Skip to main content
PMC home page
Scientific Reports logoLink to Scientific Reports
. 2017 Mar 13;7:44389. doi: 10.1038/srep44389

A novel strategy for clustering major depression individuals using whole-genome sequencing variant data

Chenglong Yu 1,2,a, Bernhard T Baune 3, Julio Licinio 1,2, Ma-Li Wong 1,2
PMCID: PMC5347377  PMID: 28287625

Abstract

Major depressive disorder (MDD) is highly prevalent, resulting in an exceedingly high disease burden. The identification of generic risk factors could lead to advance prevention and therapeutics. Current approaches examine genotyping data to identify specific variations between cases and controls. Compared to genotyping, whole-genome sequencing (WGS) allows for the detection of private mutations. In this proof-of-concept study, we establish a conceptually novel computational approach that clusters subjects based on the entirety of their WGS. Those clusters predicted MDD diagnosis. This strategy yielded encouraging results, showing that depressed Mexican-American participants were grouped closer; in contrast ethnically-matched controls grouped away from MDD patients. This implies that within the same ancestry, the WGS data of an individual can be used to check whether this individual is within or closer to MDD subjects or to controls. We propose a novel strategy to apply WGS data to clinical medicine by facilitating diagnosis through genetic clustering. Further studies utilising our method should examine larger WGS datasets on other ethnical groups.

With the development of new and cheaper whole genome sequencing (WGS) technology, patient care may move towards precision medicine. Ever since the first human genome was fully sequenced, scientists have been searching for approaches to provide personalized care1. WGS allows us to identify single nucleotide variants (SNVs), which are private genetic variants, and determine all the genetic variants within each person. Single nucleotide polymorphism (SNP) genotyping is currently the gold-standard technique for genome-wide association studies (GWAS), as WGS costs remain relatively high; however, as WGS costs are projected to drop further, researchers may have the opportunity to examine the significance of SNVs, which involve more individual characteristics.

Major depressive disorder (MDD) is a chronic condition with great medical, social, and economic impacts. MDD is a main contributor to global disease burden and produces significant morbidity and mortality2,3,4,5,6. Despite recent advances7,8,9, little is known about its underlying fundamental biology. The existing psychiatric genetic studies have not found common consistently replicated gene variants of large effect in the pathogenesis of MDD10,11,12, and thus much work still needs to be done to fully elucidate the genetic factors that confer susceptibility to this condition. For our current research, we tested whether the combined effect of all SNVs at the whole-genome sequence level could confer genetic liability to the MDD risk.

In this study, we focus on a sample of Los Angeles Mexican-American participants who had three or more grandparents born in Mexico. MDD participants were diagnosed using the Structured Clinical Interview (SCID) for Diagnostic and Statistical Manual of Mental Disorders (DSM), and the DSM-IV diagnostic criteria for current, unipolar major depressive episode with a HAM-D21 (21-Item Hamilton Depression Rating Scale) score of 18 or greater with item number 1 (depressed mood) rated 2 or greater; they participated in a pharmacogenetic study of antidepressant treatment. Controls were in general good health but were not screened for medical or psychiatric illnesses; they were age- and gender- matched Mexican-American individuals recruited from the same community in Los Angeles13,14,15,16. Here, we establish a new computational approach to cluster subjects based on all of their WGS variants. We believe that clustering of patients based on their SNV profiles may provide valuable clues for prognostics, diagnostics, and therapeutics, as it takes into account all of their genetic data. The idea for this approach arose from distance-based phylogenetic analysis of DNA/protein sequences proposed by us earlier17,18,19,20,21. In our proposed methodology we used a well-defined metric in mathematics, the Jaccard distance, to measure the similarity/dissimilarity between subjects using all the SNV information from each individual and from that we construct cluster trees based on the Jaccard distance matrices. Clustering relationships in the trees showed that Mexican-American MDD patients grouped together, and were clustered far from ethnically matched healthy controls. This discovery may be translated to clinical practice since we may be able to predict the MDD status of a given Mexican-American subject based on his/her WGS data.

Materials and Methods

The Mexican-American Sample

In our recent work16, we have investigated the whole-exome genotyping data of a Los Angeles Mexican-American cohort aged 19–65 years of 203 MDD patients and 196 healthy controls. Participants provided written informed consent, and detailed demographic, epidemiological, and clinical descriptions were previously described13,14,15. The study was registered in ClinicalTrials.gov (NCT00265291), and approved by the Institutional Review Boards of the University of California Los Angeles and University of Miami, USA, and by the Human Research Ethics Committees of the Australian National University and Bellbery Ltd, Australia13,14,15. In this study, we obtained complete WGS data for a group of 15 participants selected from the cohort, 10 MDD patients and 5 controls. In Table 1, we present the gender (all are female) and age information of the 15 Mexican-American subjects. We have confirmed that in the cohort there was no family or population structure among all those individuals16 and no any blood relationship among the 15 selected participants.

Table 1. Whole-genome sequencing variation analysis of 25 human subjects.

Subjects Gender Age Total SNVs Total INDELs dbSNP
MA-Depresson-1 Female 35 8,348,095 522,994 4,031,869
MA-Depresson-2 Female 30 7,921,961 513,462 3,993,392
MA-Depresson-3 Female 41 8,037,674 514,135 3,986,882
MA-Depresson-4 Female 32 8,021,058 511,756 3,903,495
MA-Depresson-5 Female 45 7,839,942 511,053 4,001,897
MA-Depresson-6 Female 38 7,834,986 516,002 4,021,724
MA-Depresson-7 Female 36 7,935,708 512,681 3,911,549
MA-Depresson-8 Female 59 7,694,178 514,095 3,949,370
MA-Depresson-9 Female 41 7,778,564 520,337 3,987,191
MA-Depresson-10 Female 31 8,073,958 526,792 4,045,542
MA-Control-1 Female 50 7,879,192 519,009 4,042,162
MA-Control-2 Female 45 6,974,138 517,756 4,021,858
MA-Control-3 Female 39 6,911,665 526,897 3,999,059
MA-Control-4 Female 29 7,197,066 518,675 4,011,644
MA-Control-5 Female 35 7,487,135 517,667 4,031,216
AU-Depresson-1 Male 44 3,883,255 555,785 3,888,831
AU-Depresson-2 Female 19 3,938,868 541,109 3,956,682
AU-Depresson-3 Female 19 3,925,906 560,127 3,928,997
AU-Depresson-4 Female 25 3,933,654 557,712 3,935,804
AU-Depresson-5 Female 18 3,905,386 555,542 3,920,378
AU-Control-1 Female 20 3,898,847 569,129 3,923,876
AU-Control-2 Male 18 3,920,681 558,496 3,903,217
AU-Control-3 Male 30 3,861,132 552,110 3,861,584
AU-Control-4 Female 18 3,922,531 568,501 3,911,346
AU-Control-5 Male 20 3,820,520 449,055 3,773,974
Open in a new tab

MA, Mexican-American; AU, Australian; SNVs, single nucleotide variants; INDELs, small insertions and deletions; dbSNP (the number of SNVs and INDELs that are found in the dbSNP database in NCBI).

The European-Ancestry Sample

For comparison as an outgroup sample, we also include WGS data from a group of 10 Australians of European-Ancestry. Those 10 participants gave written informed consent and were recruited under the Cognitive function and mood disorders study (conducted by the Discipline of Psychiatry, University of Adelaide, South Australia, Australia). This sample was studied under approved Human Research Ethics Committees protocols at the University of Adelaide and Flinders University, South Australia, Australia. In Table 1, we present the gender and age information of these 10 subjects.

We confirm all methods and experiments in this study were performed in accordance with relevant guidelines and regulations.

Whole-Genome Sequencing (WGS) and Analysis

Samples from fifteen Mexican-American participants (10 MDD patients and 5 controls) were whole-genome sequenced using Illumina HiSeq 2000 (BGI-Shenzhen, Shenzhen, Guangdong, China) and samples from ten European-Ancestry Australian participants (5 MDD patients and 5 controls) were whole-genome sequenced using HiSeq X (Garvan Institute, Sydney, New South Wales, Australia). After obtaining paired-end sequencing reads of those 25 participants, we did the variant calling analysis using the following pipeline. The reads of each participant were aligned to the human reference genome (hg19, Genome Reference Consortium GRCh37) using Burrows-Wheeler Aligner (BWA)22 to get SAM (sequence alignment/map) format files. SAM files were converted to the BAM (binary version of a SAM file) format files using SAMtools23. BAM files were then merged into one BAM file, and the mpileup command in SAMtools was used to calculate the likelihood of data given each possible genotype, and store the likelihoods in a binary file. The output was supplied to SAMtools/BCFtools24 which created the SNV/INDEL (small insertions and deletions) calling to generate VCF (variant call format) files. Then, ANNOVAR25 was used to annotate SNV/INDEL information and their classifications. For WGS and analysis details, please see Supplementary Information. Only the SNV information was used in the following methodology.

Clustering Subjects on SNV Sets

To take in consideration all the SNV information of those subjects, it was important to define a distance between two subjects when running the cluster analysis. We clustered the subjects at the chromosome level; consequently, we defined a distance between two people based on SNV information obtained from a given chromosome, e.g., chromosome 1. First, we considered the SNVs distribution on that chromosome. In Fig. 1, we give a hypothetical SNVs distribution on chromosome 1 for two individuals. In a real case scenario, in a given chromosome two individuals may have many same position SNVs, e.g., SNV6 in person A and SNV7 in person B in Fig. 1. Our hypothesis was that if two individuals shared more same position SNVs, then those two individuals would have more similar phenotypes, such as traits or diseases. Therefore, a proper distance (dis-similarity) between two SNV sets could be employed.

Figure 1. A hypothetical illustration of the distribution of SNVs on chromosome (chr) 1 of two individuals.

Figure 1

Open in a new tab

SNV2, SNV4 and SNV6 in person A and SNV3, SNV4 and SNV7 in person B occupy the same respective positions in chr 1.

Let S1 and S2 be SNV sets in a given chromosome from subject 1 and subject 2. We use |S| to denote the cardinality of set S. The Jaccard metric26, a statistics tool for measuring the similarity and diversity of sample sets, is introduced here. The Jaccard metric between S1 and S2 is defined as

graphic file with name srep44389-m1.jpg

If S1 and S2 are exactly the same, then Inline graphic, J(S1, S2) = 0. If S1 and S2 have no common elements, then Inline graphic, J(S1, S2) = 1. Unlike many distances used in comparative genomics (e.g., some distances based on alignment methods), the Jaccard metric satisfies the triangle inequality, i.e., Inline graphic, which guarantees that the Jaccard distance is a well-defined metric in mathematics27. Considering Fig. 1 as an example, SA = 7, SB = 8, Inline graphic, so

graphic file with name srep44389-m6.jpg

In this proof-of-principle study we use the Jaccard metric to calculate the distance matrices of those 25 participants in each chromosome. Then cluster trees based on each chromosome can be reconstructed. Clustering relationships shown in the trees may reveal significant medical information that may be translated into clinical practice.

Results

Whole-Genome Sequencing (WGS) Data Analysis

Table 1 provides the results of WGS variation in 25 human subjects and shows that Mexican-American individuals have significantly more SNVs when compared to Australian individuals of European-Ancestry. For total SNVs, Australian’s mean value is 3901078 (n = 10), Mexican-American’s mean value is 7729021.3 (n = 15), the t test p-value for the two groups is 2.09e-15. This is consistent with data from the Human Haplotype Matching Project (HapMap). We contributed the Mexican-American sample to HapMap, from the same community as subjects in this study. That study showed that Mexican-Americans have more polymorphic SNPs in Mexican-Americans than in northern Europeans28. Mexican-Americans from that Los Angeles community have median ancestry proportions that are 45% Indigenous American, 49% European and 5% African29. According to results from the International HapMap 3 Consortium and the 1000 Genomes Project Consortium, it would be expected that individuals with African ancestry, such as Mexican-Americans, have increased number of variants, and, moreover, the Spanish population have excess of rare variants28,30.

For the Mexican-American sample, both depression and control subjects have approximately 7,000,000 to 8,000,000 SNVs; 5,100,000 to 5,200,000 INDELs, and 3,900,000 to 4,000,000 SNVs in dbSNP (the SNP database). We calculated the SNV distributions on each chromosome for the Mexican-American and Australian samples. In Fig. 2a, we used boxplot to show the descriptive statistical distributions of SNVs in each chromosome for the Mexican-American control group. Descriptive statistical distributions of SNVs of each chromosome for the Mexican-American depression group are provided in Fig. 2b. Since only female Mexican-American samples were used for this study, we include chromosome X in the results. We found that the depression and control groups have basically the same SNV distributions for all chromosomes. Table S1 provides detailed information of SNV distributions for all the chromosomes in the 25 subjects.

Figure 2. Descriptive statistical distributions of SNVs on all the chromosomes.

Figure 2

Open in a new tab

(a) The Mexican-American control group. (b) The Mexican-American MDD group.

Clustering Subjects using Cluster Trees

Following the proposed method, we use the Jaccard metric and SNV sets to obtain the distance matrices between those 25 participants for each chromosome. Jaccard distance calculation was done using R programming language. We used the popular neighbor-joining method31 on the distance matrices to construct cluster trees, which were drawn using software MEGA 632. Figure 3a shows the cluster tree for 25 subjects in chromosome 1. We found that all the 10 Mexican-American MDD patients grouped together in a cluster, and 5 Mexican-American controls were separated from that group. The Australian individuals of European-Ancestry, as a different population, assembled as an obvious outgroup from the Mexican-American subjects. This fact is also consistent with the genetic distance between different populations33. We constructed cluster trees for all chromosomes. Except for the mitochondrial genome, all cluster trees clustered the Mexican-American MDD patients as group distinct from the controls. Although the Australian subjects stably stand as an outgroup, within that group the MDD and control individuals could not be well distinguished as in the Mexican-American group. Figure 3b and c show the cluster trees in chromosome 22 and chromosome X, respectively. In Figure S1, we provided the cluster trees for all the other 20 chromosomes and mitochondrial genome.

Figure 3. Cluster tree of 25 subjects for different chromosomes.

Figure 3

Open in a new tab

(a) On chromosome 1. (b) On chromosome 22. (c) On chromosome X.

WGS data analysis and Jaccard distance calculations were performed using high-performance computers in eResearch South Australia (https://www.ersa.edu.au/).

Discussion

The results obtained by our new approach support the assumption that two individuals who share more of the same position SNVs would have more similar phenotypes, such as traits or diseases. Clustering relationships in the trees show that the Mexican-American MDD patients group together, and ethnically matched controls grouped away and separately. The fact that Australian subjects fail to be clustered into case and control groups may imply that this computational method may be restricted to specific populations, with a higher degree of genetic diversity, such as Mexican-Americans. It should be noted that the choice of Jaccard metric was not random. When measuring similarity between two SNV sets, the intersection of two sets denotes the shared same position SNVs of two people, and the union of two sets is used to normalize the similarity to a value between 0 and 1. All the SNV information for two sets is fully utilized in this metric. Furthermore, the Jaccard metric is a rigorous mathematical distance. Our results showed that it is appropriate to cluster Mexican-American MDD subjects in this study. Among distance-based tree construction methods, the neighbor-joining technique does not assume a constant rate of evolution, as opposed to the molecular clock hypothesis. Due to its low computational complexity it can be performed quickly and is widely used to generate cluster trees of individuals34,35.

We have confirmed that there were no blood relatives between those Mexican-American subjects, thus the clustering relationships in the trees were not due to genetic relatedness. For the Mexican-American sample, all the subjects were female, and the MDD case group had an average age of 38.8 years with standard deviation 8.15 and the control group had an average age of 39.6 years with standard deviation 7.36. The two groups have basically the same age distribution. Thus the clustering results were not associated with gender and age. For our approach, confounding phenotypes with complex genetic architecture may be reflected in the measured distance and this could alter the observed clustering. Therefore, before performing our method, it is necessary to control confounding factors such as ethnicity, MDD diagnostic and control selection criteria, genetic relatedness, gender and age.

Our aim in this paper was not to confirm or refute previous genetic research of depression such as candidate gene studies or GWAS36 but rather to bring a novel direction using comparative genomic analysis at the whole-genome sequence level. In our methodology, the combined effect of all SNVs in the complete genome, including all genomic regions such as coding and non-coding, was considered as a genetic factor to the depression risk. Our computational approach allowed us to perform a global comparison of whole-genome information in the subjects, which no other existing method can achieve. Once a Jaccard distance matrix has been constructed, the results in the clustering tree can be displayed and viewed graphically; this is user-friendly and allows even non-expert to understand the relationships among the subjects. Furthermore, most existing genome-wide analysis methods involve many statistical models. The different choices of these models can lead to inconsistent results. Our method does not involve any statistical model and it depends only on the genetic distance between two individuals by considering their whole-genome SNV information. Therefore, our approach is stable and produces a unique analysis result.

High quality full genome sequencing costs are currently still a concern that limits obtaining larger datasets; another limitation is the high level of computational resources needed for sequencing data analysis. Future studies utilising our method should examine additional replication data on other ethnical groups.

We have developed a novel methodology to cluster subjects based on their WGS data. To the best of our knowledge, this is the first time that SNV and cluster analysis are used to study major depressive disorder. Our approach could be a useful predictive/diagnostic tool; i.e., one could test whether WGS data from a new subject could contribute to determine whether that subject would be within or close to an existing MDD or control cluster. Advances in this line of research have the potential to be rapidly translated to clinical practice and could include the ability to diagnose patients based on WGS data.

Additional Information

How to cite this article: Yu, C. et al. A novel strategy for clustering major depression individuals using whole-genome sequencing variant data. Sci. Rep. 7, 44389; doi: 10.1038/srep44389 (2017).

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Material

Supplementary Information
srep44389-s1.pdf (436.9KB, pdf)

Acknowledgments

The authors have been supported by grants APP1051931 and APP1070935 (M.L.W.), and APP1060524 (B.T.B.) from the National Health and Medical Research Council (Australia), NIH grant GM61394 (J.L. and M.L.W.), and institutional funds from the South Australian Health and Medical Research Institute.

Footnotes

The authors declare no competing financial interests.

Author Contributions C.Y. conceived of the study and performed all data analyses. C.Y., B.T.B. and M.L.W. analyzed and interpreted the results. C.Y., M.L.W. and J.L. wrote the paper. The final version was done by all authors.

References

  1. Collins F. S. & McKusick V. A. Implications of the Human Genome Project for medical science. JAMA 285, 540–544 (2001). [DOI] [PubMed] [Google Scholar]
  2. Kessler R. C. et al. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States. Results from the National Comorbidity Survey. Arch. Gen. Psychiatry 51, 8–19 (1994). [DOI] [PubMed] [Google Scholar]
  3. Lopez A. D. & Murray C. C. The global burden of disease, 1990–2020. Nat. Med. 4, 1241–1243 (1998). [DOI] [PubMed] [Google Scholar]
  4. Wong M. L. & Licinio J. Research and treatment approaches to depression. Nat. Rev. Neurosci. 2, 343–351 (2001). [DOI] [PubMed] [Google Scholar]
  5. Wong M. L. & Licinio J. From monoamines to genomic targets: a paradigm shift for drug discovery in depression. Nat. Rev. Drug Discov. 3, 136–151 (2004). [DOI] [PubMed] [Google Scholar]
  6. Kessler R. C., Chiu W. T., Demler O., Merikangas K. R. & Walters E. E. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry 62, 617–627 (2005). [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Hyde C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48(9), 1031–1036. (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Amin N. et al. Exome-sequencing in a large population-based study reveals a rare Asn396Ser variant in the LIPG gene associated with depressive symptoms. Mol. Psychiatry, doi: 10.1038/mp.2016.101 (2016). [DOI] [PubMed] [Google Scholar]
  9. CONVERGE consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523(7562), 588–591 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Sullivan P. F., Daly M. J. & O’Donovan M. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat. Rev. Genet. 13, 537–551 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Lopez-Leon S. et al. Meta-analyses of genetic studies on major depressive disorder. Mol. Psychiatry 13(8), 772–785 (2008). [DOI] [PubMed] [Google Scholar]
  12. Lohoff F. W. Overview of the genetics of major depressive disorder. Curr. Psychiatry Rep. 12(6), 539–546 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dong C., Wong M. L. & Licinio J. Sequence variations of ABCB1, SLC6A2, SLC6A3, SLC6A4, CREB1, CRHR1 and NTRK2: association with major depression and antidepressant response in Mexican-Americans. Mol. Psychiatry 14, 1105–1118 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Wong M. L., Dong C., Andreev V., Arcos-Burgos M. & Licinio J. Prediction of susceptibility to major depression by a model of interactions of multiple functional genetic variants and environmental factors. Mol. Psychiatry 17, 624–633 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Wong M. L. et al. Clinical outcomes and genome-wide association for a brain methylation site in an antidepressant pharmacogenetics study in Mexican Americans. Am. J. Psychiatry 171, 1297–1309 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Wong M. L. et al. The PHF21B gene is associated with major depression and modulates the stress response. Mol. Psychiatry doi: 10.1038/mp.2016.174 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Yu C., Liang Q., Yin C., He R. L. & Yau S. S. T. A novel construction of genome space with biological geometry. DNA Res. 17, 155–168 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Deng M., Yu C., Liang Q., He R. L. & Yau S. S. T. A novel method of characterizing genetic sequences: genome space with biological distance and applications. PloS One 6(3), e17293 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Yu C. et al. Protein space: a natural method for realizing the nature of protein universe. J. Theor. Biol. 318, 197–204 (2013). [DOI] [PubMed] [Google Scholar]
  20. Yu C., He R. L. & Yau S. S. T. Protein sequence comparison based on K-string dictionary. Gene 529(2), 250–256 (2013). [DOI] [PubMed] [Google Scholar]
  21. Hoang T. et al. A new method to cluster DNA sequences using Fourier power spectrum. J. Theor. Biol. 372, 135–145 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Li H. & Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Li H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25(16), 2078–2079 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Wang K., Li M. & Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Levandowsky M. & Winter D. Distance between sets. Nature 234(5323), 34–35 (1971). [Google Scholar]
  27. Lipkus A. H. A proof of the triangle inequality for the Tanimoto distance. J. Math. Chem. 26(1–3), 263–265 (1999). [Google Scholar]
  28. International HapMap 3 Consortium. et al. Integrating common and rare genetic variation in diverse human populations. Nature 467(7311), 52–58 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Johnson N. A. et al. Ancestral components of admixed genomes in a Mexican cohort. PLoS Genet. 7(12), e1002410 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. 1000 Genomes Project Consortium, An integrated map of genetic variation from 1,092 human genomes. Nature 491(7422), 56–65 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Saitou N. & Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4(4), 406–425 (1987). [DOI] [PubMed] [Google Scholar]
  32. Tamura K., Stecher G., Peterson D., Filipski A. & Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30(12), 2725–2729 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Nei M. Genetic distance between populations. Am. Nat. 106, 283–292 (1972). [Google Scholar]
  34. Mihaescu R., Levy D. & Pachter L. Why neighbor-joining works. Algorithmica 54(1), 1–24 (2009). [Google Scholar]
  35. Gascuel O. & Steel M. Neighbor-joining revealed. Mol. Biol. Evol. 23(11), 1997–2000 (2006). [DOI] [PubMed] [Google Scholar]
  36. Levinson D. F. et al. Genetic studies of major depressive disorder: Why are there no GWAS findings, and what can we do about it? Biol. Psychiatry 76(7), 510 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information
srep44389-s1.pdf (436.9KB, pdf)

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES