has indeed been shown that in-solution capture enrichment techniques perform better on samples with starting endogenous content greater than 1% and with little clonality [17]. Although we analyzed only six samples, it is worth noting that our results are consistent with this previously reported threshold. Indeed, as expected, the PI sample enrichment levels were systematically lower than those in the STM samples. Low complexity levels in the starting libraries also hamper the success of capture experiments, as these protocols usually involve an amplification step, which further increases the clonality. In addition, the enriched libraries were subject to an increase in the fragment length most likely driven by the probe length (105 bp) (Fig. 5). However, the shift was more pronounced for the PI samples, suggesting that a substantial proportion of the shorter fragments in the pre-capture and WGC libraries was not retrieved in the YCC and WGC + YCC. Together, these observations provide useful insights as to the features in standard libraries that should be considered when planning capture experiments, thereby opening avenues for investigating ways to optimize these protocols.

Subsequently, we noted that for the STM samples the YCC experiments yielded higher fold-enrichments of the targeted regions than did the WGC + YCC captures. In contrast, for the PI samples, the WGC + YCC performed better. While we believe the difference owes mostly to the endogenous content, it could also be in part due to the techniques employed to enrich the whole genomes of the STM and PI samples (MYbaits and WISC, respectively). Indeed, although based on the same molecular principle, those technique have slight differences in their performances [17]. However, despite the slightly higher enrichment rates in WGC + YCC libraries, as compared to YCC, drawing from our results, we do not recommend implementing enrichments for libraries similar to the PI libraries. As we show here, despite having used a more efficient extraction protocols for those samples with low endogenous content (Table 1), regardless of the capture approach and the total sequenced reads, enriched PI libraries recover only a few hundred of Y-chromosome reads. Consequently, the ability to identify informative SNPs for haplogroup assignment is limited in these cases.

Implications of Y-haplogroup assignments for the samples

Finally, the haplogroup inference was effective only for the two STM samples, for which we recovered at least 0.32 Mb of the Y-chromosome (3.7% of on-target regions). The STM1 individual bore the M343 mutation characteristic of haplogroup R1b, consistent with the back-to-Africa R1b1c-V88 haplogroup reported in [24]; however, we did not observe any of the SNPs specific to the V88 branch. Whereas for STM2, we identified a common and widespread African haplogroup characteristic of the Bantu expansion [42], E1b1a1a1-M80, consistent with the results from the analyses of the autosomal chromosomes [24]. For the remaining individuals, we could not resolve haplogroups due to the reduced complexity and endogenous content of the libraries. Additionally, our results might be impacted by the paucity of Y-SNPs that define the tips of the Native American haplogroups versus haplogroups from other well-characterized populations in the database employed. For example, no Puerto Rican males in the 1000 Genomes dataset bore Native American Y haplogroups. Rather, all possessed European or African lineages, primarily belonging to R1b and E1b clades [4], reflecting sex-biased admixture patterns during European colonization of the island (and reproduced across the Americas) [43]. This fact highlights the need to assay ancient genetic variation among pre-contact Native American samples. It is often challenging to recover DNA for such samples and the enrichment method we discuss here would certainly help in those cases.

Conclusions

In the past decade, new technologies and protocol improvements have emerged to efficiently recover ancient DNA. However, the endogenous DNA fraction continues to be a limiting step in ancient genomics studies. The first efforts to overcome this limitation have focused on targeting the mtDNA, because it is relatively short (~ 16 kB), and it is present in multiple copies per cell, unlike the autosomes (two copies) and the Y chromosome (one copy). For the Y chromosome, targeted enrichment strategies are more problematic due to its richness in repetitive and palindromic sequences. For the same reasons, Y-chromosome content is relatively poor in WGC studies, although WGC is becoming a cost-effective alternative for ancient genomics. Therefore, we used previously reported high-quality regions to capture the most phylogenetically informative portion of the Y chromosome. We confirmed the effectiveness of the method by noting that, after capture, up to 99.1% of the reads mapping to the Y chromosome fall within the targeted regions. In this study, the two libraries with endogenous DNA content of 0.12 and 1.54% yielded ~ 10-fold greater enrichment rates under YCC, as compared to WGC + YCC libraries. Despite observing a greater enrichment for WGC + YCC experiments in the four samples with low endogenous DNA proportions (0.01 to 0.04%), none of the enriched libraries yielded enough Y-chromosome SNPs to assign a haplogroup. Moreover, we observed that both YCC and WGC + YCC libraries outperformed pre-capture libraries with respect to Y-DNA content, the data generated in this study do not allow us to assert yet if carrying out WGC is advantageous or not before enriching for Y-DNA capture. However, as could be expected based on previous work [17, 44], initial levels of endogenous DNA content, library complexities and fragment lengths of the starting libraries seem to influence the performance of the libraries after consecutive rounds of capture experiments. We thus stress the need to consider the initial complexity, endogenous DNA content, and read lengths when planning these experiments. We recommend a design that includes the estimation of predictive yield and enrichment curves [40, 45], based on shallow sequencing, to inform the best sequencing strategy and avoid sequencing beyond saturation.

Finally, there is a vast potential to incorporate Y-chromosome information from aDNA samples into the study of human population history from regions beyond Eurasia. In our work, we go beyond SNP capture and present the first instance of Y-chromosome capture on ancient samples, opening new avenues of research to improve the performance of these experiments and to extract Y-chromosome information from ancient samples.

Abbreviations

WGC + YCC: 

Whole Genome Capture + Y-chromosome capture

WGC: 

Whole genome capture

YCC: 

Y-chromosome capture

Declarations

Acknowledgments

Calculations were performed on UBELIX (http://www.id.unibe.ch/hpc), the HPC cluster at the University of Bern. MCA would like to thank Jair Garcia Soto and Luis Alberto Aguilar for technical support. MNC would like to thank William J. Pestle, L. Antonio Curet and Edwin Crespo-Torres for providing the Paso del Indio samples and Meredith Carpenter, Morten Rasmussen and Rosa Fregel for assistance with computational analyses. We would like to thank Jay B. Haviser (SIMARC) for providing STM1 and STM2 samples.

Funding

MCAA’s laboratory is supported by Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica - Universidad Nacional Autónoma de México grant IA206817. DICD and ASM are funded by the Swiss National Science foundation and the European Research Council (ERC starting grant). HS was funded by the European Research Council (FP7/2007–2013, grant no. 319209, Synergy project NEXUS1492). The work on the samples from Saint Martin was partially funded by the European Commission through the Marie Curie Actions (FP7/2007–2013, grant no. 290344, EUROTAST). Funding for work with the Puerto Rico samples was provided by the Arizona State University School of International Letters and Cultures Foster Latin American Studies Support Grant, the Arizona State University Graduate and Professional Student Association and Sigma-Xi.

Availability of data and materials

YCC and WGC + YCC reads of STM samples are available in the European Nucleotide Archive under the accession number PRJEB23498 (https://www.ebi.ac.uk/ena/data/view/PRJEB23498). Y-chromosome alignments for Paso del Indio samples are available in the NCBI Short Read Archive (SRA) under BioProject PRJNA419010 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA419010).

Authors’ contributions

MCAA conceived the project with input from CDB, DP and AS. MNC, AS and HS performed laboratory work. ASM, DICD and MCAA designed the data analysis strategy. DICD performed most data analyses with input from DGP. DICD and MCAA wrote the manuscript with input from all the authors. All authors read and approved the final manuscript.

Ethics approval and consent to participate

For STM1 and STM2, permission to sample and export remains was granted by the Sint Maarten Archaeological Center and the Department of Culture of the Government of Sint Maarten. For the Paso del Indio samples (PI174, PI383, PI435, PI437), permission to export remains and conduct genomic analysis was granted by the Consejo para la Protección del Patrimonio Arqueológico Terrestre de Puerto Rico.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
(2)
International Laboratory for Human Genome Research, National Autonomous University of Mexico, Mexico, Mexico
(3)
Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
(4)
Swiss Institute of Bioinformatics, Lausanne, Switzerland
(5)
School of Human Evolution and Social Change, Arizona State University, Tempe, USA
(6)
Department of Genetics, Stanford University, Stanford, USA
(7)
23andMe, Mountain View, USA
(8)
Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
(9)
Faculty of Archaeology, Leiden University, Leiden, Netherlands
(10)
Institute of Human Origins, Arizona State University, Tempe, USA
(11)
Department of Biomedical Data Science, Stanford University, Stanford, USA

References

  1. Jobling MA, Tyler-Smith C. Human Y-chromosome variation in the genome-sequencing era. Nat Rev Genet. 2017;18(8):485–97.View ArticlePubMedGoogle Scholar
  2. Poznik GD, Henn BM, Yee M-C, Sliwerska E, Euskirchen GM, Lin AA, Snyder M, Quintana-Murci L, Kidd JM, Underhill PA, Bustamante CD. Sequencing Y chromosomes resolves discrepancy in time to common ancestor of males versus females. Science. 2013;341(6145):562–5.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Haak W, Lazaridis I, Patterson N, Rohland N, Mallick S, Llamas B, Brandt G, Nordenfelt S, Harney E, Stewardson K, Fu Q, Mittnik A, Bánffy E, Economou C, Francken M, Friederich S, Pena RG, Hallgren F, Khartanovich V, Khokhlov A, Kunst M, Kuznetsov P, Meller H, Mochalov O, Moiseyev V, Nicklisch N, Pichler SL, Risch R, Rojo Guerra M, Roth C, Szécsényi-Nagy A, Wahl J, Meyer M, Krause J, Brown D, Anthony D, Cooper A, Alt KW, Reich D. Massive migration from the steppe was a source for Indo-European languages in Europe. Nature. 2015;522(7555):207–11.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Poznik GD, Xue Y, Mendez FL, Willems TF, Massaia A, Wilson Sayres MA, Ayub Q, McCarthy SA, Narechania A, Kashin S, Chen Y, Banerjee R, Rodriguez-Flores JL, Cerezo M, Shao H, Gymrek M, Malhotra A, Louzada S, Desalle R, Ritchie GRS, Cerveira E, Fitzgerald TW, Garrison E, Marcketta A, Mittelman D, Romanovitch M, Zhang C, Zheng-Bradley X, Abecasis GR, McCarroll SA, Flicek P, Underhill PA, Coin L, Zerbino DR, Yang F, Lee C, Clarke L, Auton A, Erlich Y, Handsaker RE, Bustamante CD, Tyler-Smith C. Punctuated bursts in human male demography inferred from 1,244 worldwide Y-chromosome sequences. Nat Genet. 2016;12(9):809.Google Scholar
  5. Lazaridis I, Nadel D, Rollefson G, Merrett DC, Rohland N, Mallick S, Fernandes D, Novak M, Gamarra B, Sirak K, Connell S, Stewardson K, Harney E, Fu Q, Gonzalez-Fortes G, Jones ER, Roodenberg SA, Lengyel G, Bocquentin F, Gasparian B, Monge JM, Gregg M, Eshed V, Mizrahi A-S, Meiklejohn C, Gerritsen F, Bejenaru L, Blüher M, Campbell A, Cavalleri G, Comas D, Froguel P, Gilbert E, Kerr SM, Kovacs P, Krause J, McGettigan D, Merrigan M, Merriwether DA, O’Reilly S, Richards MB, Semino O, Shamoon-Pour M, Stefanescu G, Stumvoll M, Tönjes A, Torroni A, Wilson JF, Yengo L, Hovhannisyan NA, Patterson N, Pinhasi R, Reich D. Genomic insights into the origin of farming in the ancient Near East. Nature. 2016;536(7617):419–24.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Brandt G, Haak W, Adler CJ, Roth C, Szecsenyi-Nagy A, Karimnia S, Moller-Rieker S, Meller H, Ganslmeier R, Friederich S, Dresely V, Nicklisch N, Pickrell JK, Sirocko F, Reich D, Cooper A, Alt KW. Ancient DNA reveals key stages in the formation of central European mitochondrial genetic diversity. Science. 2013;342(6155):257–61.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Ho SYW, Gilbert MTP. Ancient mitogenomics. Mitochondrion. 2010;10(1):1–11.View ArticlePubMedGoogle Scholar
  8. Enk JM, Devault AM, Kuch M, Murgha YE, Rouillard JM, Poinar HN. Ancient whole genome enrichment using baits built from modern dna. Mol Biol Evol. 2014;31(5):1292–4.View ArticlePubMedGoogle Scholar
  9. Fu Q, Meyer M, Gao X, Stenzel U, Burbano HA, Kelso J, Pääbo S. DNA analysis of an early modern human from Tianyuan cave, China. Proc Natl Acad Sci U S A. 2013;110(6):2223–7.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Carpenter ML, Buenrostro JD, Valdiosera C, Schroeder H, Allentoft ME, Sikora M, Rasmussen M, Gravel S, Guillén S, Nekhrizov G, Leshtakov K, Dimitrova D, Theodossiev N, Pettener D, Luiselli D, Sandoval K, Moreno-Estrada A, Li Y, Wang J, Gilbert MTP, Willerslev E, Greenleaf WJ, Bustamante CD. Pulling out the 1%: whole-genome capture for the targeted enrichment of ancient DNA sequencing libraries. Am J Hum Genet. 2013;93(5):852–64.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Maricic T, Whitten M, Pääbo S. Multiplexed DNA sequence capture of mitochondrial genomes using PCR products. PLoS One. 2010;5(11):9–13.View ArticleGoogle Scholar
  12. Mathieson I, Lazaridis I, Rohland N, Mallick S, Patterson N, Roodenberg SA, Harney E, Stewardson K, Fernandes D, Novak M, Sirak K, Gamba C, Jones ER, Llamas B, Dryomov S, Pickrell J, Arsuaga JL, de Castro JMB, Carbonell E, Gerritsen F, Khokhlov A, Kuznetsov P, Lozano M, Meller H, Mochalov O, Moiseyev V, Guerra MAR, Roodenberg J, Vergès JM, Krause J, Cooper A, Alt KW, Brown D, Anthony D, Lalueza-Fox C, Haak W, Pinhasi R, Reich D. Genome-wide patterns of selection in 230 ancient Eurasians. Nature. 2015;528(7583):499–503.Google Scholar
  13. Burbano HA, Hodges E, Green RE, Briggs AW, Krause J, Meyer M, Good JM, Maricic T, Johnson PLF, Xuan Z, Rooks M, Bhattacharjee A, Brizuela L, Albert FW, de la Rasilla M, Fortea J, Rosas A, Lachmann M, Hannon GJ, Pääbo S. Targeted investigation of the Neandertal genome by array-based sequence capture. Science. 2010;328(5979):723–5.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Paijmans JLA, Fickel J, Courtiol A, Hofreiter M, Förster DW. Impact of enrichment conditions on cross-species capture of fresh and degraded DNA. Mol Ecol Resour. 2015;16:42-55.Google Scholar
  15. Avila-Arcos MCMC, Cappellini E, Romero-Navarro JA, Wales N, Moreno-Mayar JVV, Rasmussen M, Fordyce SL, Montiel R, Vielle-Calzada J-P, Willerslev E, Gilbert MTP. Application and comparison of large-scale solution-based DNA capture-enrichment methods on ancient DNA. Sci Rep. 2011;1:74.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Dabney J, Knapp M, Glocke I, Gansauge M-T, Weihmann A, Nickel B, Valdiosera C, Garcia N, Paabo S, Arsuaga J-L, Meyer M. Complete mitochondrial genome sequence of a middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc Natl Acad Sci. 2013;110(39):15758–63.View ArticlePubMedGoogle Scholar
  17. Ávila-Arcos MC, Sandoval-Velasco M, Schroeder H, Carpenter ML, Malaspinas A-S, Wales N, Peñaloza F, Bustamante CD, Gilbert MTP. Comparative performance of two whole-genome capture methodologies on ancient DNA Illumina libraries. Bunce M, editor. Methods Ecol Evol. 2015;6(6):725–34.View ArticleGoogle Scholar
  18. Mallick S, Li H, Lipson M, Mathieson I, Gymrek M, Racimo F, Zhao M, Chennagiri N, Nordenfelt S, Tandon A, Skoglund P, Lazaridis I, Sankararaman S, Fu Q, Rohland N, Renaud G, Erlich Y, Willems T, Gallo C, Spence JP, Song YS, Poletti G, Balloux F, van Driem G, de Knijff P, Romero IG, Jha AR, Behar DM, Bravi CM, Capelli C, Hervig T, Moreno-Estrada A, Posukh OL, Balanovska E, Balanovsky O, Karachanak-Yankova S, Sahakyan H, Toncheva D, Yepiskoposyan L, Tyler-Smith C, Xue Y, Abdullah MS, Ruiz-Linares A, Beall CM, Di Rienzo A, Jeong C, Starikovskaya EB, Metspalu E, Parik J, Villems R, Henn BM, Hodoglugil U, Mahley R, Sajantila A, Stamatoyannopoulos G, JTS W, Khusainova R, Khusnutdinova E, Litvinov S, Ayodo G, Comas D, Hammer MF, Kivisild T, Klitz W, Winkler CA, Labuda D, Bamshad M, Jorde LB, Tishkoff SA, Watkins WS, Metspalu M, Dryomov S, Sukernik R, Singh L, Thangaraj K, Pääbo S, Kelso J, Patterson N, Reich D. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature. 2016;538(7624):201–6.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Schlebusch CM, Malmström H, Günther T, Sjödin P, Coutinho A, Edlund H, Munters AR, Steyn M, Soodyall H, Lombard M, Jakobsson M. Ancient genomes from southern Africa pushes modern human divergence beyond 260,000 years ago. bioRxiv. 2017;155409.Google Scholar
  20. Kemp BM, Malhi RS, McDonough J, Bolnick DA, Eshleman JA, Rickards O, Martinez-Labarga C, Johnson JR, Lorenz JG, Dixon EJ, Fifield TE, Heaton TH, Worl R, Smith DG. Genetic analysis of early holocene skeletal remains from Alaska and its implications for the settlement of the Americas. Am J Phys Anthropol. 2007;132(4):605–21.View ArticlePubMedGoogle Scholar
  21. Rasmussen M, Anzick SL, Waters MR, Skoglund P, De Giorgio M, Stafford TW, Rasmussen S, Moltke I, Albrechtsen A, Doyle SM, Poznik GD, Gudmundsdottir V, Yadav R, Malaspinas A-S, White SS, Allentoft ME, Cornejo OE, Tambets K, Eriksson A, Heintzman PD, Karmin M, Korneliussen TS, Meltzer DJ, Pierre TL, Stenderup J, Saag L, Warmuth VM, Lopes MC, Malhi RS, Brunak S, Sicheritz-Ponten T, Barnes I, Collins M, Orlando L, Balloux F, Manica A, Gupta R, Metspalu M, Bustamante CD, Jakobsson M, Nielsen R, Willerslev E, SSW V, Allentoft ME, Cornejo OE, Tambets K, Eriksson A, Heintzman PD, Karmin M, Korneliussen TS, Meltzer DJ, Pierre TL, Stenderup J, Saag L, Warmuth VM, Lopes MC, Malhi RS, Brunak S, Sicheritz-Ponten T, Barnes I, Collins M, Orlando L, Balloux F, Manica A, Gupta R, Metspalu M, Bustamante CD, Jakobsson M, Nielsen R, Willerslev E. The genome of a Late Pleistocene human from a Clovis burial site in western Montana. Nature. 2014;506(7487):225–9.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Rasmussen M, Sikora M, Albrechtsen A, Korneliussen TS, Moreno-Mayar JV, Poznik GD, Zollikofer CPE, Ponce de León MS, Allentoft ME, Moltke I, Jónsson H, Valdiosera C, Malhi RS, Orlando L, Bustamante CD, Stafford TW, Meltzer DJ, Nielsen R, Willerslev E. The ancestry and affiliations of Kennewick man. Nature. 2015;523:455-58.Google Scholar
  23. Gallego Llorente M, Jones ER, Eriksson A, Siska V, Arthur KW, Arthur JW, Curtis MC, Stock JT, Coltorti M, Pieruccini P, Stretton S, Brock F, Higham T, Park Y, Hofreiter M, Bradley DG, Bhak J, Pinhasi R, Manica A. Ancient Ethiopian genome reveals extensive Eurasian admixture throughout the African continent. Science. 2015;350(6262):820–2.View ArticlePubMedGoogle Scholar
  24. Schroeder H, Ávila-Arcos MC, Malaspinas A-S, Poznik GD, Sandoval-Velasco M, Carpenter ML, Moreno-Mayar JV, Sikora M, Johnson PLF, Allentoft ME, Samaniego JA, Haviser JB, Dee MW, Stafford TW, Salas A, Orlando L, Willerslev E, Bustamante CD, Gilbert MTP. Genome-wide ancestry of 17th-century enslaved Africans from the Caribbean. Proc Natl Acad Sci. 2015;112(12):201421784.View ArticleGoogle Scholar
  25. Pestle WJ. Diet and Society in Prehistoric Puerto Rico: An Isotopic Approach. [Unpublished Ph.D. dissertation]: University of Illinois at Chicago; 2010.Google Scholar
  26. Pestle WJ, Colvard M. Bone collagen preservation in the tropics: a case study from ancient Puerto Rico. J Archaeol Sci. 2012;39(7):2079–90.View ArticleGoogle Scholar
  27. Rohland N, Hofreiter M. Ancient DNA extraction from bones and teeth. Nat Protoc. 2007;2(7):1756–62.View ArticlePubMedGoogle Scholar
  28. Schuenemann VJ, Bos K, DeWitte S, Schmedes S, Jamieson J, Mittnik A, Forrest S, Coombes BK, Wood JW, Earn DJD, White W, Krause J, Poinar HN. Targeted enrichment of ancient pathogens yielding the pPCP1 plasmid of Yersinia pestis from victims of the black death. Proc Natl Acad Sci. 2011;108(38):E746–52.View ArticlePubMedGoogle Scholar
  29. Gilbert MTP, Bandelt HJ, Hofreiter M, Barnes I. Assessing ancient DNA studies. Trends Ecol Evol. 2005;20(10):541–4.View ArticlePubMedGoogle Scholar
  30. Simbolo M, Gottardi M, Corbo V, Fassan M, Mafficini A, Malpeli G, Lawlor RT, Scarpa A. DNA Qualification Workflow for Next Generation Sequencing of Histopathological Samples. PLoS ONE. 2013; https://doi.org/10.1371/journal.pone.0062692.
  31. Meyer M, Kircher M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb Protoc. 2010; https://doi.org/10.1101/pdb.prot5448.
  32. Kircher M, Sawyer S, Meyer M. Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. 2012;40(1):1–8.View ArticleGoogle Scholar
  33. Schubert M, Lindgreen S, Orlando L. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res Notes. 2016;9(1):88.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Li H, Durbin R. Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics. 2009;25(14):1754–60.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Schubert M, Ermini L, Der Sarkissian C, Jónsson H, Ginolhac A, Schaefer R, Martin MD, Fernández R, Kircher M, McCue M, Willerslev E, Orlando L. Characterization of ancient and modern genomes by SNP detection and phylogenomic and metagenomic analysis using PALEOMIX. Nat Protoc. 2014;9(5):1056–82.View ArticlePubMedGoogle Scholar
  36. Jónsson H, Ginolhac A, Schubert M, Johnson PLF, Orlando L. MapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics. 2013;29(13):1682–4.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Core TR. R: a language and environment for statistical computing, vol. 0. Vienna: R Foundation for Statistical Computing; 2016.Google Scholar
  38. Korneliussen TS, Albrechtsen A, Nielsen R. ANGSD: analysis of next generation sequencing data. BMC Bioinformatics. 2014;15(1):356.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Skoglund P, Storå J, Götherström A, Jakobsson M. Accurate sex identi fi cation of ancient human remains using DNA shotgun sequencing. J Archaeol Sci. 2013;40(12):4477–82.View ArticleGoogle Scholar
  40. Deng C, Daley T, Smith A. Applications of species accumulation curves in large-scale biological data analysis. Quant Biol. 2015;3(3):135–44.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Cruz-Dávalos DI, Llamas B, Gaunitz C, Fages A, Gamba C, Soubrier J, Librado P, Seguin-Orlando A, Pruvost M, Alfarhan AH, Alquraishi SA, Al-Rasheid KAS, Scheu A, Beneke N, Ludwig A, Cooper A, Willerslev E, Orlando L. Experimental conditions improving in solution target enrichment for ancient DNA. Mol Ecol Resour. 2016;17(3):508-22.Google Scholar
  42. Rowold D, Garcia-Bertrand R, Calderon S, Rivera L, Benedico DP, Alfonso Sanchez MA, Chennakrishnaiah S, Varela M, Herrera RJ. At the southeast fringe of the bantu expansion: genetic diversity and phylogenetic relationships to other sub-Saharan tribes. Meta Gene. 2014;2:670–85.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Moreno-Estrada A, Gravel S, Zakharia F, McCauley JL, Byrnes JK, Gignoux CR, Ortiz-Tello PA, Martínez RJ, Hedges DJ, Morris RW, Eng C, Sandoval K, Acevedo-Acevedo S, Norman PJ, Layrisse Z, Parham P, Martínez-Cruzado JC, Burchard EG, Cuccaro ML, Martin ER, Bustamante CD. Reconstructing the Population Genetic History of the Caribbean. Tarazona-Santos E. PLoS Genet. 2013;9(11):e1003925.Google Scholar
  44. Enk J, Rouillard JM, Poinar H. Quantitative PCR as a predictor of aligned ancient DNA read counts following targeted enrichment. BioTechniques. 2013;55(6):300–9.View ArticlePubMedGoogle Scholar
  45. Daley T, Smith AD. Predicting the molecular complexity of sequencing libraries. Nat Methods. 2013;10(4):325–7.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2018

Advertisement